Degarelix is a gonadotropin-releasing hormone antagonist registered for the treatment of advanced hormone-dependent prostate cancer. Treatment causing androgen deprivation is associated with QT prolongation and this study investigated whether degarelix at supratherapeutic concentrations has an intrinsic effect per se on cardiac repolarisation and the QT interval.

Atmospheric Carbon Monoxide (CO) provides a window on the chemistry of the atmosphere since it is one of few chemical constituents that can be remotely sensed, and it can be used to determine budgets of other greenhouse gases such as ozone and OH radicals. Remote sensing platforms in geostationary Earth orbit will soon provide regional observations of CO at several vertical layers with high spatial and temporal resolution. However, cloudy locations cannot be observed and estimates of the complete CO concentration fields have to be estimated based on the cloud-free observations. The current state-of-the-art solution of this interpolation problem is to combine cloud-free observations with prior information, computed by a deterministic physical model, which might introduce uncertainties that do not derive from data. While sharing features with the physical model, this paper suggests a Bayesian hierarchical model to estimate the complete CO concentration fields. The paper also provides a direct comparison to state-of-the-art methods. To our knowledge, such a model and comparison have not been considered before.

In this article we present a spatio‐temporal dynamic model that can be realized using image warping. Image warping is a non‐linear deformation which maps every point in one image plane to a point in another image plane. Using thin‐plate splines, these deformations are defined by how a small set of points is mapped, making the method computationally tractable. In our case the dynamics of the process is modelled by thin‐plate spline deformations and how they vary in time. Thus we make no assumption of stationarity in time. Finding the deformation between two images in the space–time series is a trade‐off between a good match of the images and a smooth, physically plausible, deformation. This is formulated as a penalized likelihood problem, where the likelihood measures how good the match is and the penalty comes from a prior model on the deformation. The dynamic model we suggest can be used to make forecasts and also to estimate the uncertainties associated with these. An introduction to image warping and thin‐plate splines is given as well as an application where the methodology is applied to the problem of nowcasting radar precipitation. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper a statistical forecasting model designed for bounded areas of near-surface ocean wind speeds is implemented.Dimension reduction is achieved by decomposing the covariance structure into one large-scale and one small-scale component using empirical orthogonal functions. The large-scale component is modelled with an AR process and forecasts are calculated by applying a Kalman filter.The model is suited for stable weather situations as for unsteady situations it requires more frequent wind information. From the prediction variance fields it is possible to identify where unexpected weather usually enters the area. (C) 2004 Elsevier Ltd. All rights reserved.

In this paper we present a spatio-temporal dynamic model which can be realized using image warping, a technique used to match images. Image warping is a non-linear transformation which maps all positions in one image plane to positions in a second plane. Using thin-plate splines this transformation is defined by a small set of matching points, a feature that will make this method work even for large data sets. In our case the dynamics of the process is described by warping transformations between consecutive images in the space-time series. Finding these transformations is a trade-off between a good match of the images and a smooth, physically plausible deformation. This leads to a penalized likelihood method for finding the transformation. The deformations that can be described with this approach include all affine transformations as well as a large class of non-linear deformations. Our method is applied to the problem of nowcasting radar precipitation.

In this paper we present a spatio-temporal dynamic model which can be realized using image warping, a technique used to match images. Image warping is a non-linear transformation which maps all positions in one image plane to positions in a second plane. Using thin-plate splines this transformation is defined by a small set of matching points, a feature that will make this method work even for large data sets. In our case the dynamics of the process is described by warping transformations between consecutive images in the space-time series. Finding these transformations is a trade-off between a good match of the images and a smooth, physically plausible deformation. This leads to a penalized likelihood method for finding the transformation. The deformations that can be described with this approach include all affine transformations as well as a large class of non-linear deformations. Our method is applied to the problem of nowcasting radar precipitation.