Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

Subclinical thyroid dysfunction and the risk of heart failure events: an individual participant data analysis from 6 prospective cohorts.

BACKGROUND: American College of Cardiology/American Heart Association guidelines for the diagnosis and management of heart failure recommend investigating exacerbating conditions such as thyroid dysfunction, but without specifying the impact of different thyroid-stimulation hormone (TSH) levels. Limited prospective data exist on the association between subclinical thyroid dysfunction and heart failure events. METHODS AND RESULTS: We performed a pooled analysis of individual participant data using all available prospective cohorts with thyroid function tests and subsequent follow-up of heart failure events. Individual data on 25 390 participants with 216 248 person-years of follow-up were supplied from 6 prospective cohorts in the United States and Europe. Euthyroidism was defined as TSH of 0.45 to 4.49 mIU/L, subclinical hypothyroidism as TSH of 4.5 to 19.9 mIU/L, and subclinical hyperthyroidism as TSH <0.45 mIU/L, the last two with normal free thyroxine levels. Among 25 390 participants, 2068 (8.1%) had subclinical hypothyroidism and 648 (2.6%) had subclinical hyperthyroidism. In age- and sex-adjusted analyses, risks of heart failure events were increased with both higher and lower TSH levels (P for quadratic pattern <0.01); the hazard ratio was 1.01 (95% confidence interval, 0.81-1.26) for TSH of 4.5 to 6.9 mIU/L, 1.65 (95% confidence interval, 0.84-3.23) for TSH of 7.0 to 9.9 mIU/L, 1.86 (95% confidence interval, 1.27-2.72) for TSH of 10.0 to 19.9 mIU/L (P for trend <0.01) and 1.31 (95% confidence interval, 0.88-1.95) for TSH of 0.10 to 0.44 mIU/L and 1.94 (95% confidence interval, 1.01-3.72) for TSH <0.10 mIU/L (P for trend=0.047). Risks remained similar after adjustment for cardiovascular risk factors. CONCLUSION: Risks of heart failure events were increased with both higher and lower TSH levels, particularly for TSH ≥10 and <0.10 mIU/L.

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.

How to interprete subclinical thyroid dysfunction in the prevention and management of heart failure events? Individual participant data analysis from six prospective cohorts

Background: Guidelines of the Diagnosis and Management of Heart Failure (HF) recommend investigating exacerbating conditions, such as thyroid dysfunction, but without specifying impact of different TSH levels. Limited prospective data exist regarding the association between subclinical thyroid dysfunction and HF events. Methods: We performed a pooled analysis of individual participant data using all available prospective cohorts with thyroid function tests and subsequent follow-up of HF events. Individual data on 25,390 participants with 216,247 person-years of follow-up were supplied from 6 prospective cohorts in the United States and Europe. Euthyroidism was defined as TSH 0.45-4.49 mIU/L, subclinical hypothyroidism as TSH 4.5-19.9 mIU/L and subclinical hyperthyroidism as TSH <0.45 mIU/L, both with normal free thyroxine levels. HF events were defined as acute HF events, hospitalization or death related to HF events. Results: Among 25,390 participants, 2068 had subclinical hypothyroidism (8.1%) and 648 subclinical hyperthyroidism (2.6%). In age- and gender-adjusted analyses, risks of HF events were increased with both higher and lower TSH levels (P for quadratic pattern<0.01): hazard ratio (HR) was 1.01 (95% confidence interval [CI] 0.81-1.26) for TSH 4.5-6.9 mIU/L, 1.65 (CI 0.84-3.23) for TSH 7.0-9.9 mIU/L, 1.86 (CI 1.27-2.72) for TSH 10.0-19.9 mIUL/L (P for trend <0.01), and was 1.31 (CI 0.88-1.95) for TSH 0.10-0.44 mIU/L and 1.94 (CI 1.01-3.72) for TSH <0.10 mIU/L (P for trend=0.047). Risks remained similar after adjustment for cardiovascular risk factors. Conclusion: Risks of HF events were increased with both higher and lower TSH levels, particularly for TSH ≥10 mIU/L and for TSH <0.10 mIU/L. Our findings might help to interpret TSH levels in the prevention and investigation of HF.

Subclinical hypothyroidism and the risk of coronary heart disease and mortality: an individual participant data analysis from nine prospective cohort studies

Texte intégral: http://www.springerlink.com/content/3q68180337551r47/fulltext.pdf

On the Schoenberg transformations in data analysis: theory and illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.

Thyroid antibody status, subclinical hypothyroidism, and the risk of coronary heart disease: an individual participant data analysis.

CONTEXT: Subclinical hypothyroidism has been associated with increased risk of coronary heart disease (CHD), particularly with thyrotropin levels of 10.0 mIU/L or greater. The measurement of thyroid antibodies helps predict the progression to overt hypothyroidism, but it is unclear whether thyroid autoimmunity independently affects CHD risk. OBJECTIVE: The objective of the study was to compare the CHD risk of subclinical hypothyroidism with and without thyroid peroxidase antibodies (TPOAbs). DATA SOURCES AND STUDY SELECTION: A MEDLINE and EMBASE search from 1950 to 2011 was conducted for prospective cohorts, reporting baseline thyroid function, antibodies, and CHD outcomes. DATA EXTRACTION: Individual data of 38 274 participants from six cohorts for CHD mortality followed up for 460 333 person-years and 33 394 participants from four cohorts for CHD events. DATA SYNTHESIS: Among 38 274 adults (median age 55 y, 63% women), 1691 (4.4%) had subclinical hypothyroidism, of whom 775 (45.8%) had positive TPOAbs. During follow-up, 1436 participants died of CHD and 3285 had CHD events. Compared with euthyroid individuals, age- and gender-adjusted risks of CHD mortality in subclinical hypothyroidism were similar among individuals with and without TPOAbs [hazard ratio (HR) 1.15, 95% confidence interval (CI) 0.87-1.53 vs HR 1.26, CI 1.01-1.58, P for interaction = .62], as were risks of CHD events (HR 1.16, CI 0.87-1.56 vs HR 1.26, CI 1.02-1.56, P for interaction = .65). Risks of CHD mortality and events increased with higher thyrotropin, but within each stratum, risks did not differ by TPOAb status. CONCLUSIONS: CHD risk associated with subclinical hypothyroidism did not differ by TPOAb status, suggesting that biomarkers of thyroid autoimmunity do not add independent prognostic information for CHD outcomes.

Subclinical Hypothyroidism and the Risk of Stroke Events and Fatal Stroke: An Individual Participant Data Analysis.

OBJECTIVE: The objective was to determine the risk of stroke associated with subclinical hypothyroidism. DATA SOURCES AND STUDY SELECTION: Published prospective cohort studies were identified through a systematic search through November 2013 without restrictions in several databases. Unpublished studies were identified through the Thyroid Studies Collaboration. We collected individual participant data on thyroid function and stroke outcome. Euthyroidism was defined as TSH levels of 0.45-4.49 mIU/L, and subclinical hypothyroidism was defined as TSH levels of 4.5-19.9 mIU/L with normal T4 levels. DATA EXTRACTION AND SYNTHESIS: We collected individual participant data on 47 573 adults (3451 subclinical hypothyroidism) from 17 cohorts and followed up from 1972-2014 (489 192 person-years). Age- and sex-adjusted pooled hazard ratios (HRs) for participants with subclinical hypothyroidism compared to euthyroidism were 1.05 (95% confidence interval [CI], 0.91-1.21) for stroke events (combined fatal and nonfatal stroke) and 1.07 (95% CI, 0.80-1.42) for fatal stroke. Stratified by age, the HR for stroke events was 3.32 (95% CI, 1.25-8.80) for individuals aged 18-49 years. There was an increased risk of fatal stroke in the age groups 18-49 and 50-64 years, with a HR of 4.22 (95% CI, 1.08-16.55) and 2.86 (95% CI, 1.31-6.26), respectively (p trend 0.04). We found no increased risk for those 65-79 years old (HR, 1.00; 95% CI, 0.86-1.18) or ≥ 80 years old (HR, 1.31; 95% CI, 0.79-2.18). There was a pattern of increased risk of fatal stroke with higher TSH concentrations. CONCLUSIONS: Although no overall effect of subclinical hypothyroidism on stroke could be demonstrated, an increased risk in subjects younger than 65 years and those with higher TSH concentrations was observed.

Results of An International Study of Quantitative BCR/ABL Assays Using Defined RNA Samples

Molecular monitoring of BCR/ABL transcripts by real time quantitative reverse transcription PCR (qRT-PCR) is an essential technique for clinical management of patients with BCR/ABL-positive CML and ALL. Though quantitative BCR/ABL assays are performed in hundreds of laboratories worldwide, results among these laboratories cannot be reliably compared due to heterogeneity in test methods, data analysis, reporting, and lack of quantitative standards. Recent efforts towards standardization have been limited in scope. Aliquots of RNA were sent to clinical test centers worldwide in order to evaluate methods and reporting for e1a2, b2a2, and b3a2 transcript levels using their own qRT-PCR assays. Total RNA was isolated from tissue culture cells that expressed each of the different BCR/ABL transcripts. Serial log dilutions were prepared, ranging from 100 to 10-5, in RNA isolated from HL60 cells. Laboratories performed 5 independent qRT-PCR reactions for each sample type at each dilution. In addition, 15 qRT-PCR reactions of the 10-3 b3a2 RNA dilution were run to assess reproducibility within and between laboratories. Participants were asked to run the samples following their standard protocols and to report cycle threshold (Ct), quantitative values for BCR/ABL and housekeeping genes, and ratios of BCR/ABL to housekeeping genes for each sample RNA. Thirty-seven (n=37) participants have submitted qRT-PCR results for analysis (36, 37, and 34 labs generated data for b2a2, b3a2, and e1a2, respectively). The limit of detection for this study was defined as the lowest dilution that a Ct value could be detected for all 5 replicates. For b2a2, 15, 16, 4, and 1 lab(s) showed a limit of detection at the 10-5, 10-4, 10-3, and 10-2 dilutions, respectively. For b3a2, 20, 13, and 4 labs showed a limit of detection at the 10-5, 10-4, and 10-3 dilutions, respectively. For e1a2, 10, 21, 2, and 1 lab(s) showed a limit of detection at the 10-5, 10-4, 10-3, and 10-2 dilutions, respectively. Log %BCR/ABL ratio values provided a method for comparing results between the different laboratories for each BCR/ABL dilution series. Linear regression analysis revealed concordance among the majority of participant data over the 10-1 to 10-4 dilutions. The overall slope values showed comparable results among the majority of b2a2 (mean=0.939; median=0.9627; range (0.399 - 1.1872)), b3a2 (mean=0.925; median=0.922; range (0.625 - 1.140)), and e1a2 (mean=0.897; median=0.909; range (0.5174 - 1.138)) laboratory results (Fig. 1-3)). Thirty-four (n=34) out of the 37 laboratories reported Ct values for all 15 replicates and only those with a complete data set were included in the inter-lab calculations. Eleven laboratories either did not report their copy number data or used other reporting units such as nanograms or cell numbers; therefore, only 26 laboratories were included in the overall analysis of copy numbers. The median copy number was 348.4, with a range from 15.6 to 547,000 copies (approximately a 4.5 log difference); the median intra-lab %CV was 19.2% with a range from 4.2% to 82.6%. While our international performance evaluation using serially diluted RNA samples has reinforced the fact that heterogeneity exists among clinical laboratories, it has also demonstrated that performance within a laboratory is overall very consistent. Accordingly, the availability of defined BCR/ABL RNAs may facilitate the validation of all phases of quantitative BCR/ABL analysis and may be extremely useful as a tool for monitoring assay performance. Ongoing analyses of these materials, along with the development of additional control materials, may solidify consensus around their application in routine laboratory testing and possible integration in worldwide efforts to standardize quantitative BCR/ABL testing.

Results of An International Study of Quantitative BCR/ABL Assays Using Defined RNA Samples

Molecular monitoring of BCR/ABL transcripts by real time quantitative reverse transcription PCR (qRT-PCR) is an essential technique for clinical management of patients with BCR/ABL-positive CML and ALL. Though quantitative BCR/ABL assays are performed in hundreds of laboratories worldwide, results among these laboratories cannot be reliably compared due to heterogeneity in test methods, data analysis, reporting, and lack of quantitative standards. Recent efforts towards standardization have been limited in scope. Aliquots of RNA were sent to clinical test centers worldwide in order to evaluate methods and reporting for e1a2, b2a2, and b3a2 transcript levels using their own qRT-PCR assays. Total RNA was isolated from tissue culture cells that expressed each of the different BCR/ABL transcripts. Serial log dilutions were prepared, ranging from 100 to 10-5, in RNA isolated from HL60 cells. Laboratories performed 5 independent qRT-PCR reactions for each sample type at each dilution. In addition, 15 qRT-PCR reactions of the 10-3 b3a2 RNA dilution were run to assess reproducibility within and between laboratories. Participants were asked to run the samples following their standard protocols and to report cycle threshold (Ct), quantitative values for BCR/ABL and housekeeping genes, and ratios of BCR/ABL to housekeeping genes for each sample RNA. Thirty-seven (n=37) participants have submitted qRT-PCR results for analysis (36, 37, and 34 labs generated data for b2a2, b3a2, and e1a2, respectively). The limit of detection for this study was defined as the lowest dilution that a Ct value could be detected for all 5 replicates. For b2a2, 15, 16, 4, and 1 lab(s) showed a limit of detection at the 10-5, 10-4, 10-3, and 10-2 dilutions, respectively. For b3a2, 20, 13, and 4 labs showed a limit of detection at the 10-5, 10-4, and 10-3 dilutions, respectively. For e1a2, 10, 21, 2, and 1 lab(s) showed a limit of detection at the 10-5, 10-4, 10-3, and 10-2 dilutions, respectively. Log %BCR/ABL ratio values provided a method for comparing results between the different laboratories for each BCR/ABL dilution series. Linear regression analysis revealed concordance among the majority of participant data over the 10-1 to 10-4 dilutions. The overall slope values showed comparable results among the majority of b2a2 (mean=0.939; median=0.9627; range (0.399 - 1.1872)), b3a2 (mean=0.925; median=0.922; range (0.625 - 1.140)), and e1a2 (mean=0.897; median=0.909; range (0.5174 - 1.138)) laboratory results (Fig. 1-3)). Thirty-four (n=34) out of the 37 laboratories reported Ct values for all 15 replicates and only those with a complete data set were included in the inter-lab calculations. Eleven laboratories either did not report their copy number data or used other reporting units such as nanograms or cell numbers; therefore, only 26 laboratories were included in the overall analysis of copy numbers. The median copy number was 348.4, with a range from 15.6 to 547,000 copies (approximately a 4.5 log difference); the median intra-lab %CV was 19.2% with a range from 4.2% to 82.6%. While our international performance evaluation using serially diluted RNA samples has reinforced the fact that heterogeneity exists among clinical laboratories, it has also demonstrated that performance within a laboratory is overall very consistent. Accordingly, the availability of defined BCR/ABL RNAs may facilitate the validation of all phases of quantitative BCR/ABL analysis and may be extremely useful as a tool for monitoring assay performance. Ongoing analyses of these materials, along with the development of additional control materials, may solidify consensus around their application in routine laboratory testing and possible integration in worldwide efforts to standardize quantitative BCR/ABL testing.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.