The structure of RuO(2)(110) and the mechanism for catalytic carbon monoxide oxidation on this surface were studied by low-energy electron diffraction, scanning tunneling microscopy, and density-functional calculations. The RuO(2)(110) surface exposes bridging oxygen atoms and ruthenium atoms not capped by oxygen. The latter act as coordinatively unsaturated sites-a hypothesis introduced long ago to account for the catalytic activity of oxide surfaces-onto which carbon monoxide can chemisorb and from where it can react with neighboring lattice-oxygen to carbon dioxide. Under steady-state conditions, the consumed lattice-oxygen is continuously restored by oxygen uptake from the gas phase. The results provide atomic-scale verification of a general mechanism originally proposed by Mars and van Krevelen in 1954 and are likely to be of general relevance for the mechanism of catalytic reactions at oxide surfaces.

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Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. Methods— This was a retrospective study using a prospective cohort that enrolled patients with acute ischemic stroke. Favorable outcome was defined as modified Rankin Scale score 0, 1, or 2 at 3 months. We developed 3 machine learning models (deep neural network, random forest, and logistic regression) and compared their predictability. To evaluate the accuracy of the machine learning models, we also compared them to the Acute Stroke Registry and Analysis of Lausanne (ASTRAL) score. Results— A total of 2604 patients were included in this study, and 2043 (78%) of them had favorable outcomes. The area under the curve for the deep neural network model was significantly higher than that of the ASTRAL score (0.888 versus 0.839; P <0.001), while the areas under the curves of the random forest (0.857; P =0.136) and logistic regression (0.849; P =0.413) models were not significantly higher than that of the ASTRAL score. Using only the 6 variables that are used for the ASTRAL score, the performance of the machine learning models did not significantly differ from that of the ASTRAL score. Conclusions— Machine learning algorithms, particularly the deep neural network, can improve the prediction of long-term outcomes in ischemic stroke patients.

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RS was independently associated with good outcomes without increasing symptomatic intracranial hemorrhage or mortality. RS seemed considered in MT-failed internal carotid artery or middle cerebral artery M1 occlusion.