Brachial or wrist blood pressure in obese patients : which is the best ?

RESUME L'obésité et l'hypertension atteignent des niveaux épidémiques aussi bien dans les pays industrialisés que dans ceux en voie de développement. La coexistence de ces deux pathologies est associée à un risque cardiovasculaire augmenté. Traditionnellement on mesure la pression artérielle (PA) au bras au moyen d'un brassard qui détermine la pression systolique et diastolique en utilisant soit la méthode auscultatoire ou oscillométrique. L'utilisation d'un brassard de taille standard chez le patient avec un tour de bras augmenté peut surestimer la pression artérielle. Il semble même qu'il existe un rapport idéal entre le tour de bras, et la taille du brassard La mesure à domicile de la pression artérielle avec des appareils validés donne des valeurs de la PA valables. Plusieurs appareils existent sur le marché et depuis quelques années les appareils de mesure de la PA au poignet font leur apparition sur le marché. Cette étude vise à comparer chez des sujets sains et obèses les valeurs de PA obtenues au poignet avec celles obtenues au bras en utilisant deux appareils validés l'OMRON HEM 705-CP et l'OMRON R6. L'OMRON HEM 705-CP permet l'utilisation soit d'un brassard standard (13x30 cm) ou d'un brassard large (16x38 cm), et l'OMRON R6 mesure la PA au poignet. Nous avons comparé un groupe de sujets obèses [Body Mass Index (BMI) >35kg/m2] avec un groupe de sujets sains (BMI <25kg/m2). Ont été exclues de l'étudé les personnes prenant un traitement antihypertenseur ainsi que celles souffrant d'arythmies. La PA a été mesurée en position assise avec le bras gauche sur une table à hauteur du coeur. Un brassard large a été employé pour les sujets obèses et un brassard standard pour les sujets sains. Trois mesures ont été effectuées, la première après une pause de 5 min et chacune des suivantes avec un intervalle de 2 min. La pression d'inflation maximale a été fixée à 170 mmHg. Nous avons utilisé la formule proposée par Marks LA et al pour déterminer si le rapport entre la taille des brassards fournis avec l'OMRON .HEM 705-CP et le tour de bras de nos sujets était optimal (taille du brassard = 9.34 x log10 taille du bras). Nos résultats ne montrent pas de différence statistiquement significative de la PA diastolique entre les deux groupes, qu'elle soit mesurée au bras ou au poignet. La PA systolique mesurée au bras s'est par contre avérée significativement plus basse chez les sujets obèses que chez les sujets sains. Aucune différence n'a été trouvée lorsque la mesure est effectuée au poignet. En utilisant la formule fournie par Marks le rapport entre taille du brassard (large chez les obèses) et tour de bras a été de 10.30±30 chez les sujets obèses et 9.630.45 chez les sujets sains (p<0.001). Le rapport entre tour de bras et brassard chez les sujets obèses est nettement au-dessus de la valeur optimale, ce qui suggère une possible sous-estimation de la PA systolique chez ces sujets. Ces résultats suggèrent qu'il existe un risque de sous-estimer la PA chez le patient obèse lors de l'utilisation d'un brassard large. Cette erreur pourrait être réduite par l'utilisation d'appareils de mesure au poignet. validés chez le sujet obèse.

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.

The 2010 May Flaring Episode of Cygnus X-3 in Radio, X-rays, and γ-rays

In 2009, Cygnus X-3 (Cyg X-3) became the first microquasar to be detected in the GeV γ-ray regime, via the satellites Fermi and AGILE. The addition of this new band to the observational toolbox holds promise for building a more detailed understanding of the relativistic jets of this and other systems. We present a rich data set of radio, hard and soft X-ray, and γ-ray observations of Cyg X-3 made during a flaring episode in 2010 May. We detect a ~3 day softening and recovery of the X-ray emission, followed almost immediately by a ~1 Jy radio flare at 15 GHz, followed by a 4.3σ γ-ray flare (E > 100 MeV) ~1.5 days later. The radio sampling is sparse, but we use archival data to argue that it is unlikely the γ-ray flare was followed by any significant unobserved radio flares. In this case, the sequencing of the observed events is difficult to explain in a model in which the γ-ray emission is due to inverse Compton scattering of the companion star's radiation field. Our observations suggest that other mechanisms may also be responsible for γ-ray emission from Cyg X-3.

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.

Road Safety Audit for County Road X-37, from the Intersection of G-62 to the East Corporate Limits of Columbus City in Louisa County, Iowa, 2009

A road safety audit was conducted for a 7.75 mile section of County Road X-37 in Louisa County, Iowa. In 2006, the average annual daily traffic on this roadway was found to be 680 vehicles per day. Using crash data from 2001 to 2007, the Iowa Department of Transportation (Iowa DOT) has identified this roadway as being in the highest 5% of local rural roads in Iowa for single-vehicle runoff- road crashes. Considering these safety data, the Louisa County Engineer requested that a road safety audit be conducted to identify areas of safety concerns and recommend low-cost mitigation to address those concerns. Staff and officials from the Iowa DOT, Governor’s Traffic Safety Bureau, Federal Highway Administration, Institute for Transportation, and local law enforcement and transportation agencies met to review crash data and discuss potential safety improvements to this segment of X-37. This report outlines the findings and recommendations of the road safety audit team to address the safety concerns on this X-37 corridor and explain several selected mitigation strategies.

Study of OSEM with different subsets in grating-based X-ray differential phase-contrast imaging.

Impressive developments in X-ray imaging are associated with X-ray phase contrast computed tomography based on grating interferometry, a technique that provides increased contrast compared with conventional absorption-based imaging. A new "single-step" method capable of separating phase information from other contributions has been recently proposed. This approach not only simplifies data-acquisition procedures, but, compared with the existing phase step approach, significantly reduces the dose delivered to a sample. However, the image reconstruction procedure is more demanding than for traditional methods and new algorithms have to be developed to take advantage of the "single-step" method. In the work discussed in this paper, a fast iterative image reconstruction method named OSEM (ordered subsets expectation maximization) was applied to experimental data to evaluate its performance and range of applicability. The OSEM algorithm with different subsets was also characterized by comparison of reconstruction image quality and convergence speed. Computer simulations and experimental results confirm the reliability of this new algorithm for phase-contrast computed tomography applications. Compared with the traditional filtered back projection algorithm, in particular in the presence of a noisy acquisition, it furnishes better images at a higher spatial resolution and with lower noise. We emphasize that the method is highly compatible with future X-ray phase contrast imaging clinical applications.

Interference of genotypes x environments interaction in the genetic control of resistance to Asian rust soybean

The objectives of this work were to identify parents resistant to Asian soybean rust using diallel crosses, obtain information on the genetic control of soybean resistance to the pathogen and verify whether the combining ability estimates interact with the environment (year or time of assessment). The F1 generation was obtained in a greenhouse from crosses between five contrasting parents for the trait resistance to soybean rust, in a complete diallel without reciprocals. Two rust-severity assessments were carried out on individual soybean plants of 25 treatments (parents and F2 and F3 populations) in 2006/2007 and 2007/2008, in an experimental field at Embrapa Soja, Londrina, PR, Brazil. Additive effects predominated in the genetic control of soybean resistance to Asian rust, and the interaction of the segregant populations with the environment, although significant, did not alter the genetic parameter's general combining ability (GCA) and specific combining ability estimates, indicating that estimates obtained in one year and one assessment can be extrapolated to others. BR01-18437 inbred line is resistant to Asian rust and showed high GCA effects. This line should be used as parent if the objective is the resistance to Phakopsora pachyrhizi.

Road Safety Audits for County Road X-23 from IA 2 to the South Corporate Limits of West Point and for County Road W-62 from US 218 to IA 27 in Lee County, Iowa, 2010

On October 20–21, 2009, two road safety audits were conducted in Lee County, Iowa: one for a 6 mile section of County Road X-23 from IA 2 to the south corporate limits of West Point and one for a 9.7 mile section of County Road W-62 from US 218 to IA 27. Both roads have high severe crash histories for the years of 2001 through 2008. Using these crash data, the Iowa Department of Transportation (Iowa DOT) has identified County Road X-23 as being in the top 5 percent of similar roads for run-off-road crashes. The Iowa DOT lists County Road W-62 as a high-risk rural road that has above-average crash numbers and is eligible for funding under the Federal High-Risk Rural Road Program. Considering these issues, the Lee County Engineer and Iowa DOT requested that road safety audits be conducted to address the safety concerns and to suggest possible mitigation strategies.

Análise AMMI com dados imputados em experimentos de interação genótipo x ambiente de algodão

O objetivo deste trabalho foi avaliar a conveniência de definir o número de componentes multiplicativos dos modelos de efeitos principais aditivos com interação multiplicativa (AMMI) em experimentos de interações genótipo x ambiente de algodão com dados imputados ou desbalanceados. Um estudo de simulação foi realizado com base em uma matriz de dados reais de produtividade de algodão em caroço, obtidos em ensaios de interação genótipo x ambiente, conduzidos com 15 cultivares em 27 locais no Brasil. A simulação foi feita com retiradas aleatórias de 10, 20 e 30% dos dados. O número ótimo de componentes multiplicativos para o modelo AMMI foi determinado usando o teste de Cornelius e o teste de razão de verossimilhança sobre as matrizes completadas por imputação. Para testar as hipóteses, quando a análise é feita a partir de médias e não são disponibilizadas as repetições, foi proposta uma correção com base nas observações ausentes no teste de Cornelius. Para a imputação de dados, foram considerados métodos usando submodelos robustos, mínimos quadrados alternados e imputação múltipla. Na análise de experimentos desbalanceados, é recomendável escolher o número de componentes multiplicativos do modelo AMMI somente a partir da informação observada e fazer a estimação clássica dos parâmetros com base nas matrizes completadas por imputação.

Health consultation : E.I. Du Pont de Nemours & Co., Inc. County Road X-23 site, Lee County, Iowa EPA facility ID: IAD980685804 (2007)

The U.S. Environmental Protection Agency (EPA) is completing a third five-year review of the E.I. du Pont de Nemours & Co., Inc., County Road X-23 Superfund site in Lee County, Iowa. The site is also known as the Baier and McCarl subsites. The EPA is inviting public comment on whether the current site remedy continues to be protective of public health and the environment. The Iowa Department of Public Health in cooperation with the Agency for Toxic Substances and Disease Registry (ATSDR) prepared this health consultation to review the current status of the Baier and McCarl subsites and to provide an evaluation of the public health status of these subsites. The information in this health consultation was current at the time of writing. Data that emerges later could alter this docum ent’s conclusions and recommendations.

Feasibility of direct mapping of cerebral fluorodeoxy-D-glucose metabolism in situ at subcellular resolution using soft X-ray fluorescence.

Glucose metabolism is difficult to image with cellular resolution in mammalian brain tissue, particularly with (18) fluorodeoxy-D-glucose (FDG) positron emission tomography (PET). To this end, we explored the potential of synchrotron-based low-energy X-ray fluorescence (LEXRF) to image the stable isotope of fluorine (F) in phosphorylated FDG (DG-6P) at 1 μm(2) spatial resolution in 3-μm-thick brain slices. The excitation-dependent fluorescence F signal at 676 eV varied linearly with FDG concentration between 0.5 and 10 mM, whereas the endogenous background F signal was undetectable in brain. To validate LEXRF mapping of fluorine, FDG was administered in vitro and in vivo, and the fluorine LEXRF signal from intracellular trapped FDG-6P over selected brain areas rich in radial glia was spectrally quantitated at 1 μm(2) resolution. The subsequent generation of spatial LEXRF maps of F reproduced the expected localization and gradients of glucose metabolism in retinal Müller glia. In addition, FDG uptake was localized to periventricular hypothalamic tanycytes, whose morphological features were imaged simultaneously by X-ray absorption. We conclude that the high specificity of photon emission from F and its spatial mapping at ≤1 μm resolution demonstrates the ability to identify glucose uptake at subcellular resolution and holds remarkable potential for imaging glucose metabolism in biological tissue. © 2012 Wiley Periodicals, Inc.

Tannin-tolerant bacteria from crossbred Holstein x Zebu cows

The objective of this work was to isolate and characterize tannin-tolerant ruminal bacteria from crossbred Holstein x Zebu cows fed a chopped mixture of elephant grass (Pennisetum purpureum), young stems of "angico-vermelho" (Parapiptadenia rigida), and banana tree (Musa sp.) leaves. A total of 117 bacteria strains were isolated from enrichment cultures of rumen microflora in medium containing tannin extracts. Of these, 11 isolates were able to tolerate up to 3 g L-1 of tannins. Classical characterization procedures indicated that different morphological and physiological groups were represented. Restriction fragments profiles using Alu1 and Taq1 of 1,450 bp PCR products from the 16S rRNA gene grouped the 11 isolates into types I to VI. Sequencing of 16S rRNA PCR products was used for identification. From the 11 strains studied, seven were not identifiable by the methods used in this work, two were strains of Butyrivibrio fibrisolvens, and two of Streptococcus bovis.