BEYOND THE “LEAST LIMITING WATER RANGE”: RETHINKING SOIL PHYSICS RESEARCH IN BRAZIL

As opposed to objective definitions in soil physics, the subjective term “soil physical quality” is increasingly found in publications in the soil physics area. A supposed indicator of soil physical quality that has been the focus of attention, especially in the Brazilian literature, is the Least Limiting Water Range (RLL), translated in Portuguese as "Intervalo Hídrico Ótimo" or IHO. In this paper the four limiting water contents that define RLLare discussed in the light of objectively determinable soil physical properties, pointing to inconsistencies in the RLLdefinition and calculation. It also discusses the interpretation of RLL as an indicator of crop productivity or soil physical quality, showing its inability to consider common phenological and pedological boundary conditions. It is shown that so-called “critical densities” found by the RLL through a commonly applied calculation method are questionable. Considering the availability of robust models for agronomy, ecology, hydrology, meteorology and other related areas, the attractiveness of RLL as an indicator to Brazilian soil physicists is not related to its (never proven) effectiveness, but rather to the simplicity with which it is dealt. Determining the respective limiting contents in a simplified manner, relegating the study or concern on the actual functioning of the system to a lower priority, goes against scientific construction and systemic understanding. This study suggests a realignment of the research in soil physics in Brazil with scientific precepts, towards mechanistic soil physics, to replace the currently predominant search for empirical correlations below the state of the art of soil physics.

Analysis of the alternative pathways for the beta-oxidation of unsaturated fatty acids using transgenic plants synthesizing polyhydroxyalkanoates in peroxisomes.

Degradation of fatty acids having cis-double bonds on even-numbered carbons requires the presence of auxiliary enzymes in addition to the enzymes of the core beta-oxidation cycle. Two alternative pathways have been described to degrade these fatty acids. One pathway involves the participation of the enzymes 2, 4-dienoyl-coenzyme A (CoA) reductase and Delta(3)-Delta(2)-enoyl-CoA isomerase, whereas the second involves the epimerization of R-3-hydroxyacyl-CoA via a 3-hydroxyacyl-CoA epimerase or the action of two stereo-specific enoyl-CoA hydratases. Although degradation of these fatty acids in bacteria and mammalian peroxisomes was shown to involve mainly the reductase-isomerase pathway, previous analysis of the relative activity of the enoyl-CoA hydratase II (also called R-3-hydroxyacyl-CoA hydro-lyase) and 2,4-dienoyl-CoA reductase in plants indicated that degradation occurred mainly through the epimerase pathway. We have examined the implication of both pathways in transgenic Arabidopsis expressing the polyhydroxyalkanoate synthase from Pseudomonas aeruginosa in peroxisomes and producing polyhydroxyalkanoate from the 3-hydroxyacyl-CoA intermediates of the beta-oxidation cycle. Analysis of the polyhydroxyalkanoate synthesized in plants grown in media containing cis-10-heptadecenoic or cis-10-pentadecenoic acids revealed a significant contribution of both the reductase-isomerase and epimerase pathways to the degradation of these fatty acids.

L'exploitation de l'eau dans le département de Gaya : des projets à contresens des besoins des populations

L'objectif initial du programme dans lequel ont été réalisées les études était d'analyser les conflits d'utilisation de l'eau. Il est apparu que le principal problème en termes d'aménagements hydrauliques pour l'approvisionnement en eau potable et l'irrigation est l'inadaptation des projets aux caractéristiques hydrologiques de la région et aux besoins des populations. La fourniture d'eau potable est un enjeu majeur, les programmes d'aménagement de puits ont privilégié un objectif d'accès à l'eau par la construction de puits cimentés n'offrant aucune amélioration, comparativement aux puits traditionnels, en matière de qualité de l'eau. L'eau des puits modernes est en effet hautement contaminée du fait de la contamination généralisée des nappes. Les investissements en matière d'approvisionnement en eau domestique ont été insuffisants par rapport aux besoins. Les investissements sont par contre hors de proportion pour ce qui concerne le développement de l'irrigation. Les agriculteurs ayant développé l'irrigation sans l'appui d'un projet ont opté pour une stratégie d'investissement à bas coût. L'investissement est de quelques centaines de millier de FCFA par hectare alors qu'il se chiffre en dizaines de million pour l'aménagement des périmètres hydro agricoles et à plus d'un million dans le cas du programme d'appui à la petite irrigation privée. De plus, les conditions d'attribution d'une aide financière dans le cadre de ce programme excluent la grande majorité des agriculteurs.

Dialectología y paremiología: refranes meteorológicos y variación diatópica en la Romania

A partir de los materiales de BADARE (Base de datos sobre refranes del calendario y meteorológicos en la Romania), esta contribución reivindica el enorme interés de estudiar la variación diatópica en dicho tipo de paremias, y abre una línea de investigación que podríamos denominar"dialectología paremiológica". A partir de la muestra que aquí ofrezco, se observa que tales paremias albergan componentes individuales de índole fonética, morfosintáctica y derivativa, léxica y semántica que responden a una adscripción dialecta l concreta; y, asimismo, que las propias paremias, como entidades globales, dan pie en algunos casos a identificar"tipos parémicos" o"paremiotipos" relacionables con determinadas zonas de uso: áreas parémicas dialectales que constituyen uno de los principales objetos de atención de la"geoparemiología romance"

Caracterización meteorológica de las camapañas estivales 1994-95 y 1995-96, en la isla Livingston (Shetland del Sur).

En este trabajo se presenta el análisis de los datos de temperatura, radiación global, radiación difusa y humedad relativa, simultáneos, registrados en la isla Livingston durante las campañas de verano de 1994-95 y 1995-96. Los datos corresponden a dos estaciones, una instalada en la Base Antártica Española Juan Carlos I y otra instalada sobre el Glaciar Johnsons. Se ha establecido la correlación de las variables medidas en la Base con las obtenidas en el glaciar. A su vez, los resultados obtenidos permiten calcular por regresión los correspondientes al glaciar, en las campañas donde no se obtuvieron datos in situ. También, se caracterizan las evoluciones medias diarias de la temperatura, radiación y humedad mediante modelos periódicos. Por último se estudian la relación de la temperatura y nubosidad con respecto a la velocidad y dirección del viento. Algunos de los resultados obtenidos se han comparado con éxito con los obtenidos a partir de los registros de la base antrática de Bellingshausen.

L'analyse économique face à l'épuisement des ressources naturelles, de William Stanley Jevons à Harold Hotelling (1865-1931) : le cas de énergies fossiles

L'épuisement des énergies fossiles est un thème d'actualité dont les prémices datent, selon l'opinion courante, des années 1970 et du premier choc pétrolier. En réalité, c'est une préoccupation plus ancienne, intimement liée à l'ère industrielle. Dans la deuxième partie du XIXème siècle, les économistes se sont penchés sur la question de l'épuisement des minerais, 'objet non identifié' jusqu'alors et nécessitant la mise sur pied de nouveaux outils d'analyse (effet-rebond chez Jevons, rente minière chez Marshall-Einaudi notamment). Avec le progrès des techniques et l'apparition de nouvelles énergies (pétrole, hydro-électricité), leurs craintes de déclin industriel se sont progressivement dissipées dans les années 1910 et 1920. Mais ces évolutions tenant à l'histoire des faits ne sont pas les seules à considérer. Des facteurs internes à la discipline économique, comme l'émergence du marginalisme dans les années 1870 et de la théorie de l'épargne et du capital dans les années 1890, ont aussi changé le regard des économistes sur la question de l'épuisement des ressources. Pourquoi ? Comment ? Quels enseignements peut-on en tirer pour les défis environnementaux d'aujourd'hui ? Voilà les questions qui sont traitées dans ce travail de thèse.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Molecular bases of circadian rhythmicity in renal physiology and pathology.

The physiological processes that maintain body homeostasis oscillate during the day. Diurnal changes characterize kidney functions, comprising regulation of hydro-electrolytic and acid-base balance, reabsorption of small solutes and hormone production. Renal physiology is characterized by 24-h periodicity and contributes to circadian variability of blood pressure levels, related as well to nychthemeral changes of sodium sensitivity, physical activity, vascular tone, autonomic function and neurotransmitter release from sympathetic innervations. The circadian rhythmicity of body physiology is driven by central and peripheral biological clockworks and entrained by the geophysical light/dark cycle. Chronodisruption, defined as the mismatch between environmental-social cues and physiological-behavioral patterns, causes internal desynchronization of periodic functions, leading to pathophysiological mechanisms underlying degenerative, immune related, metabolic and neoplastic diseases. In this review we will address the genetic, molecular and anatomical elements that hardwire circadian rhythmicity in renal physiology and subtend disarray of time-dependent changes in renal pathology.

Self-organization mechanisms for the formation on nearshore crescentic and transverse sand bars

The formation and development of transverse and crescentic sand bars in the coastal marine environment has been investigated by means of a nonlinear numerical model based on the shallow-water equations and on a simpli ed sediment transport parameterization. By assuming normally approaching waves and a saturated surf zone, rhythmic patterns develop from a planar slope where random perturbations of small amplitude have been superimposed. Two types of bedforms appear: one is a crescentic bar pattern centred around the breakpoint and the other, herein modelled for the rst time, is a transverse bar pattern. The feedback mechanism related to the formation and development of the patterns can be explained by coupling the water and sediment conservation equations. Basically, the waves stir up the sediment and keep it in suspension with a certain cross-shore distribution of depth-averaged concentration. Then, a current flowing with (against) the gradient of sediment concentration produces erosion (deposition). It is shown that inside the surf zone, these currents may occur due to the wave refraction and to the redistribution of wave breaking produced by the growing bedforms. Numerical simulations have been performed in order to understand the sensitivity of the pattern formation to the parameterization and to relate the hydro-morphodynamic input conditions to which of the patterns develops. It is suggested that crescentic bar growth would be favoured by high-energy conditions and ne sediment while transverse bars would grow for milder waves and coarser sediment. In intermediate conditions mixed patterns may occur.

Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images

A discussion is presented of daytime sky imaging and techniques that may be applied to the analysis of full-color sky images to infer cloud macrophysical properties. Descriptions of two different types of skyimaging systems developed by the authors are presented, one of which has been developed into a commercially available instrument. Retrievals of fractional sky cover from automated processing methods are compared to human retrievals, both from direct observations and visual analyses of sky images. Although some uncertainty exists in fractional sky cover retrievals from sky images, this uncertainty is no greater than that attached to human observations for the commercially available sky-imager retrievals. Thus, the application of automatic digital image processing techniques on sky images is a useful method to complement, or even replace, traditional human observations of sky cover and, potentially, cloud type. Additionally, the possibilities for inferring other cloud parameters such as cloud brokenness and solar obstruction further enhance the usefulness of sky imagers

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.

Scale effect in hazard assessment - application to daily rainfall

Daily precipitation is recorded as the total amount of water collected by a rain-gauge in 24h. Events are modelled as a Poisson process and the 24h precipitation by a Generalized Pareto Distribution (GPD) of excesses. Hazard assessment is complete when estimates of the Poisson rate and the distribution parameters, together with a measure of their uncertainty, are obtained. The shape parameter of the GPD determines the support of the variable: Weibull domain of attraction (DA) corresponds to finite support variables, as should be for natural phenomena. However, Fréchet DA has been reported for daily precipitation, which implies an infinite support and a heavy-tailed distribution. We use the fact that a log-scale is better suited to the type of variable analyzed to overcome this inconsistency, thus showing that using the appropriate natural scale can be extremely important for proper hazard assessment. The approach is illustrated with precipitation data from the Eastern coast of the Iberian Peninsula affected by severe convective precipitation. The estimation is carried out by using Bayesian techniques

Can a Halo CME from the limb be geoeffective?

The probability for a halo coronal mass ejection (CME) to be geoeffective is assumed to be higher the closer the CME launch site is located to the solar central meridian. However, events far from the central meridian may produce severe geomagnetic storms, like the case in April 2000. In this work, we study the possible geoeffectiveness of full halo CMEs with the source region situated at solar limb. For this task, we select all limb full halo (LFH) CMEs that occurred during solar cycle 23, and we search for signatures of geoeffectiveness between 1 and 5 days after the first appearance of each CME in the LASCO C2 field of view. When signatures of geomagnetic activity are observed in the selected time window, interplanetary data are carefully analyzed in order to look for the cause of the geomagnetic disturbance. Finally, a possible association between geoeffective interplanetary signatures and every LFH CME in solar cycle 23 is checked in order to decide on the CME's geoeffectiveness. After a detailed analysis of solar, interplanetary, and geomagnetic data, we conclude that of the 25 investigated events, there are only four geoeffective LFH CMEs, all coming from the west limb. The geoeffectiveness of these events seems to be moderate, turning to intense in two of them as a result of cumulative effects from previous mass ejections. We conclude that ejections from solar locations close to the west limb should be considered in space weather, at least as sources of moderate disturbances.

Mesoscale numerical analysis of the historical November 1982 heavy precipitation event over Andorra (Eastern Pyrenees)

From 6 to 8 November 1982 one of the most catastrophic flash-flood events was recorded in the Eastern Pyrenees affecting Andorra and also France and Spain with rainfall accumulations exceeding 400 mm in 24 h, 44 fatalities and widespread damage. This paper aims to exhaustively document this heavy precipitation event and examines mesoscale simulations performed by the French Meso-NH non-hydrostatic atmospheric model. Large-scale simulations show the slow-evolving synoptic environment favourable for the development of a deep Atlantic cyclone which induced a strong southerly flow over the Eastern Pyrenees. From the evolution of the synoptic pattern four distinct phases have been identified during the event. The mesoscale analysis presents the second and the third phase as the most intense in terms of rainfall accumulations and highlights the interaction of the moist and conditionally unstable flows with the mountains. The presence of a SW low level jet (30 m s-1) around 1500 m also had a crucial role on focusing the precipitation over the exposed south slopes of the Eastern Pyrenees. Backward trajectories based on Eulerian on-line passive tracers indicate that the orographic uplift was the main forcing mechanism which triggered and maintained the precipitating systems more than 30 h over the Pyrenees. The moisture of the feeding flow mainly came from the Atlantic Ocean (7-9 g kg-1) and the role of the Mediterranean as a local moisture source was very limited (2-3 g kg-1) due to the high initial water vapour content of the parcels and the rapid passage over the basin along the Spanish Mediterranean coast (less than 12 h).

Glucocorticoids exert direct toxicity on microvasculature: analysis of cell death mechanisms.

Glucocorticoids (GCs) are routinely administered systemically or injected into the eye when treating numerous ocular diseases; however, their toxicity on the retinal microvasculature has not been previously investigated. In this article, the effects of hydrocortisone (Hydro), dexamethasone, dexamethasone-phosphate and triamcinolone acetonide (TA) were evaluated in vitro on human skin microcirculation cells and, bovine endothelial retinal cells, ex-vivo, on flat mounted rat retinas. The degree of GCs induced endothelial cell death varied according to the endothelial cell type and GCs chemical properties. GCs toxicity was higher in skin microvascular endothelial cells and for hydrophobic GC formulations. The mechanism of cell death differed between GCs, Hydro and TA activated the leukocyte elastase inhibitor/L-DNase II pathways but did not activate caspases. The mechanisms of cell death observed in cell cultures were similar to those observed in rat retinal explants. Taken together these results indicate that particular attention should be paid to the potential vascular side effects when administrating GCs clinically and in particular when developing sustained-release intraocular devices.