Determining Parameters of Cool Giant Stars by Modeling Spectrophotometric and Interferometric Observations Using the SAtlas Program

Context: Optical interferometry is a powerful tool for observing the intensity structure and angular diameter of stars. When combined with spectroscopy and/or spectrophotometry, interferometry provides a powerful constraint for model stellar atmospheres. Aims: The purpose of this work is to test the robustness of the spherically symmetric version of the Atlas stellar atmosphere program, SAtlas, using interferometric and spectrophotometric observations. Methods: Cubes (three dimensional grids) of model stellar atmospheres, with dimensions of luminosity, mass, and radius, are computed to fit observations for three evolved giant stars, \psi Phoenicis, \gamma Sagittae, and \alpha Ceti. The best-fit parameters are compared with previous results. Results: The best-fit angular diameters and values of \chi^2 are consistent with predictions using Phoenix and plane-parallel Atlas models. The predicted effective temperatures, using SAtlas, are about 100 to 200 K lower, and the predicted luminosities are also lower due to the differences in effective temperatures. Conclusions: It is shown that the SAtlas program is a robust tool for computing models of extended stellar atmospheres that are consistent with observations. The best-fit parameters are consistent with predictions using Phoenix models, and the fit to the interferometric data for \psi Phe differs slightly, although both agree within the uncertainty of the interferometric observations.Comment: 5 pages, 6 figures, Accepted for publication in A&A as a Research Not

Radio and IR interferometry of SiO maser stars

Radio and infrared interferometry of SiO maser stars provide complementary information on the atmosphere and circumstellar environment at comparable spatial resolution. Here, we present the latest results on the atmospheric structure and the dust condensation region of AGB stars based on our recent infrared spectro-interferometric observations, which represent the environment of SiO masers. We discuss, as an example, new results from simultaneous VLTI and VLBA observations of the Mira variable AGB star R Cnc, including VLTI near- and mid-infrared interferometry, as well as VLBA observations of the SiO maser emission toward this source. We present preliminary results from a monitoring campaign of high-frequency SiO maser emission toward evolved stars obtained with the APEX telescope, which also serves as a precursor of ALMA images of the SiO emitting region. We speculate that large-scale long-period chaotic motion in the extended molecular atmosphere may be the physical reason for observed deviations from point symmetry of atmospheric molecular layers, and for the observed erratic variability of high-frequency SiO maser emissionComment: 8 pages, 4 figures, submitted to Proc. IAU Symp. 287 "Cosmic masers - from OH to H_0", R.S. Booth, E.M.L. Humphreys, W.H.T. Vlemmings (eds.), invited pape

Novel Methods for Multivariate Ordinal Data applied to Genetic Diplotypes, Genomic Pathways, Risk Profiles, and Pattern Similarity

Introduction: Conventional statistical methods for multivariate data (e.g., discriminant/regression) are based on the (generalized) linear model, i.e., the data are interpreted as points in a Euclidian space of independent dimensions. The dimensionality of the data is then reduced by assuming the components to be related by a specific function of known type (linear, exponential, etc.), which allows the distance of each point from a hyperspace to be determined. While mathematically elegant, these approaches may have shortcomings when applied to real world applications where the relative importance, the functional relationship, and the correlation among the variables tend to be unknown. Still, in many applications, each variable can be assumed to have at least an “orientation”, i.e., it can reasonably assumed that, if all other conditions are held constant, an increase in this variable is either “good” or “bad”. The direction of this orientation can be known or unknown. In genetics, for instance, having more “abnormal” alleles may increase the risk (or magnitude) of a disease phenotype. In genomics, the expression of several related genes may indicate disease activity. When screening for security risks, more indicators for atypical behavior may constitute raise more concern, in face or voice recognition, more indicators being similar may increase the likelihood of a person being identified. Methods: In 1998, we developed a nonparametric method for analyzing multivariate ordinal data to assess the overall risk of HIV infection based on different types of behavior or the overall protective effect of barrier methods against HIV infection. By using u-statistics, rather than the marginal likelihood, we were able to increase the computational efficiency of this approach by several orders of magnitude. Results: We applied this approach to assessing immunogenicity of a vaccination strategy in cancer patients. While discussing the pitfalls of the conventional methods for linking quantitative traits to haplotypes, we realized that this approach could be easily modified into to a statistically valid alternative to a previously proposed approaches. We have now begun to use the same methodology to correlate activity of anti-inflammatory drugs along genomic pathways with disease severity of psoriasis based on several clinical and histological characteristics. Conclusion: Multivariate ordinal data are frequently observed to assess semiquantitative characteristics, such as risk profiles (genetic, genomic, or security) or similarity of pattern (faces, voices, behaviors). The conventional methods require empirical validation, because the functions and weights chosen cannot be justified on theoretical grounds. The proposed statistical method for analyzing profiles of ordinal variables, is intrinsically valid. Since no additional assumptions need to be made, the often time-consuming empirical validation can be skipped.ranking; nonparametric; robust; scoring; multivariate

From the atmosphere to the circumstellar environment in cool evolved stars

We discuss and illustrate contributions that optical interferometry has made on our current understanding of cool evolved stars. We include red giant branch (RGB) stars, asymptotic giant branch (AGB) stars, and red supergiants (RSGs). Studies using optical interferometry from visual to mid-infrared wavelengths have greatly increased our knowledge of their atmospheres, extended molecular shells, dust formation, and winds. These processes and the morphology of the circumstellar environment are important for the further evolution of these stars toward planetary nebulae (PNe) and core-collapse supernovae (SNe), and for the return of material to the interstellar medium.Comment: To appear in the Book of the VLTI School 2013, held 9-21 Sep 2013 Barcelonnette (France), "What the highest angular resolution can bring to stellar astrophysics?", Ed. Millour, Chiavassa, Bigot, Chesneau, Meilland, Stee, EAS Publications Series (2015

Towards Novel Nonparametric Statistical Methods and Bioinformatics Tools for Clinical and Translational Sciences

As the field of functional genetics and genomics is beginning to mature, we become confronted with new challenges. The constant drop in price for sequencing and gene expression profiling as well as the increasing number of genetic and genomic variables that can be measured makes it feasible to address more complex questions. The success with rare diseases caused by single loci or genes has provided us with a proof-of-concept that new therapies can be developed based on functional genomics and genetics. Common diseases, however, typically involve genetic epistasis, genomic pathways, and proteomic pattern. Moreover, to better understand the underlying biologi-cal systems, we often need to integrate information from several of these sources. Thus, as the field of clinical research moves toward complex diseases, the demand for modern data base systems and advanced statistical methods increases. The traditional statistical methods implemented in most of the bioinformatics tools currently used in the novel field of genetics and functional genomics are based on the linear model and, thus, have shortcomings when applied to nonlinear biological systems. The previous work on partially ordered data (Wittkowski 1988; 1992), when combined with theoretical results (Hoeffding 1948) and computational strategies (Deuchler 1914) has opened a new field of nonparametric statistics. With grid technology, new tools are now feasible when screening for interactions between genetics (Wittkowski, Liu 2002) and functional genomics (Wittkowski, Lee 2004). Having more complex study designs and more specific methods available increases the demand for decision support when selecting appropriate bioinformatics tools. With the advent of rapid prototyping systems for Web based database application, we have recently begun to complement previous work on knowledge based systems with graphical Web-based tools for acquisition of DESIGN and MODEL knowledge.Biostatistics Bioinformatics NIH NCRR ROADMAP