At risk of being risky: The relationship between "brain age" under emotional states and risk preference.

Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some individuals are more likely to have a greater inclination toward risk. The current study (N=212; 10-25y) examined the influence of emotional context on underlying functional brain connectivity over development and its impact on risk preference. Using functional imaging data in a neutral brain-state we first identify the "brain age" of a given individual then validate it with an independent measure of cortical thickness. We then show, on average, that "brain age" across the group during the teen years has the propensity to look younger in emotional contexts. Further, we show this phenotype (i.e. a younger brain age in emotional contexts) relates to a group mean difference in risk perception - a pattern exemplified greatest in young-adults (ages 18-21). The results are suggestive of a specified functional brain phenotype that relates to being at "risk to be risky.

Prediction of High-Grade Vesicoureteral Reflux after Pediatric Urinary Tract Infection: External Validation Study of Procalcitonin-Based Decision Rule

BACKGROUND: Predicting vesico-ureteral reflux (VUR) 653 at the time of the first urinary tract infection (UTI) would make it possible to restrict cystography to high-risk children. We previously derived the following clinical decision rule for that purpose: cystography should be performed in cases with ureteral dilation and a serum procalcitonin level 650.17 ng/mL, or without ureteral dilatation when the serum procalcitonin level 650.63 ng/mL. The rule yielded a 86% sensitivity with a 46% specificity. We aimed to test its reproducibility. STUDY DESIGN: A secondary analysis of prospective series of children with a first UTI. The rule was applied, and predictive ability was calculated. RESULTS: The study included 413 patients (157 boys, VUR 653 in 11%) from eight centers in five countries. The rule offered a 46% specificity (95% CI, 41-52), not different from the one in the derivation study. However, the sensitivity significantly decreased to 64% (95%CI, 50-76), leading to a difference of 20% (95%CI, 17-36). In all, 16 (34%) patients among the 47 with VUR 653 were misdiagnosed by the rule. This lack of reproducibility might result primarily from a difference between derivation and validation populations regarding inflammatory parameters (CRP, PCT); the validation set samples may have been collected earlier than for the derivation one. CONCLUSIONS: The rule built to predict VUR 653 had a stable specificity (ie. 46%), but a decreased sensitivity (ie. 64%) because of the time variability of PCT measurement. Some refinement may be warranted

Intraoperative Detektion der Neuroendokrinen Tumoren mit Hilfe der Gamma Sonde

L. Nichols' Waltzer - A87 - no date or location

Diagnosing the Ischaemic Heart Disease with Machine Learning

Ishaemic heart disease is one of the world's most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy and finally coronary angiography. The diagnostic process is stepwise and the results are interpreted hierarchically, i.e. the next step is necessary only if the results of the former are inconclusive. Because the suggestibility is possible, the results of each step are interpreted individually and only the results of the highest step are valid. On the other hand, Machine Learning methods may be able of objective interpretation of all available results for the same patient and in this way increase the diagnostic accuracy, sensitivity and specificity of each step. In the usual setting, the Machine Learning algorithms are tuned t..

Modeling the ocean and atmosphere during an extreme bora event in northern Adriatic using one-way and two-way atmosphere–ocean coupling

We have studied the performances of (a) a two-way coupled atmosphere–ocean modeling system and (b) one-way coupled ocean model (forced by the atmosphere model), as compared to the available in situ measurements during and after a strong Adriatic bora wind event in February 2012, which led to extreme air–sea interactions. The simulations span the period between January and March 2012. The models used were ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) (4.4 km resolution) on the atmosphere side and an Adriatic setup of Princeton ocean model (POM) (1°∕30 × 1°∕30 angular resolution) on the ocean side. The atmosphere–ocean coupling was implemented using the OASIS3-MCT model coupling toolkit. Two-way coupling ocean feedback to the atmosphere is limited to sea surface temperature. We have compared modeled atmosphere–ocean fluxes and sea temperatures from both setups to platform and CTD (conductivity, temperature, and depth) measurements from three locations in the northern Adriatic. We present objective verification of 2 m atmosphere temperature forecasts using mean bias and standard deviation of errors scores from 23 meteorological stations in the eastern part of Italy. We show that turbulent fluxes from both setups differ up to 20 % during the bora but not significantly before and after the event. When compared to observations, two-way coupling ocean temperatures exhibit a 4 times lower root mean square error (RMSE) than those from one-way coupled system. Two-way coupling improves sensible heat fluxes at all stations but does not improve latent heat loss. The spatial average of the two-way coupled atmosphere component is up to 0.3 °C colder than the one-way coupled setup, which is an improvement for prognostic lead times up to 20 h. Daily spatial average of the standard deviation of air temperature errors shows 0.15 °C improvement in the case of coupled system compared to the uncoupled. Coupled and uncoupled circulations in the northern Adriatic are predominantly wind-driven and show no significant mesoscale differences