Microsatellite variations of Quercus aquifolioides populations at varying altitudes in the Wolong Natural Reserve of China

Abstract

Allohexaploid pearl millet x elephantgrass population potential for a recurrent selection program

The objective of this work was to evaluate the potential of allohexaploid pearl millet x elephantgrass (HGL) population for a recurrent selection program through open-pollinated progenies. Seventy-eight progenies, one representative sample of the population, and two commercial cultivars, Pioneiro and Paraíso, were evaluated in a 9x9 triple lattice design, in two sites. Plant height and dry matter yield were evaluated in three and four cuts, respectively. For plant height, the 17 best progenies were similar to both commercial controls, while for dry matter yield they were higher than 'Paraíso' and lower than 'Pioneiro'. The correlation between progenies and cuts indicated that the fourth cut represents the mean of all cuts, and the possibility of using early selection. Heritability estimates considering cuts and sites were 56.9% for plant height and 58.8% for dry matter yield, and the expected response to selection was 23.4% for dry matter yield and 18.1% for plant height. These results demonstrate the promising HGL population potential for a recurrent selection program.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

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.

Iowa Pavement Asset Management Decision-Making Framework & Pavement Treatment Selection Tool for Local Agencies, TR-651, 2015

Most local agencies in Iowa currently make their pavement treatment decisions based on their limited experience due primarily to lack of a systematic decision-making framework and a decision-aid tool. The lack of objective condition assessment data of agency pavements also contributes to this problem. This study developed a systematic pavement treatment selection framework for local agencies to assist them in selecting the most appropriate treatment and to help justify their maintenance and rehabilitation decisions. The framework is based on an extensive literature review of the various pavement treatment techniques in terms of their technical applicability and limitations, meaningful practices of neighboring states, and the results of a survey of local agencies. The treatment selection framework involves three different steps: pavement condition assessment, selection of technically feasible treatments using decision trees, and selection of the most appropriate treatment considering the return-on-investment (ROI) and other non-economic factors. An Excel-based spreadsheet tool that automates the treatment selection framework was also developed, along with a standalone user guide for the tool. The Pavement Treatment Selection Tool (PTST) for Local Agencies allows users to enter the severity and extent levels of existing distresses and then, recommends a set of technically feasible treatments. The tool also evaluates the ROI of each feasible treatment and, if necessary, it can also evaluate the non-economic value of each treatment option to help determine the most appropriate treatment for the pavement. It is expected that the framework and tool will help local agencies improve their pavement asset management practices significantly and make better economic and defensible decisions on pavement treatment selection.

Ant species distribution along a topographic gradient in a "terra-firme" forest reserve in Central Amazonia

In Amazonia, topographical variations in soil and forest structure within "terra-firme" ecosystems are important factors correlated with terrestrial invertebrates' distribution. The objective of this work was to assess the effects of soil clay content and slope on ant species distribution over a 25 km² grid covering the natural topographic continuum. Using three complementary sampling methods (sardine baits, pitfall traps and litter samples extracted in Winkler sacks), 300 subsamples of each method were taken in 30 plots distributed over a wet tropical forest in the Ducke Reserve (Manaus, AM, Brazil). An amount of 26,814 individuals from 11 subfamilies, 54 genera, 85 species and 152 morphospecies was recorded (Pheidole represented 37% of all morphospecies). The genus Eurhopalothrix was registered for the first time for the reserve. Species number was not correlated with slope or clay content, except for the species sampled from litter. However, the Principal Coordinate Analysis indicated that the main pattern of species composition from pitfall and litter samples was related to clay content. Almost half of the species were found only in valleys or only on plateaus, which suggests that most of them are habitat specialists. In Central Amazonia, soil texture is usually correlated with vegetation structure and moisture content, creating different microhabitats, which probably account for the observed differences in ant community structure.

Selection strategies of segregant soybean populations for resistance to Asian rust

The objective of this work was to identify the best selection strategies for the more promising parental combinations to obtain lines with good resistance to soybean Asian rust (Phakopsora pachyrhizi). Two experiments were carried out in the field during the 2006/2007 and 2007/2008 growing seasons, to determine the percentage of infected leaf area of individual plants of five parents and their segregant F2 and F3 populations. The data obtained indicates that additive genetic variance predominates in the control of soybean resistance to Asian rust, and that the year and time of assessment do not significantly influence the estimates of the genetic parameters obtained. The narrow-sense heritability (h²r) ranged from 23.12 to 55.83%, and indicates the possibility of successful selection of resistant individuals in the early generations of the breeding program. All the procedures used to select the most promising populations to generate superior inbred lines for resistance to P. pachyrhizi presented similar results and identified the BR01-18437 x BRS 232 population as the best for inbred line selection.

Leaf plasticity in successive selection cycles of 'Saracura' maize in response to periodic soil flooding

The objective of this work was to assess the effect of successive selection cycles on leaf plasticity of 'Saracura' maize BRS-4154 under periodical flooding in field conditions. Soil flooding started at the six-leaf stage with the application of a 20-cm depth water layer three times a week. At flowering, samples of leaves were collected and fixed. Paradermic and transverse sections were observed under photonic microscope. Several changes were observed throughout the selection cycles, such as modifications in the number and size of the stomata, higher amount of vascular bundles and the resulting decrease of the distance between them, smaller diameter of the metaxylem, decrease of cuticle and epidermis thickness, decrease of number and size of bulliform cells, increase of phloem thickness, smaller sclerenchyma area. Therefore, the successive selection cycles of 'Saracura' maize resulted in changes in the leaf anatomy, which might be favorable to the plant's tolerance to the intermittent flooding of the soil.

Habitat fragmentation, ecology and sexual selection in forest bird species in Monteverde, Costa Rica

Abstract Forest fragmentation is often associated with local extinction and changes in species abundance patterns. The main topic of this thesis is the effect of forest fragmentation on birds in Monteverde, Costa Rica. This thesis also studies aspects of sexual selection and ecology of Long-tailed Manakins, Chiroxiphia Linearis. Chapter 1 investigates bird species assemblages in two degrees of forest fragmentation. It is shown that the distribution, abundance and diversity of forest bird species are strongly influenced by the amount of forest in the landscape matrix. Presence of cattle within the forest influences the presence of some bird species. The prevalence and intensity of ticks and blood parasites on birds in relation to fragmentation is described in Chapter 2. Overall tick prevalence is 3%. Understory birds are significantly more infested with ticks than species at intermediate heights. Tick prevalence on birds does not differ significantly between two degrees of forest fragmentation and individual tick loads tend to be higher in High- than in Low-fragmentation sites. Infestations by the blood parasites Haemoproteus sp. was low except in white-eared ground sparrow, Melozone leucotis, that is 28% and is significantly higher in High- than in Low-fragmentation sites. In chapter 3 results on the ecology and habitat movements of the Bare-necked Umbrellabird, Cephalopterus glabricollis, are presented. The abundance of umbrellabirds at high elevations during the breeding season coincides with the highest peak of fruit abundance. Birds leave the protected area during the non-breeding season moving to unprotected forest fragments. In chapter 4 ontogenetic changes in feather morphology through sexual maturity in Long-tailed Manakins are described. In adult males, rectrices length is positively correlated to testis volume. Changes in male morphology during ontogeny in the long-tailed manakin appear to be associated with their specific-display behaviours. Significant interpopulation differences in the morphology of Long-tailed Manakins are shown in chapter 5. These differences are more accentuated in morphological traits related to flight displays. A field experiment demonstrates that long rectrices impose flying costs for males and females. A reduction in flying ability was found to be strongest in males from a population presenting the highest degree of sexual dimorphism. Résumé La fragmentation des forêts est souvent associée avec des modifications dans l'abondance des espèces et des extinctions locales. Le thème principale de cette thèse est l'étude de l'effet de la fragmentation des forêts sur les oiseaux de Monteverde, Costa Rica. Elle décrit par ailleurs certains aspects de la sélection sexuelle et l'écologie du manakin à longue queue, Chiroxiphia linearis. Dans le Chapitre 1 je montre que la distribution, l'abondance et la diversité des assemblages d'oiseaux vivant dans la forêt sont fortement influencées pas le degré de fragmentation de celle ci. Par ailleurs, la présence ou l'absence de bétail dans les forêts influence la présence de certaines espèces d'oiseaux. Dans le chapitre 2 j'ai étudié la prévalence et l'intensité d'infestation par des tiques ainsi que la présence de parasites sanguins chez les oiseaux en relation avec la fragmentation des forêts. La prévalence globale de tiques est de 3 %, les oiseaux vivant au niveau du sol étaient plus souvent infectés par des tiques que les espèces se déplaçant à un niveau plus élevé. La prévalence de tiques sur les oiseaux n'était pas significativement différente entre les paysages avec différentes fragmentations. Les parasites sanguins du genre Haemoproteus sp. étaient présents à très basse fréquence à l'exception chez Melozone leucotis ou la prevalence était de 28% et significativement plus élevée chez les oiseaux vivant dans les forêts à forte fragmentation. Dans le Chapitre 3 je présente des résultats sur l'écologie et les mouvements entre habitats chez le "Bare-necked umbrellabird", Cephalopterus glabricollis. Cette espèce endémique du Costa Rica niche à haute altitude durant la période d'abondance des fruits et réalise une migration altitudinale vers des zones basses durant la saison de non reproduction. Dans le chapitre 4 je présente les changements ontogénétiques dans la morphologie du plumage des manakins à longue queue. Chez les mâles, les changements de morphologie semblent être associés avec leurs comportements de parade spécifiques. Dans le chapitre 5 je présente des différences morphologiques significative entre deux populations chez le manakin à longue queue et je montre que la capacité de vols chez les mâles est plus fortement influencée dans la population avec le degré de dimorphisme sexuel le plus prononcé.

In vitro negative selection of viral superantigen-reactive thymocytes by thymic dendritic cells.

Intrathymic expression of endogenous mouse mammary tumor virus (MMTV)-encoded superantigens (SAg) induces the clonal deletion of T cells bearing SAg-reactive T-cell receptor (TCR) Vbeta elements. However, the identity of the thymic antigen-presenting cells (APC) involved in the induction of SAg tolerance remains to be defined. We have analyzed the potential of dendritic cells (DC) to mediate the clonal deletion of Mtv-7-reactive TCR alphabeta P14 transgenic thymocytes in an in vitro assay. Our results show that both thymic and splenic DC induced the deletion of TCR transgenic double positive (DP) thymocytes. DC appear to be more efficient than splenic B cells as negatively selecting APC in this experimental system. Interestingly, thymic and splenic DC display a differential ability to induce CD4+ SP thymocyte proliferation. These observations suggest that thymic DC may have an important role in the induction of SAg tolerance in vivo.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Parent selection for cocoa resistance to witches'-broom

The objective of this work was to identify genotypes with high general combining ability for resistance to witches'-broom (Moniliophthora perniciosa) in populations formed from a first cycle of recurrent selection. Highly productive and resistant clones from different origins were interbred using the North Carolina II design. The clones SCA 6, CSUL 7, RB 39, CEPEC 89, OC 67, BE 4, EEG 29 and ICS 98 were used as paternal parents, while the maternal ones were NA 33, CCN 10, IMC 67, P 4B, CCN 51, CEPEC 86, SGU 54 and ICS 9. Twenty days after germination, 56 seedlings of each cross (four replicates of 14 seedlings) received the inoculation of a 1-mL suspension with 7.5x10(4 ) basidiospores mL-1. Symptoms were evaluated 60 days after inoculation. Significant differences were observed among paternal and among maternal parents, for resistance to witches'-broom assessed according to the proportion of progeny seedlings with the disease symptoms. Differences were also observed between groups of mothers or fathers previously defined as resistant, and groups previously defined as susceptible. It is possible to obtain a combination of genes that can increase the level of resistance to witches'-broom directly from the first cycle of recurrent selection.