Radboud University Nijmegen

About: Radboud University Nijmegen is a education organization based out in Nijmegen, Gelderland, Netherlands. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 35417 authors who have published 83035 publications receiving 3285064 citations. The organization is also known as: Catholic University of Nijmegen & Radboud University.

Vegetation of Europe: hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities

Abstract: Aims: Vegetation classification consistent with the Braun-Blanquet approach is widely used in Europe for applied vegetation science, conservation planning and land management. During the long history of syntaxonomy, many concepts and names of vegetation units have been proposed, but there has been no single classification system integrating these units. Here we (1) present a comprehensive, hierarchical, syntaxonomic system of alliances, orders and classes of Braun-Blanquet syntaxonomy for vascular plant, bryophyte and lichen, and algal communities of Europe; (2) briefly characterize in ecological and geographic terms accepted syntaxonomic concepts; (3) link available synonyms to these accepted concepts; and (4) provide a list of diagnostic species for all classes. LocationEuropean mainland, Greenland, Arctic archipelagos (including Iceland, Svalbard, Novaya Zemlya), Canary Islands, Madeira, Azores, Caucasus, Cyprus. Methods: We evaluated approximately 10000 bibliographic sources to create a comprehensive list of previously proposed syntaxonomic units. These units were evaluated by experts for their floristic and ecological distinctness, clarity of geographic distribution and compliance with the nomenclature code. Accepted units were compiled into three systems of classes, orders and alliances (EuroVegChecklist, EVC) for communities dominated by vascular plants (EVC1), bryophytes and lichens (EVC2) and algae (EVC3). Results: EVC1 includes 109 classes, 300 orders and 1108 alliances; EVC2 includes 27 classes, 53 orders and 137 alliances, and EVC3 includes 13 classes, 24 orders and 53 alliances. In total 13448 taxa were assigned as indicator species to classes of EVC1, 2087 to classes of EVC2 and 368 to classes of EVC3. Accepted syntaxonomic concepts are summarized in a series of appendices, and detailed information on each is accessible through the software tool EuroVegBrowser. Conclusions: This paper features the first comprehensive and critical account of European syntaxa and synthesizes more than 100 yr of classification effort by European phytosociologists. It aims to document and stabilize the concepts and nomenclature of syntaxa for practical uses, such as calibration of habitat classification used by the European Union, standardization of terminology for environmental assessment, management and conservation of nature areas, landscape planning and education. The presented classification systems provide a baseline for future development and revision of European syntaxonomy.

Planck 2015 results: XIV. Dark energy and modified gravity

Abstract: We study the implications of Planck data for models of dark energy (DE) and modified gravity (MG) beyond the standard cosmological constant scenario. We start with cases where the DE only directly affects the background evolution, considering Taylor expansions of the equation of state w(a), as well as principal component analysis and parameterizations related to the potential of a minimally coupled DE scalar field. When estimating the density of DE at early times, we significantly improve present constraints and find that it has to be below ~2% (at 95% confidence) of the critical density, even when forced to play a role for z < 50 only. We then move to general parameterizations of the DE or MG perturbations that encompass both effective field theories and the phenomenology of gravitational potentials in MG models. Lastly, we test a range of specific models, such as k-essence, f(R) theories, and coupled DE. In addition to the latest Planck data, for our main analyses, we use background constraints from baryonic acoustic oscillations, type-Ia supernovae, and local measurements of the Hubble constant. We further show the impact of measurements of the cosmological perturbations, such as redshift-space distortions and weak gravitational lensing. These additional probes are important tools for testing MG models and for breaking degeneracies that are still present in the combination of Planck and background data sets. All results that include only background parameterizations (expansion of the equation of state, early DE, general potentials in minimally-coupled scalar fields or principal component analysis) are in agreement with ΛCDM. When testing models that also change perturbations (even when the background is fixed to ΛCDM), some tensions appear in a few scenarios: the maximum one found is ~2σ for Planck TT+lowP when parameterizing observables related to the gravitational potentials with a chosen time dependence; the tension increases to, at most, 3σ when external data sets are included. It however disappears when including CMB lensing.

Changes to taxonomy and the International Code of Virus Classification and Nomenclature ratified by the International Committee on Taxonomy of Viruses (2017)

Abstract: This article lists the changes to virus taxonomy approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in March 2017.

Bayesian classification with Gaussian processes

Abstract: We consider the problem of assigning an input vector to one of m classes by predicting P(c|x) for c=1,...,m. For a two-class problem, the probability of class one given x is estimated by /spl sigma/(y(x)), where /spl sigma/(y)=1/(1+e/sup -y/). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points. We provide a Bayesian treatment, integrating over uncertainty in y and in the parameters that control the Gaussian process prior the necessary integration over y is carried out using Laplace's approximation. The method is generalized to multiclass problems (m>2) using the softmax function. We demonstrate the effectiveness of the method on a number of datasets.