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Purpose: Image‐guided radiotherapy (IGRT) improves tumor control but its intensive use may entrain late side effects caused by the additional imaging doses. There is a need to better quantify the additional imaging doses, so they can be integrated in the therapeutic workflow. Currently, no dedicated software enables to compute patient‐specific imaging doses on a wide range of systems and protocols. As a first step toward this objective, we propose a common methodology to model four different kV‐imaging systems used in radiotherapy (Varian's OBI, Elekta's XVI, Brainlab's ExacTrac, and Accuray's Cyberknife) using a new type of virtual source model based on Monte Carlo calculations. Methods: We first describe our method to build a simplified description of the photon output, or virtual source models (VSMs), of each imaging system. Instead of being constructed using measurement data, as it is most commonly the case, our VSM is used as the summary of the phase‐space files (PSFs) resulting from a first Monte Carlo simulation of the considered x‐ray tube. Second, the VSM is used as a photon generator for a second MC simulation in which we compute the dose. Then, the proposed VSM is thoroughly validated against standard MC simulation using PSFs on the XVI system. Last, each modeled system is compared to profiles and depth‐dose‐curve measurements performed in homogeneous phantom. Results: Comparisons between PSF‐based and VSM‐based calculations highlight that VSMs could provide equivalent dose results (within 1% of difference) than PSFs inside the imaging field‐of‐view (FOV). In contrast, VSMs tend to underestimate (for up to 20%) calculated doses outside of the imaging FOV due to the assumptions underlying the VSM construction. In addition, we showed that the use of VSMs allows reducing calculation time by at least a factor of 2.8. Indeed, for identical simulation times, statistical uncertainties on dose distributions computed using VSMs were much lower than those obtained from PSF‐based calculations. Conclusions: For each of the four imaging systems, VSMs were successfully validated against measurements in homogeneous phantoms, and are therefore ready to be used for future preclinical studies in heterogeneous or anthropomorphic phantoms. The cross system modeling methodology developed here should enable, later on, to estimate precisely and accurately patient‐specific 3D dose maps delivered during a large range of kV‐imaging procedures. [ABSTRACT FROM AUTHOR]

Background Evidences have suggested that larger utensils may provoke 'size-contrast illusions', influencing the perceived volume and food consumption. Objective To analyse the influence of plate size on the visual estimate of food portion size. Methods Two 400 g portions of pasta with tomato sauce were presented on two plates of different diameters (24.0 and 9.0 cm). Each participant visually estimated on an individual basis the quantities of the pasta portions (g) present on each plate. In addition, each subject classified the size of the portions on each plate as 'small', 'medium' and 'large'. The mean estimates of the amount of pasta on each plate were compared by the nonparametric Mann-Whitney. The differences in the frequencies of portion classifications between plates were evaluated by the chi-squared test. Results Forty-eight students (average 25.8 ± 8.9 years) participated in the study. There was no difference in the median amount of pasta estimated for the large and small plates (150 g; range 50-500 and 115 g; range 40-500 g, respectively). The classification of the portion size as 'large' was reported by a significantly greater number of persons when they evaluated the amount of pasta arranged on the large plate compared to the small plate (47.9 versus 22.9%, respectively; P = 0.018). Conclusion The size of the plate did not influence the estimate of food portions, even though it did influence the classification of portion size. [ABSTRACT FROM AUTHOR]

This article presents information on the registration of Carrizalito small red bean. Small red dry bean Carrizalito was developed at the Escuela Agricola Panamericana in Zamorano, Honduras, and released for Honduras in 2003 and Costa Rica in 2004, in collaboration with the National Bean Programs of Honduras and Costa Rica, and the University of Puerto Rico. Carrizalito is a high yielding, disease resistant cultivar, adapted to the midaltitude bean production regions of Central America. In 2004, Carrizalito was mechanically inoculated at the University of Puerto Rico with the NL3 strain of Bean common mosaic necrotic virus (BCMV) and found to have top necrosis caused by the presence of the dominant I gene for resistance to BCMV. Ashy stem blight is favored by warm dry conditions associated with drought stress. Seedlings of Carrizalito were inoculated in the greenhouse at the UPR with the ashy stem blight pathogen Macrophominaphaseolina and found to be resistant. Carrizalito has an indeterminate upright, Type III growth habit with a short vine. Carrizalito flowers in 35 to 37 d and matures in 68 to 70 d.