Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Relationship between obesity and the risk of clinically significant depression: Mendelian randomisation study.

BACKGROUND: Obesity has been shown to be associated with depression and it has been suggested that higher body mass index (BMI) increases the risk of depression and other common mental disorders. However, the causal relationship remains unclear and Mendelian randomisation, a form of instrumental variable analysis, has recently been employed to attempt to resolve this issue. AIMS: To investigate whether higher BMI increases the risk of major depression. METHOD: Two instrumental variable analyses were conducted to test the causal relationship between obesity and major depression in RADIANT, a large case-control study of major depression. We used a single nucleotide polymorphism (SNP) in FTO and a genetic risk score (GRS) based on 32 SNPs with well-established associations with BMI. RESULTS: Linear regression analysis, as expected, showed that individuals carrying more risk alleles of FTO or having higher score of GRS had a higher BMI. Probit regression suggested that higher BMI is associated with increased risk of major depression. However, our two instrumental variable analyses did not support a causal relationship between higher BMI and major depression (FTO genotype: coefficient -0.03, 95% CI -0.18 to 0.13, P = 0.73; GRS: coefficient -0.02, 95% CI -0.11 to 0.07, P = 0.62). CONCLUSIONS: Our instrumental variable analyses did not support a causal relationship between higher BMI and major depression. The positive associations of higher BMI with major depression in probit regression analyses might be explained by reverse causality and/or residual confounding.

Identification and prospective validation of clinically relevant chronic obstructive pulmonary disease (COPD) subtypes.

Background Chronic obstructive pulmonary disease (COPD) is increasingly considered a heterogeneous condition. It was hypothesised that COPD, as currently defined, includes different clinically relevant subtypes. Methods To identify and validate COPD subtypes, 342 subjects hospitalised for the first time because of a COPD exacerbation were recruited. Three months after discharge, when clinically stable, symptoms and quality of life, lung function, exercise capacity, nutritional status, biomarkers of systemic and bronchial inflammation, sputum microbiology, CT of the thorax and echocardiography were assessed. COPD groups were identified by partitioning cluster analysis and validated prospectively against cause-specific hospitalisations and all-cause mortality during a 4 year follow-up. Results Three COPD groups were identified: group 1 (n ¼ 126, 67 years) was characterised by severe airflow limitation (postbronchodilator forced expiratory volume in 1 s (FEV 1 ) 38% predicted) and worse performance in most of the respiratory domains of the disease; group 2 (n ¼ 125, 69 years) showed milder airflow limitation (FEV 1 63% predicted); and group 3 (n ¼ 91, 67 years) combined a similarly milder airflow limitation (FEV 1 58% predicted) with a high proportion of obesity, cardiovascular disorders, iabetes and systemic inflammation. During follow-up, group 1 had more frequent hospitalisations due to COPD (HR 3.28, p < 0.001) and higher all-cause mortality (HR 2.36, p ¼ 0.018) than the other two groups, whereas group 3 had more admissions due to cardiovascular disease (HR 2.87, p ¼ 0.014). Conclusions In patients with COPD recruited at their first hospitalisation, three different COPD subtypes were identified and prospectively validated:"severe respiratory COPD","moderate respiratory COPD", and"systemic COPD'

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Measuring Intermediary Determinants of Early Childhood Health: A Composite Index Comparing Colombian Departments

In recent years there has been growing interest in composite indicators as an efficient tool of analysis and a method of prioritizing policies. This paper presents a composite index of intermediary determinants of child health using a multivariate statistical approach. The index shows how specific determinants of child health vary across Colombian departments (administrative subdivisions). We used data collected from the 2010 Colombian Demographic and Health Survey (DHS) for 32 departments and the capital city, Bogotá. Adapting the conceptual framework of Commission on Social Determinants of Health (CSDH), five dimensions related to child health are represented in the index: material circumstances, behavioural factors, psychosocial factors, biological factors and the health system. In order to generate the weight of the variables, and taking into account the discrete nature of the data, principal component analysis (PCA) using polychoric correlations was employed in constructing the index. From this method five principal components were selected. The index was estimated using a weighted average of the retained components. A hierarchical cluster analysis was also carried out. The results show that the biggest differences in intermediary determinants of child health are associated with health care before and during delivery.

Measuring early childhood health : a composite index comparing Colombian departments

This paper presents a composite index of early childhood health using a multivariate statistical approach. The index shows how child health varies across Colombian departments, -administrative subdivisions-. In recent years there has been growing interest in composite indicators as an efficient analysis tool and a way of prioritizing policies. These indicators not only enable multi-dimensional phenomena to be simplified but also make it easier to measure, visualize, monitor and compare a country’s performance in particular issues. We used data collected from the Colombian Demographic and Health Survey, DHS, for 32 departments and the capital city, Bogotá, in 2005 and 2010. The variables included in the index provide a measure of three dimensions related to child health: health status, health determinants and the health system. In order to generate the weight of the variables and take into account the discrete nature of the data, we employed a principal component analysis, PCA, using polychoric correlation. From this method, five principal components were selected. The index was estimated using a weighted average of the components retained. A hierarchical cluster analysis was also carried out. We observed that the departments ranking in the lowest positions are located on the Colombian periphery. They are departments with low per capita incomes and they present critical social indicators. The results suggest that the regional disparities in child health may be associated with differences in parental characteristics, household conditions and economic development levels, which makes clear the importance of context in the study of child health in Colombia.

Brief report : Academic amotivation in light of the dark side of identity formation

The study intended to determine motivational profiles of first-year undergraduates and aimed their characterization in terms of identity processes. First, a cluster analysis revealed five motivational profiles: combined (i.e., high quantity of motivation, low amotivation); intrinsic (i.e., high intrinsic, low introjected and external regulation, low amotivation); "demotivated" (i.e., very low quantity of motivation and amotivation); extrinsic (i.e., high extrinsic and identified regulation and low intrinsic and amotivation); and "amotivated" (i.e., low intrinsic and identified, very high amotivation). Second, using Lebart's (2000) methodology, the most characteristic identity processes were listed for each motivational cluster. Demotivated and amotivated profiles were refined in terms of adaptive and maladaptive forms of exploration. Notably, exploration in breadth and in depth were underrepresented in demotivated students compared to the total sample; commitment and ruminative exploration were under and overrepresented respectively in amotivated students. Educational and clinical implications are proposedand future research is suggested.

The unfathomable secret remains: a reverse engineering study of SEO factors on Google's search engine

The vast majority of users don’t seek results beyond the second page offered by the search engine, so if a site fails to be among the top 20 (second page), it says that this page does not have good SEO and, therefore, is not visible to the user. The overall objective of this project is to conduct a study to discover the factors that determine (or not) the positioning of websites in a search engine.

Uma introdução à análise exploratória de dados multivariados

The modern technological ability to handle large amounts of information confronts the chemist with the necessity to re-evaluate the statistical tools he routinely uses. Multivariate statistics furnishes theoretical bases for analyzing systems involving large numbers of variables. The mathematical calculations required for these systems are no longer an obstacle due to the existence of statistical packages that furnish multivariate analysis options. Here basic concepts of two multivariate statistical techniques, principal component and hierarchical cluster analysis that have received broad acceptance for treating chemical data are discussed.

Approximation to the theory of affinities to manage the problems of the groupping process

New economic and enterprise needs have increased the interest and utility of the methods of the grouping process based on the theory of uncertainty. A fuzzy grouping (clustering) process is a key phase of knowledge acquisition and reduction complexity regarding different groups of objects. Here, we considered some elements of the theory of affinities and uncertain pretopology that form a significant support tool for a fuzzy clustering process. A Galois lattice is introduced in order to provide a clearer vision of the results. We made an homogeneous grouping process of the economic regions of Russian Federation and Ukraine. The obtained results gave us a large panorama of a regional economic situation of two countries as well as the key guidelines for the decision-making. The mathematical method is very sensible to any changes the regional economy can have. We gave an alternative method of the grouping process under uncertainty.

Classificação de cerâmicas arqueológicas da Bacia Amazônica

In order to elucidate the traditional classification of archaeological artefacts, a multielemental analytical method for characterisation of its micro and macro chemical constituents. combined with statistical multivariate analysis for classification, were used. Instrumental thermal neutron activation analysis, for elemental chemical determination, and three statistical methods: discriminant, cluster and modified cluster analysis were applied. The statistical results obtained for the samples from Iquiri, Quinari and Xapuri archaeological phases were in good agreement with the conventional archaeological classification. Iaco and Jacuru archaeological phase were not characterised as homogenous groups. Iquiri phase were the most distinct in relation to the other analysed groups. An homogeneous group for 54% collected samples at the Los Angeles site was also found, this could be characterised as a new archaeological phase.

Análises seqüenciais de agrupamentos aplicadas ao estudo de partículas atmosféricas

The present paper has as objective to apply a sequential Cluster Analysis to the atmospheric particles: Hierarchical Cluster Analysis followed by Nonhierarchical Cluster Analysis. The hierarchical cluster analysis results were used as start point for the nonhierarchical cluster analysis as an agglomerative technique. These particles were taken from two areas of the metropolitan region of Porto Alegre, Charqueadas and Sapucaia do Sul., from may /97 to may/98, using a High Volume Sampler (Hi-Vol). Around 10,000 particles were analysed by Scanning Electron Microscope with Energy-Dispersive X-Ray microanalysis (SEM-EDS). The Hierarchical Cluster Analysis allowed the identification of five groups of particles, whose amounts were differentiated according to the summer and the winter campaigns. The abundance of each type of particles inside each group according to the different sections was verified by the Nonhierarchical Cluster Analysis, resulting in information about the emissions sources. The groups of particles of Si/Al and Si and of Fe/Zn and Fe for Charqueadas were more significant in section 2 and 3 (NW and W wind directions) and in section 1 (SE wind direction), evidencing the influence of the coal power plant and steel industry, respectively located in these quadrants. In Sapucaia do Sul the data were more heterogeneous, causing a certain difficulty to identify the source as anthropogenic. Nevertheless the group of particles containing Fe was found in sectors of NW/W wind directions which shows the influence of the steel plant.

Estudo do coeficiente de partição octanol-água de bifenilas policloradas (PCBs) utilizando parâmetros topológicos

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.

Functional diversity of macrophyte communities within and between Pyrenean lakes

Submersed vegetation is a common feature in about 70% Pyrene an high mountain (>1500 m a.s.l.) lakes. Isoetids and soft-water elodeids are common elements of this underw ater flora and can form distinct vegetation units (i.e. patches of vegetation dominated by different species) within complex mosaics of vegetation in shallow waters (<7 m). Since is oetids exert a strong influence on sediment biogeochemistry due to high radial oxygen loss, we examined the small scale characteristics of the lake environment (water and sediment) associated to vegetation patches in order to ascertain potential functional differences among them. To do so, we characterised the species composition and biomass of the main vegetation units from 11 lakes, defined plant communities based on biomass data, and then related each community with sediment properties (redox and dissolved nutrient concentration in the pore water) and water nutrient concentration within plant canopy. We also characterised lake water and sediment in areas without vegetation as a reference. A total of twenty-one vegetation units were identified, ranging from one to five per lake. A cluster analysis on biomass species composition suggested seven different macrophyte communities that were named after the most dominant species:Nitella sp.,Potamogeton praelongus, Myriophyllum alterniflorum, Sparganium angustifolium , Isoetes echinospora,Isoetes lacustris and Carex rostrata . Coupling between macrophyte communities and their immediate environment (overlying water and sediment) was manifested mainly as variation in sediment redox conditions and the dominant form of inorganic nitrogen in pore-water. These effects depended on the specific compositi on of the community, and on the allocation between above- and belowground biomass, and could be predicted with a model relating the average and standard deviation of sediment redox potential from 0 down to -20 cm, across macrophyte communities. Differences in pore-water total dissolved phosphorus were related to the trophic state of the lakes. There was no correlation between sediment and water column dissolved nutrients. However, nitrate concentrations tended to be lower in the water overlaying isoetid communities, in apparent contradiction to the patterns of dissolved nitrates in the pore-water. These tendencies were robust even when comparing the water over laying communities within the same lake, thus pointing towards a potential effect of isoetids in reducing dissolved nitrogen in the lakes.

Feeding ecology and trophic position of three sympatric demersal chondrichthyans in the Northwestern Mediterranean.

Understanding how marine predators interact is a scientific challenge. In marine ecosystems, segregation in feeding habits has been largely described as a common mechanism to allow the coexistence of several competing marine predators. However, little is known about the feeding ecology of most species of chondrichthyans, which play a pivotal role in the structure of marine food webs worldwide. In this study, we examined the trophic ecology of 3 relatively abundant chondrichthyans coexisting in the Mediterranean Sea: the blackmouth catshark Galeus melastomus , the velvet belly lanternshark Etmopterus spinax and the rabbit fish Chimaera monstrosa. To examine their trophic ecology and interspecific differences in food habits, we combined the analysis of stomach content and stable isotopes. Our results highlighted a trophic segregation between C. monstrosa and the other 2 species. G. melastomus showed a diet composed mainly of cephalopods, while E. spinax preyed mainly on shrimps and C. monstrosa on crabs. Interspecific differences in the trophic niche were likely due to different feeding capabilities and body size. Each species showed different isotopic niche space and trophic level. Specifically, C. monstrosa showed a higher trophic level than E. spinax and G. melastomus. The high trophic levels of the 3 species highlighted their important role as predators in the marine food web. Our results illustrate the utility of using complementary approaches that provide information about the feeding behaviour at short (stomach content) and long-term scales (stable isotopes), which could allow more efficient monitoring of marine food-web changes in the study area.