Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster.

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

Post-mortem cardiac 3-T magnetic resonance imaging: visualization of sudden cardiac death?

OBJECTIVES: This study aimed to investigate post-mortem magnetic resonance imaging (pmMRI) for the assessment of myocardial infarction and hypointensities on post-mortem T2-weighted images as a possible method for visualizing the myocardial origin of arrhythmic sudden cardiac death. BACKGROUND: Sudden cardiac death has challenged clinical and forensic pathologists for decades because verification on post-mortem autopsy is not possible. pmMRI as an autopsy-supporting examination technique has been shown to visualize different stages of myocardial infarction. METHODS: In 136 human forensic corpses, a post-mortem cardiac MR examination was carried out prior to forensic autopsy. Short-axis and horizontal long-axis images were acquired in situ on a 3-T system. RESULTS: In 76 cases, myocardial findings could be documented and correlated to the autopsy findings. Within these 76 study cases, a total of 124 myocardial lesions were detected on pmMRI (chronic: 25; subacute: 16; acute: 30; and peracute: 53). Chronic, subacute, and acute infarction cases correlated excellently to the myocardial findings on autopsy. Peracute infarctions (age range: minutes to approximately 1 h) were not visible on macroscopic autopsy or histological examination. Peracute infarction areas detected on pmMRI could be verified in targeted histological investigations in 62.3% of cases and could be related to a matching coronary finding in 84.9%. A total of 15.1% of peracute lesions on pmMRI lacked a matching coronary finding but presented with severe myocardial hypertrophy or cocaine intoxication facilitating a cardiac death without verifiable coronary stenosis. CONCLUSIONS: 3-T pmMRI visualizes chronic, subacute, and acute myocardial infarction in situ. In peracute infarction as a possible cause of sudden cardiac death, it demonstrates affected myocardial areas not visible on autopsy. pmMRI should be considered as a feasible post-mortem investigation technique for the deceased patient if no consent for a clinical autopsy is obtained.

Classification of very high spatial resolution imagery using mathematical morphology and support vector machines

We investigate the relevance of morphological operators for the classification of land use in urban scenes using submetric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.

Postnatal intracerebroventricular administrations of NGF alter spatial memory in adulthood

The present work assessed the effects of intracerebroventricular injections (2x5 mg/2.5 ml) of recombined human nerve growth factor (rhNGF) at postnatal days 2 and 3 upon the development of spatial learning capacities in rats. The treated rats were trained at the age of 22 days to escape onto an invisible platform at a fixed position in space in a Morris navigation task. For half of the subjects, the training position was also cued, a procedure aimed at facilitating escape and reducing attention to the distant spatial cues. At the age of 2 months all the rats were retrained in the same task. Treatment effects were found in both immature and adult rats. The injection of NGF induced a slight alteration of the immature rats' performance. In contrast, a marked impairment of spatial abilities was shown in the 2-month-old rats. The most consistent effects were a significant increase in the escape latency and a decrease bias towards the training platform area during probe trials. The reduction of spatial memory was particularly marked if the subjects had been trained in a cued condition. Taken together, these experiments reveal that an acute pharmacological treatment that leads to transient modifications during early development might induce a behavioural change long after treatment. Thus, the development and the maintenance of an accurate spatial representation are tightly related to the development of brain structures that could be altered by precocious NGF administrations.

Combining ability and heterosis in common bean cultivars

The objective of this work was to determine the combining ability and heterosis, for productivity and yield components, in diallel hybrids derived from crossings between BRSMG-Talismã, IPR Uirapuru, FT Soberano, BRS Campeiro, IAC Tybatã, and IPR Juriti parent cultivars. Fifteen hybrids were generated from diallel crosses, excluding reciprocals. The general and specific combining abilities were significant for plant height, number of pods per plant, number of seeds per plant, number of seeds per pod, 50-seed weight, and grain yield, indicating the occurrence of both additive and nonadditive genetic effects. The best strategy to be adopted is the use of BRS Campeiro, FT Soberano and BRSMG-Talismã cultivars in common bean breeding programs involving selection. The most promising combinations were 'IPR Uirapuru' x 'IAC Tybatã', 'IPR Uirapuru' x 'FT Soberano', 'BRS Campeiro' x 'IPR Juriti', and 'BRS Campeiro' x 'IAC Tybatã'. The parents of these hybrids presented high estimates of specific combining abilities. Hybridization of cultivars belonging to distinguished commercial groups propitiates higher heterosis values in the segregant population.

Electron microscopic visualization of protein-DNA interactions at the estrogen responsive element and in the first intron of the Xenopus laevis vitellogenin gene.

Stable protein-DNA complexes can be assembled in vitro at the 5' end of Xenopus laevis vitellogenin genes using extracts of nuclei from estrogen-induced frog liver and visualized by electron microscopy. Complexes at the three following sites can be identified on the gene B2: the transcription initiation site, the estrogen responsive element (ERE) and in the first intron. The complex at the transcription initiation site is stabilized by dinucleotides and thus represents a ternary transcription complex. The formation of the complexes at the two other sites is enhanced by estrogen and is reduced by tamoxifen, an antagonist of estrogen, while this latter effect is reversed by adding an excess of hormone. No sequence homology is apparent between the site containing the ERE and the binding site in intron I and functional tests in MCF-7 cells suggest that these two sites are not equivalent. Finally, we made use of previously characterized deletion mutants of the 5' flanking region of the gene B1, a close relative of the gene B2, to demonstrate that the 13-bp palindromic core element of the ERE is involved in the formation of the complexes observed upstream of the transcription initiation site.

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.

Reduced genetic diversity, increased isolation and multiple introductions of invasive giant hogweed in the western Swiss Alps.

Abstract The giant hogweed (Heracleum mantegazzianum) has successfully invaded 19 European countries as well as parts of North America. It has become a problematic species due to its ability to displace native flora and to cause public health hazards. Applying population genetics to species invasion can help reconstruct invasion history and may promote more efficient management practice. We thus analysed levels of genetic variation and population genetic structure of H. mantegazzianum in an invaded area of the western Swiss Alps as well as in its native range (the Caucasus), using eight nuclear microsatellite loci together with plastid DNA markers and sequences. On both nuclear and plastid genomes, native populations exhibited significantly higher levels of genetic diversity compared to invasive populations, confirming an important founder event during the invasion process. Invasive populations were also significantly more differentiated than native populations. Bayesian clustering analysis identified five clusters in the native range that corresponded to geographically and ecologically separated groups. In the invaded range, 10 clusters occurred. Unlike native populations, invasive clusters were characterized by a mosaic pattern in the landscape, possibly caused by anthropogenic dispersal of the species via roads and direct collection for ornamental purposes. Lastly, our analyses revealed four main divergent groups in the western Swiss Alps, likely as a consequence of multiple independent establishments of H. mantegazzianum.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

Combining ability of maize grain yield under different levels of environmental stress

The objectives of this work were to caracterize the tropical maize germplasm and to compare the combining abilities of maize grain yield under different levels of environmental stress. A diallel was performed among tropical maize cultivars with wide adaptability, whose hybrid combinations were evaluated in two sowing dates, in two years. The significance of the environmental effect emphasized the environmental contrasts. Based on grain yield, the environments were classified as favorable (8,331 kg ha-1), low stress (6,637 kg ha-1), high stress (5,495 kg ha-1), and intense stress (2,443 kg ha-1). None of the genetic effects were significant in favorable and intense stress environments, indicating that there was low germplasm variability under these conditions. In low and high stresses, the specific combining ability effects (SCA) were significant, showing that the nonadditive genetic effects were the most important, and that it is possible to select parent pairs with breeding potential. SCA and grain yield showed significant correlations only between the closer environment pairs like favorable/low stress and high/intense stress. The genetic control of grain yield differed under contrasting stress environments for which maize cultivars with wide adaptability are not adequate.

Predictive Value of Readiness, Importance, and Confidence in Ability to Change Drinking and Smoking

Background: Visual analog scales (VAS) are used to assess readiness to changeconstructs, which are often considered critical for change.Objective: We studied whether 3 constructs -readiness to change, importance of changing and confidence inability to change- predict risk status 6 months later in 20 year-old men with either orboth of two behaviors: risky drinking and smoking. Methods: 577 participants in abrief intervention randomized trial were assessed at baseline and 6 months later onalcohol and tobacco consumption and with three 1-10 VAS (readiness, importance,confidence) for each behavior. For each behavior, we used one regression model foreach constructs. Models controlled for receipt of a brief intervention and used thelowest level (1-4) in each construct as the reference group (vs medium (5-7) and high(8-10) levels).Results: Among the 475 risky drinkers, mean (SD) readiness, importance and confidence to change drinking were 4.0 (3.1), 2.8 (2.2) and 7.2 (3.0).Readiness was not associated with being alcohol-risk free 6 months later (OR 1.3[0.7; 2.2] and 1.4 [0.8; 2.6] for medium and high readiness). High importance andhigh confidence were associated with being risk free (OR 0.9 [0.5; 1.8] and 2.9 [1.2;7.5] for medium and high importance; 2.1 [1.0;4.8] and 2.8 [1.5;5.6] for medium andhigh confidence). Among the 320 smokers, mean readiness, importance andconfidence to change smoking were 4.6 (2.6), 5.3 (2.6) and 5.9 (2.6). Neitherreadiness nor importance were associated with being smoking free (OR 2.1 [0.9; 4.7]and 2.1 [0.8; 5.8] for medium and high readiness; 1.4 [0.6; 3.4] and 2.1 [0.8; 5.4] formedium and high importance). High confidence was associated with being smokingfree (OR 2.2 [0.8;6.6] and 3.4 [1.2;9.8] for medium and high confidence).Conclusions: For drinking and smoking, high confidence in ability to change wasassociated -with similar magnitude- with a favorable outcome. This points to thevalue of confidence as an important predictor of successful change.

Revisiting spatial distribution and biochemical composition of calcium-containing crystals in human osteoarthritic articular cartilage.

INTRODUCTION: Calcium-containing (CaC) crystals, including basic calcium phosphate (BCP) and calcium pyrophosphate dihydrate (CPP), are associated with destructive forms of osteoarthritis (OA). We assessed their distribution and biochemical and morphologic features in human knee OA cartilage. METHODS: We prospectively included 20 patients who underwent total knee replacement (TKR) for primary OA. CaC crystal characterization and identification involved Fourier-transform infra-red spectrometry and scanning electron microscopy of 8 to 10 cartilage zones of each knee, including medial and lateral femoral condyles and tibial plateaux and the intercondyle zone. Differential expression of genes involved in the mineralization process between cartilage with and without calcification was assessed in samples from 8 different patients by RT-PCR. Immunohistochemistry and histology studies were performed in 6 different patients. RESULTS: Mean (SEM) age and body mass index of patients at the time of TKR was 74.6 (1.7) years and 28.1 (1.6) kg/m², respectively. Preoperative X-rays showed joint calcifications (chondrocalcinosis) in 4 cases only. The medial femoro-tibial compartment was the most severely affected in all cases, and mean (SEM) Kellgren-Lawrence score was 3.8 (0.1). All 20 OA cartilages showed CaC crystals. The mineral content represented 7.7% (8.1%) of the cartilage weight. All patients showed BCP crystals, which were associated with CPP crystals for 8 joints. CaC crystals were present in all knee joint compartments and in a mean of 4.6 (1.7) of the 8 studied areas. Crystal content was similar between superficial and deep layers and between medial and femoral compartments. BCP samples showed spherical structures, typical of biological apatite, and CPP samples showed rod-shaped or cubic structures. The expression of several genes involved in mineralization, including human homolog of progressive ankylosis, plasma-cell-membrane glycoprotein 1 and tissue-nonspecific alkaline phosphatase, was upregulated in OA chondrocytes isolated from CaC crystal-containing cartilages. CONCLUSIONS: CaC crystal deposition is a widespread phenomenon in human OA articular cartilage involving the entire knee cartilage including macroscopically normal and less weight-bearing zones. Cartilage calcification is associated with altered expression of genes involved in the mineralisation process.