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Ventral striatum and amygdala activity as convergence sites for early adversity and conduct disorder
(2017)
Childhood family adversity (CFA) increases the risk for conduct disorder (CD) and has been associated with alterations in regions of affective processing like ventral striatum (VS) and amygdala. However, no study so far has demonstrated neural converging effects of CFA and CD in the same sample. At age 25 years, functional MRI data during two affective tasks, i.e. a reward (N = 171) and a face-matching paradigm (N = 181) and anatomical scans (N = 181) were acquired in right-handed currently healthy participants of an epidemiological study followed since birth. CFA during childhood was determined using a standardized parent interview. Disruptive behaviors and CD diagnoses during childhood and adolescence were obtained by diagnostic interview (2–19 years), temperamental reward dependence was assessed by questionnaire (15 and 19 years).
CFA predicted increased CD and amygdala volume. Both exposure to CFA and CD were associated with a decreased VS response during reward anticipation and blunted amygdala activity during face-matching. CD mediated the effect of CFA on brain activity. Temperamental reward dependence was negatively correlated with CFA and CD and positively with VS activity. These findings underline the detrimental effects of CFA on the offspring's affective processing and support the importance of early postnatal intervention programs aiming to reduce childhood adversity factors.
We study prediction problems in which the conditional distribution of the output given the input varies as a function of task variables which, in our applications, represent space and time. In varying-coefficient models, the coefficients of this conditional are allowed to change smoothly in space and time; the strength of the correlations between neighboring points is determined by the data. This is achieved by placing a Gaussian process (GP) prior on the coefficients. Bayesian inference in varying-coefficient models is generally intractable. We show that with an isotropic GP prior, inference in varying-coefficient models resolves to standard inference for a GP that can be solved efficiently. MAP inference in this model resolves to multitask learning using task and instance kernels. We clarify the relationship between varying-coefficient models and the hierarchical Bayesian multitask model and show that inference for hierarchical Bayesian multitask models can be carried out efficiently using graph-Laplacian kernels. We explore the model empirically for the problems of predicting rent and real-estate prices, and predicting the ground motion during seismic events. We find that varying-coefficient models with GP priors excel at predicting rents and real-estate prices. The ground-motion model predicts seismic hazards in the State of California more accurately than the previous state of the art.
Fluvial planation surfaces, such as straths, commonly serve as recorders of climatic and tectonic changes and are formed by the lateral erosion of rivers, a process that remains poorly understood. Here we present a study of kilometer-wide, fluvially eroded, low-relief surfaces on rapidly uplifting folds in the foreland of the southwestern Tian Shan. A combination of field work, digital elevation model analysis, and dating of fluvial deposits reveals that despite an arid climate and rapid average rock-uplift rates of 1-3mm/yr, rivers cut extensive (>1-2km wide) surfaces with typical height variations of <6m over periods of >2-6kyr. The extent of this beveling varies in space and time, such that different beveling episodes affect individual structures. Between times of planation, beveled surfaces are abandoned, incised, and deformed across the folds. In a challenge to models that link strath cutting and abandonment primarily to changes in river incision rates, we demonstrate that lateral erosion rates of antecedent streams crossing the folds have to vary by more than 1 order of magnitude to explain the creation of beveled platforms in the past and their incision at the present day. These variations do not appear to covary with climate variability and might be caused by relatively small (much less than an order of magnitude) changes in sediment or water fluxes. It remains uncertain in which settings variations in lateral bedrock erosion rates predominate over changes in vertical erosion rates. Therefore, when studying fluvial planation and strath terraces, variability of both lateral and vertical erosion rates should be considered.
Puumala virus (PUUV) causes many human infections in large parts of Europe and can lead to mild to moderate disease. The bank vole (Myodes glareolus) is the only reservoir of PUUV in Central Europe. A commercial PUUV rapid field test for rodents was validated for bank-vole blood samples collected in two PUUV-endemic regions in Germany (North Rhine-Westphalia and Baden-Wurttemberg). A comparison of the results of the rapid field test and standard ELISAs indicated a test efficacy of 93-95%, largely independent of the origin of the antigens used in the ELISA. In ELISAs, reactivity for the German PUUV strain was higher compared to the Swedish strain but not compared to the Finnish strain, which was used for the rapid field test. In conclusion, the use of the rapid field test can facilitate short-term estimation of PUUV seroprevalence in bank-vole populations in Germany and can aid in assessing human PUUV infection risk.
In this study, we validate and compare elevation accuracy and geomorphic metrics of satellite-derived digital elevation models (DEMs) on the southern Central Andean Plateau. The plateau has an average elevation of 3.7 km and is characterized by diverse topography and relief, lack of vegetation, and clear skies that create ideal conditions for remote sensing. At 30m resolution, SRTM-C, ASTER GDEM2, stacked ASTER L1A stereopair DEM, ALOS World 3D, and TanDEM-X have been analyzed. The higher-resolution datasets include 12m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X DEMs, and 5m ALOS World 3D. These DEMs are state of the art for optical (ASTER and ALOS) and radar (SRTM-C and TanDEM-X) spaceborne sensors. We assessed vertical accuracy by comparing standard deviations of the DEM elevation versus 307 509 differential GPS measurements across 4000m of elevation. For the 30m DEMs, the ASTER datasets had the highest vertical standard deviation at > 6.5 m, whereas the SRTM-C, ALOS World 3D, and TanDEM-X were all < 3.5 m. Higher-resolution DEMs generally had lower uncertainty, with both the 12m TanDEM-X and 5m ALOSWorld 3D having < 2m vertical standard deviation. Analysis of vertical uncertainty with respect to terrain elevation, slope, and aspect revealed the low uncertainty across these attributes for SRTM-C (30 m), TanDEM-X (12–30 m), and ALOS World 3D (5–30 m). Single-CoSSC TerraSAR-X/TanDEM-X 10m DEMs and the 30m ASTER GDEM2 displayed slight aspect biases, which were removed in their stacked counterparts (TanDEM-X and ASTER Stack). Based on low vertical standard deviations and visual inspection alongside optical satellite data, we selected the 30m SRTM-C, 12–30m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X, and 5m ALOS World 3D for geomorphic metric comparison in a 66 km2 catchment with a distinct river knickpoint. Consistent m=n values were found using chi plot channel profile analysis, regardless of DEM type and spatial resolution. Slope, curvature, and drainage area were calculated and plotting schemes were used to assess basin-wide differences in the hillslope-to-valley transition related to the knickpoint. While slope and hillslope length measurements vary little between datasets, curvature displays higher magnitude measurements with fining resolution. This is especially true for the optical 5m ALOS World 3D DEM, which demonstrated high-frequency noise in 2–8 pixel steps through a Fourier frequency analysis. The improvements in accurate space-radar DEMs (e.g., TanDEM-X) for geomorphometry are promising, but airborne or terrestrial data are still necessary for meter-scale analysis.