Greenland records of aerosol source and atmospheric lifetime changes from the Eemian to the Holocene.

The Northern Hemisphere experienced dramatic changes during the last glacial, featuring vast ice sheets and abrupt climate events, while high northern latitudes during the last interglacial (Eemian) were warmer than today. Here we use high-resolution aerosol records from the Greenland NEEM ice core to reconstruct the environmental alterations in aerosol source regions accompanying these changes. Separating source and transport effects, we find strongly reduced terrestrial biogenic emissions during glacial times reflecting net loss of vegetated area in North America. Rapid climate changes during the glacial have little effect on terrestrial biogenic aerosol emissions. A strong increase in terrestrial dust emissions during the coldest intervals indicates higher aridity and dust storm activity in East Asian deserts. Glacial sea salt aerosol emissions in the North Atlantic region increase only moderately (50%), likely due to sea ice expansion. Lower aerosol concentrations in Eemian ice compared to the Holocene are mainly due to shortened atmospheric residence time, while emissions changed little.

ABC stenosis morphology classification and outcome of coronary angioplasty: reassessment with computing techniques.

BACKGROUND: The American College of Cardiology/American Heart Association (ACC/AHA) stenosis morphology classification (MC) stratifies coronary lesions for probability of success and complications after coronary angioplasty (PTCA). Modern computing techniques were used to evaluate the individual predictive value of MC in random PTCA cases. METHODS AND RESULTS: MC was attributed to the target lesions by consensus of 2 observers. The predictive value regarding procedural success (PS) and major adverse cardiac events (MACE) of MC was analyzed by conventional logistic regression analyses and by inductive machine learning models. The study was adequately powered for the methods applied with 325 target lesions of 250 cases. Overall, PS decreased and MACE increased from type A to type C lesions. Regression analysis identified no single factor as predictive. Logistic regression showed an error rate of 42%. Machine learning techniques achieved an individual predictive error of only 10%, which could be further reduced to 2% by addition of parameters. For PS, MC parameters showed a high ranking for building the model. For MACE, variables of the medical history showed more impact. CONCLUSIONS: MC per se cannot individually predict PS or MACE. However, when all MC parameters are integrated together with additional lesion-specific and history variables, a high individual predictive value can be achieved. This technique may be clinically helpful for risk stratification in the catheterization laboratory and improvement of classification systems in interventional cardiology.