Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Binding under Conflict Conditions: State-Space Analysis of Multivariate EEG Synchronization.

Real-world objects are often endowed with features that violate Gestalt principles. In our experiment, we examined the neural correlates of binding under conflict conditions in terms of the binding-by-synchronization hypothesis. We presented an ambiguous stimulus ("diamond illusion") to 12 observers. The display consisted of four oblique gratings drifting within circular apertures. Its interpretation fluctuates between bound ("diamond") and unbound (component gratings) percepts. To model a situation in which Gestalt-driven analysis contradicts the perceptually explicit bound interpretation, we modified the original diamond (OD) stimulus by speeding up one grating. Using OD and modified diamond (MD) stimuli, we managed to dissociate the neural correlates of Gestalt-related (OD vs. MD) and perception-related (bound vs. unbound) factors. Their interaction was expected to reveal the neural networks synchronized specifically in the conflict situation. The synchronization topography of EEG was analyzed with the multivariate S-estimator technique. We found that good Gestalt (OD vs. MD) was associated with a higher posterior synchronization in the beta-gamma band. The effect of perception manifested itself as reciprocal modulations over the posterior and anterior regions (theta/beta-gamma bands). Specifically, higher posterior and lower anterior synchronization supported the bound percept, and the opposite was true for the unbound percept. The interaction showed that binding under challenging perceptual conditions is sustained by enhanced parietal synchronization. We argue that this distributed pattern of synchronization relates to the processes of multistage integration ranging from early grouping operations in the visual areas to maintaining representations in the frontal networks of sensory memory.

Spatial relational learning and memory abilities do not differ between men and women in a real-world, open-field environment

This study assesses gender differences in spatial and non-spatial relational learning and memory in adult humans behaving freely in a real-world, open-field environment. In Experiment 1, we tested the use of proximal landmarks as conditional cues allowing subjects to predict the location of rewards hidden in one of two sets of three distinct locations. Subjects were tested in two different conditions: (1) when local visual cues marked the potentially-rewarded locations, and (2) when no local visual cues marked the potentially-rewarded locations. We found that only 17 of 20 adults (8 males, 9 females) used the proximal landmarks to predict the locations of the rewards. Although females exhibited higher exploratory behavior at the beginning of testing, males and females discriminated the potentially-rewarded locations similarly when local visual cues were present. Interestingly, when the spatial and local information conflicted in predicting the reward locations, males considered both spatial and local information, whereas females ignored the spatial information. However, in the absence of local visual cues females discriminated the potentially-rewarded locations as well as males. In Experiment 2, subjects (9 males, 9 females) were tested with three asymmetrically-arranged rewarded locations, which were marked by local cues on alternate trials. Again, females discriminated the rewarded locations as well as males in the presence or absence of local cues. In sum, although particular aspects of task performance might differ between genders, we found no evidence that women have poorer allocentric spatial relational learning and memory abilities than men in a real-world, open-field environment.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

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.

Artificial evolution on network structures : how time and space influence dynamics

Abstract The main objective of this work is to show how the choice of the temporal dimension and of the spatial structure of the population influences an artificial evolutionary process. In the field of Artificial Evolution we can observe a common trend in synchronously evolv¬ing panmictic populations, i.e., populations in which any individual can be recombined with any other individual. Already in the '90s, the works of Spiessens and Manderick, Sarma and De Jong, and Gorges-Schleuter have pointed out that, if a population is struc¬tured according to a mono- or bi-dimensional regular lattice, the evolutionary process shows a different dynamic with respect to the panmictic case. In particular, Sarma and De Jong have studied the selection pressure (i.e., the diffusion of a best individual when the only selection operator is active) induced by a regular bi-dimensional structure of the population, proposing a logistic modeling of the selection pressure curves. This model supposes that the diffusion of a best individual in a population follows an exponential law. We show that such a model is inadequate to describe the process, since the growth speed must be quadratic or sub-quadratic in the case of a bi-dimensional regular lattice. New linear and sub-quadratic models are proposed for modeling the selection pressure curves in, respectively, mono- and bi-dimensional regu¬lar structures. These models are extended to describe the process when asynchronous evolutions are employed. Different dynamics of the populations imply different search strategies of the resulting algorithm, when the evolutionary process is used to solve optimisation problems. A benchmark of both discrete and continuous test problems is used to study the search characteristics of the different topologies and updates of the populations. In the last decade, the pioneering studies of Watts and Strogatz have shown that most real networks, both in the biological and sociological worlds as well as in man-made structures, have mathematical properties that set them apart from regular and random structures. In particular, they introduced the concepts of small-world graphs, and they showed that this new family of structures has interesting computing capabilities. Populations structured according to these new topologies are proposed, and their evolutionary dynamics are studied and modeled. We also propose asynchronous evolutions for these structures, and the resulting evolutionary behaviors are investigated. Many man-made networks have grown, and are still growing incrementally, and explanations have been proposed for their actual shape, such as Albert and Barabasi's preferential attachment growth rule. However, many actual networks seem to have undergone some kind of Darwinian variation and selection. Thus, how these networks might have come to be selected is an interesting yet unanswered question. In the last part of this work, we show how a simple evolutionary algorithm can enable the emrgence o these kinds of structures for two prototypical problems of the automata networks world, the majority classification and the synchronisation problems. Synopsis L'objectif principal de ce travail est de montrer l'influence du choix de la dimension temporelle et de la structure spatiale d'une population sur un processus évolutionnaire artificiel. Dans le domaine de l'Evolution Artificielle on peut observer une tendence à évoluer d'une façon synchrone des populations panmictiques, où chaque individu peut être récombiné avec tout autre individu dans la population. Déjà dans les année '90, Spiessens et Manderick, Sarma et De Jong, et Gorges-Schleuter ont observé que, si une population possède une structure régulière mono- ou bi-dimensionnelle, le processus évolutionnaire montre une dynamique différente de celle d'une population panmictique. En particulier, Sarma et De Jong ont étudié la pression de sélection (c-à-d la diffusion d'un individu optimal quand seul l'opérateur de sélection est actif) induite par une structure régulière bi-dimensionnelle de la population, proposant une modélisation logistique des courbes de pression de sélection. Ce modèle suppose que la diffusion d'un individu optimal suit une loi exponentielle. On montre que ce modèle est inadéquat pour décrire ce phénomène, étant donné que la vitesse de croissance doit obéir à une loi quadratique ou sous-quadratique dans le cas d'une structure régulière bi-dimensionnelle. De nouveaux modèles linéaires et sous-quadratique sont proposés pour des structures mono- et bi-dimensionnelles. Ces modèles sont étendus pour décrire des processus évolutionnaires asynchrones. Différentes dynamiques de la population impliquent strategies différentes de recherche de l'algorithme résultant lorsque le processus évolutionnaire est utilisé pour résoudre des problèmes d'optimisation. Un ensemble de problèmes discrets et continus est utilisé pour étudier les charactéristiques de recherche des différentes topologies et mises à jour des populations. Ces dernières années, les études de Watts et Strogatz ont montré que beaucoup de réseaux, aussi bien dans les mondes biologiques et sociologiques que dans les structures produites par l'homme, ont des propriétés mathématiques qui les séparent à la fois des structures régulières et des structures aléatoires. En particulier, ils ont introduit la notion de graphe sm,all-world et ont montré que cette nouvelle famille de structures possède des intéressantes propriétés dynamiques. Des populations ayant ces nouvelles topologies sont proposés, et leurs dynamiques évolutionnaires sont étudiées et modélisées. Pour des populations ayant ces structures, des méthodes d'évolution asynchrone sont proposées, et la dynamique résultante est étudiée. Beaucoup de réseaux produits par l'homme se sont formés d'une façon incrémentale, et des explications pour leur forme actuelle ont été proposées, comme le preferential attachment de Albert et Barabàsi. Toutefois, beaucoup de réseaux existants doivent être le produit d'un processus de variation et sélection darwiniennes. Ainsi, la façon dont ces structures ont pu être sélectionnées est une question intéressante restée sans réponse. Dans la dernière partie de ce travail, on montre comment un simple processus évolutif artificiel permet à ce type de topologies d'émerger dans le cas de deux problèmes prototypiques des réseaux d'automates, les tâches de densité et de synchronisation.

Analysis of space-time environmental data in complex regions

This thesis develops a comprehensive and a flexible statistical framework for the analysis and detection of space, time and space-time clusters of environmental point data. The developed clustering methods were applied in both simulated datasets and real-world environmental phenomena; however, only the cases of forest fires in Canton of Ticino (Switzerland) and in Portugal are expounded in this document. Normally, environmental phenomena can be modelled as stochastic point processes where each event, e.g. the forest fire ignition point, is characterised by its spatial location and occurrence in time. Additionally, information such as burned area, ignition causes, landuse, topographic, climatic and meteorological features, etc., can also be used to characterise the studied phenomenon. Thereby, the space-time pattern characterisa- tion represents a powerful tool to understand the distribution and behaviour of the events and their correlation with underlying processes, for instance, socio-economic, environmental and meteorological factors. Consequently, we propose a methodology based on the adaptation and application of statistical and fractal point process measures for both global (e.g. the Morisita Index, the Box-counting fractal method, the multifractal formalism and the Ripley's K-function) and local (e.g. Scan Statistics) analysis. Many measures describing the space-time distribution of environmental phenomena have been proposed in a wide variety of disciplines; nevertheless, most of these measures are of global character and do not consider complex spatial constraints, high variability and multivariate nature of the events. Therefore, we proposed an statistical framework that takes into account the complexities of the geographical space, where phenomena take place, by introducing the Validity Domain concept and carrying out clustering analyses in data with different constrained geographical spaces, hence, assessing the relative degree of clustering of the real distribution. Moreover, exclusively to the forest fire case, this research proposes two new methodologies to defining and mapping both the Wildland-Urban Interface (WUI) described as the interaction zone between burnable vegetation and anthropogenic infrastructures, and the prediction of fire ignition susceptibility. In this regard, the main objective of this Thesis was to carry out a basic statistical/- geospatial research with a strong application part to analyse and to describe complex phenomena as well as to overcome unsolved methodological problems in the characterisation of space-time patterns, in particular, the forest fire occurrences. Thus, this Thesis provides a response to the increasing demand for both environmental monitoring and management tools for the assessment of natural and anthropogenic hazards and risks, sustainable development, retrospective success analysis, etc. The major contributions of this work were presented at national and international conferences and published in 5 scientific journals. National and international collaborations were also established and successfully accomplished. -- Cette thèse développe une méthodologie statistique complète et flexible pour l'analyse et la détection des structures spatiales, temporelles et spatio-temporelles de données environnementales représentées comme de semis de points. Les méthodes ici développées ont été appliquées aux jeux de données simulées autant qu'A des phénomènes environnementaux réels; nonobstant, seulement le cas des feux forestiers dans le Canton du Tessin (la Suisse) et celui de Portugal sont expliqués dans ce document. Normalement, les phénomènes environnementaux peuvent être modélisés comme des processus ponctuels stochastiques ou chaque événement, par ex. les point d'ignition des feux forestiers, est déterminé par son emplacement spatial et son occurrence dans le temps. De plus, des informations tels que la surface bru^lée, les causes d'ignition, l'utilisation du sol, les caractéristiques topographiques, climatiques et météorologiques, etc., peuvent aussi être utilisées pour caractériser le phénomène étudié. Par conséquent, la définition de la structure spatio-temporelle représente un outil puissant pour compren- dre la distribution du phénomène et sa corrélation avec des processus sous-jacents tels que les facteurs socio-économiques, environnementaux et météorologiques. De ce fait, nous proposons une méthodologie basée sur l'adaptation et l'application de mesures statistiques et fractales des processus ponctuels d'analyse global (par ex. l'indice de Morisita, la dimension fractale par comptage de boîtes, le formalisme multifractal et la fonction K de Ripley) et local (par ex. la statistique de scan). Des nombreuses mesures décrivant les structures spatio-temporelles de phénomènes environnementaux peuvent être trouvées dans la littérature. Néanmoins, la plupart de ces mesures sont de caractère global et ne considèrent pas de contraintes spatiales com- plexes, ainsi que la haute variabilité et la nature multivariée des événements. A cet effet, la méthodologie ici proposée prend en compte les complexités de l'espace géographique ou le phénomène a lieu, à travers de l'introduction du concept de Domaine de Validité et l'application des mesures d'analyse spatiale dans des données en présentant différentes contraintes géographiques. Cela permet l'évaluation du degré relatif d'agrégation spatiale/temporelle des structures du phénomène observé. En plus, exclusif au cas de feux forestiers, cette recherche propose aussi deux nouvelles méthodologies pour la définition et la cartographie des zones périurbaines, décrites comme des espaces anthropogéniques à proximité de la végétation sauvage ou de la forêt, et de la prédiction de la susceptibilité à l'ignition de feu. A cet égard, l'objectif principal de cette Thèse a été d'effectuer une recherche statistique/géospatiale avec une forte application dans des cas réels, pour analyser et décrire des phénomènes environnementaux complexes aussi bien que surmonter des problèmes méthodologiques non résolus relatifs à la caractérisation des structures spatio-temporelles, particulièrement, celles des occurrences de feux forestières. Ainsi, cette Thèse fournit une réponse à la demande croissante de la gestion et du monitoring environnemental pour le déploiement d'outils d'évaluation des risques et des dangers naturels et anthro- pogéniques. Les majeures contributions de ce travail ont été présentées aux conférences nationales et internationales, et ont été aussi publiées dans 5 revues internationales avec comité de lecture. Des collaborations nationales et internationales ont été aussi établies et accomplies avec succès.

New York City: "gilt cage" or "promised land" ? : representations of urban space in Edith Wharton and Anzia Yezierska

This thesis explores the importance of literary New York City in the urban narratives of Edith Wharton and Anzia Yezierska. It specifically looks at the Empire City of the Progressive Period when the concept of the city was not only a new theme but also very much a typical American one which was as central to the American experience as had been the Western frontier. It could be argued, in fact, that the American city had become the new frontier where modern experiences like urbanization, industrialization, immigration, and also women's emancipation and suffrage, caused all kinds of sensations on the human scale from smoothly lived assimilation and acculturation to deeply felt alienation because of the constantly shifting urban landscape. The developing urban space made possible the emergence of new female literary protagonists like the working girl, the reformer, the prostitute, and the upper class lady dedicating her life to 'conspicuous consumption'. Industrialization opened up city space to female exploration: on the one hand, upper and middle class ladies ventured out of the home because of the many novel urban possibilities, and on the other, lower class and immigrant girls also left their domestic sphere to look for paid jobs outside the home. New York City at the time was not only considered the epicenter of the world at large, it was also a city of great extremes. Everything was constantly in flux: small brownstones made way for ever taller skyscrapers and huge waves of immigrants from Europe pushed native New Yorkers further uptown on the island, adding to the crowdedness and intensity of the urban experience. The city became a polarized urban space with Fifth Avenue representing one end of the spectrum and the Lower East Side the other. Questions of space and the urban home greatly mattered. It has been pointed out that the city setting functions as an ideal means for the display of human nature as well as social processes. Narrative representations of urban space, therefore, provide a similar canvas for a protagonist's journey and development. From widely diverging vantage points both Edith Wharton and Anzia Yezierska thus create a polarized city where domesticity is a primal concern. Looking at all of their New York narratives by close readings of exterior and interior city representations, this thesis shows how urban space greatly affects questions of identity, assimilation, and alienation in literary protagonists who cannot escape the influence of their respective urban settings. Edith Wharton's upper class "millionaire" heroines are framed and contained by the city interiors of "old" New York, making it impossible for them to truly participate in the urban landscape in order to develop outside of their 'Gilt Cages'. On the other side are Anzia Yezierska's struggling "immigrant" protagonists who, against all odds, never give up in their urban context of streets, rooftops, and stoops. Their New York City, while always challenging and perpetually changing, at least allows them perspectives of hope for a 'Promised Land' in the making. Central for both urban narrative approaches is the quest for a home as an architectural structure, a spiritual resting place, and a locus for identity forming. But just as the actual city embraces change, urban protagonists must embrace change also if they desire to find fulfillment and success. That this turns out to be much easier for Anzia Yezierska's driven immigrants rather than for Edith Wharton's well established native New Yorkers is a surprising conclusion to this urban theme.

Photodynamic therapy : a pathway for enhanced delivery of liposomal doxorubicin to tumors

Abstract Part I : Background : Isolated lung perfusion (ILP) was designed for the treatment of loco-regional malignancies of the lung. In contrast to intravenous (IV) drug application, ILP allows for a selective administration of cytostatic agents such as doxorubicin to the lung while sparing non-affected tissues. However, the clinical results with ILP were disappointing. Doxorubicinbased ILP on sarcoma rodent lungs suggested high overall doxorubicin concentrations within the perfused lung but a poor penetration of the cytostatic agent into tumors. The same holds true for liposomal-encapsulated macromolecular doxorubicin (LiporubicinTM) In specific conditions, low-dose photodynamic therapy (PDT) can enhance the distribution of macromolecules across the endothelial bamer in solid tumors. It was recently postulated that tumor neovessels were more responsive to PDT than the normal vasculature. We therefore hypothesized that Visudyne®-mediated PDT could selectively increase liposomal doxorubicin (LiporubicinTM) uptake in sarcoma tumors to rodent lungs during intravenous (IV) drug administration and isolated lung perfusion (ILP). Material and Methods : A sarcoma tumor was generated in the left lung of Fisher rats by subpleural injection of a sarcoma cell ,suspension via thoracotomy. Ten days later, LiporubicinTM is administered IV or by single pass antegrade ILP, with or without Visudyne® -mediated low-dose PDT pre-treatment of the sarcoma bearing lung. The drug concentration and distribution were assessed separately in tumors and lung tissues by high pressure liquid chromatography (HPLC) and fluorescence microscopy (FNI~, respectively. Results : PDT pretreatment before IV LiporubicinTM administration resulted in a significantly higher tumor drug uptake and tumor to lung drug ratio compared to IV drug injection alone without affecting the blood flow and drug distribution in the lung. PDT pre-treatment before LiporubicinTM-based ILP also resulted in a higher tumor drug uptake and a higher tumor to lung drug ratio compared to ILP alone, however, these differences were not significant due to a heterogeneous blood flow drug distribution during ILP which was further accentuated by PDT. Conclusions : Low-dose Visudyne®-mediated PDT pre-treatment has the potential to selectively enhance liposomal encapsulated doxorubicin uptake in tumors but not in normal lung tissue after IV drug application in a rat model of sarcoma tumors to the lung which opens new perspectives for the treatment of superficially spreading chemoresistant tumors of the chest cavity such as mesothelioma or malignant effusion. However, the impact of PDT on macromolecular drug uptake during ILP is limited since its therapeutic advantage is circumvented by ILP-induced heterogeneicity of blood flow and drug distribution Abstract Part II Background : Photodynamic therapy (PDT) with Visudyne® acts by direct cellular phototoxicity and/or by an indirect vascular-mediated effect. Here, we demonstrate that the vessel integrity interruption by PDT can promote the extravasation of a macromolecular agent in normal tissue. To obtain extravasation in normal tissue PDT conditions were one order of magnitude more intensive than the ones in tissue containing neovessels reported in the literature. Material and Methods : Fluorescein isothiocyanate dextran (FITC-D, 2000kDa), a macromolecular agent, was intravenously injected 10 minutes before (LKO group, n=14) or 2 hours (LK2 group, n=16) after Visudyne® mediated PDT in nude mice bearing a dorsal skin fold chamber. Control animals had no PDT (CTRL group, n=8). The extravasation of FITC-D from blood vessels in striated muscle tissue was observed in both groups in real-time for up to 2500 seconds after injection. We also monitored PDT-induced leukocyte rolling in-vivo and assessed, by histology, the corresponding inflammatory reaction score in the dorsal skin fold chambers. Results : In all animals, at the applied PDT conditions, FITC-D extravasation was significantly enhanced in the PDT treated areas as compared to the surrounding non-treated areas (p<0.0001). There was no FITC-D leakage in the control animals. Animals from the LKO group had significantly less FITC-D extravasation than those from the LK2 group (p = 0.0002). In the LKO group FITC-D leakage correlated significantly with the inflammation (p < 0.001). Conclusions: At the selected conditions, Visudyne-mediated PDT promotes vascular leakage and FITC-D extravasation into the interstitial space of normal tissue. The intensity of vascular leakage depends on the time interval between PDT and FITC-D injection. This concept could be used to locally modulate the delivery of macromolecules in vivo. Résumé : La perfusion cytostatique isolée du poumon permet une administration sélective des agents cytostatiques sans implication de la circulation systémique avec une forte accumulation au niveau du poumon mais une faible pénétration dans les tumeurs. La thérapie photodynamique (PDT) qui consiste en l'application d'un sensibilisateur activé par lumière laser non- thermique d'une longueur d'onde définie permet dans certaines conditions, une augmentation de la pénétration des agents cytostatiques macromoléculaires à travers la barrière endothéliale tumorale. Nous avons exploré cet avantage thérapeutique de la PDT dans un modèle expérimental afin d'augmenter d'une manière sélective la pénétration tumorale de la doxorubicin pegylée, liposomal- encapsulée macromoléculaire (Liporubicin). Une tumeur sarcomateuse a été générée au niveau du poumon de rongeur suivie d'administration de Liporubicin, soit par voie intraveineuse soit par perfusion isolée du poumon (ILP). Une partie des animaux ont reçus un prétraitement de la tumeur et du poumon sous jacent par PDT avec Visudyne comme photosensibilisateur. Les résultats ont démontrés que la PDT permet, sous certaines conditions, une augmentation sélective de Liporubicin dans les tumeurs mais pas dans le parenchyme pulmonaire sous jacent. Après administration intraveineuse de Liporubicin et prétraitement par PDT, l'accumulation dans les tumeurs était significative par rapport au poumon, et aux tumeurs sans PDT. Le même phénomène est observé après ILP du poumon. Cependant, les différences avec ou sans PDT n'étaient pas significatives lié à und distribution hétérogène de Liporubicin dans le poumon perfusé après ILP. Dans une deuxième partie de l'expérimentation, nous avons exploré la microscopie intra-vitale pour déterminer l'extravasion des substances macromoléculaires (FITS) à travers la barrière endothéliale avec ou sans Visudyne-PDT au niveau des chambres dorsales des souris nues. Les résultats montrent qu'après PDT, l'extravasion de FITS a été augmentée de manière significative par rapport au tissu non traité. L'intensité de l'extravasion de FITS dépendait également de l'intervalle entre PDT et injection de FITS. En conclusion, les expérimentations montrent que la PDT est capable, sous certaines conditions, d'augmenter de manière significative l'extravasion des macromolécules à travers la barrière endothéliale et leur accumulation dans des tumeurs mais pas dans le parenchyme pulmonaire. Ces résultats permettent une nouvelle perspective de traitement pour des tumeurs superficielles intrathoraciques chimio-résistent comme l'épanchement pleural malin ou le mésothéliome pleural.

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization.

The goal of the present work was assess the feasibility of using a pseudo-inverse and null-space optimization approach in the modeling of the shoulder biomechanics. The method was applied to a simplified musculoskeletal shoulder model. The mechanical system consisted in the arm, and the external forces were the arm weight, 6 scapulo-humeral muscles and the reaction at the glenohumeral joint, which was considered as a spherical joint. The muscle wrapping was considered around the humeral head assumed spherical. The dynamical equations were solved in a Lagrangian approach. The mathematical redundancy of the mechanical system was solved in two steps: a pseudo-inverse optimization to minimize the square of the muscle stress and a null-space optimization to restrict the muscle force to physiological limits. Several movements were simulated. The mathematical and numerical aspects of the constrained redundancy problem were efficiently solved by the proposed method. The prediction of muscle moment arms was consistent with cadaveric measurements and the joint reaction force was consistent with in vivo measurements. This preliminary work demonstrated that the developed algorithm has a great potential for more complex musculoskeletal modeling of the shoulder joint. In particular it could be further applied to a non-spherical joint model, allowing for the natural translation of the humeral head in the glenoid fossa.

Increasing respiratory dead space improves sleep disordered breathing and hypoxemia in patients with chronic mountain sickness

Background: Chronic mountain sickness (CMS), which is characterised by hypoxemia, erythrocytosis and pulmonary hypertension, is a major public health problem in high-altitude dwellers. The only existing treatment is descent to low altitude, an option that for social reasons almost never exists. Sleep disordered breathing may represent an underlying mechanism. We recently found that in mountaineers increasing the respiratory dead space markedly improves sleep disordered breathing. The aim of the present study was to assess the effects of this procedure on sleep disordered breathing in patients with CMS. Methods: In 10 male Bolivian high-altitude dwellers (mean ± SD age, 59 ± 9 y) suffering from CMS (haemoglobin >20 g/L) full night sleep recordings (Embletta, RespMed) were obtained in La Paz (3600 m). In random order, one night was spent with a 500 ml increase in dead space through a custom designed full face mask and the other night without it. Exclusion criteria were: secondary erythrocytosis, smoking, drug intake, acute infection, cardio- pulmonary or neurologic disease and travelling to low altitude in the preceding 6 months. Results: The major new finding was that added dead space dramatically improved sleep disordered breathing in patients suffering from CMS. The apnea/hypopnea index decreased by >50% (from 34.5 ± 25.0 to 16.8 ± 14.9, P = 0.003), the oxygen desaturation index decreased from 46.2 ± 23.0 to 27.2 ± 20.0 (P = 0.0004) and hypopnea index from 28.8 ± 20.9 to 16.3 ± 14.0 (P = 0.01), whereas nocturnal oxygen saturation increased from 79.8 ± 3.6 to 80.9 ± 3.0% (P = 0.009). The procedure was easily accepted and well tolerated. Conclusion: Here, we show for the very first time that an increase in respiratory dead space through a fitted mask dramatically improves nocturnal breathing in high-altitude dwellers suffering from CMS. We speculate that when used in the long-term, this procedure will improve erythrocytosis and pulmonary hypertension and offer an inexpensive and easily implementable treatment for this major public health problem.

Faire la ville juste : une analyse "in itinere" de la maîtrise publique d'ouvrage du projet urbain Carré de Soie (métropole lyonnaise)

Dans certaines portions des agglomérations (poches de pauvreté de centre-ville, couronnes suburbaines dégradées, espaces périurbains sans aménité), un cumul entre des inégalités sociales (pauvreté, chômage, etc.) et environnementales (exposition au bruit, aux risques industriels, etc.) peut être observé. La persistance de ces inégalités croisées dans le temps indique une tendance de fond : la capacité d'accéder à un cadre de vie de qualité n'est pas équitablement partagée parmi les individus. Ce constat interroge : comment se créent ces inégalités ? Comment infléchir cette tendance et faire la ville plus juste ?¦Apporter des réponses à cette problématique nécessite d'identifier les facteurs de causalités qui entrent en jeu dans le système de (re)production des inégalités urbaines. Le fonctionnement des marchés foncier et immobilier, la « tyrannie des petites décisions » et les politiques publiques à incidence spatiale sont principalement impliqués. Ces dernières, agissant sur tous les éléments du système, sont placées au coeur de ce travail. On va ainsi s'intéresser précisément à la manière dont les collectivités publiques pilotent la production de la ville contemporaine, en portant l'attention sur la maîtrise publique d'ouvrage (MPO) des grands projets urbains.¦Poser la question de la justice dans la fabrique de la ville implique également de questionner les référentiels normatifs de l'action publique : à quelle conception de la justice celle-ci doit- elle obéir? Quatre perspectives (radicale, substantialiste, procédurale et intégrative) sont caractérisées, chacune se traduisant par des principes d'action différenciés. Une méthodologie hybride - empruntant à la sociologie des organisations et à l'analyse des politiques publiques - vient clore le volet théorique, proposant par un détour métaphorique d'appréhender le projet urbain comme une pièce de théâtre dont le déroulement dépend du jeu d'acteurs.¦Cette méthodologie est utilisée dans le volet empirique de la recherche, qui consiste en une analyse de la MPO d'un projet urbain en cours dans la première couronne de l'agglomération lyonnaise : le Carré de Soie. Trois grands objectifs sont poursuivis : descriptif (reconstruire le scénario), analytique (évaluer la nature de la pièce : conte de fée, tragédie ou match d'improvisation ?) et prescriptif (tirer la morale de l'histoire). La description de la MPO montre le déploiement successif de quatre stratégies de pilotage, dont les implications sur les temporalités, le contenu du projet (programmes, morphologies) et les financements publics vont être déterminantes. Sur la base de l'analyse, plusieurs recommandations peuvent être formulées - importance de l'anticipation et de l'articulation entre planification et stratégie foncière notamment - pour permettre à la sphère publique de dominer le jeu et d'assurer la production de justice par le projet urbain (réalisation puis entretien des équipements et espaces publics, financement de logements de qualité à destination d'un large éventail de populations, etc.). Plus généralement, un décalage problématique peut être souligné entre les territoires stratégiques pour le développement de l'agglomération et les capacités de portage limitées des communes concernées. Ce déficit plaide pour le renforcement des capacités d'investissement de la structure intercommunale.¦La seule logique du marché (foncier, immobilier) mène à la polarisation sociale et à la production d'inégalités urbaines. Faire la ville juste nécessite une forte volonté des collectivités publiques, laquelle doit se traduire aussi bien dans l'ambition affichée - une juste hiérarchisation des priorités dans le développement urbain - que dans son opérationnalisation - une juste maîtrise publique d'ouvrage des projets urbains.¦Inner-city neighborhoods, poor outskirts, and peri-urban spaces with no amenities usually suffer from social and environmental inequalities, such as poverty, unemployment, and exposure to noise and industrial hazards. The observed persistence of these inequalities over time points to an underlying trend - namely, that access to proper living conditions is fundamentally unequal, thus eliciting the question of how such inequalities are effected and how this trend can be reversed so as to build a more equitable city.¦Providing answers to such questions requires that the causal factors at play within the system of (re)production of urban inequalities be identified. Real estate markets, "micromotives and macrobehavior", and public policies that bear on space are mostly involved. The latter are central in that they act on all the elements of the system. This thesis therefore focuses on the way public authorities shape the production of contemporary cities, by studying the public project ownership of major urban projects.¦The study of justice within the urban fabric also implies that the normative frames of reference of public action be questioned: what conception of justice should public action refer to? This thesis examines four perspectives (radical, substantialist, procedural, and integrative) each of which results in different principles of action. This theoretical part is concluded by a hybrid methodology that draws from sociology of organizations and public policy analysis and that suggests that the urban project may be understood as a play, whose outcome hinges on the actors' acting.¦This methodology is applied to the empirical analysis of the public project ownership of an ongoing urban project in the Lyon first-ring suburbs: the Carré de Soie. Three main objectives are pursued: descriptive (reconstructing the scenario), analytical (assessing the nature of the play - fairy tale, tragedy or improvisation match), and prescriptive (drawing the moral of the story). The description of the public project ownership shows the successive deployment of four control strategies, whose implications on deadlines, project content (programs, morphologies), and public funding are significant. Building on the analysis, several recommendations can be made to allow the public sphere to control the process and ensure the urban project produces equity (most notably, anticipation and articulation of planning and real- estate strategy, as well as provision and maintenance of equipment and public spaces, funding of quality housing for a wide range of populations, etc.). More generally, a gap can be highlighted between those territories that are strategic to the development of the agglomeration and the limited resources of the municipalities involved. This deficit calls for strengthening the investment abilities of the intermunicipal structure.¦By itself, the real-estate market logic brings about social polarization and urban inequalities. Building an equitable city requires a strong will on the part of public authorities, a will that must be reflected both in the stated ambition - setting priorities of urban development equitably - and in its implementation managing urban public projects fairly.