University of Vienna

About: University of Vienna is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Population & Context (language use). The organization has 44686 authors who have published 95840 publications receiving 2907492 citations.

Complete nitrification by Nitrospira bacteria

Abstract: Nitrification, the oxidation of ammonia via nitrite to nitrate, has always been considered to be a two-step process catalysed by chemolithoautotrophic microorganisms oxidizing either ammonia or nitrite. No known nitrifier carries out both steps, although complete nitrification should be energetically advantageous. This functional separation has puzzled microbiologists for a century. Here we report on the discovery and cultivation of a completely nitrifying bacterium from the genus Nitrospira, a globally distributed group of nitrite oxidizers. The genome of this chemolithoautotrophic organism encodes the pathways both for ammonia and nitrite oxidation, which are concomitantly activated during growth by ammonia oxidation to nitrate. Genes affiliated with the phylogenetically distinct ammonia monooxygenase and hydroxylamine dehydrogenase genes of Nitrospira are present in many environments and were retrieved on Nitrospira-contigs in new metagenomes from engineered systems. These findings fundamentally change our picture of nitrification and point to completely nitrifying Nitrospira as key components of nitrogen-cycling microbial communities.

VALD{2: Progress of the Vienna Atomic Line Data Base ?

Abstract: We describe the updated version of the “Vienna Atomic Line Data Base” (VALD, Piskunov et al. 1995) which represents a considerable improvement over the first installation from 1994. The original line lists have been complemented with critically evaluated data obtained from experimental measurements and theoretical calculations which are necessary for computing state-of-the-art line opacities in stellar atmospheres, as well as for synthesizing spectra for high precision analyses. In this paper, we present new and improved data sets for neutral species and ions of Si, P, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Y, Zr, Ru, Xe, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm, Yb, Lu, Re, Pt, Au, Hg, and Pb. For some species data are available in VALD for the first time. We explain our choice of quality rankings by reviewing the literature for the new data and by comparison with source lists included into VALD. For some cases, we produced new line data by weighted averaging of data from different sources with individual error estimates in order to increase the reliability of VALD line lists. Software modifications allow remote users of VALD to specify individual extraction parameters as an alternative to the default settings of the VALD team and to have direct control over the quality ranking of line data. A World–Wide–Web interface is described which provides easy access to all new features. To simplify proper crediting of all authors of atomic data, VALD now includes a compilation of all publications used in each type of reply. Finally, we briefly discuss the future roadmap of VALD developments, including the incorporation of molecular transitions and integration with external data bases.

A solution to the problem of separation in logistic regression

Abstract: The phenomenon of separation or monotone likelihood is observed in the fitting process of a logistic model if the likelihood converges while at least one parameter estimate diverges to +/- infinity. Separation primarily occurs in small samples with several unbalanced and highly predictive risk factors. A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. It produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald tests and confidence intervals are available but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. The clear advantage of the procedure over previous options of analysis is impressively demonstrated by the statistical analysis of two cancer studies.

Partial differential equations

Abstract: Entering now the vast field of partial differential equations, we immediately announce that our discussion shall be restricted to those types of equations that are of major importance in physics. These are the quasilinear PDEs of second order, which may be written in the general form $${a_{11}}\frac{{{\partial ^2}u}}{{\partial {x^2}}} + 2{a_{12}}\frac{{{\partial ^2}u}}{{\partial x\partial y}} + {a_{22}}\frac{{{\partial ^{2u}}}}{{a{y^2}}} + f\left( {x,y,u,\frac{{\partial u}}{{\partial x}},\frac{{\partial u}}{{\partial y}}} \right) = 0$$ (5.1) (“Quasilinear” means that the second derivatives of u appear in linear order only).