The spatially resolved Kennicutt-Schmidt relation in the HI dominated regions of spiral and dwarf irregular galaxies

We study the Kennicutt-Schmidt relation between average star formation rate and average cold gas surface density in the Hi dominated ISM of nearby spiral and dwarf irregular galaxies. We divide the galaxies into grid cells varying from sub-kpc to tens of kpc in size. Grid-cell measurements of low SFRs using H-alpha emission can be biased and scatter may be introduced because of non-uniform sampling of the IMF or because of stochastically varying star formation. In order to alleviate these issues, we use far-ultraviolet emission to trace SFR, and we sum up the fluxes from different bins with the same gas surface density to calculate the average $\Sigma_{SFR}$ at a given value of $\Sigma_{gas}$. We study the resulting Kennicutt-Schmidt relation in 400 pc, 1 kpc and 10 kpc scale grids in nearby massive spirals and in 400 pc scale grids in nearby faint dwarf irregulars. We find a relation with a power law slope of 1.5 in the HI-dominated regions for both kinds of galaxies. The relation is offset towards longer gas consumption timescales compared to the molecular hydrogen dominated centres of spirals, but the offset is an order-of-magnitude less than that quoted by earlier studies. Our results lead to the surprising conclusion that conversion of gas to stars is independent of metallicity in the HI dominated regions of star-forming galaxies. Our observed relations are better fit by a model of star formation based on thermal and hydrostatic equilibrium in the ISM, in which feedback driven turbulence sets the thermal pressure.Comment: 11 pages, 7 figures, 5 tables. Accepted for publication in MNRAS Main Journal. For the definitive version visit http://mnras.oxfordjournals.org

Nonparametric Estimation of the Bivariate Recurrence Time Distribution

This paper considers statistical models in which two different types of events, such as the diagnosis of a disease and the remission of the disease, occur alternately over time and are observed subject to right censoring. We propose nonparametric estimators for the joint distribution of bivariate recurrence times and the marginal distribution of the first recurrence time. In general, the marginal distribution of the second recurrence time cannot be estimated due to an identifiability problem, but a conditional distribution of the second recurrence time can be estimated non-parametrically. In literature, statistical methods have been developed to estimate the joint distribution of bivariate recurrence times based on data of the first pair of censored bivariate recurrence times. These methods are efficient in the current model because recurrence times of higher orders are not used. Asymptotic properties of the estimators are established. Numerical studies demonstrate the estimator performs well with practical sample sizes. We apply the proposed method to a Denmark psychiatric case register data set for illustration of the methods and theory

Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data

Recurrent event data are commonly encountered in longitudinal follow-up studies related to biomedical science, econometrics, reliability, and demography. In many studies, recurrent events serve as important measurements for evaluating disease progression, health deterioration, or insurance risk. When analyzing recurrent event data, an independent censoring condition is typically required for the construction of statistical methods. Nevertheless, in some situations, the terminating time for observing recurrent events could be correlated with the recurrent event process and, as a result, the assumption of independent censoring is violated. In this paper, we consider joint modeling of a recurrent event process and a failure time in which a common subject-specific latent variable is used to model the association between the intensity of the recurrent event process and the hazard of the failure time. The proposed joint model is flexible in that no parametric assumptions on the distributions of censoring times and latent variables are made and, under the model, informative censoring is allowed for observing both the recurrent events and failure times. We propose a ‘borrow-strength estimation procedure’ by first estimating the value of the latent variable from recurrent event data, and next using the estimated value in the failure time model. Some interesting implications and trajectories of the proposed model will be presented. Properties of the regression parameter estimates and the estimated baseline cumulative hazard functions are also studied

BST2/CD317 counteracts human coronavirus 229E productive infection by tethering virions at the cell surface

AbstractBone marrow stromal antigen 2 (BST2), an interferon-inducible antiviral factor, has been shown to block the release of various enveloped viruses from cells. It has also been identified as an innate immune system component. Most enveloped viruses subject to BST2 restriction bud at the plasma membrane. Here we report our findings that (a) the production of human coronavirus 229E (HCoV-229E) progeny viruses, whose budding occurs at the ER-Golgi intermediate compartment (ERGIC), markedly decreases in the presence of BST2; and (b) BST2 knockdown expression results in enhanced HCoV-229E virion production. Electron microscopy analyses indicate that HCoV-229E virions are tethered to cell surfaces or intracellular membranes by BST2. Our results suggest that BST2 exerts a broad blocking effect against enveloped virus release, regardless of whether budding occurs at the plasma membrane or intracellular compartments