Enhanced light-harvesting is an area of interest for optimising both natural photosynthesis and artificial solar energy capture 1,2. While iridescence has been shown to exist widely and in diverse forms in plants and other photosynthetic organisms and symbioses 3,4 , there has yet to be any direct link demonstrated between iridescence and photosynthesis. Here we show that epidermal chloroplasts, also known as iridoplasts, in shade-dwelling species of Begonia 5 , notable for their brilliant blue iridescence, have a photonic crystal structure formed from a periodic arrangement of the light-absorbing thylakoid tissue itself. This structure enhances photosynthesis in two ways: by increasing light capture at the predominantly green wavelengths available in shade conditions, and by directly enhancing quantum yield by 10-15% under low light conditions. These findings together imply that the iridoplast is a highly modified chloroplast structure adapted to make best use of the extremely low light conditions in the tropical forest understory in which it is found 5,6. A phylogenetically diverse range of shade-dwelling plant species have been found to produce similarly structured chloroplasts 7-9 , suggesting that the ability to produce chloroplasts whose

Fully quantitative CMR MBF pixel maps can be generated automatically, and the results agree well with manual quantification. These methods can discriminate regional perfusion variations and have high diagnostic performance for detecting significant CAD. (Technical Development of Cardiovascular Magnetic Resonance Imaging; NCT00027170).

BackgroundQuantitative assessment of myocardial blood flow (MBF) with first-pass perfusion cardiovascular magnetic resonance (CMR) requires a measurement of the arterial input function (AIF). This study presents an automated method to improve the objectivity and reduce processing time for measuring the AIF from first-pass perfusion CMR images. This automated method is used to compare the impact of different AIF measurements on MBF quantification.MethodsGadolinium-enhanced perfusion CMR was performed on a 1.5 T scanner using a saturation recovery dual-sequence technique. Rest and stress perfusion series from 270 clinical studies were analyzed. Automated image processing steps included motion correction, intensity correction, detection of the left ventricle (LV), independent component analysis, and LV pixel thresholding to calculate the AIF signal. The results were compared with manual reference measurements using several quality metrics based on the contrast enhancement and timing characteristics of the AIF. The median and 95 % confidence interval (CI) of the median were reported. Finally, MBF was calculated and compared in a subset of 21 clinical studies using the automated and manual AIF measurements.ResultsTwo clinical studies were excluded from the comparison due to a congenital heart defect present in one and a contrast administration issue in the other. The proposed method successfully processed 99.63 % of the remaining image series. Manual and automatic AIF time-signal intensity curves were strongly correlated with median correlation coefficient of 0.999 (95 % CI [0.999, 0.999]). The automated method effectively selected bright LV pixels, excluded papillary muscles, and required less processing time than the manual approach. There was no significant difference in MBF estimates between manually and automatically measured AIFs (p = NS). However, different sizes of regions of interest selection in the LV cavity could change the AIF measurement and affect MBF calculation (p = NS to p = 0.03).ConclusionThe proposed automatic method produced AIFs similar to the reference manual method but required less processing time and was more objective. The automated algorithm may improve AIF measurement from the first-pass perfusion CMR images and make quantitative myocardial perfusion analysis more robust and readily available.