A short training program improves the accuracy of portion-size estimates in future dietitians

[EN] The objective of this study was to determine whether a short training program, using real foods, would decreased their portion-size estimation errors after training. 90 student volunteers (20.18±0.44 y old) of the University of the Basque Country (Spain) were trained in observational techniques and tested in food-weight estimation during and after a 3-hour training period. The program included 57 commonly consumed foods that represent a variety of forms (125 different shapes). Estimates of food weight were compared with actual weights. Effectiveness of training was determined by examining change in the absolute percentage error for all observers and over all foods over time. Data were analyzed using SPSS vs. 13.0. The portion-size errors decreased after training for most of the foods. Additionally, the accuracy of their estimates clearly varies by food group and forms. Amorphous was the food type estimated least accurately both before and after training. Our findings suggest that future dietitians can be trained to estimate quantities by direct observation across a wide range of foods. However this training may have been too brief for participants to fully assimilate the application.

Fitting BICEP2 with defects, primordial gravitational waves and dust

In this work we discuss the possibility of cosmic defects being responsible for the B-mode signal measured by the BICEP2 collaboration. We also allow for the presence of other cosmological sources of B-modes such as inflationary gravitational waves and polarized dust foregrounds, which might contribute to or dominate the signal. On the one hand, we find that defects alone give a poor fit to the data points. On the other, we find that defects help to improve the fit at higher multipoles when they are considered alongside inflationary gravitational waves or polarized dust. Finally, we derive new defect constraints from models combining defects and dust. This proceeding is based on previous works [1, 2].