A semianalytical model for sheet flow layer thickness with application to the swash zone

A new semianalytical model for the time-dependent thickness of the sheet flow layer that includes the effects of pressure gradients, bed slope, boundary layer growth, and bore turbulence is presented. The shear stress and boundary layer growth are computed using the boundary layer integral method. The model is expressed as two coupled ordinary differential equations that are solved numerically given a prescribed time series of free-stream velocity, horizontal pressure gradient and bore turbulence, which together represent the hydrodynamic forcing. The model was validated against two data sets of sheet flow layer thickness collected in oscillatory flow tunnels and one data set collected in the swash zone of a prototype-scale laboratory experiment. In the oscillatory flow tunnel data sets, sheet flow is mostly generated by shear stress, with pressure gradients providing an important secondary forcing around flow reversal. In the swash zone, pressure gradients and shear stresses alone are not sufficient to generate the large sheet flow layer thickness observed at the initial stages of uprush. Bore turbulence is most likely the dominant generation mechanism for this intense sheet flow

Scientific support regarding hydrodynamics and sand transport in the coastal zone: hindcast of the morphological impact of the 5-6 December 2013 storm using XBeach

A storm occurred in the North Sea on 5-6 December 2013 (also known as the Sinterklaasstorm) which generated high surge levels and moderately high wave heights along the Belgian coast, resulting in beach and dune erosion. Measurements of beach profile change along 122 cross-shore survey transects were compared with the numerical model XBeach. First, a novel configuration for XBeach was developed that takes into account the effects of wave directional spreading since XBeach cannot resolve directionally spreading of long waves in 1D mode (which is generally used to predict cross-shore profile change). The effect of wave directional spreading on hydrodynamics and sediment transport is investigated using idealized and realistic simulations. Idealized simulations show that the near-shore long wave field is more energetic in 1D than in an equivalent 2D model with an alongshore-uniform bathymetry. Realistic storm scenarios show that this leads to predicted erosion volumes that are 25-57% higher than in the 2D model. Sparse 2D models with 5 to 8 alongshore grid cells provide a reasonable approximation (both for hydrodynamics and sediment transport) of the full 2D model (with 50 alongshore grid cells) at a lower computational cost. The December 2013 storm was therefore simulated using a sparse 2D model with 5 grid cells in the alongshore direction. Good agreement with measured erosion volumes was found when using a recently established set of calibration values provided by Deltares. Default calibration values from an older version of XBeach lead to an overestimation of the erosion volumes, as does the use of a 1D model instead of a sparse 2D model. The sensitivity of the predicted erosion volumes to increased surge levels and wave heights was also investigated

Improved functional prediction of proteins by learning kernel combinations in multilabel settings

Background We develop a probabilistic model for combining kernel matrices to predict the function of proteins. It extends previous approaches in that it can handle multiple labels which naturally appear in the context of protein function. Results Explicit modeling of multilabels significantly improves the capability of learning protein function from multiple kernels. The performance and the interpretability of the inference model are further improved by simultaneously predicting the subcellular localization of proteins and by combining pairwise classifiers to consistent class membership estimates. Conclusion For the purpose of functional prediction of proteins, multilabels provide valuable information that should be included adequately in the training process of classifiers. Learning of functional categories gains from co-prediction of subcellular localization. Pairwise separation rules allow very detailed insights into the relevance of different measurements like sequence, structure, interaction data, or expression data. A preliminary version of the software can be downloaded from http://www.inf.ethz.ch/personal/vroth/KernelHMM/.ISSN:1471-210

Supervised selective kernel fusion for membrane protein prediction

Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

<p>Abstract</p> <p>Background</p> <p>Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases.</p> <p>Results</p> <p>We propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases.</p> <p>Conclusions</p> <p>ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at <url>http://cbio.ensmp.fr/prodige</url>.</p