Twisted sl(3, C)(k)((2)) current algebra: free field representation and screening currents

Motivated by application of twisted current algebra in description of the entropy of Ads(3) black hole, we investigate the simplest twisted current algebra sl(3, c)(k)((2)). Free field representation of the twisted algebra, and the corresponding twisted Sugawara energy-momentum tensor are obtained by using three (beta, gamma) pairs and two scalar fields. Primary fields and two screening currents of the first kind are presented. (C) 2001 Published by Elsevier Science B.V.

Free field and parafermionic realizations of twisted su(3)(k)((2)) current algebra

Free field and twisted parafermionic representations of twisted su(3)(k)((2)) current algebra are obtained. The corresponding twisted Sugawara energy-momentum tensor is given in terms of three (beta, gamma) pairs and two scalar fields and also in terms of twisted parafermionic currents and one scalar field. Two screening currents of the first kind are presented in terms of the free fields.

Potassium Current in Mature Bovine Spermatozoa

Ion channels have been assigned a pivotal importance in various sperm functions and are therefore promising targets for contraceptive development. The lack of data on channel functionality and pharmacology has hampered this goal. This is a consequence of technical problems of applying electrophysiological techniques to spermatozoa due to their small size and form. By using a laminin coating to increase adherence of spermatozoa and nystatin in the patch pipette for pore formation, we have adapted the whole-cell recording technique to study currents in mature uncapacitated bovine spermatozoa. Employing these conditions, in the head region, patched spermatozoa could be transferred into the whole-cell configuration. For the first time we document an outward rectifying current in mature bovine spermatozoa was blocked by tetraethyl ammonium (TEA) chloride. The observation of a shift in the reversal potential as a response to changes in the extracellular concentration of K+ ions allowed us to identify this current as K+ selective. This result shows that K+ channels in the head region of mature uncapacitated bovine spermatozoa can be suitably investigated using the whole-cell recording patch-clamp technique.

A transient outward-rectifying K+ channel current down-regulated by cytosolic Ca2+ in Arabidopsis thaliana guard cells

Sustained (noninactivating) outward-rectifying K+ channel currents have been identified in a variety of plant cell types and species. Here, in Arabidopsis thaliana guard cells, in addition to these sustained K+ currents, an inactivating outward-rectifying K+ current was characterized (plant A-type current: IAP). IAP activated rapidly with a time constant of 165 ms and inactivated slowly with a time constant of 7.2 sec at +40 mV. IAP was enhanced by increasing the duration (from 0 to 20 sec) and degree (from +20 to −100 mV) of prepulse hyperpolarization. Ionic substitution and relaxation (tail) current recordings showed that outward IAP was mainly carried by K+ ions. In contrast to the sustained outward-rectifying K+ currents, cytosolic alkaline pH was found to inhibit IAP and extracellular K+ was required for IAP activity. Furthermore, increasing cytosolic free Ca2+ in the physiological range strongly inhibited IAP activity with a half inhibitory concentration of ≈ 94 nM. We present a detailed characterization of an inactivating K+ current in a higher plant cell. Regulation of IAP by diverse factors including membrane potential, cytosolic Ca2+ and pH, and extracellular K+ and Ca2+ implies that the inactivating IAP described here may have important functions during transient depolarizations found in guard cells, and in integrated signal transduction processes during stomatal movements.

The importance of Na(plus)-K(plus)-Cl- cotransport in nitrogen mustard induced cell death

The incubation of murine leukaemic L1210 cells in vitro for 4 hours (hr) with 10uM nitrogen mustard (HN2), a bifunctional alkylating agent, inhibited the influx of the potassium congener, 88rubidium+ ( 86Rb+) by the selective inhibition of the Na+-K+-CI- cotransporter. The aim of this project was to investigate the importance of this lesion in HN2-induced cytotoxicity. 86Rb+ uptake in human erythrocytes was inhibited by high concentrations of HN2 (2mM) and occurred in two phases.In the first hour both the Na+/K+ ATPase pump and the Na+-K+-CI- cotransporter were equally inhibited but after 2 hrs exposure to 2mM HN2, the Na+ -K+ -CI- cotransporter was significantly more inhibited than the Na+/K+ ATPase pump. In contrast, both potassium transport systems were equally inhibited in L1210 cells incubated for 10 minutes with 1mM HN2. The selective inhibition of the Na+-K+-CI- cotransporter, after a 3 hrs exposure to 10uM HN2, was not absolved by coincubation with 5ug/ml cycloheximide (CHX), an inhibitor of protein synthesis. Incubation of L1210 cells with concentrations of diuretics which completely inhibited Na+-K+-CI- cotransport did not enhance the cytotoxicity of either HN2 or its monofunctional analogue 2-chloroethyldimethylamine (Me-HN1). The incubation of L1210 cells with a twice strength Rosewell Park Memorial Institute 1640 media did not enhance the toxicity of HN2. An L1210 cell line (L1210FR) was prepared which was able to grow in toxic concentrations of furosemide and exhibited a similiar sensitivity to HN2 as parental L1210 cells. Treatment of L1210 cells with 10uM HN2 resulted in a decrease in cell volume which was concurrent with the inhibition of the Na+-K+-CI- cotransporter. This was not observed in L1210 cells treated with either 1 or O.SuM HN2. Thus, possible differences in the cell death, in terms of necrosis and apoptosis, induced by the different concentrations of HN2 was investigated. The cell cycle of L1210 cells appeared to be blocked non-specifically by 10uM HN2 and in S and G2/M by either 1 or 0.5uM HN2. There were no significant changes in the cytosolic calcium concentrations of L1210 cells for up to 48 hrs after exposure to the three concentrations of HN2. No protection against th_ toxic effects of HN2 was observed in L1210 cells incubated with 5ug/ml CHX for up to 6 hrs. Incubation for 12 or 18 hrs with a non-toxic concentration (5mM) of L-Azetidine-2- carboxylic acid (ACA) enhanced the toxicity of low concentrations (<0.5uM) of HN2.

The crustacean gill (Na(+),K(+))-ATPase: Allosteric modulation of high- and low-affinity ATP-binding sites by sodium and potassium

The blue crab, Callinectes danae, tolerates exposure to a wide salinity range employing mechanisms of compensatory ion uptake when in dilute media. Although the gill (Na(+), K(+))-ATPase is vital to hyperosmoregulatory ability, the interactions occurring at the sites of ATP binding on the molecule itself are unknown. Here, we investigate the modulation by Na(+) and K(+) of homotropic interactions between the ATP-binding sites, and of phosphoenzyme formation of the (Na(+),K(+))-ATPase from the posterior gills of this euryhaline crab. The contribution of the high- and low-affinity ATP-binding sites to maximum velocity was similar for both Na(+) and K(+). However, in contrast to Na(+), a threshold K(+) concentration triggers the appearance of the high-affinity binding sites, displacing the saturation curve to lower ATP concentrations. Further, a low-affinity site for phosphorylation is present on the enzyme. These findings reveal notable differences in the catalytic mechanism of the crustacean (Na(+),K(+))-ATPase compared to the vertebrate enzyme. (C) 2008 Elsevier Inc. All rights reserved.

Loss-of-function mutations in the KCNJ8-encoded Kir6.1 K(ATP) channel and sudden infant death syndrome.

BACKGROUND Approximately 10% of sudden infant death syndrome (SIDS) may stem from cardiac channelopathies. The KCNJ8-encoded Kir6.1 (K(ATP)) channel critically regulates vascular tone and cardiac adaptive response to systemic metabolic stressors, including sepsis. KCNJ8-deficient mice are prone to premature sudden death, particularly with infection. We determined the spectrum, prevalence, and function of KCNJ8 mutations in a large SIDS cohort. METHODS AND RESULTS Using polymerase chain reaction, denaturing high-performance liquid chromatography, and DNA sequencing, comprehensive open reading frame/splice-site mutational analysis of KCNJ8 was performed on genomic DNA isolated from necropsy tissue on 292 unrelated SIDS cases (178 males, 204 white; age, 2.9±1.9 months). KCNJ8 mutations were coexpressed heterologously with SUR2A in COS-1 cells and characterized using whole-cell patch-clamp. Two novel KCNJ8 mutations were identified. A 5-month-old white male had an in-frame deletion (E332del) and a 2-month-old black female had a missense mutation (V346I). Both mutations localized to Kir6.1's C-terminus, involved conserved residues and were absent in 400 and 200 ethnic-matched reference alleles respectively. Both cases were negative for mutations in established channelopathic genes. Compared with WT, the pinacidil-activated K(ATP) current was decreased 45% to 68% for Kir6.1-E332del and 40% to 57% for V346I between -20 mV and 40 mV. CONCLUSIONS Molecular and functional evidence implicated loss-of-function KCNJ8 mutations as a novel pathogenic mechanism in SIDS, possibly by predisposition of a maladaptive cardiac response to systemic metabolic stressors akin to the mouse models of KCNJ8 deficiency.

Gain-of-function mutation S422L in the KCNJ8-encoded cardiac K(ATP) channel Kir6.1 as a pathogenic substrate for J-wave syndromes.

BACKGROUND J-wave syndromes have emerged conceptually to encompass the pleiotropic expression of J-point abnormalities including Brugada syndrome (BrS) and early repolarization syndrome (ERS). KCNJ8, which encodes the cardiac K(ATP) Kir6.1 channel, recently has been implicated in ERS following identification of the functionally uncharacterized missense mutation S422L. OBJECTIVE The purpose of this study was to further explore KCNJ8 as a novel susceptibility gene for J-wave syndromes. METHODS Using polymerase chain reaction, denaturing high-performance liquid chromatography, and direct DNA sequencing, comprehensive open reading frame/splice site mutational analysis of KCNJ8 was performed in 101 unrelated patients with J-wave syndromes, including 87 with BrS and 14 with ERS. Six hundred healthy individuals were examined to assess the allelic frequency for all variants detected. KCNJ8 mutation(s) was engineered by site-directed mutagenesis and coexpressed heterologously with SUR2A in COS-1 cells. Ion currents were recorded using whole-cell configuration of the patch-clamp technique. RESULTS One BrS case and one ERS case hosted the identical missense mutation S422L, which was reported previously. KCNJ8-S422L involves a highly conserved residue and was absent in 1,200 reference alleles. Both cases were negative for mutations in all known BrS and ERS susceptibility genes. K(ATP) current of the Kir6.1-S422L mutation was increased significantly over the voltage range from 0 to 40 mV compared to Kir6.1-WT channels (n = 16-21; P <.05). CONCLUSION These findings further implicate KCNJ8 as a novel J-wave syndrome susceptibility gene and a marked gain of function in the cardiac K(ATP) Kir6.1 channel secondary to KCNJ8-S422L as a novel pathogenic mechanism for the phenotypic expression of both BrS and ERS.

Measurements of ocean microstructure, physical oceanography and current in the Weddell Sea, Antarctica, during the RRS Ernest Shackleton cruises ES033 and ES060

Turbulence profile measurements made on the upper continental slope and shelf of the southeastern Weddell Sea reveal striking contrasts in dissipation and mixing rates between the two sites. The mean profiles of dissipation rates from the upper slope are 1-2 orders of magnitude greater than the profiles collected over the shelf in the entire water column. The difference increases toward the bottom where the dissipation rate of turbulent kinetic energy and the vertical eddy diffusivity on the slope exceed 10?7 W kg?1 and 10?2 m2 s?1, respectively. Elevated levels of turbulence on the slope are concentrated within a 100 m thick bottom layer, which is absent on the shelf. The upper slope is characterized by near-critical slopes and is in close proximity to the critical latitude for semidiurnal internal tides. Our observations suggest that the upper continental slope of the southern Weddell Sea is a generation site of semidiurnal internal tide, which is trapped along the slope along the critical latitude, and dissipates its energy in a inline image m thick layer near the bottom and within inline image km across the slope.

In vitro susceptibility of Paracoccidioides brasiliensis yeast form to antifungal agents

A study was conducted to determine the susceptibility of P. brasiliensis yeast form to amphotericin B (A), ketoconazole (K), 5-fluorocytosine (5-FC) and rifampin (R). The three isolates tested produced minimal inhibitory concentrations (MICs) (mcg/ml) in the following range: A: 0.09-0.18; K: 0.001-0.007; 5-FC: 62.5-250 and R: 40-80. The minimal fungicidal concentrations (MFC) were several times higher than the corresponding MICs. Precise MFC for 5-FC were not obtained (> 500 mcg/ml). Combination of K plus A proved synergic, with the fractional inhibitory concentration (FIC) indices revealing synergy when the drugs were combined at the 1 to 1 and 1 to 5 MIC ratios. R (40 mcg/ml) appeared to antagonize K. These results indicate promise for the combined use of K plus A as a therapeutical regimen.

Toxic cardenolides: chemical ecology and coevolution of specialized plant-herbivore interactions.

CONTENTS: Summary 28 I. Historic background and introduction 29 II. Diversity of cardenolide forms 29 III. Biosynthesis 30 IV. Cardenolide variation among plant parts 31 V. Phylogenetic distribution of cardenolides 32 VI. Geographic distribution of cardenolides 34 VII. Ecological genetics of cardenolide production 34 VIII. Environmental regulation of cardenolide production 34 IX. Biotic induction of cardenolides 36 X. Mode of action and toxicity of cardenolides 38 XI. Direct and indirect effects of cardenolides on specialist and generalist insect herbivores 39 XII. Cardenolides and insect oviposition 39 XIII. Target site insensitivity 40 XIV. Alternative mechanisms of cardenolide resistance 40 XV. Cardenolide sequestration 41 Acknowledgements 42 References 42 SUMMARY: Cardenolides are remarkable steroidal toxins that have become model systems, critical in the development of theories for chemical ecology and coevolution. Because cardenolides inhibit the ubiquitous and essential animal enzyme Na(+) /K(+) -ATPase, most insects that feed on cardenolide-containing plants are highly specialized. With a huge diversity of chemical forms, these secondary metabolites are sporadically distributed across 12 botanical families, but dominate the Apocynaceae where they are found in > 30 genera. Studies over the past decade have demonstrated patterns in the distribution of cardenolides among plant organs, including all tissue types, and across broad geographic gradients within and across species. Cardenolide production has a genetic basis and is subject to natural selection by herbivores. In addition, there is strong evidence for phenotypic plasticity, with the biotic and abiotic environment predictably impacting cardenolide production. Mounting evidence indicates a high degree of specificity in herbivore-induced cardenolides in Asclepias. While herbivores of cardenolide-containing plants often sequester the toxins, are aposematic, and possess several physiological adaptations (including target site insensitivity), there is strong evidence that these specialists are nonetheless negatively impacted by cardenolides. While reviewing both the mechanisms and evolutionary ecology of cardenolide-mediated interactions, we advance novel hypotheses and suggest directions for future work.

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.

Configuration et exploitation d'une machine émotionnelle

Dans ce travail, nous explorons la faisabilité de doter les machines de la capacité de prédire, dans un contexte d'interaction homme-machine (IHM), l'émotion d'un utilisateur, ainsi que son intensité, de manière instantanée pour une grande variété de situations. Plus spécifiquement, une application a été développée, appelée machine émotionnelle, capable de «comprendre» la signification d'une situation en se basant sur le modèle théorique d'évaluation de l'émotion Ortony, Clore et Collins (OCC). Cette machine est apte, également, à prédire les réactions émotionnelles des utilisateurs, en combinant des versions améliorées des k plus proches voisins et des réseaux de neurones. Une procédure empirique a été réalisée pour l'acquisition des données. Ces dernières ont fourni une connaissance consistante aux algorithmes d'apprentissage choisis et ont permis de tester la performance de la machine. Les résultats obtenus montrent que la machine émotionnelle proposée est capable de produire de bonnes prédictions. Une telle réalisation pourrait encourager son utilisation future dans des domaines exploitant la reconnaissance automatique de l'émotion.

Concentrado de vinhaça biodigerida como fertilizante: efeito no substrato, no crescimento e nutrição da cana-de-açúcar

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)