Serotonin transporter polymorphisms and panic disorder

Panic disorder (PD) is the most common anxiety disorder. Although PD seems to occur unprovoked and the underlying etiology is not well understood, studies have consistently shown that genetic factors explain approximately 48% of the variance. Moreover, family and twin studies support the view that the majority of PD cases have a complex genetic basis. Promising findings have most recently implicated the polymorphisms at the 3' end of the serotonin transporter gene SLC6A4 as PD risk variants. If independent studies can replicate the observed association with the SLC6A4 variants and their functional effects on gene expression, this would have a great impact on our understanding of the disease pathophysiology and would provide opportunities to investigate genotype-phenotype correlations

Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

The age of forest stands is critical information for many aspects of forest management and conservation but area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between 58{\deg} and 65{\deg} northern latitude in a 181,773 km2 study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root-mean-squared-errors (RMSE) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between -1 and 3 years. The models improved with increasing SI and the RMSE were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot-level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%). Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI, that could be considered for practical applications but see considerable potential for improvements, if better SI maps were available

Homeostatic plasticity for single node delay-coupled reservoir computing

© 2015 Massachusetts Institute of Technology. Supplementing a differential equation with delays results in an infinitedimensional dynamical system. This property provides the basis for a reservoir computing architecture, where the recurrent neural network is replaced by a single nonlinear node, delay-coupled to itself. Instead of the spatial topology of a network, subunits in the delay-coupled reservoir are multiplexed in time along one delay span of the system. The computational power of the reservoir is contingent on this temporal multiplexing. Here, we learn optimal temporal multiplexing by means of a biologically inspired homeostatic plasticity mechanism. Plasticity acts locally and changes the distances between the subunits along the delay, depending on how responsive these subunits are to the input. After analytically deriving the learning mechanism, we illustrate its role in improving the reservoir's computational power. To this end, we investigate, first, the increase of the reservoir's memory capacity. Second, we predict a NARMA-10 time series, showing that plasticity reduces the normalized root-mean-square error by more than 20%. Third, we discuss plasticity's influence on the reservoir's input-information capacity, the coupling strength between subunits, and the distribution of the readout coefficients

Development of Comorbid Depression after Social Fear Conditioning in Mice and Its Effects on Brain Sphingolipid Metabolism

Currently, there are no animal models for studying both specific social fear and social fear with comorbidities. Here, we investigated whether social fear conditioning (SFC), an animal model with face, predictive and construct validity for social anxiety disorder (SAD), leads to the development of comorbidities at a later stage over the course of the disease and how this affects the brain sphingolipid metabolism. SFC altered both the emotional behavior and the brain sphingolipid metabolism in a time-point-dependent manner. While social fear was not accompanied by changes in non-social anxiety-like and depressive-like behavior for at least two to three weeks, a comorbid depressive-like behavior developed five weeks after SFC. These different pathologies were accompanied by different alterations in the brain sphingolipid metabolism. Specific social fear was accompanied by increased activity of ceramidases in the ventral hippocampus and ventral mesencephalon and by small changes in sphingolipid levels in the dorsal hippocampus. Social fear with comorbid depression, however, altered the activity of sphingomyelinases and ceramidases as well as the sphingolipid levels and sphingolipid ratios in most of the investigated brain regions. This suggests that changes in the brain sphingolipid metabolism might be related to the short- and long-term pathophysiology of SAD

Grapevine germplasm collections of Switzerland

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