Solar ultraviolet-B radiation and vitamin D: a cross-sectional population-based study using data from the 2007 to 2009 Canadian Health Measures Survey

Background: Exposure to solar ultraviolet-B (UV-B) radiation is a major source of vitamin D3. Chemistry climate models project decreases in ground-level solar erythemal UV over the current century. It is unclear what impact this will have on vitamin D status at the population level. The purpose of this study was to measure the association between ground-level solar UV-B and serum concentrations of 25-hydroxyvitamin D (25(OH)D) using a secondary analysis of the 2007 to 2009 Canadian Health Measures Survey (CHMS). Methods: Blood samples collected from individuals aged 12 to 79 years sampled across Canada were analyzed for 25(OH)D (n=4,398). Solar UV-B irradiance was calculated for the 15 CHMS collection sites using the Tropospheric Ultraviolet and Visible Radiation Model. Multivariable linear regression was used to evaluate the association between 25(OH)D and solar UV-B adjusted for other predictors and to explore effect modification. Results: Cumulative solar UV-B irradiance averaged over 91 days (91-day UV-B) prior to blood draw correlated significantly with 25(OH)D. Independent of other predictors, a 1 kJ/m 2 increase in 91-day UV-B was associated with a significant 0.5 nmol/L (95% CI 0.3-0.8) increase in mean 25(OH)D (P =0.0001). The relationship was stronger among younger individuals and those spending more time outdoors. Based on current projections of decreases in ground-level solar UV-B, we predict less than a 1 nmol/L decrease in mean 25(OH)D for the population. Conclusions: In Canada, cumulative exposure to ambient solar UV-B has a small but significant association with 25(OH)D concentrations. Public health messages to improve vitamin D status should target safe sun exposure with sunscreen use, and also enhanced dietary and supplemental intake and maintenance of a healthy body weight.

A unified neurofuzzy model for classification

This work proposes a unified neurofuzzy modelling scheme. To begin with, the initial fuzzy base construction method is based on fuzzy clustering utilising a Gaussian mixture model (GMM) combined with the analysis of covariance (ANOVA) decomposition in order to obtain more compact univariate and bivariate membership functions over the subspaces of the input features. The mean and covariance of the Gaussian membership functions are found by the expectation maximisation (EM) algorithm with the merit of revealing the underlying density distribution of system inputs. The resultant set of membership functions forms the basis of the generalised fuzzy model (GFM) inference engine. The model structure and parameters of this neurofuzzy model are identified via the supervised subspace orthogonal least square (OLS) learning. Finally, instead of providing deterministic class label as model output by convention, a logistic regression model is applied to present the classifier’s output, in which the sigmoid type of logistic transfer function scales the outputs of the neurofuzzy model to the class probability. Experimental validation results are presented to demonstrate the effectiveness of the proposed neurofuzzy modelling scheme.

Particle swarm optimisation assisted classification using elastic net prefiltering

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how “Occam's razor” is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.

Association between serotonin 5-HT-2C receptor gene (HTR2C) polymorphisms and obesity- and mental health-related phenotypes in a large population-based cohort

OBJECTIVE: Studies have shown that common single-nucleotide polymorphisms (SNPs) in the serotonin 5-HT-2C receptor (HTR2C) are associated with antipsychotic agent-induced weight gain and the development of behavioural and psychological symptoms. We aimed to analyse whether variation in the HTR2C is associated with obesity- and mental health-related phenotypes in a large population-based cohort. METHOD: Six tagSNPs, which capture all common genetic variation in the HTR2C gene, were genotyped in 4978 men and women from the European Prospective Investigation into Cancer (EPIC)-Norfolk study, an ongoing prospective population-based cohort study in the United Kingdom. To confirm borderline significant associations, the -759C/T SNP (rs3813929) was genotyped in the remaining 16 003 individuals from the EPIC-Norfolk study. We assessed social and psychological circumstances using the Health and Life Experiences Questionnaire. Genmod models were used to test associations between the SNPs and the outcomes. Logistic regression was performed to test for association of SNPs with obesity- and mental health- related phenotypes. RESULTS: Of the six HTR2C SNPs, only the T allele of the -759C/T SNP showed borderline significant associations with higher body mass index (BMI) (0.23 kg m(-2); (95% confidence interval (CI): 0.01-0.44); P=0.051) and increased risk of lifetime major depressive disorder (MDD) (Odds ratio (OR): 1.13 (95% CI: 1.01-1.22), P=0.02). The associations between the -759C/T and BMI and lifetime MDD were independent. As associations only achieved borderline significance, we aimed to validate our findings on the -759C/T SNP in the full EPIC-Norfolk cohort (n=20 981). Although the association with BMI remained borderline significant (beta=0.20 kg m(-2); 95% CI: 0.04-0.44, P=0.09), that with lifetime MDD (OR: 1.01; 95% CI: 0.94-1.09, P=0.73) was not replicated. CONCLUSIONS: Our findings suggest that common HTR2C gene variants are unlikely to have a major role in obesity- and mental health-related traits in the general population.

The stochastic component in choice and regression to the mean

In this article, we illustrate experimentally an important consequence of the stochastic component in choice behaviour which has not been acknowledged so far. Namely, its potential to produce ‘regression to the mean’ (RTM) effects. We employ a novel approach to individual choice under risk, based on repeated multiple-lottery choices (i.e. choices among many lotteries), to show how the high degree of stochastic variability present in individual decisions can distort crucially certain results through RTM effects. We demonstrate the point in the context of a social comparison experiment.

Measurements of stratospheric ozone at a mid-latitude observing station Valentia, Ireland (51.94° N, 10.25° W), using ground-based and ozonesonde observations from 1994 to 2009

Sixteen years (1994 – 2009) of ozone profiling by ozonesondes at Valentia Meteorological and Geophysical Observatory, Ireland (51.94° N, 10.23° W) along with a co-located MkIV Brewer spectrophotometer for the period 1993–2009 are analyzed. Simple and multiple linear regression methods are used to infer the recent trend, if any, in stratospheric column ozone over the station. The decadal trend from 1994 to 2010 is also calculated from the monthly mean data of Brewer and column ozone data derived from satellite observations. Both of these show a 1.5 % increase per decade during this period with an uncertainty of about ±0.25 %. Monthly mean data for March show a much stronger trend of ~ 4.8 % increase per decade for both ozonesonde and Brewer data. The ozone profile is divided between three vertical slots of 0–15 km, 15–26 km, and 26 km to the top of the atmosphere and a 11-year running average is calculated. Ozone values for the month of March only are observed to increase at each level with a maximum change of +9.2 ± 3.2 % per decade (between years 1994 and 2009) being observed in the vertical region from 15 to 26 km. In the tropospheric region from 0 to 15 km, the trend is positive but with a poor statistical significance. However, for the top level of above 26 km the trend is significantly positive at about 4 % per decade. The March integrated ozonesonde column ozone during this period is found to increase at a rate of ~6.6 % per decade compared with the Brewer and satellite positive trends of ~5 % per decade.

Relationships between plant traits and climate in the Mediterranean region: an analysis based on pollen data

Question: What are the correlations between the degree of drought stress and temperature, and the adoption of specific adaptive strategies by plants in the Mediterranean region? Location: 602 sites across the Mediterranean region. Method: We considered 12 plant morphological and phenological traits, and measured their abundance at the sites as trait scores obtained from pollen percentages. We conducted stepwise regression analyses of trait scores as a function of plant available moisture (α) and winter temperature (MTCO). Results: Patterns in the abundance for the plant traits we considered are clearly determined by α, MTCO or a combination of both. In addition, trends in leaf size, texture, thickness, pubescence and aromatic leaves and other plant level traits such as thorniness and aphylly, vary according to the life form (tree, shrub, forb), the leaf type (broad, needle) and phenology (evergreen, summer-green). Conclusions: Despite conducting this study based on pollen data we have identified ecologically plausible trends in the abundance of traits along climatic gradients. Plant traits other than the usual life form, leaf type and leaf phenology carry strong climatic signals. Generally, combinations of plant traits are more climatically diagnostic than individual traits. The qualitative and quantitative relationships between plant traits and climate parameters established here will help to provide an improved basis for modelling the impact of climate changes on vegetation and form a starting point for a global analysis of pollen-climate relationships

MODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests

Context: Variation in photosynthetic activity of trees induced by climatic stress can be effectively evaluated using remote sensing data. Although adverse effects of climate on temperate forests have been subjected to increased scrutiny, the suitability of remote sensing imagery for identification of drought stress in such forests has not been explored fully. Aim: To evaluate the sensitivity of MODIS-based vegetation index to heat and drought stress in temperate forests, and explore the differences in stress response of oaks and beech. Methods: We identified 8 oak and 13 beech pure and mature stands, each covering between 4 and 13 MODIS pixels. For each pixel, we extracted a time series of MODIS NDVI from 2000 to 2010. We identified all sequences of continuous unseasonal NDVI decline to be used as the response variable indicative of environmental stress. Neural Networks-based regression modelling was then applied to identify the climatic variables that best explain observed NDVI declines. Results: Tested variables explained 84–97% of the variation in NDVI, whilst air temperature-related climate extremes were found to be the most influential. Beech showed a linear response to the most influential climatic predictors, while oak responded in a unimodal pattern suggesting a better coping mechanism. Conclusions: MODIS NDVI has proved sufficiently sensitive as a stand-level indicator of climatic stress acting upon temperate broadleaf forests, leading to its potential use in predicting drought stress from meteorological observations and improving parameterisation of forest stress indices.

Aircraft-based observations of air–sea turbulent fluxes around the British Isles

Observations of turbulent fluxes of momentum, heat and moisture from low-level aircraft data are presented. Fluxes are calculated using the eddy covariance technique from flight legs typically ∼40 m above the sea surface. Over 400 runs of 2 min (∼12 km) from 26 flights are evaluated. Flight legs are mainly from around the British Isles although a small number are from around Iceland and Norway. Sea-surface temperature (SST) observations from two on-board sensors (the ARIES interferometer and a Heimann radiometer) and a satellite-based analysis (OSTIA) are used to determine an improved SST estimate. Most of the observations are from moderate to strong wind speed conditions, the latter being a regime short of validation data for the bulk flux algorithms that are necessary for numerical weather prediction and climate models. Observations from both statically stable and unstable atmospheric boundary-layer conditions are presented. There is a particular focus on several flights made as part of the DIAMET (Diabatic influence on mesoscale structures in extratropical storms) project. Observed neutral exchange coefficients are in the same range as previous studies, although higher for the momentum coefficient, and are broadly consistent with the COARE 3.0 bulk flux algorithm, as well as the surface exchange schemes used in the ECMWF and Met Office models. Examining the results as a function of aircraft heading shows higher fluxes and exchange coefficients in the across-wind direction, compared to along-wind (although this comparison is limited by the relatively small number of along-wind legs). A multi-resolution spectral decomposition technique demonstrates a lengthening of spatial scales in along-wind variances in along-wind legs, implying the boundary-layer eddies are elongated in the along-wind direction. The along-wind runs may not be able to adequately capture the full range of turbulent exchange that is occurring because elongation places the largest eddies outside of the run length.

Rule-based morphological processing in a second language: a behavioural investigation

According to dual-system accounts of English past-tense processing, regular forms are decomposed into their stem and affix (played=play+ed) based on an implicit linguistic rule, whereas irregular forms (kept) are retrieved directly from the mental lexicon. In second language (L2) processing research, it has been suggested that L2 learners do not have rule-based decomposing abilities, so they process regular past-tense forms similarly to irregular ones (Silva & Clahsen 2008), without applying the morphological rule. The present study investigates morphological processing of regular and irregular verbs in Greek-English L2 learners and native English speakers. In a masked-priming experiment with regular and irregular prime-target verb pairs (playedplay/kept-keep), native speakers showed priming effects for regular pairs, compared to unrelated pairs, indicating decomposition; conversely, L2 learners showed inhibitory effects. At the same time, both groups revealed priming effects for irregular pairs. We discuss these findings in the light of available theories on L2 morphological processing.

An event-based approach to understanding decadal fluctuations in the Atlantic meridional overturning circulation

Many previous studies have shown that unforced climate model simulations exhibit decadal-scale fluctuations in the Atlantic meridional overturning circulation (AMOC), and that this variability can have impacts on surface climate fields. However, the robustness of these surface fingerprints across different models is less clear. Furthermore, with the potential for coupled feedbacks that may amplify or damp the response, it is not known whether the associated climate signals are linearly related to the strength of the AMOC changes, or if the fluctuation events exhibit nonlinear behaviour with respect to their strength or polarity. To explore these questions, we introduce an objective and flexible method for identifying the largest natural AMOC fluctuation events in multicentennial/multimillennial simulations of a variety of coupled climate models. The characteristics of the events are explored, including their magnitude, meridional coherence and spatial structure, as well as links with ocean heat transport and the horizontal circulation. The surface fingerprints in ocean temperature and salinity are examined, and compared with the results of linear regression analysis. It is found that the regressions generally provide a good indication of the surface changes associated with the largest AMOC events. However, there are some exceptions, including a nonlinear change in the atmospheric pressure signal, particularly at high latitudes, in HadCM3. Some asymmetries are also found between the changes associated with positive and negative AMOC events in the same model. Composite analysis suggests that there are signals that are robust across the largest AMOC events in each model, which provides reassurance that the surface changes associated with one particular event will be similar to those expected from regression analysis. However, large differences are found between the AMOC fingerprints in different models, which may hinder the prediction and attribution of such events in reality.

Nonlinear identification using orthogonal forward regression with nested optimal regularization

An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

Does repeated burial of skeletal muscle tissue (Ovis aries) in soil affect subsequent decomposition?

The repeated introduction of an organic resource to soil can result in its enhanced degradation. This phenomenon is of primary importance in agroecosystems, where the dynamics of repeated nutrient, pesticide, and herbicide amendment must be understood to achieve optimal yield. Although not yet investigated, the repeated introduction of cadaveric material is an important area of research in forensic science and cemetery planning. It is not currently understood what effects the repeated burial of cadaveric material has on cadaver decomposition or soil processes such as carbon mineralization. To address this gap in knowledge, we conducted a laboratory experiment using ovine (Ovis aries) skeletal muscle tissue (striated muscle used for locomotion) and three contrasting soils (brown earth, rendzina, podsol) from Great Britain. This experiment comprised two stages. In Stage I skeletal muscle tissue (150 g as 1.5 g cubes) was buried in sieved (4.6 mm) soil (10 kg dry weight) calibrated to 60% water holding capacity and allowed to decompose in the dark for 70 days at 22 °C. Control samples comprised soil without skeletal muscle tissue. In Stage II, soils were weighed (100 g dry weight at 60% WHC) into 1285 ml incubation microcosms. Half of the soils were designated for a second tissue amendment, which comprised the burial (2.5 cm) of 1.5 g cube of skeletal muscle tissue. The remaining half of the samples did not receive tissue. Thus, four treatments were used in each soil, reflecting all possible combinations of tissue burial (+) and control (−). Subsequent measures of tissue mass loss, carbon dioxide-carbon evolution, soil microbial biomass carbon, metabolic quotient and soil pH show that repeated burial of skeletal muscle tissue was associated with a significantly greater rate of decomposition in all soils. However, soil microbial biomass following repeated burial was either not significantly different (brown earth, podsol) or significantly less (rendzina) than new gravesoil. Based on these results, we conclude that enhanced decomposition of skeletal muscle tissue was most likely due to the proliferation of zymogenous soil microbes able to better use cadaveric material re-introduced to the soil.

Objective Empirical Mode Decomposition metric

Empirical Mode Decomposition (EMD) is a data driven technique for extraction of oscillatory components from data. Although it has been introduced over 15 years ago, its mathematical foundations are still missing which also implies lack of objective metrics for decomposed set evaluation. Most common technique for assessing results of EMD is their visual inspection, which is very subjective. This article provides objective measures for assessing EMD results based on the original definition of oscillatory components.

Interactions between uncoupling protein 2 gene polymorphisms, obesity and alcohol intake on liver function: a large meta-analysed population-based study

BACKGROUND AND OBJECTIVE: Given the role of uncoupling protein 2 (UCP2) in the accumulation of fat in the hepatocytes and in the enhancement of protective mechanisms in acute ethanol intake, we hypothesised that UCP2 polymorphisms are likely to cause liver disease through their interactions with obesity and alcohol intake. To test this hypothesis, we investigated the interaction between tagging polymorphisms in the UCP2 gene (rs2306819, rs599277 and rs659366), alcohol intake and obesity traits such as BMI and waist circumference (WC) on alanine aminotransferase (ALT) and gamma glutamyl transferase (GGT) in a large meta-analysis of data sets from three populations (n=20 242). DESIGN AND METHODS: The study populations included the Northern Finland Birth Cohort 1966 (n=4996), Netherlands Study of Depression and Anxiety (n=1883) and LifeLines Cohort Study (n=13 363). Interactions between the polymorphisms and obesity and alcohol intake on dichotomised ALT and GGT levels were assessed using logistic regression and the likelihood ratio test. RESULTS: In the meta-analysis of the three cohorts, none of the three UCP2 polymorphisms were associated with GGT or ALT levels. There was no evidence for interaction between the polymorphisms and alcohol intake on GGT and ALT levels. In contrast, the association of WC and BMI with GGT levels varied by rs659366 genotype (Pinteraction=0.03 and 0.007, respectively; adjusted for age, gender, high alcohol intake, diabetes, hypertension and serum lipid concentrations). CONCLUSION: In conclusion, our findings in 20 242 individuals suggest that UCP2 gene polymorphisms may cause liver dysfunction through the interaction with body fat rather than alcohol intake.