Using group role-playing games with gifted children and adolescents: A psychosocial intervention model

Gifted children develop asynchronously, often advanced for their age cognitively, but at or between their chronological and mental ages socially and emotionally (Robinson, 2008). In order to help gifted children and adolescents develop and practice social and emotional self-regulation skills, we investigated the use of an Adlerian play therapy approach during pen-and-paper role-playing games. Additionally, we used Goffman's (1961, 1974) social role identification and distance to encourage participants to experiment with new identities. Herein, we propose a psychosocial model of interactions during role-playing games based on Goffman's theory and Adlerian play therapy techniques, and suggest that role-playing games are an effective way of intervening with gifted children and adolescents to improve their intra- and interpersonal skills. We specifically targeted intrapersonal skills of exercising creativity, becoming self-aware, and setting individual goals by raising participants' awareness of their privately logical reasons for making decisions and their levels of social interest. We also targeted their needs and means of seeking significance in the group to promote collaboration and interaction skills with other gifted peers through role analysis, embracement, and distancing. We report results from a case study and conclude that role-playing games deserve more attention, both from researchers and clinical practitioners, because they encourage change while improving young clients' social and emotional development.

Les enjeux spatiaux et sociaux d'une dynamique interethnique en transition dans les quartiers de centre-ville. La construction des diverses formes de représentation dans la manière de définir un quartier de Nice.

Résumé Cette thèse de doctorat se fixe comme objectif général de définir la spécificité d'un espace urbain situé en centre-ville à travers sa dynamique sociale et interethnique. Il s'agit de relever les différentes formes et manifestations ethniques, mais également de comprendre la manière dont est vécue cette socialité, tout en tenant compte du contexte urbain dans lequel elles se situent. La particularité de ce travail est le fait de proposer une démarche alliant deux disciplines à savoir la géographie et la sociologie, dans le but de proposer une recherche transdisciplinaire, avec comme conséquence la mise en oeuvre d'une démarche commune nourrie des deux disciplines. Les quartiers ethniques situés en centre-ville ne se présentent pas comme des espaces figés, puisque des changements fréquents sont observés aussi bien au niveau de leur population que de l'affectation des locaux commerciaux et des logements. Ils semblent davantage se caractériser comme des « espaces à l'avenir indéterminé » dans le sens où leur avenir aussi bien au niveau de leur structure urbaine que de leurs rapports sociaux ne peut être prévisible et qu'ils sont par ailleurs susceptibles d'être soumis à de nombreux changements, d'où l'idée également d' « espaces en mutations ». Pour aborder ces thématiques de recherche, nous avons opté pour une étude fondée sur le principe de la représentation et de l'une des formes essentielles à travers lesquelles elles se produisent et se manifestent, à savoir les images. Cette recherche privilégie ainsi une analyse dynamique du phénomène interethnique, l'inscrivant dans le courant constructiviste qui s'intéresse notamment à l'étude des pratiques des acteurs sociaux et leurs représentations, dans le sens qu'elles sont le fruit d'une construction sociale. Dans ce type de démarche analytique, il s'agit de prendre en considération à la fois l'analyse du chercheur et la réalité objective et subjective du vécu des acteurs urbains qui regroupent les riverains, les commerçants, les membres associatifs, les politiques et les médias. C'est justement l'association de ces deux approches qui permet de définir un quartier urbain. Abstract "The spatial and social stakes of an interethnic dynamics in transition in the downtown area. The construction of the various forms of representations in order to define an urban quarter in Nice" The general objective of this PHD is to define the specificity of an urban space located in the downtown area with social and interethnic dynamics. Specifically the diverse ethnic form and sign to include the understanding mariner where society has lived in a specific urban context. The particularity of this work is to advance an analytic method using two disciplines: geography and sociology, in order to bring up a transdisciplinarian research with a common method. The ethnic quarters in the inner-city area are not presented as fixed spaces, since frequent changes were observed in the population level and at the allotment level of the commercial premises and the lodgings. Defined as "undetermined future spaces" as their future in the urban structure or the social relations can not be foresee and are supposed to be subjective to many changes. Therefore speaking of "mutations spaces". The option of this type of thematic research is the study of the principle of representation and essential form which shows and produces the images. Research promotes a dynamic analyses of the interethnic phenomena, inside the constructivist which is focused the practices of social actors, their own representations, and products of a social construction. The production of analytical approach is necessary in order to consider the analyses of the research worker and the objective and subjective reality of the life of the urban actors. It gathers the residents, tradesmen, members of associations, politicians and the press. The association of these types of approach define a urban quarter.

Occupational exposure to solar ultraviolet radiation of outdoor workers in France

Question: Outdoor occupational exposure could be associated with important cumulative and intense exposure to ultraviolet (UV) solar radiation. Such exposure would increase risk of skin cancer. However, little information exists on jobs associated with intense UV exposure. The objective of this study was to characterise occupational UV exposure in a representative sample in France. Methods: A population-based survey was conducted in May-June 2012 through computer-assisted telephonic interviews in population 25 to 69 years of age. Individual UV irradiation was computed with declared time and place of residence matched to UV records from satellite measurement (Eurosun project). We analysed factors influencing exposure to UV (annual average and seasonal peak). Results: A total of 1442 individuals declared having an occupational exposure to UV which represents 18% of population aged 25 to 69 years. Outdoor workers were more frequently men (58%), aged 40-54 (43%), with a phototype III or IV (69%). Occupations associated with highest UV exposure were: construction workers (annual daily average 62.8 Joules/m2), gardeners (62.6), farmers (52.8), culture/art/social sciences workers (52.0) and transport workers/mail carriers (49.5). The maximum of UVA exposure was found for occupation with a strong seasonality of exposure: culture, art or social sciences works (98.1 Joules/m2), construction works (97.2), gardening (96.7) and farming (95.0). Significant factors associated with high occupational UV exposure were gender (men vs. women: 53.6 vs. 42.6), phototype (IV vs. I: 51.9 vs. 45.5) and taking lunch outdoors (always vs. never: 59.8 vs. 48.6). Conclusion: Our study showed that some occupations were associated with particularly intense UV exposure such as farmers, gardeners, construction workers. Other unexpected occupations were also associated with high UV exposure such as transport workers, mail carriers and culture/art/social sciences workers.

Analyse de réseau piéton et gestion scolaire: sinuosité, centricité et transport optimal régularisé

Que ce soit d'un point de vue, urbanistique, social, ou encore de la gouvernance, l'évolution des villes est un défi majeur de nos sociétés contemporaines. En offrant la possibilité d'analyser des configurations spatiales et sociales existantes ou en tentant de simuler celles à venir, les systèmes d'information géographique sont devenus incontournables dans la gestion et dans la planification urbaine. En cinq ans la population de la ville de Lausanne est passée de 134'700 à 140'570 habitants, alors que les effectifs de l'école publique ont crû de 12'200 à 13'500 élèves. Cet accroissement démographique associé à un vaste processus d'harmonisation de la scolarité obligatoire en Suisse ont amené le Service des écoles à mettre en place et à développer en collaboration avec l'université de Lausanne des solutions SIG à même de répondre à différentes problématiques spatiales. Établies en 1989, les limites des établissements scolaires (bassins de recrutement) ont dû être redéfinies afin de les réadapter aux réalités d'un paysage urbain et politique en pleine mutation. Dans un contexte de mobilité et de durabilité, un système d'attribution de subventions pour les transports publics basé sur la distance domicile-école et sur l'âge des écoliers, a été conçu. La réalisation de ces projets a nécessité la construction de bases de données géographiques ainsi que l'élaboration de nouvelles méthodes d'analyses exposées dans ce travail. Cette thèse s'est ainsi faite selon une dialectique permanente entre recherches théoriques et nécessités pratiques. La première partie de ce travail porte sur l'analyse du réseau piéton de la ville. La morphologie du réseau est investiguée au travers d'approches multi-échelles du concept de centralité. La première conception, nommée sinuo-centralité ("straightness centrality"), stipule qu'être central c'est être relié aux autres en ligne droite. La deuxième, sans doute plus intuitive, est intitulée centricité ("closeness centrality") et exprime le fait qu'être central c'est être proche des autres (fig. 1, II). Les méthodes développées ont pour but d'évaluer la connectivité et la marchabilité du réseau, tout en suggérant de possibles améliorations (création de raccourcis piétons). Le troisième et dernier volet théorique expose et développe un algorithme de transport optimal régularisé. En minimisant la distance domicile-école et en respectant la taille des écoles, l'algorithme permet de réaliser des scénarios d'enclassement. L'implémentation des multiplicateurs de Lagrange offre une visualisation du "coût spatial" des infrastructures scolaires et des lieux de résidence des écoliers. La deuxième partie de cette thèse retrace les aspects principaux de trois projets réalisés dans le cadre de la gestion scolaire. À savoir : la conception d'un système d'attribution de subventions pour les transports publics, la redéfinition de la carte scolaire, ou encore la simulation des flux d'élèves se rendant à l'école à pied. *** May it be from an urbanistic, a social or from a governance point of view, the evolution of cities is a major challenge in our contemporary societies. By giving the opportunity to analyse spatial and social configurations or attempting to simulate future ones, geographic information systems cannot be overlooked in urban planning and management. In five years, the population of the city of Lausanne has grown from 134'700 to 140'570 inhabitants while the numbers in public schools have increased from 12'200 to 13'500 students. Associated to a considerable harmonisation process of compulsory schooling in Switzerland, this demographic rise has driven schooling services, in collaboration with the University of Lausanne, to set up and develop GIS capable of tackling various spatial issues. Established in 1989, the school districts had to be altered so that they might fit the reality of a continuously changing urban and political landscape. In a context of mobility and durability, an attribution system for public transport subventions based on the distance between residence and school and on the age of the students was designed. The implementation of these projects required the built of geographical databases as well as the elaboration of new analysis methods exposed in this thesis. The first part of this work focuses on the analysis of the city's pedestrian network. Its morphology is investigated through multi-scale approaches of the concept of centrality. The first conception, named the straightness centrality, stipulates that being central is being connected to the others in a straight line. The second, undoubtedly more intuitive, is called closeness centrality and expresses the fact that being central is being close to the others. The goal of the methods developed is to evaluate the connectivity and walkability of the network along with suggesting possible improvements (creation of pedestrian shortcuts).The third and final theoretical section exposes and develops an algorithm of regularised optimal transport. By minimising home to school distances and by respecting school capacity, the algorithm enables the production of student allocation scheme. The implementation of the Lagrange multipliers offers a visualisation of the spatial cost associated to the schooling infrastructures and to the student home locations. The second part of this thesis recounts the principal aspects of three projects fulfilled in the context of school management. It focuses namely on the built of an attribution system for public transport subventions, a school redistricting process and on simulating student pedestrian flows.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

JULIDE: a software tool for 3D reconstruction and statistical analysis of autoradiographic mouse brain sections.

In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. This software tool has been developed in the open-source ITK software framework and is freely available under a GPL license. The article presents the complete image processing chain from raw data acquisition to 3D statistical group analysis. Results of the group comparison in the context of a study on spatial learning are shown as an illustration of the data that can be obtained with this tool.

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.

Benchmarking therapeutic drug monitoring software: A review of available computer tools

Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.

MSight: an image analysis software for liquid chromatography-mass spectrometry.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.

3D visualization software to analyze topological outcomes of topoisomerase reactions.

The action of various DNA topoisomerases frequently results in characteristic changes in DNA topology. Important information for understanding mechanistic details of action of these topoisomerases can be provided by investigating the knot types resulting from topoisomerase action on circular DNA forming a particular knot type. Depending on the topological bias of a given topoisomerase reaction, one observes different subsets of knotted products. To establish the character of topological bias, one needs to be aware of all possible topological outcomes of intersegmental passages occurring within a given knot type. However, it is not trivial to systematically enumerate topological outcomes of strand passage from a given knot type. We present here a 3D visualization software (TopoICE-X in KnotPlot) that incorporates topological analysis methods in order to visualize, for example, knots that can be obtained from a given knot by one intersegmental passage. The software has several other options for the topological analysis of mechanisms of action of various topoisomerases.

Coltop3D: A new software for structural analysis with high resolution 3D point clouds and DEM

Coltop3D is a software that performs structural analysis by using digital elevation model (DEM) and 3D point clouds acquired with terrestrial laser scanners. A color representation merging slope aspect and slope angle is used in order to obtain a unique code of color for each orientation of a local slope. Thus a continuous planar structure appears in a unique color. Several tools are included to create stereonets, to draw traces of discontinuities, or to compute automatically density stereonet. Examples are shown to demonstrate the efficiency of the method.

Optimization of spatial resolution for peripheral magnetic resonance angiography.

RATIONALE AND OBJECTIVES: To determine optimum spatial resolution when imaging peripheral arteries with magnetic resonance angiography (MRA). MATERIALS AND METHODS: Eight vessel diameters ranging from 1.0 to 8.0 mm were simulated in a vascular phantom. A total of 40 three-dimensional flash MRA sequences were acquired with incremental variations of fields of view, matrix size, and slice thickness. The accurately known eight diameters were combined pairwise to generate 22 "exact" degrees of stenosis ranging from 42% to 87%. Then, the diameters were measured in the MRA images by three independent observers and with quantitative angiography (QA) software and used to compute the degrees of stenosis corresponding to the 22 "exact" ones. The accuracy and reproducibility of vessel diameter measurements and stenosis calculations were assessed for vessel size ranging from 6 to 8 mm (iliac artery), 4 to 5 mm (femoro-popliteal arteries), and 1 to 3 mm (infrapopliteal arteries). Maximum pixel dimension and slice thickness to obtain a mean error in stenosis evaluation of less than 10% were determined by linear regression analysis. RESULTS: Mean errors on stenosis quantification were 8.8% +/- 6.3% for 6- to 8-mm vessels, 15.5% +/- 8.2% for 4- to 5-mm vessels, and 18.9% +/- 7.5% for 1- to 3-mm vessels. Mean errors on stenosis calculation were 12.3% +/- 8.2% for observers and 11.4% +/- 15.1% for QA software (P = .0342). To evaluate stenosis with a mean error of less than 10%, maximum pixel surface, the pixel size in the phase direction, and the slice thickness should be less than 1.56 mm2, 1.34 mm, 1.70 mm, respectively (voxel size 2.65 mm3) for 6- to 8-mm vessels; 1.31 mm2, 1.10 mm, 1.34 mm (voxel size 1.76 mm3), for 4- to 5-mm vessels; and 1.17 mm2, 0.90 mm, 0.9 mm (voxel size 1.05 mm3) for 1- to 3-mm vessels. CONCLUSION: Higher spatial resolution than currently used should be selected for imaging peripheral vessels.

Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.