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Results: 18
European mushroom assemblages are darker in cold climates
Franz-Sebastian Krah, Ulf Büntgen, Hanno Schaefer, Jörg Müller, Carrie Andrew, Lynne Boddy, Jeffrey Diez, Simon Egli, Robert Freckleton, Alan C. Gange, Rune Halvorsen, Einar Heegaard, Antje Heideroth, Christoph Heibl, Jacob Heilmann-Clausen, Klaus Høiland, Ritwika Kar, Håvard Kauserud, Paul M. Kirk, Thomas W. Kuyper, Irmgard Krisai-Greilhuber, Jenni Norden, Phillip Papastefanou, Beatrice Senn-Irlet, Claus Bässler
Jun 27, 2020
Abstract: Thermal melanism theory states that dark-colored ectotherm organisms are at an advantage at low temperature due to increased warming. This theory is generally supported for ectotherm animals, however, the function of...
European mushroom assemblages are darker in cold climates.
Franz-Sebastian Krah, Ulf Büntgen, Hanno Schaefer, Jörg Müller, Carrie Andrew, Lynne Boddy, Jeffrey Diez, Simon Egli, Robert Freckleton, Alan C Gange, Rune Halvorsen, Einar Heegaard, Antje Heideroth, Christoph Heibl, Jacob Heilmann-Clausen, Klaus Høiland, Ritwika Kar, Håvard Kauserud, Paul M Kirk, Thomas W Kuyper, Irmgard Krisai-Greilhuber, Jenni Norden, Phillip Papastefanou, Beatrice Senn-Irlet, Claus Bässler
Sep 04, 2019
Thermal melanism theory states that dark-colored ectotherm organisms are at an advantage at low temperature due to increased warming. This theory is generally supported for ectotherm animals, however, the function of colors in...
Bayesian Spatial Modelling with R-INLA
The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009)...
Bayesian computing with INLA
The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. New...
Does non-stationary spatial data always require non-stationary random fields?
A stationary spatial model is an idealization and we expect that the true dependence structures of physical phenomena are spatially varying, but how should we handle this non-stationarity in practice? We study the challenges...
Bayesian computing with INLA
The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. New...
Bayesian adaptive smoothing splines using stochastic differential equations
The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. However, there are two...
Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
Gaussian random fields (GRFs) constitute an important part of spatial modelling, but can be computationally infeasible for general covariance structures. An efficient approach is to specify GRFs via stochastic partial...
Bayesian adaptive smoothing splines using stochastic differential equations
The smoothing spline is one of the most popular curve-fitting methods, partly because of empirical evidence supporting its effectiveness and partly because of its elegant mathematical formulation. However, there are two...
Bayesian Spatial Modelling with R-INLA
The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009)...
Penalising model component complexity
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base...
Does non-stationary spatial data always require non-stationary random fields?
A stationary spatial model is an idealization and we expect that the true dependence structures of physical phenomena are spatially varying, but how should we handle this non-stationarity in practice? We study the challenges...
Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
Gaussian random fields (GRFs) constitute an important part of spatial modelling, but can be computationally infeasible for general covariance structures. An efficient approach is to specify GRFs via stochastic partial...
Maximal strength training-induced increase in efferent neural drive is not reflected in relative protein expression of SERCA.
Tiril Tøien, Håvard Haglo, Stian Kwak Nyberg, Shalini Vasudev Rao, Astrid Kamilla Stunes, Mats Peder Mosti, Eivind Wang
Nov 05, 2021
Penalising model component complexity
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base...
The association of electronic health literacy with behavioural and psychological coronary artery disease risk factors in patients after percutaneous coronary intervention
Gunhild Brørs, Håvard Dalen, Heather Allore, Christi Deaton, Bengt Fridlund, Cameron D Norman, Pernille Palm, Tore Wentzel-Larsen, Tone M Norekvål
Apr 17, 2023
AIMS: Fundamental roadblocks, such as non-use and low electronic health (eHealth) literacy, prevent the implementation of eHealth resources. The aims were to study internet usage for health information and eHealth literacy in...