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Psychological stress and physical activity are interrelated, constituting a relevant association to human health, especially in children. However, the association’s nature remains elusive, i.e., why psychological stress predicts both decreased and increased physical activity. To test whether effects vary as a function of the level of analyses, we derived intensive longitudinal data via accelerometers and stress questionnaires from 74 children across 7 days as they went about their daily routines (n = 513 assessments). Multilevel modelling analyses revealed that between children, higher psychological stress predicted decreased physical activity (standardized beta coefficient = −0.14; p = 0.046). Concurrently, within those children, higher psychological stress predicted increased physical activity across days (standardized beta coefficient = 0.09; p = 0.015). Translated to practice, children who experienced more stress than others moved less, but children were more active on days when they experienced heightened stress. This suggests that the analyses level is crucial to the understanding of the association between psychological stress and physical activity and should be considered to receive unequivocal results. If replicated, e.g., including high-frequency sampling and experimental manipulation in everyday life for in-depth insights on underlying mechanisms and causality, our findings may be translated to individually tailored (digital) prevention and intervention strategies which target children’s distress-feelings despite impairing their heightened physical activity in stressful situations and identify tipping points of chronic stress phases. Therefore, we especially call for more intensive longitudinal data approaches to tackle thus far neglected within-subject issues in the field of physical activity, sport and exercise research.

Background Heterogeneity in the phenotypic presentation of autism spectrum disorder (ASD) is apparent in the profile and the severity of sensory features. Here, we applied factor mixture modelling (FMM) to test a multidimensional factor model of sensory processing in ASD. We aimed to identify homogeneous sensory subgroups in ASD that differ intrinsically in their severity along continuous factor scores. We also investigated sensory subgroups in relation to clinical variables: sex, age, IQ, social-communication symptoms, restricted and repetitive behaviours, adaptive functioning and symptoms of anxiety and attention-deficit/hyperactivity disorder. Methods Three hundred thirty-two children and adults with ASD between the ages of 6 and 30 years with IQs varying between 40 and 148 were included. First, three different confirmatory factor models were fit to the 38 items of the Short Sensory Profile (SSP). Then, latent class models (with two-to-six subgroups) were evaluated. The best performing factor model, the 7-factor structure, was subsequently used in two FMMs that varied in the number of subgroups: a two-subgroup, seven-factor model and a three-subgroup and seven-factor model. Results The ‘three-subgroup/seven-factor’ FMM was superior to all other models based on different fit criteria. Identified subgroups differed in sensory severity from severe, moderate to low. Accounting for the potential confounding effects of age and IQ, participants in these sensory subgroups had different levels of social-communicative symptoms, restricted and repetitive behaviours, adaptive functioning skills and symptoms of inattention and anxiety. Limitations Results were derived using a single parent-report measure of sensory features, the SSP, which limits the generalisability of findings. Conclusion Sensory features can be best described by three homogeneous sensory subgroups that differ in sensory severity gradients along seven continuous factor scores. Identified sensory subgroups were further differentiated by the severity of core and co-occurring symptoms, and level of adaptive functioning, providing novel evidence on the associated clinical correlates of sensory subgroups. These sensory subgroups provide a platform to further interrogate the neurobiological and genetic correlates of altered sensory processing in ASD.

Contains fulltext : 202693.pdf (Publisher’s version ) (Open Access) Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

A comprehensive ice nucleation parameterization has been implemented in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations (ICNCs). The parameterization of Barahona and Nenes (2009, hereafter BN09) allows for the treatment of ice nucleation taking into account the competition for water vapour between homogeneous and heterogeneous nucleation in cirrus clouds. Furthermore, the influence of chemically heterogeneous, polydisperse aerosols is considered by applying one of the multiple ice nucleating particle parameterizations which are included in BN09 to compute the heterogeneously formed ice crystals. BN09 has been modified in order to consider the pre-existing ice crystal effect and implemented to operate both in the cirrus and in the mixed-phase regimes. Compared to the standard EMAC parameterizations, BN09 produces fewer ice crystals in the upper troposphere but higher ICNCs in the middle troposphere, especially in the Northern Hemisphere where ice nucleating mineral dust particles are relatively abundant. Overall, ICNCs agree well with the observations, especially in cold cirrus clouds (at temperatures below205 K), although they are underestimated between200and220 K. As BN09 takes into account processes which were previously neglected by the standard version of the model, it is recommended for future EMAC simulations.

Major population centers (MPCs) or mega-cities represent the largest of growing urban agglomerations with major societal and environmental implications. In terms of air quality they are seen as localized but strong emission sources of aerosols and trace gases which in turn affect air pollution levels in the city or in downwind regions. In the state-of-the-art atmospheric chemistry general circulation model EMAC, generic aerosol and gas phase tracers with equal emission source strengths at 46 MPC locations are used to study the balance between local pollution build up and pollution export, either vertically into the upper troposphere or horizontally, but remaining in the lower atmosphere. The insoluble gas phase tracers with fixed lifetimes are transported with the atmospheric circulation, while the aerosol tracers also undergo gravitational sedimentation as well as dry and wet deposition processes. The strength of low-level tracer export depends on the location of the emission source and prevailing meteorology, in particular on atmospheric stability and the height of the boundary layer and the mixing out of this layer. In contrast, vertical transport of tracer mass depends on the tracer's solubility: the more soluble a tracer is the less mass reaches altitudes above five kilometers. Hence, the mass of insoluble gas phase tracer above five kilometers can be up to ten times higher than the hydrophilic aerosol mass from the same source. In the case of aerosol tracers, pollution build up around the source is determined by meteorological factors which have only indirect effects on tracer lifetime, like surface wind, boundary layer height, and turbulent mixing as well as those which affect the lifetime of the tracers such as precipitation. The longer a tracer stays in the atmosphere, the lower is the relative importance of the location of the source to the atmospheric mass and thus the lower is the relative local pollution build up. We further use aerosol deposition fields to estimate regions with high deposition, that is more than 1% or more than 5% of the corresponding tracer emission deposited in this region. In doing so, we find that the high deposition areas are larger for larger aerosols, and these differ less between the MPCs than for smaller aerosols due to faster deposition. Furthermore, cities in regions with high precipitation rates or unfavorable geographic location, e.g. in a basin, suffer most of this high deposition. Most of the high deposition occurs over land, although about 50% of the MPCs are located along coastlines. By folding the aerosol deposition fields with geographical distributions of cropland, pasture, and forest, the impact on different land ecosystems is assessed. In general, forest are exhibited most to deposition from MPCs while pasture land is least affected. Moreover, the impact on humans, measured with a threshold exceedance of pollutant surface mixing ratios, is more dependent on population densities than on the size of the area holding a certain mixing ratio.

We present a numerical modelling study investigating the impact of mineral dust on cloud formation over the Eastern Mediterranean for two case studies: (i) 25 September 2008 and (ii) 28/29 January 2003. In both cases dust plumes crossed the Mediterranean and interacted with clouds forming along frontal systems. For our investigation we used the fully online coupled model WRF-chem. The results show that increased aerosol concentrations due to the presence of mineral dust can enhance the formation of ice crystals. This leads to slight shifts of the spatial and temporal precipitation patterns compared to scenarios where dust was not considered to act as ice nuclei. However, the total amount of precipitation did not change significantly. The only exception occurred when dust entered into an area of orographic ascent, causing glaciation of the clouds, leading to a local enhancement of rainfall. The impact of dust particles acting as giant cloud condensation nuclei on precipitation formation was found to be small. Based on our simulations the contribution of dust to the CCN population is potentially significant only for warm phase clouds. Nevertheless, the dust-induced differences in the microphysical structure of the clouds can contribute to a significant radiative forcing, which is important from a climate perspective.

Self-reporting is a well-established approach within the medical and psychological sciences. In order to avoid recall bias, i.e. past events being remembered inaccurately, the reports can be filled out on a smartphone in real-time and in the natural environment. This is often referred to as ambulatory assessment and the reports are usually triggered at regular time intervals. With this sampling scheme, however, rare events (e.g. a visit to a park or recreation area) are likely to be missed. When addressing the correlation between mood and the environment, it may therefore be beneficial to include participant locations within the ambulatory assessment sampling scheme. Based on the geographical coordinates, the database query system then decides if a self-report should be triggered or not. We simulated four different ambulatory assessment sampling schemes based on movement data (coordinates by minute) from 143 voluntary participants tracked for seven consecutive days. Two location-based sampling schemes incorporating the environmental characteristics (land use and population density) at each participant’s location were introduced and compared to a time-based sampling scheme triggering a report on the hour as well as to a sampling scheme incorporating physical activity. We show that location-based sampling schemes trigger a report less often, but we obtain more unique trigger positions and a greater spatial spread in comparison to sampling strategies based on time and distance. Additionally, the location-based methods trigger significantly more often at rarely visited types of land use and less often outside the study region where no underlying environmental data are available.

The new global anthropogenic emission inventory (EDGAR-CIRCE) of gas and aerosol pollutants has been incorporated in the chemistry general circulation model EMAC (ECHAM5/MESSy Atmospheric Chemistry). A relatively high horizontal resolution simulation is performed for the years 2005–2008 to evaluate the capability of the model and the emissions to reproduce observed aerosol concentrations and aerosol optical depth (AOD) values. Model output is compared with observations from different measurement networks (CASTNET, EMEP and EANET) and AODs from remote sensing instruments (MODIS and MISR). A good spatial agreement of the distribution of sulfate and ammonium aerosol is found when compared to observations, while calculated nitrate aerosol concentrations show some discrepancies. The simulated temporal development of the inorganic aerosols is in line with measurements of sulfate and nitrate aerosol, while for ammonium aerosol some deviations from observations occur over the USA, due to the wrong temporal distribution of ammonia gas emissions. The calculated AODs agree well with the satellite observations in most regions, while negative biases are found for the equatorial area and in the dust outflow regions (i.e. Central Atlantic and Northern Indian Ocean), due to an underestimation of biomass burning and aeolian dust emissions, respectively. Aerosols and precursors budgets for five different regions (North America, Europe, East Asia, Central Africa and South America) are calculated. Over East-Asia most of the emitted aerosols (precursors) are also deposited within the region, while in North America and Europe transport plays a larger role. Further, it is shown that a simulation with monthly varying anthropogenic emissions typically improves the temporal correlation by 5–10% compared to one with constant annual emissions.

In this study we report measurements of hydrogen peroxide (H2O2), methyl hydroperoxide* (MHP* as a proxy of MHP based on an unspecific measurement of total organic peroxides) and formaldehyde (HCHO) from the HOx OVer EuRope (HOOVER) project (HOx = OH+HO2). HOOVER included two airborne field campaigns, in October 2006 and July 2007. Measurement flights were conducted from the base of operation Hohn (Germany, 54° N, 9° E) towards the Mediterranean and to the subpolar regions over Norway. We find negative concentration gradients with increasing latitude throughout the troposphere for H2O2 and CH3OOH*. In contrast, observed HCHO is almost homogeneously distributed over central and northern Europe and is elevated over the Mediterranean. In general, the measured gradients tend to be steepest entering the Mediterranean region, where we also find the highest abundances of the 3 species. Mixing ratios of these tracers generally decrease with altitude. H2O2 and CH3OOH* show maxima above the boundary layer at 2–5 km, being more distinct over southern than over northern Europe. We also present a comparison of our data with simulations by two global 3-D-models, MATCH-MPIC and EMAC, and with the box model CAABA. The models realistically represent altitude and latitude gradients for both HCHO and hydroperoxides (ROOH). In contrast, the models have problems reproducing the absolute mixing ratios, in particular of H2O2. Large uncertainties about retention coefficients and cloud microphysical parameters suggest that cloud scavenging might be a large source of error for the simulation of H2O2. A sensitivity study with EMAC shows a strong influence of cloud and precipitation scavenging on the budget of H2O2 as simulations improve significantly with this effect switched off.

International audience; This is the first article of a series presenting a detailed analysis of bromine chemistry simulated with the atmospheric chemistry general circulation model ECHAM5/MESSy. Release from sea salt is an important bromine source, hence the model explicitly calculates aerosol chemistry and phase partitioning for coarse mode aerosol particles. Many processes including chemical reaction rates are influenced by the particle size distribution, and aerosol associated water strongly affects the aerosol pH. Knowledge of the aerosol pH is important as it determines the aerosol chemistry, e.g., the efficiency of sulphur oxidation and bromine release. Here, we focus on the simulated sea salt aerosol size distribution and the coarse mode aerosol pH. A comparison with available field data shows that the simulated aerosol distributions agree reasonably well within the range of measurements. In spite of the small number of aerosol pH measurements and the uncertainty in its experimental determination, the simulated aerosol pH compares well with the observations. The aerosol pH ranges from alkaline aerosol in areas of strong production down to pH-values of 1 over regions of medium sea salt production and high levels of gas phase acids, mostly polluted regions over the oceans in the Northern Hemisphere.

We apply a cloud slicing technique (CST), originally developed for Total Ozone Mapping Spectrometer (TOMS) ozone observations, to CO vertical column densities retrieved from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). CST makes use of the shielding effect of clouds and combines trace gas column measurements of cloudy pixels with different cloud heights to retrieve fractional columns aloft. Here we determine seasonal mean tropospheric CO profiles at a vertical resolution of about 1 km, which is much finer than what can be obtained from thermal infrared (IR) instruments. However, since both the atmospheric CO profiles and the effective cloud heights depend systematically on meteorology, and in addition part of the retrieved signal originates from the clear part of the satellite ground pixel, the profiles retrieved from the CST have to be interpreted with care. We compare the seasonal mean SCIAMACHY CO profiles with the output from two atmospheric models sampled in the same way as the satellite observations. We find in general good agreement of the spatial patterns, but systematic differences in the absolute values are observed in both hemispheres (more strongly in the Northern Hemisphere), indicating that the source strengths in the emission inventories are probably underestimated.

Recent years have seen considerable progress in epidemiological and molecular genetic research into environmental and genetic factors in schizophrenia, but methodological uncertainties remain with regard to validating environmental exposures, and the population risk conferred by individual molecular genetic variants is small. There are now also a limited number of studies that have investigated molecular genetic candidate gene-environment interactions (G × E), however, so far, thorough replication of findings is rare and G × E research still faces several conceptual and methodological challenges. In this article, we aim to review these recent developments and illustrate how integrated, large-scale investigations may overcome contemporary challenges in G × E research, drawing on the example of a large, international, multi-center study into the identification and translational application of G × E in schizophrenia. While such investigations are now well underway, new challenges emerge for G × E research from late-breaking evidence that genetic variation and environmental exposures are, to a significant degree, shared across a range of psychiatric disorders, with potential overlap in phenotype. © 2014 The Author.