The DNA-binding domain (DBD) is essential for NR2E3 dimerization and interaction with Crx

Purpose:NR2E3 (PNR) is an orphan nuclear receptor essential for proper photoreceptor determination and differentiation. In humans, mutations in NR2E3 have been associated with the recessively inherited enhanced short wavelength sensitive (S-) cone syndrome (ESCS) and, more recently, with autosomal dominant retinitis pigmentosa (adRP). NR2E3 acts in concert with the transcription factors Crx and Nrl to repress cone-specific genes and activate rod-specific genes. NR2E3 and Crx have been shown to physically interact by their DNA-binding domain (DBD), which may also be implicated in the dimerization process of the nuclear receptor. However, neither NR2E3 homodimerization nor NR2E3/Crx complex formation has been investigated in detail. Methods:In this present work, we analyzed the dimerization of the NR2E3 protein and its interaction with Crx by bioluminescence resonance energy transfer (BRET2) which utilizes Renilla luciferase (hRluc) protein and its substrate DeepBlueC as an energy donor and a mutant green fluorescent protein (GFP2) as the acceptor. We investigated, on whole intact cells, the role of NR2E3 DBD-mutations in dimerization and association with Crx. Results:We clearly showed that NR2E3 formed homodimers in HEK-293T cells. Moreover, all causative NR2E3 mutations present in the DBD of the protein showed an alteration in dimerization, except for the R76Q and the R104W mutants. Interestingly, the adRP-linked G56R mutant was the only DBD-NR2E3 mutant that showed a correct interaction with Crx. Finally, we observed a decrease in rhodospin gene transactivation for all DBD-NR2E3 mutants tested and no potentiation for the adRP-linked G56R mutant. In addition, the p.G56R mutant enhanced the transrepression of M-opsin promoter, while all other DBD-NR2E3 mutants did not repress M-opsin transactivation. Conclusions:A defect, either in the dimer formation or in the interaction of NR2E3 with Crx, leads to abnormal transcriptional activity on rhodopsin and M-opsin promoter and to an atypical retinal development; while the titration of Crx by p.G56R-NR2E3 leads to low levels of rhodopsin and M-opsin expression and may be responsible for the strong adRP phenotype.

Two-step postmortem angiography with a modified heart-lung machine: preliminary results.

OBJECTIVE: The purpose of this study was to adapt and improve a minimally invasive two-step postmortem angiographic technique for use on human cadavers. Detailed mapping of the entire vascular system is almost impossible with conventional autopsy tools. The technique described should be valuable in the diagnosis of vascular abnormalities. MATERIALS AND METHODS: Postmortem perfusion with an oily liquid is established with a circulation machine. An oily contrast agent is introduced as a bolus injection, and radiographic imaging is performed. In this pilot study, the upper or lower extremities of four human cadavers were perfused. In two cases, the vascular system of a lower extremity was visualized with anterograde perfusion of the arteries. In the other two cases, in which the suspected cause of death was drug intoxication, the veins of an upper extremity were visualized with retrograde perfusion of the venous system. RESULTS: In each case, the vascular system was visualized up to the level of the small supplying and draining vessels. In three of the four cases, vascular abnormalities were found. In one instance, a venous injection mark engendered by the self-administration of drugs was rendered visible by exudation of the contrast agent. In the other two cases, occlusion of the arteries and veins was apparent. CONCLUSION: The method described is readily applicable to human cadavers. After establishment of postmortem perfusion with paraffin oil and injection of the oily contrast agent, the vascular system can be investigated in detail and vascular abnormalities rendered visible.

Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images

A discussion is presented of daytime sky imaging and techniques that may be applied to the analysis of full-color sky images to infer cloud macrophysical properties. Descriptions of two different types of skyimaging systems developed by the authors are presented, one of which has been developed into a commercially available instrument. Retrievals of fractional sky cover from automated processing methods are compared to human retrievals, both from direct observations and visual analyses of sky images. Although some uncertainty exists in fractional sky cover retrievals from sky images, this uncertainty is no greater than that attached to human observations for the commercially available sky-imager retrievals. Thus, the application of automatic digital image processing techniques on sky images is a useful method to complement, or even replace, traditional human observations of sky cover and, potentially, cloud type. Additionally, the possibilities for inferring other cloud parameters such as cloud brokenness and solar obstruction further enhance the usefulness of sky imagers

Scanning electron microscopy of the interaction between Cryptococcus magnus and Colletotrichum gloeosporioides on papaya fruit

The objective of this work was to investigate possible modes of action of the yeast Cryptococcus magnus in controlling anthracnose (Colletotrichum gloeosporioides) on post harvested papaya fruits. Scanning electron microscopy was used to analyze the effect of the yeast on inoculations done after harvest. Results showed that C. magnus is able to colonize wound surfaces much faster than the pathogen, outcompeting the later for space and probably for nutrients. In addition, C. magnus produces a flocculent matrix, which affects hyphae integrity. The competition for space and the production of substances that affect hyphae integrity are among the most important modes of action of this yeast.

Phosphorylation regulates the microtubule-destabilizing activity of stathmin and its interaction with tubulin.

Stathmin is a regulator of microtubule dynamics which undergoes extensive phosphorylation during the cell cycle as well as in response to various extracellular factors. Four serine residues are targets for protein kinases: Ser-25 and Ser-38 for proline-directed kinases such as mitogen-activated protein kinase and cyclin-dependent protein kinase, and Ser-16 and Ser-63 for cAMP-dependent protein kinase. We studied the effect of phosphorylation on the microtubule-destabilizing activity of stathmin and on its interaction with tubulin in vitro. We show that triple phosphorylation on Ser-16, Ser-25, and Ser-38 efficiently inhibits its activity and prevents its binding to tubulin.

Horizontal vestibulo-ocular reflex dynamics in acute vestibular neuritis and viral labyrinthitis: evidence of otolith-canal interaction.

OBJECTIVE: To evaluate the dynamic properties of the horizontal vestibulo-ocular reflex (h-VOR) in the acute stage of two common labyrinthine diseases that provoke severe attacks of vertigo with spontaneous nystagmus: vestibular neuritis (vestibular loss alone) and viral labyrinthitis (cochleovestibular loss). MATERIAL AND METHODS: Sixty-three patients were investigated: 42 were diagnosed with vestibular neuritis and 21 with viral labyrinthitis. The h-VOR function was evaluated by conventional caloric and impulsive testing. A simplified model of vestibular function was used to analyze the vestibulo-ocular response to rotational stimulation. RESULTS: The results showed a significant difference in h-VOR characteristics between the two pathologies. Patients with vestibular neuritis exhibited a strong horizontal semicircular canal deficit, but no h-VOR asymmetry between the two rotational directions. In contrast, patients with viral labyrinthitis demonstrated moderate canal paresis and a marked h-VOR deficit in rotation toward the affected ear. CONCLUSION: These findings support the hypothesis that the h-VOR dynamic asymmetry that occurs after an acute unilateral inner ear lesion is not due to canal dysfunction alone, but involves complex adaptive changes in the central VOR that may implicate the otolith system. Based on histopathologic and clinical differences in the two pathologies reported in the literature, we postulate that this otolith-canal interaction is mainly linked to the loss of saccular function.

Études des forces d'interaction entre Staphylococcus aureus et fibrinogène par microscopie à force atomique

Ce travail s'inscrit dans le cadre d'un projet dont l'objectif est d'étudier les propriétés d'adhésion du ClfA au fibrinogène à l'aide de l'AFM. Plus précisément, le mode « Force spectroscopy » de l'AFM sera utilisé afin de mesurer les forces d'interactions entre le fibrinogène et le ClfA cloné à des bactéries ne comportant pas de MSCRAMMs et n'étant pas pathogène pour l'homme. Puis les forces d'interactions seront mesurées entre le fibrinogène et la surface des S. aureus. Une meilleure connaissance des propriétés d'adhésion des S. aureus au ClfA contribuerait ainsi au développement de la recherche dans ce domaine et à de potentielle future thérapie contre les infections à S. aureus.

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.

Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).

Multivariate analyses of genotype x environment interaction of popcorn

The objectives of this work were to evaluate the genotype x environment (GxE) interaction for popcorn and to compare two multivariate analyses methods. Nine popcorn cultivars were sown on four dates one month apart during each of the agricultural years 1998/1999 and 1999/2000. The experiments were carried out using randomized block designs, with four replicates. The cv. Zélia contributed the least to the GxE interaction. The cv. Viçosa performed similarly to cv. Rosa-claro. Optimization of GxE was obtained for cv. CMS 42 for a favorable mega-environment, and for cv. CMS 43 for an unfavorable environment. Multivariate analysis supported the results from the method of Eberhart & Russell. The graphic analysis of the Additive Main effects and Multiplicative Interaction (AMMI) model was simple, allowing conclusions to be made about stability, genotypic performance, genetic divergence between cultivars, and the environments that optimize cultivar performance. The graphic analysis of the Genotype main effects and Genotype x Environment interaction (GGE) method added to AMMI information on environmental stratification, defining mega-environments and the cultivars that optimized performance in those mega-environments. Both methods are adequate to explain the genotype x environment interactions.

Mid- to Late Holocene shoreline reconstruction and human occupation in Ancient Eretria (South Central Euboea, Greece)

Few studies have aimed to reconstruct landscape change in the area of Eretria (South Central Euboea, Greece) during the last 6000 years. The aim of this paper is to partially fill in this gap by examining the interaction be- tween Mid- to Late Holocene shoreline evolution and human occupation, which is documented in the area from the Late Neolithic to the Late Roman period (with discontinuities). Evidence of shoreline displacements is derived from the study of five boreholes (maximum depth of 5.25 m below the surface) drilled in the lowlands of Eretria. Based on sedimentological analyses and micro/macrofaunal identifications, different facies have been identified in the cores and which reveal typical features of deltaic progradation with marine, lagoonal, fluvio- deltaic and fluvial environments. In addition, a chronostratigraphy has been obtained based on 20 AMS 14C radio- carbon dates performed on samples of plant remains and marine/lagoonal shells found in situ. The main sequences of landscape reconstruction in the plain of Eretria can be summarized as follows: a marine environ- ment predominated from ca. 4000 to 3200 cal. BC and a gradual transition to shallow marine conditions is ob- served ca. 3200-3000 cal. BC due to the general context of deltaic progradation west of the ancient city. Subsequently, from ca. 3000 to 2000 cal. BC, a lagoon occupied the area in the vicinity of the Temple of Apollo and the settlement's development was restricted to several fluvio-deltaic levees, thus severely limiting human activities in the plain. From ca. 2000 to 800 cal. BC, a phase of shallow marine presence prevailed and constrained settlement on higher ground, forcing abandonment of the major part of the plain. Finally, since the eighth century BC, the sea has regressed southward and created the modern landscape.