Introduction

Although increasing our knowledge of the properties of networks of cities is essential, these properties can be measured at the city level, and must be assessed by analyzing actor networks. The present volume focuses less on individual characteristics and more on the interactions of actors and institutions that create functional territories in which the structure of existing links constrains emerging links. Rather than basing explanations on external factors, the goal is to determine the extent to which network properties reflect spatial distributions and create local synergies at the meso level that are incorporated into global networks at the macro level where different geographical scales occur. The paper introduces the way to use the graphs structure to identify empirically relevant groups and levels that explain dynamics. It defines what could be called âeurooemulti-levelâeuro, âeurooemulti-scaleâeuro, or âeurooemultidimensionalâeuro networks in the context of urban geography. It explains how the convergence of the network multi-territoriality paradigm collaboratively formulated, and manipulated by geographers and computer scientists produced the SPANGEO project, which is exposed in this volume.

Objective assessment of image quality in conventional and digital mammography taking into account dynamic range.

The goal of this work is to develop a method to objectively compare the performance of a digital and a screen-film mammography system in terms of image quality. The method takes into account the dynamic range of the image detector, the detection of high and low contrast structures, the visualisation of the images and the observer response. A test object, designed to represent a compressed breast, was constructed from various tissue equivalent materials ranging from purely adipose to purely glandular composition. Different areas within the test object permitted the evaluation of low and high contrast detection, spatial resolution and image noise. All the images (digital and conventional) were captured using a CCD camera to include the visualisation process in the image quality assessment. A mathematical model observer (non-prewhitening matched filter), that calculates the detectability of high and low contrast structures using spatial resolution, noise and contrast, was used to compare the two technologies. Our results show that for a given patient dose, the detection of high and low contrast structures is significantly better for the digital system than for the conventional screen-film system studied. The method of using a test object with a large tissue composition range combined with a camera to compare conventional and digital imaging modalities can be applied to other radiological imaging techniques. In particular it could be used to optimise the process of radiographic reading of soft copy images.

A Small World Perspective on Urban Systems

The theory of small-world networks as initiated by Watts and Strogatz (1998) has drawn new insights in spatial analysis as well as systems theory. The theoryâeuro?s concepts and methods are particularly relevant to geography, where spatial interaction is mainstream and where interactions can be described and studied using large numbers of exchanges or similarity matrices. Networks are organized through direct links or by indirect paths, inducing topological proximities that simultaneously involve spatial, social, cultural or organizational dimensions. Network synergies build over similarities and are fed by complementarities between or inside cities, with the two effects potentially amplifying each other according to the âeurooepreferential attachmentâeuro hypothesis that has been explored in a number of different scientific fields (Barabási, Albert 1999; Barabási A-L 2002; Newman M, Watts D, Barabàsi A-L). In fact, according to Barabási and Albert (1999), the high level of hierarchy observed in âeurooescale-free networksâeuro results from âeurooepreferential attachmentâeuro, which characterizes the development of networks: new connections appear preferentially close to nodes that already have the largest number of connections because in this way, the improvement in the network accessibility of the new connection will likely be greater. However, at the same time, network regions gathering dense and numerous weak links (Granovetter, 1985) or network entities acting as bridges between several components (Burt 2005) offer a higher capacity for urban communities to benefit from opportunities and create future synergies. Several methodologies have been suggested to identify such denser and more coherent regions (also called communities or clusters) in terms of links (Watts, Strogatz 1998; Watts 1999; Barabási, Albert 1999; Barabási 2002; Auber 2003; Newman 2006). These communities not only possess a high level of dependency among their member entities but also show a low level of âeurooevulnerabilityâeuro, allowing for numerous redundancies (Burt 2000; Burt 2005). The SPANGEO project 2005âeuro"2008 (SPAtial Networks in GEOgraphy), gathering a team of geographers and computer scientists, has included empirical studies to survey concepts and measures developed in other related fields, such as physics, sociology and communication science. The relevancy and potential interpretation of weighted or non-weighted measures on edges and nodes were examined and analyzed at different scales (intra-urban, inter-urban or both). New classification and clustering schemes based on the relative local density of subgraphs were developed. The present article describes how these notions and methods contribute on a conceptual level, in terms of measures, delineations, explanatory analyses and visualization of geographical phenomena.

L'angio-CT post-mortem: un nouvel outil diagnostique

L'angio-CT post-mortem est un examen peu invasif qui permet d'investiguer le système vasculaire d'une manière détaillée impossible à obtenir lors d'une autopsie conventionnelle. Le groupe de recherche lausannois sur l'angio-CT a développé un protocole standardisé pour une technique appelée «angio-CT post-mortem en phases multiples», qui permet de réaliser des angio-CT de manière simple et d'améliorer le diagnostic radiologique. De plus, de nouveaux équipements incluant une pompe à perfusion, du matériel prêt à l'emploi et un produit de contraste spécifique ont été développés. L'angio-CT permet la détection d'une source d'hémorragie, d'une malformation du système vasculaire, de lésions d'artériosclérose, d'une occlusion d'un vaisseau et la visualisation de l'anatomie vasculaire.

Identification and functional characterization of auxiliary enzymes of the β-oxidation in " Arabidopsis thaliana "

Abstract: The ß-oxidation is the universal pathway that allows living organisms to degrade fatty acids. leading to lipid homeostasis and carbon and energy recovery from the fatty acid molecules. This pathway is centred on four core enzymatic activities sufficient to degrade saturated fatty acids. Additional auxiliary enzymes of the ß-oxidation are necessary for the complete degradation of a larger array of molecules encompassing the unsaturated fatty acids. The main pathways of the ßoxidation of fatty acids have been investigated extensively and auxiliary enzymes are well-known in mammals and yeast. The comparison of the established ß-oxidation systems suggests that the activities that are required to proceed to the full degradation of unsaturated fatty acids are present regardless of the organism and rely on common active site templates. The precise identity of the plant enzymes was unknown. By homology searches in the genome of Arabidopsis thaliana, I identified genes. encoding for proteins that could be orthologous to the yeast or animal auxiliary enzymes Δ 3, Δ 2-enoyl-CoA isomerase, Δ 3,5, Δ 2,4 -dienoyl-CoA isomerase, and type 2 enoyl-CoA hydratase. I established that these genes are expressed in Arabidopsis and that their expression can be correlated to the expression of core ß-oxidation genes. Through the observation of chimeric fluorescent protein fusions, I demonstrated that the identified proteins are localized in the peroxisóme, the only organelle where the ß-oxidation occurs in plants. Enzymatic assays were performed with the partially purified enzymes to demonstrate that the identified enzymes can catalyze the same in vitro reactions as their non-plant orthologs. The activities in vivo of the plant enzymes were demonstrated by heterologous complementation of the corresponding yeast Saccharomyces cerevisiae mutants. The complementation was visualized using the artificial polyhydroxyalkanoate (PHA) production in yeast peroxisomes. The recombinant strains, expressing a Pseudomonas aeruginosa PHA synthase modified for a peroxisomal localization, produce this polymer that serves as a trap for the 3-hydroxyacyl-CoA intermediaries of the ßoxidation and that reflects qualitatively and quantitatively the array of molecules that are processed through the ß-oxidation. This complementation demonstrated the implication of the plant Δ 3, Δ 2-enoyl-CoA isomerases and Δ3,5, Δ2,4-dienoyl-CoA isomerase in the degradation of odd chain position unsaturated fatty acids. The presence of a monofunctional type 2 enoyl-CoA hydratase is a novel in eukaryotes. Downregulation of the corresponding gene expression in an Arabidopsis line, modified to produce PHA in the peroxisome, demonstrated thàt this enzyme participates in vivo to the conversion of the intermediate 3R-hydroxyacyl-CoA, generated by the metabolism of fatty acids with a cis (Z)-unsaturated bond on an even-numbered carbon, to the 2Eenoyl-CoA for further degradation through the core ß-oxidation cycle. Résumé: La ß-oxydation est une voie universelle de dégradation des acides gras qui permet aux organismes vivants d'assurer une homéostasie lipidique et de récupérer l'énergie et le carbone contenus dans les acides gras. Le coeur de cette voie est composé de quatre réactions enzymatiques suffisantes à la dégradation des acides gras saturés. La présence des enzymes auxiliaires de la ß-oxydation est nécessaire à la dégradation d'une gamme plus étendue de molécules comprenant les acides gras insaturés. Les voies principales de la ß-oxydation des acides gras ont été étudiées en détail et les enzymes auxiliaires sont déterminées chez les mammifères et la levure. La comparaison entre les systèmes de ß-oxydation connus suggère que les activités requises pour la dégradation complète des acides gras insaturés reposent sur la présence de site actifs similaires. L'identité précise des enzymes auxiliaires chez les plantes était inconnue. En cherchant par homologie dans le génome de la plante modèle Arabidopsis thaliana, j'ai identifié des gènes codant pour des protéines pouvant être orthologues aux enzymes auxiliaires Δ3 Δ2-enoyl-CoA isomérase, Δ 3,5 Δ 2,4-dienoyl-CoA isomérase et enoyl-CoA hydratase de type 2 d'origine fongique ou mammalienne. J'ai établi la corrélation de l'expression de ces gènes dans Arabidopsis avec celle de gènes des enzymes du coeur de la ß-oxydation. En observant des chimères de fusion avec des protéines fluorescentes, j'ai démontré que les protéines identifiées sont localisées dans le péroxysomes, le seul organelle où la ß-oxydation se déroule chez les plantes. Des essais enzymatiques ont été conduits avec ces enzymes partiellement purifiées pour démontrer que les enzymes identifiées sont capables de catalyser in vitro les mêmes réactions que leurs orthologues non végétaux. Les activités des enzymes végétales in vivo ont été .démontrées par complémentation hétérologue des mutants de délétion correspondants de levure Saccharomyces cerevisiae. La visualisation de la complémentation est rendue possible par la synthèse de polyhydroxyalcanoate (PHA) dans les péroxysomes de levure. Les souches recombinantes expriment la PHA synthase de Pseudomonas aeruginosa modifiée pour être localisée dans le péroxysome produisent ce polymère qui sert de piège pour les 3-hydroxyacylCoAs intermédiaires de la ß-oxydation et qui reflète qualitativement et quantitativement la gamme de molécules qui subit la ß-oxydation. Cette complémentation a permis de démontrer que les Δ3, Δ2-enoyl-CoA isomérases, et la Δ3.5, Δ2,4-dienoyl-CoA isomérase végétales sont impliquées dans la dégradation des acides gras insaturés en position impaire. L'enoyl-CoA hydratase de type 2 monofonctionelle est une enzyme nouvelle chez les eucaryotes. La sous-expression du gène correspondant dans une lignée d'Arabidopsis modifiée pour produite du PHA dans le péroxysome a permis de démontrer que cette enzyme participe in vivo à la dégradation des acides gras ayant une double liaison en conformation cis (Z) en position paire.

Evaluation of the Video Intubation Unit in morbid obese patients.

BACKGROUND: Tracheal intubation may be more difficult in morbidly obese (MO) patients than in the non-obese. The aim of this study was to evaluate clinically if the use of the Video Intubation Unit (VIU), a video-optical intubation stylet, could improve the laryngoscopic view compared with the standard Macintosh laryngoscope in this specific population. METHODS: We studied 40 MO patients (body mass index >35 kg/m(2)) scheduled for bariatric surgery. Each patient had a conventional laryngoscopy and a VIU inspection. The laryngoscopic grades (LG) using the Cormack and Lehane scoring system were noted and compared. Thereafter, the patients were randomised to be intubated with one of the two techniques. In one group, the patients were intubated with the help of the VIU and in the control group, tracheal intubation was performed conventionally. The duration of intubation, as well as the minimal SpO(2) achieved during the procedure, were measured. RESULTS: Patient characteristics were similar in both groups. Seventeen patients had a direct LG of 2 or 3 (no patient had a grade of 4). Out of these 17 patients, the LG systematically improved with the VIU and always attained grade 1 (P<0.0001). The intubation time was shorter within the VIU group, but did not attain significance. There was no difference in the SpO(2) post-intubation. CONCLUSION: In MO patients, the use of the VIU significantly improves the visualisation of the larynx, thereby improving the intubation conditions.

Tree edit distance as a baseline approach for paraphrase representation

Finding an adequate paraphrase representation formalism is a challenging issue in Natural Language Processing. In this paper, we analyse the performance of Tree Edit Distance as a paraphrase representation baseline. Our experiments using Edit Distance Textual Entailment Suite show that, as Tree Edit Distance consists of a purely syntactic approach, paraphrase alternations not based on structural reorganizations do not find an adequate representation. They also show that there is much scope for better modelling of the way trees are aligned.

Technique de reconstruction itérative VEO : évaluation en tomodensitométrie thoracique chez l'enfant.

Objectifs: Evaluer la technique de reconstruction itérative VEO en tomodensitométrie (TDM) du thorax chez l'enfant. Matériels et méthodes: Etude prospective, basée sur 20 patients (7-18 ans), suivis pour mucoviscidose et adressés pour TDM de suivi. Dix patients (groupe A) ont eu une acquisition basse-dose habituelle (BD). Dix patients (groupe B) ont eu une acquisition très-basse-dose (TBD) et ultra-basse-dose (UBD). Les acquisitions BD étaient reconstruites par rétroprojection filtrée (RPF), les acquisitions TBD et UBD étaient reconstruites par RPF et VEO. L'évaluation de VEO était basée sur la réduction de dose et la qualité des images (mesures de bruit et scores de visualisation des structures pulmonaires). Résultats: Une réduction de dose d'environ 50% était obtenue dans le groupe B. La réduction du bruit en VEO par rapport aux RPF était de 55% en TBD et de 75% en UBD. En VEO, une amélioration des scores de visualisation des structures pulmonaires était obtenue en TBD et UBD. Cependant, en VEO-UBD, la visualisation des structures distales demeuraient parfois insuffisante et celle des structures proximales était altérée par une modification de texture de l'image. Conclusion: Malgré une altération possible de la texture de l'image en UBD, la technique de reconstruction VEO est performante en réduction de dose et amélioration des images.

Silicon quantum dots embedded in a SiO2 matrix: From structural study to carrier transport properties

We study the details of electronic transport related to the atomistic structure of silicon quantum dots embedded in a silicon dioxide matrix using ab initio calculations of the density of states. Several structural and composition features of quantum dots (QDs), such as diameter and amorphization level, are studied and correlated with transport under transfer Hamiltonian formalism. The current is strongly dependent on the QD density of states and on the conduction gap, both dependent on the dot diameter. In particular, as size increases, the available states inside the QD increase, while the QD band gap decreases due to relaxation of quantum confinement. Both effects contribute to increasing the current with the dot size. Besides, valence band offset between the band edges of the QD and the silica, and conduction band offset in a minor grade, increases with the QD diameter up to the theoretical value corresponding to planar heterostructures, thus decreasing the tunneling transmission probability and hence the total current. We discuss the influence of these parameters on electron and hole transport, evidencing a correlation between the electron (hole) barrier value and the electron (hole) current, and obtaining a general enhancement of the electron (hole) transport for larger (smaller) QD. Finally, we show that crystalline and amorphous structures exhibit enhanced probability of hole and electron current, respectively.

Visualising microorganisms from molecules to cells.

10 images from FEMS articles have been selected to show the diversity of visualisation used in microbiology.

Modeling and control of electromechanical systems

The material presented in the these notes covers the sessions Modelling of electromechanical systems, Passive control theory I and Passive control theory II of the II EURON/GEOPLEX Summer School on Modelling and Control of Complex Dynamical Systems.We start with a general description of what an electromechanical system is from a network modelling point of view. Next, a general formulation in terms of PHDS is introduced, and some of the previous electromechanical systems are rewritten in this formalism. Power converters, which are variable structure systems (VSS), can also be given a PHDS form.We conclude the modelling part of these lectures with a rather complex example, showing the interconnection of subsystems from several domains, namely an arrangement to temporally store the surplus energy in a section of a metropolitan transportation system based on dc motor vehicles, using either arrays of supercapacitors or an electric poweredflywheel. The second part of the lectures addresses control of PHD systems. We first present the idea of control as power connection of a plant and a controller. Next we discuss how to circumvent this obstacle and present the basic ideas of Interconnection and Damping Assignment (IDA) passivity-based control of PHD systems.

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.

Interest rates mapping

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well. (C) 2008 Elsevier B.V. All rights reserved.

Modeling morphogen gradient formation from arbitrary realistically shaped sources.

Much of the analytical modeling of morphogen profiles is based on simplistic scenarios, where the source is abstracted to be point-like and fixed in time, and where only the steady state solution of the morphogen gradient in one dimension is considered. Here we develop a general formalism allowing to model diffusive gradient formation from an arbitrary source. This mathematical framework, based on the Green's function method, applies to various diffusion problems. In this paper, we illustrate our theory with the explicit example of the Bicoid gradient establishment in Drosophila embryos. The gradient formation arises by protein translation from a mRNA distribution followed by morphogen diffusion with linear degradation. We investigate quantitatively the influence of spatial extension and time evolution of the source on the morphogen profile. For different biologically meaningful cases, we obtain explicit analytical expressions for both the steady state and time-dependent 1D problems. We show that extended sources, whether of finite size or normally distributed, give rise to more realistic gradients compared to a single point-source at the origin. Furthermore, the steady state solutions are fully compatible with a decreasing exponential behavior of the profile. We also consider the case of a dynamic source (e.g. bicoid mRNA diffusion) for which a protein profile similar to the ones obtained from static sources can be achieved.

Successful migration of three tracers without identification of sentinel nodes during intraoperative lymphatic mapping for non-small cell lung cancer.

Prospective comparative evaluation of patent V blue, fluorescein and (99m)TC-nanocolloids for intraoperative sentinel lymph node (SLN) mapping during surgery for non-small cell lung cancer (NSCLC). Ten patients with peripherally localised clinical stage I NSCLC underwent thoracotomy and peritumoral subpleural injection of 2 ml of patent V blue dye, 1 ml of 10% fluorescein and 1ml of (99m)Tc-nanocolloids (0.4 mCi). The migration and spatial distribution pattern of the tracers was assessed by direct visualisation (patent V blue), visualisation of fluorescence signalling by a lamp of Wood (fluorescein) and radioactivity counting with a hand held gamma-probe ((99m)Tc-nanocolloids). Lymph nodes at interlobar (ATS 11), hilar (ATS 10) and mediastinal (right ATS 2,4,7; left ATS 5,6,7) levels were systematically assessed every 10 min up to 60 min after injection, followed by lobectomy and formal lymph node dissection. Successful migration from the peritumoral area to the mediastinum was observed for all three tracers up to 60 min after injection. The interlobar lympho-fatty tissue (station ATS 11) revealed an early and preferential accumulation of all three tracers for all tumours assessed and irrespective of the tumour localisation. However, no preferential accumulation in one or two distinct lymph nodes was observed up to 60 min after injection for all three tracers assessed. Intraoperative SLN mapping revealed successful migration of the tracers from the site of peritumoral injection to the mediastinum, but in a diffuse pattern without preferential accumulation in sentinel lymph nodes.