C oronary CT angiography has proven prognostic value for cardiac events (1-6). It depicts vessel lumen and wall characteristics including stenoses, remodeling, plaque thickness, and degree of calcification (7). Imaging improves prognostic accuracy beyond that offered by traditional risk estimation methods, and data suggest that it might be useful for primary assessment of coronary risk under some circumstances (8-10). A practical problem has been how to score atherosclerotic features for use in prognosis estimation models. The most common approach is to divide the coronary tree into 16 segments and then score each segment according to certain simple criteria (11-13). For example, the segmental plaque score scores the amount of plaque from 0 to 3 for each segment and takes the sum. The Coronary Artery Disease Reporting and Data System (CAD-RADS), a standardized reporting system, was recently introduced for clinical use (14). These scoring systems are necessarily an abstraction from the underlying pathologic condition, and there is the chance of discarding useful information along the way. Machine learning can explore a large number of possible models and construct a good model without overlooking important input features or including unnecessary ones (15). In this study, patients were followed after coronary CT angiography for the occurrence of death and myocardial infarction. The hypothesis was that machine learning, compared with conventional scoring systems, could find a combination of arterial features that better discriminated patients who did not experience an adverse event from those who did. We analyzed data as summarized by the reading radiologists (ie, from human visual analysis, not

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A space-resolving flux detector (SRFD) is developed to measure the X-ray flux emitted from a specified region in hohlraum with a high resolution up to 0.11mm for the first time. This novel detector has been used successfully to measure the distinct X-ray fluxes emitted from hot laser spot and cooler re-emitting region simultaneously, in the hohlraum experiments on SGIII prototype laser facility. According to our experiments, the ratio of laser spot flux to re-emitted flux shows a strong time-dependent behavior, and the area-weighted flux post-processed from the measured laser spot flux and re-emitting wall flux agrees with that measured from Laser Entrance Hole by using flat-response X-ray detector (F-XRD). The experimental observations is reestablished by our two-dimensional hydrodynamic simulations and is well understood with the power balance relationship.

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