Rating impacts in a multi‐stressor world: a quantitative assessment of 50 stressors affecting the Great Lakes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116318/1/eap2015253717.pd

Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo

Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated data sets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS data sets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely, excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions, physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in nonphysical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells, and in living organisms.National Science Foundation (U.S.). Physics of Living Systems ProgramNational Institute of Mental Health (U.S.) (Award U01MH106011

The plasticity of adolescent cognitions: Data from a novel cognitive bias modification training task.

Many adult anxiety problems emerge in adolescence. Investigating how adolescent anxiety arises and abates is critical for understanding and preventing adult psychiatric problems. Drawing threat interpretations from ambiguous material is linked to adolescent anxiety but little research has clarified the causal nature of this relationship. Work in adults using Cognitive Bias Modification of Interpretations (CBM-I) training show that manipulating negative interpretational style alters negative affect. Conversely, 'boosting' positive interpretations improves affect. Here, we extend CBM-I investigations to adolescents. Thirty-nine adolescents (13-18 years), varying in trait anxiety and self-efficacy, were randomly allocated to receive positive or negative training. Training-congruent differences emerged for subsequent interpretation style. Induced negative biases predicted a decline in positive affect in low self-efficacious adolescents only. Tentatively, our data suggest that cognitive biases predict adolescent affective symptoms in vulnerable individuals. The acquisition of positive cognitions through training has implications for prevention