Wavelet analysis residual kriging vs. neural network residual kriging

This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical prediction (kriging) is proposed. The method - wavelet analysis residual kriging (WARK) - is developed in order to assess the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing global statistical characteristics of the distribution and spatial correlation structure.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Ecological correlations between food availability and mortality: more than you can eat?

Introduction: There is little information regarding the impact of diet on disease incidence and mortality in Switzerland. Objectives: We aimed at assessing the associations between food availability and disease using ecological correlations. Methods: Time-trend ecologic study for period 1970 to 2009. Food availability was measured through the FAO food balance sheets. Standardized mortality rates (SMRs) were obtained from the Swiss Federal Office of Statistics. Cancer incidence data was obtained from the WHO Health for all database and the Vaud cancer registry. Association between food availability and mortality/incidence was assessed at lags 0, 5, 10 and 15 years by Spearman correlation. Results: Alcoholic beverages and fruit availability were positively associated with SMRs from all types of cardiovascular disease, while fish availability was negatively associated. Animal products, meat and animal fats were positively associated with SMR from ischemic heart disease only. For cancers, opposite results were found whether the association used SMRs or incidence rates. For all cancers, alcoholic beverages and fruits were positively associated with SMRs but negatively associated with incidence rates. Similar findings were obtained for all other foods, with the exception of vegetables, which were weakly and negatively associated with SMRs and incidence rates. Finally, a 15 years lag time reversed the association for animal and vegetal products, weakened the association for alcohol and fruits and strengthened the association for fish. Conclusion: Ecologic associations between food availability and disease vary considerably whether mortality or incidence rates are used. Great care should be taken when interpreting the results.

Is human lactation a particularly efficient process?

The recommended dietary allowances of many expert committees (UK DHSS 1979, FAO/WHO/UNU 1985, USA NRC 1989) have set out the extra energy requirements necessary to support lactation on the basis of an efficiency of 80 per cent for human milk production. The metabolic efficiency of milk synthesis can be derived from the measurements of resting energy expenditure in lactating women and in a matched control group of non-pregnant non-lactating women. The results of the present study in Gambian women, as well as a review of human studies on energy expenditure during lactation performed in different countries, suggest an efficiency of human milk synthesis greater than the value currently used by expert committees. We propose that an average figure of 95 per cent would be more appropriate to calculate the energy cost of human lactation.

BME-based uncertainty assessment of the Chernobyl fallout

The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements (?hard? data) are combined with secondary information on local uncertainties (treated as ?soft? data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study.

Heterogeneous 40Ar* distributions in naturally deformed muscovite: in situ UV-laser ablation evidence for micro structurally controlled intragrain diffusion

Micas are commonly used in Ar-40/Ar-39 thermochronological studies of variably deformed rocks yet the physical basis by which deformation may affect radiogenic argon retention in mica is poorly constrained. This study examines the relationship between deformation and deformation-induced microstructures on radiogenic argon retention in muscovite, A combination of furnace step-heating and high-spatial resolution in situ UV-laser ablation Ar-40/Ar-39 analyses are reported for deformed muscovites sampled from a granitic pegmatite vein within the Siviez-Mischabel Nappe, western Swiss Alps (Penninic domain, Brianconnais unit). The pegmatite forms part of the Variscan (similar to 350 Ma) Alpine basement and exhibits a prominent Alpine S-C fabric including numerous mica `fish' that developed under greenschist facies metamorphic conditions, during the dominant Tertiary Alpine tectonic phase of nappe emplacement. Furnace step-heating of milligram quantities of separated muscovite grains yields an Ar-40/Ar-39 age spectrum with two distinct staircase segments but without any statistical plateau, consistent with a previous study from the same area. A single (3 X 5 mm) muscovite porphyroclast (fish) was investigated by in situ UV-laser ablation. A histogram plot of 170 individual Ar-40/Ar-39 UV-laser ablation ages exhibit a range from 115 to 387 Ma with modes at approximately 340 and 260 Ma. A variogram statistical treatment of the (40)Ad/Ar-39 results reveals ages correlated with two directions; a highly correlated direction at 310 degrees and a lesser correlation at 0 degrees relative to the sense of shearing. Using the highly correlated direction a statistically generated (Kriging method) age contour map of the Ar-40/Ar-39 data reveals a series of elongated contours subparallel to the C-surfaces which where formed during Tertiary nappe emplacement. Similar data distributions and slightly younger apparent ages are recognized in a smaller mica fish. The observed intragrain age variations are interpreted to reflect the partial loss of radiogenic argon during Alpine (similar to 35 Ma) greenschist facies metamorphism. One-dirnensional diffusion modelling results are consistent with the idea that the zones of youngest apparent age represent incipient shear band development within the mica porphyroclasts, thus providing a network of fast diffusion pathways. During Alpine greenschist facies metamorphism the incipient shear bands enhanced the intragrain loss of radiogenic argon. The structurally controlled intragrain age variations observed in this investigation imply that deformation has a direct control on the effective length scale for argon diffusion, which is consistent with the heterogeneous nature of deformation. (C) 2001 Elsevier Science B.V. All rights reserved.

Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations.

High blood pressure (BP) is more prevalent and contributes to more severe manifestations of cardiovascular disease (CVD) in African Americans than in any other United States ethnic group. Several small African-ancestry (AA) BP genome-wide association studies (GWASs) have been published, but their findings have failed to replicate to date. We report on a large AA BP GWAS meta-analysis that includes 29,378 individuals from 19 discovery cohorts and subsequent replication in additional samples of AA (n = 10,386), European ancestry (EA) (n = 69,395), and East Asian ancestry (n = 19,601). Five loci (EVX1-HOXA, ULK4, RSPO3, PLEKHG1, and SOX6) reached genome-wide significance (p < 1.0 × 10(-8)) for either systolic or diastolic BP in a transethnic meta-analysis after correction for multiple testing. Three of these BP loci (EVX1-HOXA, RSPO3, and PLEKHG1) lack previous associations with BP. We also identified one independent signal in a known BP locus (SOX6) and provide evidence for fine mapping in four additional validated BP loci. We also demonstrate that validated EA BP GWAS loci, considered jointly, show significant effects in AA samples. Consequently, these findings suggest that BP loci might have universal effects across studied populations, demonstrating that multiethnic samples are an essential component in identifying, fine mapping, and understanding their trait variability.

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity.

9-cis-retinoic acid enhances fatty acid-induced expression of the liver fatty acid-binding protein gene.

The role of retinoic acids (RA) on liver fatty acid-binding protein (L-FABP) expression was investigated in the well differentiated FAO rat hepatoma cell line. 9-cis-Retinoic acid (9-cis-RA) specifically enhanced L-FABP mRNA levels in a time- and dose-dependent manner. The higher induction was found 6 h after addition of 10(-6) M 9-cis-RA in the medium. RA also enhanced further both L-FABP mRNA levels and cytosolic L-FABP protein content induced by oleic acid. The retinoid X receptor (RXR) and the peroxisome proliferator-activated receptor (PPAR), which are known to be activated, respectively, by 9-cis-RA and long chain fatty acid (LCFA), co-operated to bind specifically the peroxisome proliferator-responsive element (PPRE) found upstream of the L-FABP gene. Our result suggest that the PPAR-RXR complex is the molecular target by which 9-cis-RA and LCFA regulate the L-FABP gene.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Pancreatic islet adaptation to fasting is dependent on peroxisome proliferator-activated receptor alpha transcriptional up-regulation of fatty acid oxidation.

The cellular response to fasting and starvation in tissues such as heart, skeletal muscle, and liver requires peroxisome proliferator-activated receptor-alpha (PPARalpha)-dependent up-regulation of energy metabolism toward fatty acid oxidation (FAO). PPARalpha null (PPARalphaKO) mice develop hyperinsulinemic hypoglycemia in the fasting state, and we previously showed that PPARalpha expression is increased in islets at low glucose. On this basis, we hypothesized that enhanced PPARalpha expression and FAO, via depletion of lipid-signaling molecule(s) for insulin exocytosis, are also involved in the normal adaptive response of the islet to fasting. Fasted PPARalphaKO mice compared with wild-type mice had supranormal ip glucose tolerance due to increased plasma insulin levels. Isolated islets from the PPARalpha null mice had a 44% reduction in FAO, normal glucose use and oxidation, and enhanced glucose-induced insulin secretion. In normal rats, fasting for 24 h increased islet PPARalpha, carnitine palmitoyltransferase 1, and uncoupling protein-2 mRNA expression by 60%, 62%, and 82%, respectively. The data are consistent with the view that PPARalpha, via transcriptionally up-regulating islet FAO, can reduce insulin secretion, and that this mechanism is involved in the normal physiological response of the pancreatic islet to fasting such that hypoglycemia is avoided.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.