BACKGROUND: In the field of root biology there has been a remarkable progress in root phenotyping, which is the efficient acquisition and quantitative description of root morphology. What is currently missing are means to efficiently explore, exchange and present the massive amount of acquired, and often time dependent root phenotypes. RESULTS: In this work, we present visual summaries of root ensembles by aggregating root images with identical genetic characteristics. We use the generalized box plot concept with a new formulation of data depth. In addition to spatial distributions, we created a visual representation to encode temporal distributions associated with the development of root individuals. CONCLUSIONS: The new formulation of data depth allows for much faster implementation close to interactive frame rates. This allows us to present the statistics from bootstrapping that characterize the root sample set quality. As a positive side effect of the new data-depth formulation we are able to define the geometric median for the curve ensemble, which was well received by the domain experts.

The impact of landslide is enormous in towns adjacent to the rift escarpments and highlands of Ethiopia, especially in rainy seasons. To study the seasonal variation and trend of slope instability in these areas with support of the current radar technology, 40 spotlight mode of TerraSAR-X/TanDEM-X (TSX/TDX) radar imageries were acquired from German Aerospace Agency (DLR). The conventional 2-pass DInSAR method has been applied to process the SAR data, and the DInSAR time series analysis result obtained by stacking 11 and 22 days interferograms from both ascending and descending orbits showed that there are 2-3 cm mass movements or deformation in the rainy season and 1-2 cm in the dry months. The results obtained from the DInSAR analysis were compared with a landslide inventory map containing more than 20 active landslides of the area of interest, and generally a good consistency was observed.

To get radarinterferometry-based measurement technique accepted as a measurement technique in terms of geodetic monitoring, e.g. in mining areas, a precise and extensive description and quantification of errors is necessary. Especially for short-term object-specific deformation monitoring with the classical DInSAR method, topographic phase errors lead to high measurement errors depending on sensor specific acquisition parameters and the used DEM. These measurement errors were commonly underestimated, especially in flat areas. The paper presents results of theoretical and practical analyses of topographic influences on the accuracy of radarinterferometric deformation monitoring. We give recommendations on the choice of key parameters for precise observation of deformations.

We present results from a deformation monitoring to demonstrate potential and limitations of TerraSAR-X interferometry to measure vertical displacements due to the tunneling of main sewerage pipes along the river Emscher in Germany. In spite of higher sensitivity for deformation gradients the potential for deformation monitoring benefits from high spatial and temporal resolution of the TerraSAR-X data. We analyzed a large stack of TerraSAR-X stripmap scenes to derive regional pattern of vertical displacements with differential SAR Interferometry and small-scale displacements and deformation of objects (infrastructure and houses) in time series of SAR-scenes with Persistent Scatterer Interferometry (PSI). First results from PSI are promising with a great number of detected PS. We show deformation measurements with Artificial Corner Reflectors. Short-time interferograms (11 or 22 days) show high coherence for large areas and therefore are likely less infected by unwrapping errors. Atmospheric errors are important for X-Band SAR. Expected deformation in our application is in the range of mm to cm, similar to tropospheric delay features in their spatial and temporal extent. The atmospheric phase screen in PSI and stacking procedures are smoothing the nonuniform deformation history of progressing tunneling.

MOTIVATION: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules. RESULTS: We propose a new algorithm that combines the benefits of existing motif finding with the ones of support vector machines (SVMs) to find degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana, we were able to show that the newly developed strategy significantly improves the recognition of TF targets. AVAILABILITY: The python source code (open source-licensed under GPL), the data for the experiments and a Galaxy-based web service are available at http://www.fml.mpg.de/raetsch/suppl/kirmes/.