ICD-11 for quality and safety: overview of the who quality and safety topic advisory group.

This paper outlines the approach that the WHO's Family of International Classifications (WHO-FIC) network is undertaking to create ICD-11. We also outline the more focused work of the Quality and Safety Topic Advisory Group, whose activities include the following: (i) cataloguing existing ICD-9 and ICD-10 quality and safety indicators; (ii) reviewing ICD morbidity coding rules for main condition, diagnosis timing, numbers of diagnosis fields and diagnosis clustering; (iii) substantial restructuring of the health-care related injury concepts coded in the ICD-10 chapters 19/20, (iv) mapping of ICD-11 quality and safety concepts to the information model of the WHO's International Classification for Patient Safety and the AHRQ Common Formats; (v) the review of vertical chapter content in all chapters of the ICD-11 beta version and (vi) downstream field testing of ICD-11 prior to its official 2015 release. The transition from ICD-10 to ICD-11 promises to produce an enhanced classification that will have better potential to capture important concepts relevant to measuring health system safety and quality-an important use case for the classification.

Geomorphological method in the elaboration of hazard maps for flash-floods in the municipality of Jucuarán (El Salvador)

This work deals with the elaboration of flood hazard maps. These maps reflect the areas prone to floods based on the effects of Hurricane Mitch in the Municipality of Jucuarán of El Salvador. Stream channels located in the coastal range in the SE of El Salvador flow into the Pacific Ocean and generate alluvial fans. Communities often inhabit these fans can be affected by floods. The geomorphology of these stream basins is associated with small areas, steep slopes, well developed regolite and extensive deforestation. These features play a key role in the generation of flash-floods. This zone lacks comprehensive rainfall data and gauging stations. The most detailed topographic maps are on a scale of 1:25 000. Given that the scale was not sufficiently detailed, we used aerial photographs enlarged to the scale of 1:8000. The effects of Hurricane Mitch mapped on these photographs were regarded as the reference event. Flood maps have a dual purpose (1) community emergency plans, (2) regional land use planning carried out by local authorities. The geomorphological method is based on mapping the geomorphological evidence (alluvial fans, preferential stream channels, erosion and sedimentation, man-made terraces). Following the interpretation of the photographs this information was validated on the field and complemented by eyewitness reports such as the height of water and flow typology. In addition, community workshops were organized to obtain information about the evolution and the impact of the phenomena. The superimposition of this information enables us to obtain a comprehensive geomorphological map. Another aim of the study was the calculation of the peak discharge using the Manning and the paleohydraulic methods and estimates based on geomorphologic criterion. The results were compared with those obtained using the rational method. Significant differences in the order of magnitude of the calculated discharges were noted. The rational method underestimated the results owing to short and discontinuous periods of rainfall data with the result that probabilistic equations cannot be applied. The Manning method yields a wide range of results because of its dependence on the roughness coefficient. The paleohydraulic method yielded higher values than the rational and Manning methods. However, it should be pointed out that it is possible that bigger boulders could have been moved had they existed. These discharge values are lower than those obtained by the geomorphological estimates, i.e. much closer to reality. The flood hazard maps were derived from the comprehensive geomorphological map. Three categories of hazard were established (very high, high and moderate) using flood energy, water height and velocity flow deduced from geomorphological and eyewitness reports.

A new locus for congenital cataract, microcornea, microphthalmia, and atypical iris coloboma maps to chromosome 2.

OBJECTIVE: To report a novel phenotype of autosomal dominant atypical congenital cataract associated with variable expression of microcornea, microphthalmia, and iris coloboma linked to chromosome 2. Molecular analysis of this phenotype may improve our understanding of anterior segment development. DESIGN: Observational case study, genome linkage analysis, and gene mutation screening. PARTICIPANTS: Three families, 1 Egyptian and 2 Belgians, with a total of 31 affected were studied. METHODS: Twenty-one affected subjects and 9 first-degree relatives underwent complete ophthalmic examination. In the Egyptian family, exclusion of PAX6, CRYAA, and MAF genes was demonstrated by haplotype analysis using microsatellite markers on chromosomes 11, 16, and 21. Genome-wide linkage analysis was then performed using 385 microsatellite markers on this family. In the 2 Belgian families, the PAX6 gene was screened for mutations by direct sequencing of all exons. MAIN OUTCOME MEASURES: Phenotype description, genome-wide linkage of the phenotype, linkage to the PAX6, CRYAA, and MAF genes, and mutation detection in the PAX6 gene. RESULTS: Affected members of the 3 families had bilateral congenital cataracts inherited in an autosomal dominant pattern. A novel form of hexagonal nuclear cataract with cortical riders was expressed. Among affected subjects with available data, 95% had microcornea, 39% had microphthalmia, and 38% had iris coloboma. Seventy-five percent of the colobomata were atypical, showing a nasal superior location in 56%. A positive lod score of 4.86 was obtained at theta = 0 for D2S2309 on chromosome 2, a 4.9-Mb common haplotype flanked by D2S2309 and D2S2358 was obtained in the Egyptian family, and linkage to the PAX6, CRYAA, or MAF gene was excluded. In the 2 Belgian families, sequencing of the junctions and all coding exons of PAX6 did not reveal any molecular change. CONCLUSIONS: We describe a novel phenotype that includes the combination of a novel form of congenital hexagonal cataract, with variably expressed microcornea, microphthalmia, and atypical iris coloboma, not caused by PAX6 and mapping to chromosome 2. FINANCIAL DISCLOSURE(S): The authors have no proprietary or commercial interest in any materials discussed in this article.

Genetic linkage maps of 'Pêra' sweet orange and 'Cravo' mandarin with RAPD markers

The objective of this work was to construct linkage maps of 'Pêra' sweet orange [Citrus sinensis (L.) Osbeck] and 'Cravo' mandarin (Citrus reticulata Blanco) using RAPD markers and the pseudo-testcross strategy. The parents were chosen according to the resistance/susceptibility to citrus variegate chlorosis (CVC). The segregation of 176 markers was analyzed in 94 progeny of F1 hybrids, which were obtained from controlled crossings. The linkage map of 'Pêra' sweet orange had 117 markers defined by 12 linkage groups, which spanned 612.1 cM. Only six markers could not be linked to the linkage group and 48.7% of the markers showed segregation distortion. The linkage map of 'Cravo' mandarin had 51 markers defined by 12 linkage groups, which spanned 353.3 cM. Only two markers did not link to the groups and 15.7% showed segregation distortion. The construction of linkage maps is relevant to future mapping studies of the inheritance of CVC, citrus canker and leprosis.

Let's K-Map Play: Juego de aprendizaje de Karnagh Maps para móvil

En este informe se describe el trabajo de fin de máster, centrado en el estudio de la gamificación como herramienta de aprendizaje aplicada a dispositivos móviles. Se ha realizado una revisión de los artículos científicos que tratan sobre el tema de la gamificación como herramienta educativa,para terminar el trabajo desarrollando un prototipo de juego para el aprendizaje de mapas de Karnaugh. Se ha optado por un desarrollo multiplataforma y se han revisado los frameworks de desarrollo más populares para desarrollo móvil multiplataforma así como los motores de juegos aplicables a este caso. Tras la implementación, se ha probado el prototipo en dos sistemas operativos móviles libres: Android y Firefox OS.

Global habitat suitability models of terrestrial mammals.

Detailed large-scale information on mammal distribution has often been lacking, hindering conservation efforts. We used the information from the 2009 IUCN Red List of Threatened Species as a baseline for developing habitat suitability models for 5027 out of 5330 known terrestrial mammal species, based on their habitat relationships. We focused on the following environmental variables: land cover, elevation and hydrological features. Models were developed at 300 m resolution and limited to within species' known geographical ranges. A subset of the models was validated using points of known species occurrence. We conducted a global, fine-scale analysis of patterns of species richness. The richness of mammal species estimated by the overlap of their suitable habitat is on average one-third less than that estimated by the overlap of their geographical ranges. The highest absolute difference is found in tropical and subtropical regions in South America, Africa and Southeast Asia that are not covered by dense forest. The proportion of suitable habitat within mammal geographical ranges correlates with the IUCN Red List category to which they have been assigned, decreasing monotonically from Least Concern to Endangered. These results demonstrate the importance of fine-resolution distribution data for the development of global conservation strategies for mammals.

Global periodicity conditions for maps and recurrences via Normal Forms

We face the problem of characterizing the periodic cases in parametric families of (real or complex) rational diffeomorphisms having a fixed point. Our approach relies on the Normal Form Theory, to obtain necessary conditions for the existence of a formal linearization of the map, and on the introduction of a suitable rational parametrization of the parameters of the family. Using these tools we can find a finite set of values p for which the map can be p-periodic, reducing the problem of finding the parameters for which the periodic cases appear to simple computations. We apply our results to several two and three dimensional classes of polynomial or rational maps. In particular we find the global periodic cases for several Lyness type recurrences

Periodic orbits of integrable birational maps on the plane: blending dynamics and algebraic geometry, the Lyness' case

Contingut del Pòster presentat al congrés New Trends in Dynamical Systems

Tarracomap: Development of an archaeological application on Google Maps navigation System

This paper describes the result of a research about diverse areas of the information technology world applied to cartography. Its final result is a complete and custom geographic information web system, designed and implemented to manage archaeological information of the city of Tarragona. The goal of the platform is to show on a web-focused application geographical and alphanumerical data and to provide concrete queries to explorate this. Various tools, between others, have been used: the PostgreSQL database management system in conjunction with its geographical extension PostGIS, the geographic server GeoServer, the GeoWebCache tile caching, the maps viewer and maps and satellite imagery from Google Maps, locations imagery from Google Street View, and other open source libraries. The technology has been chosen from an investigation of the requirements of the project, and has taken great part of its development. Except from the Google Maps tools which are not open source but are free, all design has been implemented with open source and free tools.

Let's K-map play : Juego de aprendizaje de Karnaugh Maps para móvil

En este informe se describe el trabajo de fin de máster, centrado en el estudio de la gamificación como herramienta de aprendizaje aplicada a dispositivos móviles. Se ha realizado una revisión de los artículos científicos que tratan sobre el tema de la gamificación como herramienta educativa, para terminar el trabajo desarrollando un prototipo de juego para el aprendizaje de mapas de Karnaugh. Se ha optado por un desarrollo multiplataforma y se han revisado los frameworks de desarrollo más populares para desarrollo móvil multiplataforma, así como los motores de juegos aplicables a este caso. Tras la implementación, se ha probado el prototipo en dos sistemas operativos móviles libres: Android y Firefox OS.

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.

On the preservation of combinatorial types for maps on trees

We study the preservation of the periodic orbits of an A-monotone tree map f:T→T in the class of all tree maps g:S→S having a cycle with the same pattern as A. We prove that there is a period-preserving injective map from the set of (almost all) periodic orbits of ƒ into the set of periodic orbits of each map in the class. Moreover, the relative positions of the corresponding orbits in the trees T and S (which need not be homeomorphic) are essentially preserved

Sierpinski curve Julia sets for quadratic rational maps

We investigate under which dynamical conditions the Julia set of a quadratic rational map is a Sierpiński curve.