Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

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.

CleanEx: a database of heterogeneous gene expression data based on a consistent gene nomenclature and linked to an improved annotation system

Résumé: L'automatisation du séquençage et de l'annotation des génomes, ainsi que l'application à large échelle de méthodes de mesure de l'expression génique, génèrent une quantité phénoménale de données pour des organismes modèles tels que l'homme ou la souris. Dans ce déluge de données, il devient très difficile d'obtenir des informations spécifiques à un organisme ou à un gène, et une telle recherche aboutit fréquemment à des réponses fragmentées, voir incomplètes. La création d'une base de données capable de gérer et d'intégrer aussi bien les données génomiques que les données transcriptomiques peut grandement améliorer la vitesse de recherche ainsi que la qualité des résultats obtenus, en permettant une comparaison directe de mesures d'expression des gènes provenant d'expériences réalisées grâce à des techniques différentes. L'objectif principal de ce projet, appelé CleanEx, est de fournir un accès direct aux données d'expression publiques par le biais de noms de gènes officiels, et de représenter des données d'expression produites selon des protocoles différents de manière à faciliter une analyse générale et une comparaison entre plusieurs jeux de données. Une mise à jour cohérente et régulière de la nomenclature des gènes est assurée en associant chaque expérience d'expression de gène à un identificateur permanent de la séquence-cible, donnant une description physique de la population d'ARN visée par l'expérience. Ces identificateurs sont ensuite associés à intervalles réguliers aux catalogues, en constante évolution, des gènes d'organismes modèles. Cette procédure automatique de traçage se fonde en partie sur des ressources externes d'information génomique, telles que UniGene et RefSeq. La partie centrale de CleanEx consiste en un index de gènes établi de manière hebdomadaire et qui contient les liens à toutes les données publiques d'expression déjà incorporées au système. En outre, la base de données des séquences-cible fournit un lien sur le gène correspondant ainsi qu'un contrôle de qualité de ce lien pour différents types de ressources expérimentales, telles que des clones ou des sondes Affymetrix. Le système de recherche en ligne de CleanEx offre un accès aux entrées individuelles ainsi qu'à des outils d'analyse croisée de jeux de donnnées. Ces outils se sont avérés très efficaces dans le cadre de la comparaison de l'expression de gènes, ainsi que, dans une certaine mesure, dans la détection d'une variation de cette expression liée au phénomène d'épissage alternatif. Les fichiers et les outils de CleanEx sont accessibles en ligne (http://www.cleanex.isb-sib.ch/). Abstract: The automatic genome sequencing and annotation, as well as the large-scale gene expression measurements methods, generate a massive amount of data for model organisms. Searching for genespecific or organism-specific information througout all the different databases has become a very difficult task, and often results in fragmented and unrelated answers. The generation of a database which will federate and integrate genomic and transcriptomic data together will greatly improve the search speed as well as the quality of the results by allowing a direct comparison of expression results obtained by different techniques. The main goal of this project, called the CleanEx database, is thus to provide access to public gene expression data via unique gene names and to represent heterogeneous expression data produced by different technologies in a way that facilitates joint analysis and crossdataset comparisons. A consistent and uptodate gene nomenclature is achieved by associating each single gene expression experiment with a permanent target identifier consisting of a physical description of the targeted RNA population or the hybridization reagent used. These targets are then mapped at regular intervals to the growing and evolving catalogues of genes from model organisms, such as human and mouse. The completely automatic mapping procedure relies partly on external genome information resources such as UniGene and RefSeq. The central part of CleanEx is a weekly built gene index containing crossreferences to all public expression data already incorporated into the system. In addition, the expression target database of CleanEx provides gene mapping and quality control information for various types of experimental resources, such as cDNA clones or Affymetrix probe sets. The Affymetrix mapping files are accessible as text files, for further use in external applications, and as individual entries, via the webbased interfaces . The CleanEx webbased query interfaces offer access to individual entries via text string searches or quantitative expression criteria, as well as crossdataset analysis tools, and crosschip gene comparison. These tools have proven to be very efficient in expression data comparison and even, to a certain extent, in detection of differentially expressed splice variants. The CleanEx flat files and tools are available online at: http://www.cleanex.isbsib. ch/.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

Data bases for the assessment of medical technologies: examples from Europe.

The assessment of medical technologies has to answer several questions ranging from safety and effectiveness to complex economical, social, and health policy issues. The type of data needed to carry out such evaluation depends on the specific questions to be answered, as well as on the stage of development of a technology. Basically two types of data may be distinguished: (a) general demographic, administrative, or financial data which has been collected not specifically for technology assessment; (b) the data collected with respect either to a specific technology or to a disease or medical problem. On the basis of a pilot inquiry in Europe and bibliographic research, the following categories of type (b) data bases have been identified: registries, clinical data bases, banks of factual and bibliographic knowledge, and expert systems. Examples of each category are discussed briefly. The following aims for further research and practical goals are proposed: criteria for the minimal data set required, improvement to the registries and clinical data banks, and development of an international clearinghouse to enhance information diffusion on both existing data bases and available reports on medical technology assessments.

Adult human adipose tissue contains several types of multipotent cells.

Multipotent mesenchymal stromal cells (MSCs) are a type of adult stem cells that can be easily isolated from various tissues and expanded in vitro. Many reports on their pluripotency and possible clinical applications have raised hopes and interest in MSCs. In an attempt to unify the terminology and the criteria to label a cell as MSC, in 2006 the International Society for Cellular Therapy (ISCT) proposed a standard set of rules to define the identity of these cells. However, MSCs are still extracted from different tissues, by diverse isolation protocols, are cultured and expanded in different media and conditions. All these variables may have profound effects on the selection of cell types and the composition of heterogeneous subpopulations, on the selective expansion of specific cell populations with totally different potentials and ergo, on the long-term fate of the cells upon in vitro culture. Therefore, specific molecular and cellular markers that identify MSCs subsets as well as standardization of expansion protocols for these cells are urgently needed. Here, we briefly discuss new useful markers and recent data supporting the rapidly emerging concept that many different types of progenitor cells are found in close association with blood vessels. This knowledge may promote the necessary technical improvements required to reduce variability and promote higher efficacy and safety when isolating and expanding these cells for therapeutic use. In the light of the discussed data, particularly the identification of new markers, and advances in the understanding of fundamental MSC biology, we also suggest a revision of the 2006 ISCT criteria.

The role of ceramide trihexoside (globotriaosylceramide) in the diagnosis and follow-up of the efficacy of treatment of Fabry disease: a review of the literature.

Fabry disease is caused by a deficiency of a-galactosidase A which leads to the progressive intra-lysosomal accumulation of ceramide trihexoside (CTH), also known as globotriaosylceramide (Gb3), in different cell types and body fluids. The clinical manifestations are multisystemic and predominantly affect the heart, kidney and central nervous system. The role of CTH in the pathophysiological process of Fabry disease is not established, and the link between the degree of accumulation and disease manifestations is not systematic. The use of CTH as a diagnostic tool has been proposed for several decades. The recent introduction of a specific treatment for Fabry disease in the form of enzyme replacement therapy (ERT) has led to the need for a biological marker, in place of a clinical sign, for evaluating the efficacy of treatment and also as a tool for following the long term effects of treatment. The ideal biomarker must adhere to strict criteria, and there should be a correlation between the degree of clinical efficacy of treatment and a change in its concentration. This review of the literature assesses the utility of CTH as a diagnostic tool and as a marker of the efficacy of ERT in patients with Fabry disease. Several techniques have been developed for measuring CTH; the principles and the sensitivity thresholds of these methods and the units used to express the results should be taken into consideration when interpreting data. The use of CTH measurement in Fabry disease should be re-evaluated in light of recent published data.

Uncovering hidden duplicated content in public transcriptomics data.

As part of the development of the database Bgee (a dataBase for Gene Expression Evolution), we annotate and analyse expression data from different types and different sources, notably Affymetrix data from GEO and ArrayExpress, and RNA-Seq data from SRA. During our quality control procedure, we have identified duplicated content in GEO and ArrayExpress, affecting ∼14% of our data: fully or partially duplicated experiments from independent data submissions, Affymetrix chips reused in several experiments, or reused within an experiment. We present here the procedure that we have established to filter such duplicates from Affymetrix data, and our procedure to identify future potential duplicates in RNA-Seq data. Database URL: http://bgee.unil.ch/

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy

MIMAS 3.0 is a Multiomics Information Management and Annotation System.

BACKGROUND: DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types. DESCRIPTION: MIMAS was originally conceived as a simple, convenient and local Microarray Information Management and Annotation System focused on GeneChips for expression profiling studies. MIMAS 3.0 enables users to manage data from high-density oligonucleotide SNP Chips, expression arrays (both 3'UTR and tiling) and promoter arrays, BeadArrays as well as UHTS data using MIAME-compliant standardized vocabulary. Importantly, researchers can export data in MAGE-TAB format and upload them to the EBI's ArrayExpress certified data repository using a one-step procedure. CONCLUSION: We have vastly extended the capability of the system such that it processes the data output of six types of GeneChips (Affymetrix), two different BeadArrays for mRNA and miRNA (Illumina) and the Genome Analyzer (a popular Ultra-High Throughput DNA Sequencer, Illumina), without compromising on its flexibility and user-friendliness. MIMAS, appropriately renamed into Multiomics Information Management and Annotation System, is currently used by scientists working in approximately 50 academic laboratories and genomics platforms in Switzerland and France. MIMAS 3.0 is freely available via http://multiomics.sourceforge.net/.

Genetic investigation of the functions of c-Myc and N-Myc in Hematopoietic stem cells

Résumé : c-Myc, le premier facteur de transcription de la famille Myc a été découvert il y a maintenant trente ans. Il reste à l'heure actuelle parmi les plus puissants proto-oncogènes connus. c-Myc est dérégulé dans plus de 50% des cancers, où il promeut la prolifération, la croissance cellulaire, et la néoangiogenèse. Myc peut aussi influencer de nombreuses autres fonctions de par sa capacité à activer ou à réprimer la transcription de nombreux gènes, et à agir globalement sur le génome à travers des modifications épigénétiques de la chromatine. La famille d'oncogènes Myc comprend, chez les mammifères, trois protéines structurellement proches: c-Myc, N-Myc et L-Myc. Ces protéines ont les mêmes proprietés biochimiques, exercent les mêmes fonctions mais sont le plus souvent exprimées de façon mutuellement exclusive. Myc a été récemment identifié comme un facteur clef dans la maintenance des cellules souches embryonnaires et adultes ainsi que dans la réacquisition des proprietés des cellules souches. Nous avons précédemment démontré que l'élimination de c-Myc provoque une accumulation de cellules souches hématopoïétiques (CSH) suite à un défaut de différenciation lié à la niche. Les CSH sont responsables de la production de tous les éléments cellulaires du sang pour toute la vie de l'individu et sont définies par leur capacité à s'auto-renouveler tout en produisant des précurseurs hématopoïétiques. Afin de mieux comprendre la fonction de Myc dans les CSH, nous avons choisi de combiner l'utilisation de modèles de souris génétiquement modifiées à une caractérisation systématique des schémas d'expression de c-Myc, N-Myc et L-Myc dans tout le système hématopoïétique. Nous avons ainsi découvert que les CSH les plus immatures expriment des quantités équivalentes de transcrits de c-myc et N-myc. Si les CSH déficientes en N-myc seulement ont une capacité d'auto-renouvellement à long-terme réduite, l'invalidation combinée des gènes c-myc et N-myc conduit à une pan-cytopénie suivie d'une mort rapide de l'animal, pour cause d'apoptose de tous les types cellulaires hématopoïétiques. En particulier, les CSH en cours d'auto-renouvelemment, mais pas les CSH quiescentes, accumulent du Granzyme B (GrB), une molécule fortement cytotoxique qui provoque une mort cellulaire rapide. Ces données ont ainsi mis au jour un nouveau mécanisme dont dépend la survie des CSH, à savoir la répression du GrB, une enzyme typiquement utilisée par le système immunitaire inné pour éliminer les tumeurs et les cellules infectées par des virus. Dans le but d'évaluer l'étendue de la redondance entre c-Myc et N-Myc dans les CSH, nous avons d'une part examiné des souris dans lesquelles les séquences codantes de c-myc sont remplacées par celles de N-myc (NCR) et d'autre part nous avons géneré une série allèlique de myc en éliminant de façon combinatoire un ou plusieurs allèles de c-myc et/ou de N-myc. Alors que l'analyse des souris NCR suggère que c-Myc et N-Myc sont qualitativement redondants, la série allélique indique que les efficiences avec lesquelles ces deux protéines influencent des procédés essentiels à la maintenance des CSH sont différentes. En conclusion, nos données génétiques montrent que l'activité générale de MYC, fournie par c-Myc et N-Myc, contrôle plusieurs aspects cruciaux de la fonction des CSH, notamment l'auto-renouvellement, la survie et la différenciation. Abstract : c-Myc, the first Myc transcription factor was discovered 30 years ago and is to date one of the most potent proto-oncogenes described. It is found to be misregulated in over 50% of all cancers, where it drives proliferation, cell growth and neo-angiogenesis. Myc can also influence a variety of other functions, owing to its ability to activate and repress transcription of many target genes and to globally regulate the genome via epigenetic modifications of the chromatin. The Myc family of oncogenes consists of three closely related proteins in mammals: c-Myc, N-Myc and L-Myc. These proteins share the same biochemical properties, exert mostly the same functions, but are most often expressed in mutually exclusive patterns. Myc is now emerging as a key factor in maintenance of embryonic and adult stem cells as well as in reacquisition of stem cell properties, including induced reprogramming. We previously showed that c-Myc deficiency can cause the accumulation of hematopoietic stem cells (HSCs) due to a niche dependent differentiation defect. HSCs are responsible for life-long replenishment of all blood cell types, and are defined by their ability to self-renew while concomitantly giving rise to more commited progenitors. To gain further insight into the function of Myc in HSCs, in this study we combine the use of genetically-modified mouse models with the systematic characterization of c-myc, N-myc and L-myc transcription patterns throughout the hematopoietic system. Interestingly, the most immature HSCs express not only c-myc, but also about equal amounts of N-myc transcripts. Although conditional deletion of N-myc alone in the bone marrow does not affect steady-state hematopoiesis, N-myc null HSCs show impaired long-term self-renewal capacity. Strikingly, combined deficiency of c-Myc and N-Myc results in pan-cytopenia and rapid lethality, due to the apoptosis of most hematopoietic cell types. In particular, self-renewing HSCs, but not quiescent HSCs or progenitor cell types rapidly up-regulate and accumulate the potent cytotoxic molecule GranzymeB (GrB), causing their rapid cell death. These data uncover a novel pathway on which HSC survival depends on, namely repression of GrB, a molecule typically used by the innate immune system to eliminate tumor and virus infected cells. To evaluate the extent of redundancy between c-Myc and N-Myc in HSCs, we examined mice in which c-myc coding sequences are replaced by that of N-myc (NCR) and also generated an allelic series of myc, by combinatorially deleting one or several c-myc and/or N-myc alleles. While the analysis of NCR mice suggests that c-Myc and N-Myc are qualitatively functionally redundant, our allelic series indicates that the efficiencies with which these two proteins affect crucial HSC maintenance processes are likely to be distinct. Collectively, our genetic data show that general "MYC" activity delivered by c-Myc and N-Myc controls crucial aspects of HSC function, including self-renewal, survival and niche dependent differentiation.

Assessment of Physical Activity and Energy Expenditure by GPS Combined With Accelerometry in Real-Life Conditions.

Background: Physical activity (PA) and related energy expenditure (EE) is often assessed by means of a single technique. Because of inherent limitations, single techniques may not allow for an accurate assessment both PA and related EE. The aim of this study was to develop a model to accurately assess common PA types and durations and thus EE in free-living conditions, combining data from global positioning system (GPS) and 2 accelerometers. Methods: Forty-one volunteers participated in the study. First, a model was developed and adjusted to measured EE with a first group of subjects (Protocol I, n = 12) who performed 6 structured and supervised PA. Then, the model was validated over 2 experimental phases with 2 groups (n = 12 and n = 17) performing scheduled (Protocol I) and spontaneous common activities in real-life condition (Protocol II). Predicted EE was compared with actual EE as measured by portable indirect calorimetry. Results: In protocol I, performed PA types could be recognized with little error. The duration of each PA type could be predicted with an accuracy below 1 minute. Measured and predicted EE were strongly associated (r = .97, P < .001). Conclusion: Combining GPS and 2 accelerometers allows for an accurate assessment of PA and EE in free-living situations.

Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data.

Pooling of dentin microtensile bond strength data improves clinical correlation.

Purpose: To evaluate whether the correlation between in vitro bond strength data and estimated clinical retention rates of cervical restorations after two years depends on pooled data obtained from multicenter studies or single-test data. Materials and Methods: Pooled mean data for six dentin adhesive systems (Adper Prompt L-Pop, Clearfil SE, OptiBond FL, Prime & Bond NT, Single Bond, and Scotchbond Multipurpose) and four laboratory methods (macroshear, microshear, macrotensile and microtensile bond strength test) (Scherrer et al, 2010) were correlated to estimated pooled two-year retention rates of Class V restorations using the same adhesive systems. For bond strength data from a single test institute, the literature search in SCOPUS revealed one study that tested all six adhesive systems (microtensile) and two that tested five of the six systems (microtensile, macroshear). The correlation was determined with a database designed to perform a meta-analysis on the clinical performance of cervical restorations (Heintze et al, 2010). The clinical data were pooled and adjusted in a linear mixed model, taking the study effect, dentin preparation, type of isolation and bevelling of enamel into account. A regression analysis was carried out to evaluate the correlation between clinical and laboratory findings. Results: The results of the regression analysis for the pooled data revealed that only the macrotensile (adjusted R2 = 0.86) and microtensile tests (adjusted R2 = 0.64), but not the shear and the microshear tests, correlated well with the clinical findings. As regards the data from a single-test institute, the correlation was not statistically significant. Conclusion: Macrotensile and microtensile bond strength tests showed an adequate correlation with the retention rate of cervical restorations after two years. Bond strength tests should be carried out by different operators and/or research institutes to determine the reliability and technique sensitivity of the material under investigation.

Activity measurements of 18F and 90Y with commercial radionuclide calibrators for nuclear medicine in Switzerland.

The activity of radiopharmaceuticals in nuclear medicine is measured before patient injection with radionuclide calibrators. In Switzerland, the general requirements for quality controls are defined in a federal ordinance and a directive of the Federal Office of Metrology (METAS) which require each instrument to be verified. A set of three gamma sources (Co-57, Cs-137 and Co-60) is used to verify the response of radionuclide calibrators in the gamma energy range of their use. A beta source, a mixture of (90)Sr and (90)Y in secular equilibrium, is used as well. Manufacturers are responsible for the calibration factors. The main goal of the study was to monitor the validity of the calibration factors by using two sources: a (90)Sr/(90)Y source and a (18)F source. The three types of commercial radionuclide calibrators tested do not have a calibration factor for the mixture but only for (90)Y. Activity measurements of a (90)Sr/(90)Y source with the (90)Y calibration factor are performed in order to correct for the extra-contribution of (90)Sr. The value of the correction factor was found to be 1.113 whereas Monte Carlo simulations of the radionuclide calibrators estimate the correction factor to be 1.117. Measurements with (18)F sources in a specific geometry are also performed. Since this radionuclide is widely used in Swiss hospitals equipped with PET and PET-CT, the metrology of the (18)F is very important. The (18)F response normalized to the (137)Cs response shows that the difference with a reference value does not exceed 3% for the three types of radionuclide calibrators.