Aggerkirkeby5175

54;

= .048). Viewing the headline increased the odds (OR = 1.81,

= .030) of past-year ecstasy users' intention to test their ecstasy for adulterants.

Knowledge that ecstasy is commonly adulterated may help reduce the risk for future use among non-recent users and increase the willingness of users to test their ecstasy. This information can be used to target those at risk for ecstasy/Molly use.

Knowledge that ecstasy is commonly adulterated may help reduce the risk for future use among non-recent users and increase the willingness of users to test their ecstasy. This information can be used to target those at risk for ecstasy/Molly use.The Ossa-Morena Zone (OMZ) has a complex geological history including both Cadomian and Variscan orogenic events. Therefore, the OMZ plays an important role in understanding the geodynamic evolution of Iberia. However, the P-T-t evolution of the OMZ is poorly documented. Here, we combine structural and metamorphic analyses with new geochronological data and geochemical analyses of mafic bodies in Ediacaran metasediments (in Iberia known as Série Negra) to constrain the geodynamic evolution of the OMZ. In the studied mafic rocks, two metamorphic stages were obtained by phase equilibria modelling (1) a high-pressure/low-temperature event of 1.0 ± 0.1 GPa and 470-510 °C, and (2) a medium-pressure/higher-temperature event of 0.6 ± 0.2 GPa and 550-600 °C. The increase in metamorphic temperature is attributed to the intrusion of the Beja Igneous Complex (around 350 Ma) and/or the Évora Massif (around 318 Ma). New U-Pb dating on zircons from the mafic rocks with tholeiitic affinity yields an age between 815 and 790 Ma. If the zircons crystallised from the tholeiitic magma, their age would set a minimum age for the pre-Cadomian basement. Vactosertib solubility dmso The ca. 800 Ma protolith age of HP-LT tholeiitic dykes with a different metamorphic history than the host Série Negra lead us to conclude that (1) the HP-LT mafic rocks and HP-LT marbles with dykes were included in the Ediacaran metasediments as olistoliths; (2) the blueschist metamorphism is older than 550 Ma (between ca. 790 Ma and ca. 550 Ma, e.g., Cadomian).Ecological Momentary Assessment (EMA) studies aim to explore the interaction between subjects' psychological states and real environmental factors. During the EMA studies, participants can receive prompted assessments intensively across days and within each day, which results in three-level longitudinal data, e.g., subject-level (level-3), day-level nested in subject (level-2) and assessment-level nested in each day (level-1). Those three-level data may exhibit complex longitudinal correlation structure but ignoring or mis-specifying the within-subject correlation structure can lead to bias on the estimation of the key effects and the intraclass correlation. Given the three-level EMA data and the time stamps of the responses, we proposed a linear mixed effects model with random effects at each level. In this model, we accounted for level-2 autocorrelation and level-1 autocorrelation and showed how structural information from the three-level data improved the fit of the model. With real time stamps of the assessments, we also provided a useful extension of this proposed model to deal with the issue of irregular-spacing in EMA assessments.Reconstructing the distribution of fine particulate matter (PM2.5) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM2.5 on a 1×1 km2 resolution for a 13 state region in the Northeastern USA for the years 2000-2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.We present the geomagnetic field model COV-OBS.x2 that covers the period 1840-2020. It is primarily constrained by observatory series, satellite data, plus older surveys. Over the past two decades, we consider annual differences of 4-monthly means at ground-based stations (since 1996), and virtual observatory series derived from magnetic data of the satellite missions CHAMP (over 2001-2010) and Swarm (since 2013). A priori information is needed to complement the constraints carried by geomagnetic records and solve the ill-posed geomagnetic inverse problem. We use for this purpose temporal cross-covariances associated with auto-regressive stochastic processes of order 2, whose parameters are chosen so as to mimic the temporal power spectral density observed in paleomagnetic and observatory series. We aim this way to obtain as far as possible realistic posterior model uncertainties. These can be used to infer for instance the core dynamics through data assimilation algorithms, or an envelope for short-term magnetic field forecasts.