A dusty pinwheel nebula around the massive star WR 104

Wolf-Rayet (WR) stars are luminous massive blue stars thought to be immediate precursors to the supernova terminating their brief lives. The existence of dust shells around such stars has been enigmatic since their discovery some 30 years ago; the intense radiation field from the star should be inimical to dust survival. Although dust-creation models, including those involving interacting stellar winds from a companion star, have been put forward, high-resolution observations are required to understand this phenomena. Here we present resolved images of the dust outflow around Wolf-Rayet WR 104, obtained with novel imaging techniques, revealing detail on scales corresponding to about 40 AU at the star. Our maps show that the dust forms a spatially confined stream following precisely a linear (or Archimedian) spiral trajectory. Images taken at two separate epochs show a clear rotation with a period of 220 +/- 30 days. Taken together, these findings prove that a binary star is responsible for the creation of the circumstellar dust, while the spiral plume makes WR 104 the prototype of a new class of circumstellar nebulae unique to interacting wind systems.Comment: 7 pages, 2 figures, Appearing in Nature (1999 April 08

Bayesian astrostatistics: a backward look to the future

This perspective chapter briefly surveys: (1) past growth in the use of Bayesian methods in astrophysics; (2) current misconceptions about both frequentist and Bayesian statistical inference that hinder wider adoption of Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian modeling as a major future direction for research in Bayesian astrostatistics, exemplified in part by presentations at the first ISI invited session on astrostatistics, commemorated in this volume. It closes with an intentionally provocative recommendation for astronomical survey data reporting, motivated by the multilevel Bayesian perspective on modeling cosmic populations: that astronomers cease producing catalogs of estimated fluxes and other source properties from surveys. Instead, summaries of likelihood functions (or marginal likelihood functions) for source properties should be reported (not posterior probability density functions), including nontrivial summaries (not simply upper limits) for candidate objects that do not pass traditional detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistics. Version 2 has minor clarifications and an additional referenc

Optimal gridding and degridding in radio interferometry imaging

In radio interferometry imaging, the gridding procedure of convolving visibilities with a chosen gridding function is necessary to transform visibility values into uniformly sampled grid points. We propose here a parameterised family of "least-misfit gridding functions" which minimise an upper bound on the difference between the DFT and FFT dirty images for a given gridding support width and image cropping ratio. When compared with the widely used spheroidal function with similar parameters, these provide more than 100 times better alias suppression and RMS misfit reduction over the usable dirty map. We discuss how appropriate parameter selection and tabulation of these functions allow for a balance between accuracy, computational cost and storage size. Although it is possible to reduce the errors introduced in the gridding or degridding process to the level of machine precision, accuracy comparable to that achieved by CASA requires only a lookup table with 300 entries and a support width of 3, allowing for a greatly reduced computation cost for a given performance

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

MEM and CLEAN Imaging of VLBA Polarisation Observations of Compact Active Galactic Nuclei

The Maximum Entropy Method (MEM) for the deconvolution of radio interferometry images is mathematically well based and presents a number of advantages over the usual CLEAN deconvolution, such as appreciably higher resolution. The application of MEM for polarisation imaging remains relatively little studied. CLEAN and MEM intensity and polarisation techniques are discussed in application to recently obtained 18cm VLBA polarisation data for a sample of Active Galactic Nuclei.Comment: From the proceedings of Beamed and Unbeamed Gamma-Rays from Galaxies, April 11-15, 2011, Muonio, Finland. 6 pages, 3 figure

In-cell NMR characterization of the secondary structure populations of a disordered conformation of α-Synuclein within E. coli cells

α-Synuclein is a small protein strongly implicated in the pathogenesis of Parkinson’s disease and related neurodegenerative disorders. We report here the use of in-cell NMR spectroscopy to observe directly the structure and dynamics of this protein within E. coli cells. To improve the accuracy in the measurement of backbone chemical shifts within crowded in-cell NMR spectra, we have developed a deconvolution method to reduce inhomogeneous line broadening within cellular samples. The resulting chemical shift values were then used to evaluate the distribution of secondary structure populations which, in the absence of stable tertiary contacts, are a most effective way to describe the conformational fluctuations of disordered proteins. The results indicate that, at least within the bacterial cytosol, α-synuclein populates a highly dynamic state that, despite the highly crowded environment, has the same characteristics as the disordered monomeric form observed in aqueous solution

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes

A MAP6-Related Protein Is Present in Protozoa and Is Involved in Flagellum Motility

In vertebrates the microtubule-associated proteins MAP6 and MAP6d1 stabilize cold-resistant microtubules. Cilia and flagella have cold-stable microtubules but MAP6 proteins have not been identified in these organelles. Here, we describe TbSAXO as the first MAP6-related protein to be identified in a protozoan, Trypanosoma brucei. Using a heterologous expression system, we show that TbSAXO is a microtubule stabilizing protein. Furthermore we identify the domains of the protein responsible for microtubule binding and stabilizing and show that they share homologies with the microtubule-stabilizing Mn domains of the MAP6 proteins. We demonstrate, in the flagellated parasite, that TbSAXO is an axonemal protein that plays a role in flagellum motility. Lastly we provide evidence that TbSAXO belongs to a group of MAP6-related proteins (SAXO proteins) present only in ciliated or flagellated organisms ranging from protozoa to mammals. We discuss the potential roles of the SAXO proteins in cilia and flagella function

BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors

<p>Abstract</p> <p>Background</p> <p>We have incorporated Bayesian model regularization with biophysical modeling of protein-DNA interactions, and of genome-wide nucleosome positioning to study protein-DNA interactions, using a high-throughput dataset. The newly developed method (BayesPI) includes the estimation of a transcription factor (TF) binding energy matrices, the computation of binding affinity of a TF target site and the corresponding chemical potential.</p> <p>Results</p> <p>The method was successfully tested on synthetic ChIP-chip datasets, real yeast ChIP-chip experiments. Subsequently, it was used to estimate condition-specific and species-specific protein-DNA interaction for several yeast TFs.</p> <p>Conclusion</p> <p>The results revealed that the modification of the protein binding parameters and the variation of the individual nucleotide affinity in either recognition or flanking sequences occurred under different stresses and in different species. The findings suggest that such modifications may be adaptive and play roles in the formation of the environment-specific binding patterns of yeast TFs and in the divergence of TF binding sites across the related yeast species.</p