Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

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.

Extension of Murray's law using a non-Newtonian model of blood flow.

BACKGROUND: So far, none of the existing methods on Murray's law deal with the non-Newtonian behavior of blood flow although the non-Newtonian approach for blood flow modelling looks more accurate. MODELING: In the present paper, Murray's law which is applicable to an arterial bifurcation, is generalized to a non-Newtonian blood flow model (power-law model). When the vessel size reaches the capillary limitation, blood can be modeled using a non-Newtonian constitutive equation. It is assumed two different constraints in addition to the pumping power: the volume constraint or the surface constraint (related to the internal surface of the vessel). For a seek of generality, the relationships are given for an arbitrary number of daughter vessels. It is shown that for a cost function including the volume constraint, classical Murray's law remains valid (i.e. SigmaR(c) = cste with c = 3 is verified and is independent of n, the dimensionless index in the viscosity equation; R being the radius of the vessel). On the contrary, for a cost function including the surface constraint, different values of c may be calculated depending on the value of n. RESULTS: We find that c varies for blood from 2.42 to 3 depending on the constraint and the fluid properties. For the Newtonian model, the surface constraint leads to c = 2.5. The cost function (based on the surface constraint) can be related to entropy generation, by dividing it by the temperature. CONCLUSION: It is demonstrated that the entropy generated in all the daughter vessels is greater than the entropy generated in the parent vessel. Furthermore, it is shown that the difference of entropy generation between the parent and daughter vessels is smaller for a non-Newtonian fluid than for a Newtonian fluid.

Effect of Cumulating Exposure to Abacavir on the Risk of Cardiovascular Disease Events in Patients From the Swiss HIV Cohort Study.

BACKGROUND: Patients with HIV exposed to the antiretroviral drug abacavir may have an increased risk of cardiovascular disease (CVD). There is concern that this association arises because of a channeling bias. Even if exposure is a risk, it is not clear how that risk changes as exposure cumulates. METHODS: We assess the effect of exposure to abacavir on the risk of CVD events in the Swiss HIV Cohort Study. We use a new marginal structural Cox model to estimate the effect of abacavir as a flexible function of past exposures while accounting for risk factors that potentially lie on a causal pathway between exposure to abacavir and CVD. RESULTS: A total of 11,856 patients were followed for a median of 6.6 years; 365 patients had a CVD event (4.6 events per 1000 patient-years). In a conventional Cox model, recent--but not cumulative--exposure to abacavir increased the risk of a CVD event. In the new marginal structural Cox model, continued exposure to abacavir during the past 4 years increased the risk of a CVD event (hazard ratio = 2.06; 95% confidence interval: 1.43 to 2.98). The estimated function for the effect of past exposures suggests that exposure during the past 6-36 months caused the greatest increase in risk. CONCLUSIONS: Abacavir increases the risk of a CVD event: the effect of exposure is not immediate, rather the risk increases as exposure cumulates over the past few years. This gradual increase in risk is not consistent with a rapidly acting mechanism, such as acute inflammation.

Identification of particular groups of microRNAs that positively or negatively impact on beta cell function in obese models of type 2 diabetes.

AIMS/HYPOTHESIS: MicroRNAs are key regulators of gene expression involved in health and disease. The goal of our study was to investigate the global changes in beta cell microRNA expression occurring in two models of obesity-associated type 2 diabetes and to assess their potential contribution to the development of the disease. METHODS: MicroRNA profiling of pancreatic islets isolated from prediabetic and diabetic db/db mice and from mice fed a high-fat diet was performed by microarray. The functional impact of the changes in microRNA expression was assessed by reproducing them in vitro in primary rat and human beta cells. RESULTS: MicroRNAs differentially expressed in both models of obesity-associated type 2 diabetes fall into two distinct categories. A group including miR-132, miR-184 and miR-338-3p displays expression changes occurring long before the onset of diabetes. Functional studies indicate that these expression changes have positive effects on beta cell activities and mass. In contrast, modifications in the levels of miR-34a, miR-146a, miR-199a-3p, miR-203, miR-210 and miR-383 primarily occur in diabetic mice and result in increased beta cell apoptosis. These results indicate that obesity and insulin resistance trigger adaptations in the levels of particular microRNAs to allow sustained beta cell function, and that additional microRNA deregulation negatively impacting on insulin-secreting cells may cause beta cell demise and diabetes manifestation. CONCLUSIONS/INTERPRETATION: We propose that maintenance of blood glucose homeostasis or progression toward glucose intolerance and type 2 diabetes may be determined by the balance between expression changes of particular microRNAs.

Elf-1 contributes to the function of the complex interleukin (IL)-2-responsive enhancer in the mouse IL-2 receptor alpha gene.

Lymphocytes regulate their responsiveness to IL-2 through the transcriptional control of the IL-2R alpha gene, which encodes a component of the high affinity IL-2 receptor. In the mouse IL-2R alpha gene this control is exerted via two regulatable elements, a promoter proximal region, and an IL-2-responsive enhancer (IL-2rE) 1.3 kb upstream. In vitro and in vivo functional analysis of the IL-2rE in the rodent thymic lymphoma-derived, CD4- CD8- cell line PC60 demonstrated that three separate elements, sites I, II, and III, were necessary for IL-2 responsiveness; these three sites demonstrate functional cooperation. Site III contains a consensus binding motif for members of the Ets family of transcription factors. Here we demonstrate that Elf-1, an Ets-like protein, binds to site III and participates in IL-2 responsiveness. In vitro site III forms a complex with a protein constitutively present in nuclear extracts from PC60 cells as well as from normal CD4- CD8- thymocytes. We have identified this molecule as Elf-1 according to a number of criteria. The complex possesses an identical electrophoretic mobility to that formed by recombinant Elf-1 protein and is super-shifted by anti-Elf-1 antibodies. Biotinylated IL-2rE probes precipitate Elf-1 from PC60 extracts provided site III is intact and both recombinant and PC60-derived proteins bind with the same relative affinities to different mutants of site III. In addition, by introducing mutations into the core of the site III Ets-like motif and comparing the corresponding effects on the in vitro binding of Elf-1 and the in vivo IL-2rE activity, we provide strong evidence that Elf-1 is directly involved in IL-2 responsiveness. The nature of the functional cooperativity observed between Elf-1 and the factors binding sites I and II remains unresolved; experiments presented here however suggest that this effect may not require direct interactions between the proteins binding these three elements.

TIE-2 and VEGFR Kinase Activities Drive Immunosuppressive Function of TIE-2-Expressing Monocytes in Human Breast Tumors.

PURPOSE: Tumor-associated TIE-2-expressing monocytes (TEM) are highly proangiogenic cells critical for tumor vascularization. We previously showed that, in human breast cancer, TIE-2 and VEGFR pathways control proangiogenic activity of TEMs. Here, we examine the contribution of these pathways to immunosuppressive activity of TEMs. EXPERIMENTAL DESIGN: We investigated the changes in immunosuppressive activity of TEMs and gene expression in response to specific kinase inhibitors of TIE-2 and VEGFR. The ability of tumor TEMs to suppress tumor-specific T-cell response mediated by tumor dendritic cells (DC) was measured in vitro. Characterization of TEM and DC phenotype in addition to their interaction with T cells was done using confocal microscopic images analysis of breast carcinomas. RESULTS: TEMs from breast tumors are able to suppress tumor-specific immune responses. Importantly, proangiogenic and suppressive functions of TEMs are similarly driven by TIE-2 and VEGFR kinase activity. Furthermore, we show that tumor TEMs can function as antigen-presenting cells and elicit a weak proliferation of T cells. Blocking TIE-2 and VEGFR kinase activity induced TEMs to change their phenotype into cells with features of myeloid dendritic cells. We show that immunosuppressive activity of TEMs is associated with high CD86 surface expression and extensive engagement of T regulatory cells in breast tumors. TIE-2 and VEGFR kinase activity was also necessary to maintain high CD86 surface expression levels and to convert T cells into regulatory cells. CONCLUSIONS: These results suggest that TEMs are plastic cells that can be reverted from suppressive, proangiogenic cells into cells that are able to mediate an antitumoral immune response. Clin Cancer Res; 19(13); 3439-49. ©2013 AACR.

Progesterone down-regulates the open probability of the amiloride-sensitive epithelial sodium channel via a Nedd4-2-dependent mechanism.

Activation of the mitogen-activated protein (MAP) kinase cascade by progesterone in Xenopus oocytes leads to a marked down-regulation of activity of the amiloride-sensitive epithelial sodium channel (ENaC). Here we have studied the signaling pathways involved in progesterone effect on ENaC activity. We demonstrate that: (i) the truncation of the C termini of the alphabetagammaENaC subunits results in the loss of the progesterone effect on ENaC; (ii) the effect of progesterone was also suppressed by mutating conserved tyrosine residues in the Pro-X-X-Tyr (PY) motif of the C termini of the beta and gamma ENaC subunits (beta(Y618A) and gamma(Y628A)); (iii) the down-regulation of ENaC activity by progesterone was also suppressed by co-expression ENaC subunits with a catalytically inactive mutant of Nedd4-2, a ubiquitin ligase that has been previously demonstrated to decrease ENaC cell-surface expression via a ubiquitin-dependent internalization/degradation mechanism; (iv) the effect of progesterone was significantly reduced by suppression of consensus sites (beta(T613A) and gamma(T623A)) for ENaC phosphorylation by the extracellular-regulated kinase (ERK), a MAP kinase previously shown to facilitate the binding of Nedd4 ubiquitin ligases to ENaC; (v) the quantification of cell-surface-expressed ENaC subunits revealed that progesterone decreases ENaC open probability (whole cell P(o), wcP(o)) and not its cell-surface expression. Collectively, these results demonstrate that the binding of active Nedd4-2 to ENaC is a crucial step in the mechanism of ENaC inhibition by progesterone. Upon activation of ERK, the effect of Nedd4-2 on ENaC open probability can become more important than its effect on ENaC cell-surface expression.

Nimesulide, a cyclooxygenase-2 preferential inhibitor, impairs renal function in the newborn rabbit.

Tocolysis with nonsteroidal anti-inflammatory drugs (NSAIDs) has been widely accepted for several years. Recently, the use of the cyclooxygenase-2 (COX2) preferential NSAID nimesulide has been proposed. However, data reporting neonatal acute renal failure or irreversible end-stage renal failure after maternal ingestion of nimesulide question the safety of this drug for the fetus and the neonate. Therefore, this study was designed to define the renal effects of nimesulide in newborn rabbits. Experiments were performed in 28 newborn rabbits. Renal function and hemodynamic parameters were measured using inulin and para-aminohippuric acid clearances as markers of GFR and renal blood flow, respectively. After a control period, nimesulide 2, 20, or 200 microg/kg was given as an i.v. bolus, followed by a 0.05, 0.5, or 5 microg.kg(-1).min(-1) infusion. Nimesulide administration induced a significant dose-dependent increase in renal vascular resistance (29, 37, and 92%, respectively), with a concomitant decrease in diuresis (-5, -23, and -44%), GFR (-12, -23, and -47%), and renal blood flow (-23, -23, and -48%). These results are in contrast with recent reports claiming that selective COX2 inhibition could be safer for the kidney than nonselective NSAIDs. These experiments confirm that prostaglandins, by maintaining renal vasodilation, play a key role in the delicate balance regulating neonatal GFR. We conclude that COX2-selective/preferential inhibitors thus should be prescribed with the same caution as nonselective NSAIDs during pregnancy and in the neonatal period.

Distinct ASIC currents are expressed in rat putative nociceptors and are modulated by nerve injury.

The H(+)-gated acid-sensing ion channels (ASICs) are expressed in dorsal root ganglion (DRG) neurones. Studies with ASIC knockout mice indicated either a pro-nociceptive or a modulatory role of ASICs in pain sensation. We have investigated in freshly isolated rat DRG neurones whether neurones with different ASIC current properties exist, which may explain distinct cellular roles, and we have investigated ASIC regulation in an experimental model of neuropathic pain. Small-diameter DRG neurones expressed three different ASIC current types which were all preferentially expressed in putative nociceptors. Type 1 currents were mediated by ASIC1a homomultimers and characterized by steep pH dependence of current activation in the pH range 6.8-6.0. Type 3 currents were activated in a similar pH range as type 1, while type 2 currents were activated at pH < 6. When activated by acidification to pH 6.8 or 6.5, the probability of inducing action potentials correlated with the ASIC current density. Nerve injury induced differential regulation of ASIC subunit expression and selective changes in ASIC function in DRG neurones, suggesting a complex reorganization of ASICs during the development of neuropathic pain. In summary, we describe a basis for distinct cellular functions of different ASIC types in small-diameter DRG neurones.

Effects of dietary protein type on oxidized cholesterol-induced alteration in age-related modulation of lipid metabolism and indices of immune function in rats.

Exogenous oxidized cholesterol disturbs both lipid metabolism and immune functions. Therefore, it may perturb these modulations with ageing. Effects of the dietary protein type on oxidized cholesterol-induced modulations of age-related changes in lipid metabolism and immune function was examined using differently aged (4 weeks versus 8 months) male Sprague-Dawley rats when casein, soybean protein or milk whey protein isolate (WPI) was the dietary protein source, respectively. The rats were given one of the three proteins in diet containing 0.2% oxidized cholesterols mixture. Soybean protein, as compared with the other two proteins, significantly lowered both the serum thiobarbituric acid reactive substances value and cholesterol, whereas it elevated the ratio of high density lipoprotein-cholesterol/cholesterol in young rats, but not in adult. Moreover, soybean protein, but not casein and WPI, suppressed the elevation of Delta6 desaturation indices of phospholipids in both liver and spleen, particularly in young. On the other hand, WPI, compared to the other two proteins, inhibited the leukotriene B4 production of spleen, irrespective of age. Soybean protein reduced the ratio of CD4(+)/CD8(+) T-cells in splenic lymphocytes. Therefore, the levels of immunoglobulin (Ig)A, IgE and IgG in serum were lowered in rats given soybean protein in both age groups except for IgA in adult, although these observations were not shown in rats given other proteins. Thus, various perturbations of lipid metabolism and immune function caused by oxidized cholesterol were modified depending on the type of dietary protein. The moderation by soybean protein on the change of lipid metabolism seems to be susceptible in young rats whose homeostatic ability is immature. These observations may be exerted through both the promotion of oxidized cholesterol excretion to feces and the change of hormonal release, while WPI may suppress the disturbance of immune function by oxidized cholesterol in both ages. This alleviation may be associated with a large amount of lactoglobulin in WPI. These results thus showed a possibility that oxidized cholesterol-induced perturbations of age-related changes of lipid metabolism and immune function can be moderated by both the selection and combination of dietary protein.

Have all the men and women with a fragility fracture a low bone mineral density? A Nationwide Swiss Survey : FM3

Background: The prevalence of a low bone mineral density (T-score <-1 SD) in postmenopausal women with a fragility fracture may vary from 70% to less than 50%. In one study (Siris ES. Arch Intern Med. 2004;164:1108-12), the prevalence of osteoporosis was very low at 6.4%. The corresponding values in men are rarely reported. Methods: In a nationwide Swiss survey, all consecutive patients aged 50+ presenting with one or more fractures to the emergency ward, were recruited by 8 participating hospitals (University Hospitals: Basel, Bern, and Lausanne; cantonal hospitals: Fribourg, Luzern, and St Gallen; regional hospitals: Estavayer and Riaz) between 2004 and 2006. Diagnostic workup was collected for descriptive analysis. Results: 3667 consecutive patients with a fragility fracture, 2797 women (73.8 ± 11.6 years) and 870 men (70.0 ± 12.1 years), were included. DXA measurement was performed in 1152 (44%) patients. The mean of the lowest T-score values was -2.34 SD in women and -2.16 SD in men. In the 908 women, the prevalence of osteoporosis and osteopenia according to the fracture type was: sacrum (100%, 0%), rib (100%, 0%), thoracic vertebral (78%, 22%), femur trochanter (67%, 26%), pelvis (66%, 32%), lumbar vertebral (63%, 28%), femoral neck (53%, 34%), femur shaft (50%, 50%), proximal humerus (50%, 34%), distal forearm (41%, 45%), tibia proximal (41%, 31%), malleolar lateral (28%, 46%), malleolar median (13%, 47%). The corresponding percentages in the 244 men were: distal forearm (70%, 19%), rib (63%, 11%), pelvis (60%, 20%), malleolar median (60%, 32%), femur trochanter (48%, 31%), thoracic vertebral (47%, 53%), lumbar vertebral (43%, 36%), proximal humerus (40%, 43%), femoral neck (28%, 55%), tibia proximal (26%, 36%), malleolar lateral (18%, 56%). Conclusion: The probability of underlying osteoporosis or osteopenia in men and women aged 50+ who experienced a fragility fracture was beyond 75% in fractures of the sacrum, pelvis, spine, femur, proximal humerus and distal forearm. The medial and lateral malleolar fractures had the lowest predictive value in women, not in men.

Guard cell-specific calcium sensitivity of high density and activity SV/TPC1 channels.

The slow vacuolar (SV) channel, a Ca2+-regulated vacuolar cation conductance channel, in Arabidopsis thaliana is encoded by the single-copy gene AtTPC1. Although loss-of-function tpc1 mutants were reported to exhibit a stoma phenotype, knowledge about the underlying guard cell-specific features of SV/TPC1 channels is still lacking. Here we demonstrate that TPC1 transcripts and SV current density in guard cells were much more pronounced than in mesophyll cells. Furthermore, the SV channel in motor cells exhibited a higher cytosolic Ca2+ sensitivity than in mesophyll cells. These distinct features of the guard cell SV channel therefore probably account for the published stomatal phenotype of tpc1-2.

Preserved capillary density of dorsal finger skin in treated hypertensive patients with or without type 2 diabetes

RESUME : La raréfaction des vaisseaux capillaires est une caractéristique de l'hypertension artérielle non traitée. Des données récentes indiquent que cette raréfaction peut être renversée par un traitement antihypertenseur chez les patients hypertendus non diabétiques. Malgré la fréquente association du diabète et de l'hypertension, on ne sait rien de la densité capillaire de patients diabétiques traités, souffrant d'hypertension artérielle. Nous avons dès lors recruté 21 patients normotendus (groupe contrôle), 25 patients souffrant uniquement d'hypertension artérielle , et 21 patients diabétiques (Diabète de type 2) souffrant également d'hypertension artérielle. Tous les patients hypertendus ont été traités avec un inhibiteur du système rénine-angiotensine, et une majorité présentait une tension artérielle moyenne en auto-contrôle à domicile de 135/85 mmHg ou moins. La densité capillaire a été évaluée par vidéomicroscopie sur la peau du dos des doigts et avec laser Doppler sur la peau de l'avant-bras (vasodilatation maximale induite par le chauffage local). Au final, il n'y avait pas de différence entre les groupes de l'étude, que ce soit lors des mesures de la densité capillaire sur le dos du doigt (groupe contrôle 101 ±11 capillaires, groupe des patients non- diabétiques hypertendus 99 ± 16, groupe des patients hypertendus et diabétiques 96 ± 18, p>0,5) ou lors des mesures de débit sanguin maximal sur la peau de l'avant-bras, un témoin indirect de la densité capillaire dans ce territoire (contrôles 666 ±114 unités de perfusion, non diabétique hypertendu 612 ± 126, hypertendus diabétiques 620 ±103, p> 0,5). En conclusion, notre étude est la première à démontrer que indépendamment de la présence ou non d'un diabète de type 2, la densité capillaire est normale chez les patients hypertendus présentant un contrôle raisonnable de la pression artérielle obtenue avec un bloqueur du système rénine-angiotensine.