Normal type Ia supernovae from violent mergers of white dwarf binaries

One of the most important questions regarding the progenitor systems of Type Ia supernovae (SNe Ia) is whether mergers of two white dwarfs can lead to explosions that reproduce observations of normal events. Here we present a fully three-dimensional simulation of a violent merger of two carbon-oxygen white dwarfs with masses of 0.9 M and 1.1 M combining very high resolution and exact initial conditions. A well-tested combination of codes is used to study the system. We start with the dynamical inspiral phase and follow the subsequent thermonuclear explosion under the plausible assumption that a detonation forms in the process of merging. We then perform detailed nucleosynthesis calculations and radiative transfer simulations to predict synthetic observables from the homologously expanding supernova ejecta. We find that synthetic color light curves of our merger, which produces about 0.62 M of Ni, show good agreement with those observed for normal SNe Ia in all wave bands from U to K. Line velocities in synthetic spectra around maximum light also agree well with observations. We conclude that violent mergers of massive white dwarfs can closely resemble normal SNe Ia. Therefore, depending on the number of such massive systems available these mergers may contribute at least a small fraction to the observed population of normal SNe Ia. © 2012 The American Astronomical Society. All rights reserved.

On the brightness distribution of type ia supernovae from violent white dwarf mergers

We investigate the brightness distribution expected for thermonuclear explosions that might result from the ignition of a detonation during the violent merger of white dwarf (WD) binaries. Violent WD mergers are a subclass of the canonical double degenerate scenario where two carbon-oxygen (CO) WDs merge when the larger WD fills its Roche lobe. Determining their brightness distribution is critical for evaluating whether such an explosion model could be responsible for a significant fraction of the observed population of Type Ia supernovae (SNe Ia). We argue that the brightness of an explosion realized via the violent merger model is mainly determined by the mass of Ni produced in the detonation of the primary COWD. To quantify this link, we use a set of sub-Chandrasekhar mass WD detonation models to derive a relationship between primary WD mass (m) and expected peak bolometric brightness (M). We use this m-M relationship to convert the masses of merging primary WDs from binary population models to a predicted distribution of explosion brightness. We also investigate the sensitivity of our results to assumptions about the conditions required to realize a detonation during violent mergers ofWDs. We find a striking similarity between the shape of our theoretical peak-magnitude distribution and that observed for SNe Ia: our model produces a M distribution that roughly covers the range and matches the shape of the one observed for SNe Ia. However, this agreement hinges on a particular phase of mass accretion during binary evolution: the primary WD gains ~0.15-0.35M? from a slightly evolved helium star companion. In our standard binary evolution model, such an accretion phase is predicted to occur for about 43 per cent of all binary systems that ultimately give rise to binary CO WD mergers. We also find that with high probability, violent WD mergers involving the most massive primaries (?1.3M?, which should produce bright SNe) have delay times ?500 Myr. © 2012 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.

The developmental character of cardiac autonomic responses to an acute noxious event in 4- and 8-month-old healthy infants

Heart rate (HR) has been widely studied as a measure of an individual's response to painful stimuli. It remains unclear whether changes in mean HR or the variability of HR are specifically related to the noxious stimulus (i.e. pain). Neither is it well understood how such changes reflect underlying neurologic control mechanisms that produce these responses, or how these mechanisms change during the first year of life. To study the changes in cardiac autonomic modulation that occur with acute pain and with age during early infancy, the relationship between respiratory activity and short-term variations of HR (i.e. respiratory sinus arrhythmia) was quantified in a longitudinal study of term born healthy infants who underwent a finger lance blood collection at 4 months of age (n = 24) and again at 8 months of age (n = 20). Quantitative respiratory activity and HR were obtained during baseline, lance, and recovery periods. Time and frequency domain analyses from 2.2-min epochs of data yielded mean values, spectral measures of low (0.04-0.15 Hz) and high (0.15-0.80 Hz) frequency power (LF and HF), and the LF/HF ratio. To determine sympathetic and parasympathetic cardiac activity, the transfer relation between respiration and HR was used. At both 4 and 8 months, mean HR increased significantly with the noxious event (p > 0.01). There were age-related differences in the pattern of LF, HF, and LF/HF ratio changes. Although these parameters all decreased (p > 0.01) at 4 months, LF and LF/HF increased at 8 months and at 8 months HF remained stable in response to the noxious stimulus. Transfer gain changes with the lance demonstrated a change from predominant vagal baseline to a sympathetic condition at both ages. The primary finding of this study is that a response to an acute noxious stimulus appears to produce an increase in respiratory-related sympathetic HR control and a significant decrease in respiratory-related parasympathetic control at both 4 and 8 months. Furthermore, with increasing age, the sympathetic and parasympathetic changes appear to be less intense, but more sustained.

Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations

Background. From the mid-1980s to mid-1990s, the WHO MONICA Project monitored coronary events and classic risk factors for coronary heart disease (CHD) in 38 populations from 21 countries. We assessed the extent to which changes in these risk factors explain the variation in the trends in coronary-event rates, across the populations. Methods. In men and women aged 35-64 years, non-fatal myocardial infarction and coronary deaths were registered continuously to assess trends in rates of coronary events. We carried out population surveys to estimate trends in risk factors. Trends in event rates were regressed on trends in risk score and in individual risk factors. Findings. Smoking rates decreased in most male populations but trends were mixed in women; mean blood pressures and cholesterol concentrations decreased, body-mass index increased, and overall risk scores and coronary-event rates decreased. The model of trends in 10-year coronary-event rates against risk scores and single risk factors showed a poor fit, but this was improved with a 4-year time lag for coronary events. The explanatory power of the analyses was limited by imprecision of the estimates and homogeneity of trends in the study populations. Interpretation. Changes in the classic risk factors seem to partly explain the variation in population trends in CHD. Residual variance is attributable to difficulties in measurement and analysis, including time lag, and to factors that were not included, such as medical interventions. The results support prevention policies based on the classic risk factors but suggest potential for prevention beyond these.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.