HST/NICMOS Observations of Massive Stellar Clusters Near the Galactic Center

We report Hubble Space Telescope (HST) Near-infrared Camera and Multi-object Spectrometer (NICMOS) observations of the Arches and Quintuplet clusters, two extraordinary young clusters near the Galactic Center. For the first time, we have identified main sequence stars in the Galactic Center with initial masses well below 10 Msun. We present the first determination of the initial mass function (IMF) for any population in the Galactic Center, finding an IMF slope which is significantly more positive (Gamma approx -0.65) than the average for young clusters elsewhere in the Galaxy (Gamma approx -1.4). The apparent turnoffs in the color-magnitude diagrams suggest cluster ages which are consistent with the ages implied by the mixture of spectral types in the clusters; we find tau(age) approx 2+/-1 Myr for the Arches cluster, and tau(age) approx 4+/-1 Myr for the Quintuplet. We estimate total cluster masses by adding the masses of observed stars down to the 50% completeness limit, and then extrapolating down to a lower mass cutoff of 1 Msun. Using this method, we find > 10^4 Msun for the total mass of the Arches cluster. Such a determination for the Quintuplet cluster is complicated by the double-valued mass-magnitude relationship for clusters with ages > 3 Myr. We find a lower limit of 6300 Msun for the total cluster mass, and suggest a best estimate of twice this value which accounts for the outlying members of the cluster. Both clusters have masses which place them as the two most massive clusters in the Galaxy.Comment: accepted by ApJ higher resolution versions of figures 1 and 2 can be found at: ftp://quintup.astro.ucla.edu/nicmos1

How to check a simulation study

Simulation studies are powerful tools in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings in which answers are already known. They should be coded in stages, with data-generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates surprisingly powerful tools. Failed estimation and outlying estimates should be identified and dealt with by changing data-generating mechanisms or coding realistic hybrid analysis procedures. Finally, we give a series of ideas that have been useful to us in the past for checking unexpected results. Following our advice may help to prevent errors and to improve the quality of published simulation studies

Multiple imputation for an incomplete covariate that is a ratio.

We are concerned with multiple imputation of the ratio of two variables, which is to be used as a covariate in a regression analysis. If the numerator and denominator are not missing simultaneously, it seems sensible to make use of the observed variable in the imputation model. One such strategy is to impute missing values for the numerator and denominator, or the log-transformed numerator and denominator, and then calculate the ratio of interest; we call this 'passive' imputation. Alternatively, missing ratio values might be imputed directly, with or without the numerator and/or the denominator in the imputation model; we call this 'active' imputation. In two motivating datasets, one involving body mass index as a covariate and the other involving the ratio of total to high-density lipoprotein cholesterol, we assess the sensitivity of results to the choice of imputation model and, as an alternative, explore fully Bayesian joint models for the outcome and incomplete ratio. Fully Bayesian approaches using Winbugs were unusable in both datasets because of computational problems. In our first dataset, multiple imputation results are similar regardless of the imputation model; in the second, results are sensitive to the choice of imputation model. Sensitivity depends strongly on the coefficient of variation of the ratio's denominator. A simulation study demonstrates that passive imputation without transformation is risky because it can lead to downward bias when the coefficient of variation of the ratio's denominator is larger than about 0.1. Active imputation or passive imputation after log-transformation is preferable

Planning a method for covariate adjustment in individually randomised trials: a practical guide

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. // Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. // Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. // Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely

Phases of methodological research in biostatistics—Building the evidence base for new methods

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems

Caenorhabditis elegans RIG-I Homolog Mediates Antiviral RNA Interference Downstream of Dicer-Dependent Biogenesis of Viral Small Interfering RNAs.

Dicer enzymes process virus-specific double-stranded RNA (dsRNA) into small interfering RNAs (siRNAs) to initiate specific antiviral defense by related RNA interference (RNAi) pathways in plants, insects, nematodes, and mammals. Antiviral RNAi in Caenorhabditis elegans requires Dicer-related helicase 1 (DRH-1), not found in plants and insects but highly homologous to mammalian retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs), intracellular viral RNA sensors that trigger innate immunity against RNA virus infection. However, it remains unclear if DRH-1 acts analogously to initiate antiviral RNAi in C. elegans Here, we performed a forward genetic screen to characterize antiviral RNAi in C. elegans Using a mapping-by-sequencing strategy, we uncovered four loss-of-function alleles of drh-1, three of which caused mutations in the helicase and C-terminal domains conserved in RLRs. Deep sequencing of small RNAs revealed an abundant population of Dicer-dependent virus-derived small interfering RNAs (vsiRNAs) in drh-1 single and double mutant animals after infection with Orsay virus, a positive-strand RNA virus. These findings provide further genetic evidence for the antiviral function of DRH-1 and illustrate that DRH-1 is not essential for the sensing and Dicer-mediated processing of the viral dsRNA replicative intermediates. Interestingly, vsiRNAs produced by drh-1 mutants were mapped overwhelmingly to the terminal regions of the viral genomic RNAs, in contrast to random distribution of vsiRNA hot spots when DRH-1 is functional. As RIG-I translocates on long dsRNA and DRH-1 exists in a complex with Dicer, we propose that DRH-1 facilitates the biogenesis of vsiRNAs in nematodes by catalyzing translocation of the Dicer complex on the viral long dsRNA precursors.IMPORTANCE The helicase and C-terminal domains of mammalian RLRs sense intracellular viral RNAs to initiate the interferon-regulated innate immunity against RNA virus infection. Both of the domains from human RIG-I can substitute for the corresponding domains of DRH-1 to mediate antiviral RNAi in C. elegans, suggesting an analogous role for DRH-1 as an intracellular dsRNA sensor to initiate antiviral RNAi. Here, we developed a forward genetic screen for the identification of host factors required for antiviral RNAi in C. elegans Characterization of four distinct drh-1 mutants obtained from the screen revealed that DRH-1 did not function to initiate antiviral RNAi. We show that DRH-1 acted in a downstream step to enhance Dicer-dependent biogenesis of viral siRNAs in C. elegans As mammals produce Dicer-dependent viral siRNAs to target RNA viruses, our findings suggest a possible role for mammalian RLRs and interferon signaling in the biogenesis of viral siRNAs

Radiological Pitfalls in Patients with Inducible Dynamic Proptosis

We report two patients presenting with marked clinical unilateral enophthalmos who had positional variability and dynamic proptosis on valsalva. On orbital imaging, enophthalmos was not documented and in fact, globe proptosis of the same side was reported for one of the patients. During CT and MRI scanning patients are often instructed to hold their breath to eliminate motion artefact. This may inadvertently induce dynamic proptosis. The radiological pitfalls of imaging patients with inducible dynamic proptosis and how to identify such patients are discussed

High Resolution Infrared Imaging and Spectroscopy of the Pistol Nebula: Evidence for Ejection

We present new NICMOS/HST infrared images and CGS4/UKIRT Br-alpha (4.05 um) spectroscopy of the Pistol Star and its associated nebula, finding strong evidence to support the hypothesis that the Pistol Nebula was ejected from the Pistol Star. The Pa-alpha NICMOS image shows that the nebula completely surrounds the Pistol Star, although the line intensity is much stronger on its northern and western edges. The Br-alpha spectra show the classical ring-like signature of quasi-spherical expansion, with weak blueshifted emission (V_max approx -60 km/s) and strong redshifted emission (V_max approx +10 km/s), where the velocities are with respect to the velocity of the Pistol Star; further, the redshifted emission appears to be "flattened" in the position-velocity diagram. These data suggest that the nebula was ejected from the star several thousand years ago, with a velocity between the current terminal velocity of the stellar wind (95 km/s) and the present expansion velocity of gas in the outer shell of the nebula (60 km/s). The Pa-alpha image reveals several emission-line stars in the region, including two newly-identified emission-line stars north of the Pistol Star with spectral types earlier than WC8 (T_eff > 50,000 K). The presence of these stars, the morphology of the Pa-alpha emission, and the velocity field in the gas suggest that the side of the nebula furthest from us is approaching, and being ionized by, the hot stars of the Quintuplet, and that the highest velocity redshifted gas has been decelerated by winds from the Quintuplet stars. We also discuss the possibility that the nebular gas might be magnetically confined by the ambient magnetic field delineated by the nearby nonthermal filaments.Comment: Figure 1 is included as a JPG file. Figure 1 and 2 also available at ftp://quintup.astro.ucla.edu/pistol2

artcat: Sample-size calculation for an ordered categorical outcome

We describe a new command, artcat, that calculates sample size or power for a randomized controlled trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional-odds model. artcat implements the method of Whitehead (1993, Statistics in Medicine 12: 2257–2271). We also propose and implement a new method that 1) allows the user to specify a treatment effect that does not obey the proportional-odds assumption, 2) offers greater accuracy for large treatment effects, and 3) allows for noninferiority trials. We illustrate the command and explore the value of an ordered categorical outcome over a binary outcome in various settings. We show by simulation that the methods perform well and that the new method is more accurate than Whitehead’s method