Natural genetic variation in Arabidopsis: tools, traits and prospects for evolutionary ecology.

BACKGROUND: The model plant Arabidopsis thaliana (Arabidopsis) shows a wide range of genetic and trait variation among wild accessions. Because of its unparalleled biological and genomic resources, the potential of Arabidopsis for molecular genetic analysis of this natural variation has increased dramatically in recent years. SCOPE: Advanced genomics has accelerated molecular phylogenetic analysis and gene identification by quantitative trait loci (QTL) mapping and/or association mapping in Arabidopsis. In particular, QTL mapping utilizing natural accessions is now becoming a major strategy of gene isolation, offering an alternative to artificial mutant lines. Furthermore, the genomic information is used by researchers to uncover the signature of natural selection acting on the genes that contribute to phenotypic variation. The evolutionary significance of such genes has been evaluated in traits such as disease resistance and flowering time. However, although molecular hallmarks of selection have been found for the genes in question, a corresponding ecological scenario of adaptive evolution has been difficult to prove. Ecological strategies, including reciprocal transplant experiments and competition experiments, and utilizing near-isogenic lines of alleles of interest will be a powerful tool to measure the relative fitness of phenotypic and/or allelic variants. CONCLUSIONS: As the plant model organism, Arabidopsis provides a wealth of molecular background information for evolutionary genetics. Because genetic diversity between and within Arabidopsis populations is much higher than anticipated, combining this background information with ecological approaches might well establish Arabidopsis as a model organism for plant evolutionary ecology.

Genomic islands: tools of bacterial horizontal gene transfer and evolution.

Abstract Bacterial genomes evolve through mutations, rearrangements or horizontal gene transfer. Besides the core genes encoding essential metabolic functions, bacterial genomes also harbour a number of accessory genes acquired by horizontal gene transfer that might be beneficial under certain environmental conditions. The horizontal gene transfer contributes to the diversification and adaptation of microorganisms, thus having an impact on the genome plasticity. A significant part of the horizontal gene transfer is or has been facilitated by genomic islands (GEIs). GEIs are discrete DNA segments, some of which are mobile and others which are not, or are no longer mobile, which differ among closely related strains. A number of GEIs are capable of integration into the chromosome of the host, excision, and transfer to a new host by transformation, conjugation or transduction. GEIs play a crucial role in the evolution of a broad spectrum of bacteria as they are involved in the dissemination of variable genes, including antibiotic resistance and virulence genes leading to generation of hospital 'superbugs', as well as catabolic genes leading to formation of new metabolic pathways. Depending on the composition of gene modules, the same type of GEIs can promote survival of pathogenic as well as environmental bacteria.

Biomarkers of angiogenesis for the development of antiangiogenic therapies in oncology: tools or decorations?

Since 2004, four antiangiogenic drugs have been approved for clinical use in patients with advanced solid cancers, on the basis of their capacity to improve survival in phase III clinical studies. These achievements validated the concept introduced by Judah Folkman that the inhibition of tumor angiogenesis could control tumor growth. It has been suggested that biomarkers of angiogenesis would greatly facilitate the clinical development of antiangiogenic therapies. For these four drugs, the pharmacodynamic effects observed in early clinical studies were important to corroborate activities, but were not essential for the continuation of clinical development and approval. Furthermore, no validated biomarkers of angiogenesis or antiangiogenesis are available for routine clinical use. Thus, the quest for biomarkers of angiogenesis and their successful use in the development of antiangiogenic therapies are challenges in clinical oncology and translational cancer research. We review critical points resulting from the successful clinical trials, review current biomarkers, and discuss their potential impact on improving the clinical use of available antiangiogenic drugs and the development of new ones.

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.

Effects of mental stress on insulin-mediated glucose metabolism and energy expenditure in lean and obese women.

The effects of the sympathetic activation elicited by a mental stress on insulin sensitivity and energy expenditure (VO(2)) were studied in 11 lean and 8 obese women during a hyperinsulinemic-euglycemic clamp. Six lean women were restudied under nonselective beta-adrenergic blockade with propranolol to determine the role of beta-adrenoceptors in the metabolic response to mental stress. In lean women, mental stress increased VO(2) by 20%, whole body glucose utilization ([6,6-(2)H(2)]glucose) by 34%, and cardiac index (thoracic bioimpedance) by 25%, whereas systemic vascular resistance decreased by 24%. In obese women, mental stress increased energy expenditure as in lean subjects, but it neither stimulated glucose uptake nor decreased systemic vascular resistance. In the six lean women who were restudied under propranolol, the rise in VO(2), glucose uptake, and cardiac output and the decrease in systemic vascular resistance during mental stress were all abolished. It is concluded that 1) in lean subjects, mental stress stimulates glucose uptake and energy expenditure and produces vasodilation; activation of beta-adrenoceptors is involved in these responses; and 2) in obese patients, the effects of mental stress on glucose uptake and systemic vascular resistance, but not on energy expenditure, are blunted.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Comparison of aortic elasticity determined by cardiovascular magnetic resonance imaging in obese versus lean adults.

The vascular properties of large vessels in the obese have not been adequately studied. We used cardiovascular magnetic resonance imaging to quantify the cross-sectional area and elastic properties of the ascending thoracic and abdominal aorta in 21 clinically healthy obese young adult men and 25 men who were age-matched lean controls. Obese subjects had greater maximal cross-sectional area of the ascending thoracic aorta (984 +/- 252 vs 786 +/- 109 mm(2), p <0.01) and of the abdominal aorta (415 +/- 71 vs 374 +/- 51 mm(2), p <0.05). When indexed for height the differences persisted, but when indexed for body surface area, a significant difference between groups was found only for the maximal abdominal aortic cross-sectional area. The obese subjects also had decreased abdominal aortic elasticity, characterized by 24% lower compliance (0.0017 +/- 0.0004 vs 0.0021 +/- 0.0005 mm(2)/kPa/mm, p <0.01), 22% higher stiffness index beta (6.0 +/- 1.5 vs 4.9 +/- 0.7, p <0.005), and 41% greater pressure-strain elastic modulus (72 +/- 25 vs 51 +/- 9, p <0.005). At the ascending thoracic aorta, only the pressure-strain elastic modulus was different between obese and lean subjects (85 +/- 42 vs 65 +/- 26 kPa, respectively; p <0.05), corresponding to a 31% difference-but arterial compliance and stiffness index were not significantly different between groups. In clinically healthy young adult obese men, obesity is associated with increased cross-sectional aortic area and decreased aortic elasticity.

Revisiting sodium and water reabsorption with functional genomics tools.

PURPOSE OF REVIEW: The kidney plays an essential role in maintaining sodium and water balance, thereby controlling the volume and osmolarity of the extracellular body fluids, the blood volume and the blood pressure. The final adjustment of sodium and water reabsorption in the kidney takes place in cells of the distal part of the nephron in which a set of apical and basolateral transporters participate in vectorial sodium and water transport from the tubular lumen to the interstitium and, finally, to the general circulation. According to a current model, the activity and/or cell-surface expression of these transporters is/are under the control of a gene network composed of the hormonally regulated, as well as constitutively expressed, genes. It is proposed that this gene network may include new candidate genes for salt- and water-losing syndromes and for salt-sensitive hypertension. A new generation of functional genomics techniques have recently been applied to the characterization of this gene network. The purpose of this review is to summarize these studies and to discuss the potential of the different techniques for characterization of the renal transcriptome. RECENT FINDINGS: Recently, DNA microarrays and serial analysis of gene expression have been applied to characterize the kidney transcriptome in different in-vivo and in-vitro models. In these studies, a set of new interesting genes potentially involved in the regulation of sodium and water reabsorption by the kidney have been identified and are currently under detailed investigation. SUMMARY: Characterization of the kidney transcriptome is greatly expanding our knowledge of the gene networks involved in multiple kidney functions, including the maintenance of sodium and water homeostasis.

Design of new tools to improve the efficacy of DNA-damaging drugs used in cancer therapy

Résumé Les tumeurs sont diverses et hétérogènes, mais toutes partagent la capacité de proliférer sans contrôle. Une prolifération dérégulée de cellules couplée à une insensibilité à une réponse apoptotique constitue une condition minimale pour que l'évolution d'une tumeur se produise. Un des traitements les plus utilisés pour traité le cancer à l'heure actuelle sont les chimiothérapies, qui sont fréquemment des composés chimiques qui induisent des dommages dans l'ADN. Les agents anticancéreux sont efficaces seulement quand les cellules tumorales sont plus aisément tuées que le tissu normal environnant. L'efficacité de ces agents est en partie déterminée par leur capacité à induire l'apoptose. Nous avons récemment démontré que la protéine RasGAP est un substrat non conventionnel des caspases parce elle peut induire à la fois des signaux anti et pro-apoptotiques, selon l'ampleur de son clivage par les caspases. A un faible niveau d'activité des caspases, RasGAP est clivé, générant deux fragments (le fragment N et le fragment C). Le fragment N semble être un inhibiteur général de l'apoptose en aval de l'activation des caspases. À des niveaux plus élevés d'activité des caspases, la capacité du fragment N de contrecarrer l'apoptose est supprimée quand il est clivé à nouveau par les caspases. Ce dernier clivage produit deux nouveaux fragments, N 1 et N2, qui contrairement au fragment N sensibilisent efficacement des cellules cancéreuses envers des agents chimiothérapeutiques. Dans cette étude nous avons prouvé qu'un peptide, appelé par la suite TAT-RasGAP317-326, qui est dérivé du fragment N2 de RasGAP et est rendu perméable aux cellules, sensibilise spécifiquement des cellules cancéreuses à trois génotoxines différentes utilisées couramment dans des traitements anticancéreux, et cela dans des modèles in vitro et in vivo. Il est important de noté que ce peptide semble ne pas avoir d'effet sur des cellules non cancéreuses. Nous avons également commencé à caractériser les mécanismes moléculaires expliquant les fonctions de sensibilisation de TAT-RasGAP317-326. Nous avons démontré que le facteur de transcription p53 et une protéine sous son activité transcriptionelle, nommée Puma, sont indispensables pour l'activité de TAT-RasGAP317-326. Nous avons également prouvé que TAT-RasGAP317-326 exige la présence d'une protéine appelée G3BP1, une protéine se liant a RasGAP, pour potentialisé les effets d'agents anticancéreux. Les données obtenues dans cette étude montrent qu'il pourrait être possible d'augmenter l'efficacité des chimiothérapies à l'aide d'un composé capable d'augmenter la sensibilité des tumeurs aux génotoxines et ainsi pourrait permettre de traiter de manière plus efficace des patients sous traitement chimiothérapeutiques. Summary Tumors are diverse and heterogeneous, but all share the ability to proliferate without control. Deregulated cell proliferation coupled with suppressed apoptotic sensitivity constitutes a minimal requirement upon which tumor evolution occurs. One of the most commonly used treatments is chemotherapy, which frequently uses chemical compounds that induce DNA damages. Anticancer agents are effective only when tumors cells are more readily killed than the surrounding normal tissue. The efficacy of these agents is partly determined by their ability to induce apoptosis. We have recently demonstrated that the protein RasGAP is an unconventional caspase substrate because it can induce both anti- and pro-apoptotic signals, depending on the extent of its cleavage by caspases. At low levels of caspase activity, RasGAP is cleaved, generating an N-terminal fragment (fragment N) and a C-terminal fragment (fragment C). Fragment N appears to be a general Mocker of apoptosis downstream of caspase activation. At higher levels of caspase activity, the ability of fragment N to counteract apoptosis is suppressed when it is further cleaved. This latter cleavage event generates two fragments, N1 and N2, which in contrast to fragment N potently sensitizes cancer cells toward DNA-damaging agents induced apoptosis. In the present study we show that a cell permeable peptide derived from the N2 fragment of RasGAP, thereafter called TAT-RasGAP317-326, specifically sensitizes cancer cells to three different genotoxins commonly used in chemotherapy in vitro and in vivo models. Importantly this peptide seems not to have any effect on non cancer cells. We have also started to characterize the molecular mechanisms underlying the sensitizing functions of TAT-RasGAP317-326. We have demonstrated that the p53 transcription factor and a protein under its transcriptional activity, called Puma, are required for the activity of TATRasGAP317-326. We have also showed that TAT-RasGAP317-326 requires the presence of a protein called G3BP1, which have been shown to interact with RasGAP, to increase the effect of the DNA-damaging drug cisplatin. The data obtained in this study showed that it is possible to increase the efficacy of current used chemotherapies with a compound able to increase the efficacy of genotoxins which could be beneficial for patients subjected to chemotherapy.

Anthracene-tethered ruthenium(II) arene complexes as tools to visualize the cellular localization of putative organometallic anticancer compounds.

Anthracene derivatives of ruthenium(II) arene compounds with 1,3,5-triaza-7-phosphatricyclo[3.3.1.1]decane (pta) or a sugar phosphite ligand, viz., 3,5,6-bicyclophosphite-1,2-O-isopropylidene-α-d-glucofuranoside, were prepared in order to evaluate their anticancer properties compared to the parent compounds and to use them as models for intracellular visualization by fluorescence microscopy. Similar IC(50) values were obtained in cell proliferation assays, and similar levels of uptake and accumulation were also established. The X-ray structure of [{Ru(η(6)-C(6)H(5)CH(2)NHCO-anthracene)Cl(2)(pta)] is also reported.

New experimental tools for bioassays with whitefly in laboratory

The objective of this work was to develop an experimental kit for assessments of repellency, deterrence for oviposition, and insecticidal activity on adults of the whitefly Bemisia tabaci biotype B. The kit, which consisted of arenas and nebulizer, was effective for conducting bioassays, and the application of aqueous extracts by inhaler was adequate. The techniques are simple, cheap, and may contribute to research on this insect.

Apertium Advanced Web Interface : A first step towards interactivity and language tools convergence

This documents sums up a projectaimed at building a new web interfaceto the Apertium machine translationplatform, including pre-editing andpost-editing environments. It containsa description of the accomplished workon this project, as well as an overviewof possible evolutions.

Work Organization Practices for Lean Production: A Study in Spanish and Brazilian Manufacturers

This work aims to discuss particular work organization practices in human resources reported as successful by companies adopting lean production principles, and investigate them through real examples observed on manufacturing plants in Spain and Brazil. A set of work organization practices was previously established by one of the authors, through interviews with practitioners of lean production and review of the literature. A series of visits and informal interviews at Spanish and Brazilian automotive parts manufacturers that follow lean production were conducted to verify that the set of policies are indeed practiced.

Beta-adrenergic blockade and intravenous nutrient-induced thermogenesis in lean and obese women.

Continuous respiratory exchange measurements were performed in nine obese and eight lean women for 1 h before, 3 h during, and 1 h after the intravenous administration of a nutrient mixture infused at twice the postabsorptive resting energy expenditure (REE). This experiment was conducted without or with beta-adrenergic blockade (iv propranolol). Propranolol administration did not change the postabsorptive REE [i.e., 1.03 +/- 0.07 before vs. 1.01 +/- 0.02 kcal/min after administration in lean women and 1.16 +/- 0.04 vs. 1.15 +/- 0.03 kcal/min (NS) in obese women]. The mean overall thermogenic response expressed as a percentage of the infused energy was similar in both groups and was not significantly blunted after propranolol infusion [6.9 +/- 0.4 vs. 5.9 +/- 0.6% in the lean women and 7.5 +/- 0.5 vs. 7.1 +/- 0.6% (NS) in the obese women]. During beta-adrenergic blockade the rate of lipid oxidation decreased in the lean group but was unchanged in the obese group and the glycemic response to nutrient administration was significantly higher in both groups than without propranolol. It is concluded that beta-adrenergic blockade has no effect on REE and on intravenous nutrient-induced thermogenesis in both lean and obese women.