MATERIALS AND METHODS: Prospectively ECG-triggered CCTA was performed using five commercially available CT scanners: 64-detector-row single source CT (SSCT), 2 × 32-detector-row-dual source CT (DSCT), 2 × 64-detector-row DSCT and 320-detector-row SSCT scanners. Absorbed doses were measured in 34 organs using pre-calibrated optically stimulated luminescence dosimeters (OSLDs) placed inside a standard female adult anthropomorphic phantom. HE was calculated from the measured organ doses and compared to the HE derived from the air kerma-length product (PKL) using the conversion coefficient of 0.014 mSv∙mGy-1∙cm-1 for the chest region.
RESULTS: Both breasts and lungs received the highest radiation dose during CCTA examination. The highest HE was received from 2 × 32-detector-row DSCT scanner (6.06 ± 0.72 mSv), followed by 64-detector-row SSCT (5.60 ± 0.68 and 5.02 ± 0.73 mSv), 2 × 64-detector-row DSCT (1.88 ± 0.25 mSv) and 320-detector-row SSCT (1.34 ± 0.48 mSv) scanners. HE calculated from the measured organ doses were about 38 to 53% higher than the HE derived from the PKL-to-HE conversion factor.
CONCLUSION: The radiation doses received from a prospectively ECG-triggered CCTA are relatively small and are depending on the scanner technology and imaging protocols. HE as low as 1.34 and 1.88 mSv can be achieved in prospectively ECG-triggered CCTA using 320-detector-row SSCT and 2 × 64-detector-row DSCT scanners.
RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.
CONCLUSIONS: Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .
SIGNIFICANCE: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.