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.

Notch signaling in hematopoiesis and lymphopoiesis: lessons from Drosophila.

The evolutionarily conserved Notch signaling pathway regulates a broad spectrum of cell fate decisions and differentiation processes during fetal and postnatal life. It is involved in embryonic organogenesis as well as in the maintenance of homeostasis of self-renewing systems. In this article, we review the role of Notch signaling in the hematopoietic system with particular emphasis on lymphocyte development and highlight the similarities in Notch function between Drosophila and mammalian differentiation processes. Recent studies indicating that aberrant NOTCH signaling is frequently linked to the induction of T leukemia in humans will also be discussed.

Research in psychopathology: epistemologic issues.

Etiologic research in psychiatry relies on an objectivist epistemology positing that human cognition is specified by the "reality" of the outer world, which consists of a totality of mind-independent objects. Truth is considered as some sort of correspondence relation between words and external objects, and mind as a mirror of nature. In our view, this epistemology considerably impedes etiologic research. Objectivist epistemology has been recently confronting a growing critique from diverse scientific fields. Alternative models in neurosciences (neuronal selection), artificial intelligence (connectionism), and developmental psychology (developmental biodynamics) converge in viewing living organisms as self-organizing systems. In this perspective, the organism is not specified by the outer world, but enacts its environment by selecting relevant domains of significance that constitute its world. The distinction between mind and body or organism and environment is a matter of observational perspective. These models from empirical sciences are compatible with fundamental tenets of philosophical phenomenology and hermeneutics. They imply consequences for research in psychopathology: symptoms cannot be viewed as disconnected manifestations of discrete localized brain dysfunctions. Psychopathology should therefore focus on how the person's self-coherence is maintained and on the understanding and empirical investigation of the systemic laws that govern neurodevelopment and the organization of human cognition.

Clinical performance of cervical restorations: a meta-analysis.

OBJECTIVES: To carry out a meta-analysis in order to assess the influencing factors on retention loss and marginal discoloration of cervical restorations made of composites and glass ionomer (derivates). METHODS: The literature was searched for prospective clinical studies on cervical restorations with an observation period of at least 18 months. RESULTS: Fifty clinical studies involving 40 adhesive systems matched the inclusion criteria. On average, 10% of the cervical fillings were lost and 24% exhibited marginal discoloration after 3 years. The variability ranged from 0% to 50% for retention loss and from 0% to 74% for marginal discoloration. Hardly any secondary caries was detected. When linear mixed models with a study and experiment effect were used, the analysis revealed that the adhesive/restorative class had the most significant influence, with 2-step self-etching adhesive systems performing best and 1-step self-etching adhesive systems performing worst; 3-step etch-and-rinse systems, glass ionomers/resin-modified glass ionomers, 2-step etch-and-rinse systems and polyacid-modified resin composites were ranked in between. Restorations placed in teeth whose dentin/enamel had been prepared/roughened showed a statistically significant higher retention rate than those placed in teeth with unprepared dentin (p<0.05). Beveling of the enamel and the type of isolation used (rubberdam/cotton rolls) had no significant influence. SIGNIFICANCE: The clinical performance of cervical restorations is significantly influenced by the type of adhesive system used and/or the adhesive class to which the system belonged and whether the dentin/enamel is prepared or not. 2-Step self-etching- and 3-step etch&rinse systems shall be chosen over 1-step self-etching systems and glass ionomer derivates. The dentin (and enamel) surface shall be roughened before placement of the restoration.

A dynamic model for stem cell homeostasis and patterning in Arabidopsis meristems.

Plants maintain stem cells in their meristems as a source for new undifferentiated cells throughout their life. Meristems are small groups of cells that provide the microenvironment that allows stem cells to prosper. Homeostasis of a stem cell domain within a growing meristem is achieved by signalling between stem cells and surrounding cells. We have here simulated the origin and maintenance of a defined stem cell domain at the tip of Arabidopsis shoot meristems, based on the assumption that meristems are self-organizing systems. The model comprises two coupled feedback regulated genetic systems that control stem cell behaviour. Using a minimal set of spatial parameters, the mathematical model allows to predict the generation, shape and size of the stem cell domain, and the underlying organizing centre. We use the model to explore the parameter space that allows stem cell maintenance, and to simulate the consequences of mutations, gene misexpression and cell ablations.

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Paradigms of notch signaling in mammals.

Notch proteins regulate a broad spectrum of cell fate decisions and differentiation processes during fetal and postnatal life. These proteins are involved in organogenesis during embryonic development as well as in the maintenance of homeostasis of self-renewing systems. The paradigms of Notch function, such as stem and progenitor cell maintenance, lineage specification mediated by binary cell fate decisions, and induction of terminal differentiation, were initially established in invertebrates and subsequently confirmed in mammals. Moreover, aberrant Notch signaling is linked to tumorigenesis. In this review, we discuss the origin of postulated Notch functions, give examples from different mammalian organ systems, and try to relate them to the hematopoietic system.

Correlation between microtensile bond strength data and clinical outcome of Class V restorations.

OBJECTIVE: To determine if the results of resin-dentin microtensile bond strength (µTBS) is correlated with the outcome parameters of clinical studies on non-retentive Class V restorations. METHODS: Resin-dentin µTBS data were obtained from one test center; the in vitro tests were all performed by the same operator. The µTBS testing was performed 8h after bonding and after 6 months of storing the specimens in water. Pre-test failures (PTFs) of specimens were included in the analysis, attributing them a value of 1MPa. Prospective clinical studies on cervical restorations (Class V) with an observation period of at least 18 months were searched in the literature. The clinical outcome variables were retention loss, marginal discoloration and marginal integrity. Furthermore, an index was formulated to be better able to compare the laboratory and clinical results. Estimates of adhesive effects in a linear mixed model were used to summarize the clinical performance of each adhesive between 12 and 36 months. Spearman correlations between these clinical performances and the µTBS values were calculated subsequently. RESULTS: Thirty-six clinical studies with 15 adhesive/restorative systems for which µTBS data were also available were included in the statistical analysis. In general 3-step and 2-step etch-and-rinse systems showed higher bond strength values than the 2-step/3-step self-etching systems, which, however, produced higher values than the 1-step self-etching and the resin modified glass ionomer systems. Prolonged water storage of specimens resulted in a significant decrease of the mean bond strength values in 5 adhesive systems (Wilcoxon, p<0.05). There was a significant correlation between µTBS values both after 8h and 6 months of storage and marginal discoloration (r=0.54 and r=0.67, respectively). However, the same correlation was not found between µTBS values and the retention rate, clinical index or marginal integrity. SIGNIFICANCE: As µTBS data of adhesive systems, especially after water storage for 6 months, showed a good correlation with marginal discoloration in short-term clinical Class V restorations, longitudinal clinical trials should explore whether early marginal staining is predictive for future retention loss in non-carious cervical restorations.

Clinical effectiveness of direct class II restorations: a meta-analysis.

Purpose: More than five hundred million direct dental restorations are placed each year worldwide. In about 55% of the cases, resin composites or compomers are used, and in 45% amalgam. The longevity of posterior resin restorations is well documented. However, data on resin composites that are placed without enamel/dentin conditioning and resin composites placed with self-etching adhesive systems are missing. Material and Methods: The database SCOPUS was searched for clinical trials on posterior resin composites without restricting the search to the year of publication. The inclusion criteria were: (1) prospective clinical trial with at least 2 years of observation; (2) minimum number of restorations at last recall = 20; (3) report on dropout rate; (4) report of operative technique and materials used; (5) utilization of Ryge or modified Ryge evaluation criteria. For amalgam, only those studies were included that directly compared composite resin restorations with amalgam. For the statistical analysis, a linear mixed model was used with random effects to account for the heterogeneity between the studies. P-values under 0.05 were considered significant. Results: Of the 373 clinical trials, 59 studies met the inclusion criteria. In 70% of the studies, Class II and Class I restorations had been placed. The overall success rate of composite resin restorations was about 90% after 10 years, which was not different from that of amalgam. Restorations with compomers had a significantly lower longevity. The main reason for replacement were bulk fractures and caries adjacent to restorations. Both of these incidents were infrequent in most studies and accounted only for about 6% of all replaced restorations after 10 years. Restorations with macrofilled composites and compomer suffered significantly more loss of anatomical form than restorations with other types of material. Restorations that were placed without enamel acid etching and a dentin bonding agent showed significantly more marginal staining and detectable margins compared to those restorations placed using the enamel-etch or etch-and-rinse technique; restorations with self-etching systems were between the other groups. Restorations with compomer suffered significantly more chippings (repairable fracture) than restorations with other materials, which did not statistically differ among each other. Restorations that were placed with a rubber-dam showed significantly fewer material fractures that needed replacement, and this also had a significant effect on the overall longevity. Conclusion: Restorations with hybrid and microfilled composites that were placed with the enamel-etching technique and rubber-dam showed the best overall performance; the longevity of these restorations was similar to amalgam restorations. Compomer restorations, restorations placed with macrofilled composites, and resin restorations with no-etching or self-etching adhesives demonstrated significant shortcomings and shorter longevity.

Clinical effectiveness of direct anterior restorations-A meta-analysis.

OBJECTIVES: This is the first meta-analysis on the efficacy of composite resin restorations in anterior teeth. The objective of the present meta-analysis was to verify whether specific material classes, tooth conditioning methods and operational procedures influence the result for Class III and Class IV restorations. MATERIAL AND METHODS: The database SCOPUS and PubMed were searched for clinical trials on anterior resin composites without restricting the search to the year of publication. The inclusion criteria were: (1) prospective clinical trial with at least 2 years of observation; (2) minimal number of restorations at last recall=20; (3) report on drop-out rate; (4) report of operative technique and materials used in the trial, and (5) utilization of Ryge or modified Ryge evaluation criteria. For the statistical analysis, a linear mixed model was used with random effects to account for the heterogeneity between the studies. p-Values smaller than 0.05 were considered to be significant. RESULTS: Of the 84 clinical trials, 21 studies met the inclusion criteria, 14 of them for Class III restorations, 6 for Class IV restorations and 1 for closure of diastemata; the latter was included in the Class IV group. Twelve of the 21 studies started before 1991 and 18 before 2001. The estimated median overall success rate (without replacement) after 10 years for Class III composite resin restorations was 95% and for Class IV restorations 90%. The main reason for the replacement of Class IV restorations was bulk fractures, which occurred significantly more frequently with microfilled composites than with hybrid and macrofilled composites. Caries adjacent to restorations was infrequent in most studies and accounted only for about 2.5% of all replaced restorations after 10 years irrespective of the cavity class. Class III restorations with glass ionomer derivates suffered significantly more loss of anatomical form than did fillings with other types of material. When the enamel was acid-etched and no bonding agent was applied, significantly more restorations showed marginal staining and detectable margins compared to enamel etching with enamel bonding or the total etch technique; fillings with self-etching systems were in between of these two outcome variables. Bevelling of the enamel was associated with a significantly reduced deterioration of the anatomical form compared to no bevelling but not with less marginal staining or less detectable margins. The type of isolation (absolute/relative) had a statistically significant influence on marginal caries which, however, might be a random finding.

Transitions from reproductive systems governed by two self-incompatible Loci to one in fungi.

Self-incompatibility (SI), a reproductive system broadly present in plants, chordates, fungi, and protists, might be controlled by one or several multiallelic loci. How a transition in the number of SI loci can occur and the consequences of such events for the population's genetics and dynamics have not been studied theoretically. Here, we provide analytical descriptions of two transition mechanisms: linkage of the two SI loci (scenario 1) and the loss of function of one incompatibility gene within a mating type of a population with two SI loci (scenario 2). We show that invasion of populations by the new mating type form depends on whether the fitness of the new type is lowered, and on the allelic diversity of the SI loci and the recombination between the two SI loci in the starting population. Moreover, under scenario 1, it also depends on the frequency of the SI alleles that became linked. We demonstrate that, following invasion, complete transitions in the reproductive system occurs under scenario 2 and is predicted only for small populations under scenario 1. Interestingly, such events are associated with a drastic reduction in mating type number.

Extended access cocaine self-administration differentially activates dorsal raphe and amygdala corticotropin-releasing factor systems in rats.

Cocaine-induced neuroadaptation of stress-related circuitry and increased access to cocaine each putatively contribute to the transition from cocaine use to cocaine dependence. The present study tested the hypothesis that rats receiving extended versus brief daily access to cocaine would exhibit regional differences in levels of the stress-regulatory neuropeptide corticotropin-releasing factor (CRF). A secondary goal was to explore how CRF levels change in relation to the time since cocaine self-administration. Male Wistar rats acquired operant self-administration of cocaine and were assigned to receive daily long access (6 hours/day, LgA, n = 20) or short access (1 hour/day, ShA, n = 18) to intravenous cocaine self-administration (fixed ratio 1, ∼0.50 mg/kg/infusion). After at least 3 weeks, tissue CRF immunoreactivity was measured at one of three timepoints: pre-session, post-session or 3 hours post-session. LgA, but not ShA, rats showed increased total session and first-hour cocaine intake. CRF immunoreactivity increased within the dorsal raphe (DR) and basolateral, but not central, nucleus of the amygdala (BLA, CeA) of ShA rats from pre-session to 3 hours post-session. In LgA rats, CRF immunoreactivity increased from pre-session to 3 hours post-session within the CeA and DR but tended to decrease in the BLA. LgA rats showed higher CRF levels than ShA rats in the DR and, pre-session, in the BLA. Thus, voluntary cocaine intake engages stress-regulatory CRF systems of the DR and amygdala. Increased availability of cocaine promotes greater tissue CRF levels in these extrahypothalamic brain regions, changes associated here with a model of cocaine dependence.

Density-dependent pollen limitation and reproductive assurance in a wind-pollinated herb with contrasting sexual systems

1. Wind pollination is thought to have evolved in response to selection for mechanisms to promote pollination success, when animal pollinators become scarce or unreliable. We might thus expect wind-pollinated plants to be less prone to pollen limitation than their insect-pollinated counterparts. Yet, if pollen loads on stigmas of wind-pollinated species decline with distance from pollen donors, seed set might nevertheless be pollen-limited in populations of plants that cannot self-fertilize their progeny, but not in self-compatible hermaphroditic populations.2. Here, we test this hypothesis by comparing pollen limitation between dioecious and hermaphroditic (monoecious) populations of the wind-pollinated herb Mercurialis annua.3. In natural populations, seed set was pollen-limited in low-density patches of dioecious, but not hermaphroditic, M. annua, a finding consistent with patterns of distance-dependent seed set by females in an experimental array. Nevertheless, seed set was incomplete in both dioecious and hermaphroditic populations, even at high local densities. Further, both factors limited the seed set of females and hermaphrodites, after we manipulated pollen and resource availability in a common garden experiment.4. Synthesis. Our results are consistent with the idea that pollen limitation plays a role in the evolution of combined vs. separate sexes in M. annua. Taken together, they point to the potential importance of pollen transfer between flowers on the same plant (geitonogamy) by wind as a mechanism of reproductive assurance and to the dual roles played by pollen and resource availability in limiting seed set. Thus, seed set can be pollen-limited in sparse populations of a wind-pollinated species, where mates are rare or absent, having potentially important demographic and evolutionary implications.

Concise reviews: in vitro-produced pancreas organogenesis models in three dimensions: self-organization from few stem cells or progenitors.

Three-dimensional models of organ biogenesis have recently flourished. They promote a balance between stem/progenitor cell expansion and differentiation without the constraints of flat tissue culture vessels, allowing for autonomous self-organization of cells. Such models allow the formation of miniature organs in a dish and are emerging for the pancreas, starting from embryonic progenitors and adult cells. This review focuses on the currently available systems and how these allow new types of questions to be addressed. We discuss the expected advancements including their potential to study human pancreas development and function as well as to develop diabetes models and therapeutic cells.