Why does the apparent mass of a coronal mass ejection increase?

Mass is one of the most fundamental parameters characterizing the dynamics of a coronal mass ejection (CME). It has been found that CME apparent mass measured from the brightness enhancement in coronagraph images shows an increasing trend during its evolution in the corona. However, the physics behind it is not clear. Does the apparent mass gain come from the mass outflow from the dimming regions in the low corona, or from the pileup of the solar wind plasma around the CME when it propagates outwards from the Sun? We analyzed the mass evolution of six CME events. Their mass can increase by a factor of 1.6 to 3.2 from 4 to 15 Rs in the field of view (FOV) of the coronagraph on board the Solar Terrestrial Relations Observatory (STEREO). Over the distance about 7 to 15 Rs, where the coronagraph occulting effect can be negligible, the mass can increase by a factor of 1.3 to 1.7. We adopted the `snow-plough' model to calculate the mass contribution of the piled-up solar wind in the height range from about 7 to 15 Rs. For 2/3 of the events, the solar wind pileup is not sufficient to explain the measured mass increase. In the height range from about 7 to 15 Rs, the ratio of the modeled to the measured mass increase is roughly larger than 0.55. Although the ratios are believed to be overestimated, the result gives evidence that the solar wind pileup probably makes a non-negligible contribution to the mass increase. It is not clear yet whether the solar wind pileup is a major contributor to the final mass derived from coronagraph observations. However, our study suggests that the solar wind pileup plays increasingly important role in the mass increase as a CME moves further away from the Sun.Comment: 27 pages, 2 tables, 9 figures, accepted by Ap

Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement

AIMS The coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID-19 patients and to identify ECG parameters that are associated with cardiac involvement. METHODS AND RESULTS The study included patients who were hospitalized with COVID-19 diagnosis and had cardiac biomarker assessments and simultaneous 12-lead surface ECGs. Sixty-three hospitalized patients (median 53 [inter-quartile range, 43-65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre-existing co-morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38-4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03-1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age. CONCLUSIONS In COVID-19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification