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Reconstructing geometric shapes from point clouds is a common task that is often accomplished by experts manually modeling geometries in CAD-capable software. State-of-the-art workflows based on fully automatic geometry extraction are limited by point cloud density and memory constraints, and require pre- and post-processing by the user. In this work, we present a framework for interactive, user-driven, feature-assisted geometry reconstruction from arbitrarily sized point clouds. Based on seeded region-growing point cloud segmentation, the user interactively extracts planar pieces of geometry and utilizes contextual suggestions to point out plane surfaces, normal and tangential directions, and edges and corners. We implement a set of feature-assisted tools for high-precision modeling tasks in architecture and urban surveying scenarios, enabling instant-feedback interactive point cloud manipulation on large-scale data collected from real-world building interiors and facades. We evaluate our results through systematic measurement of the reconstruction accuracy, and interviews with domain experts who deploy our framework in a commercial setting and give both structured and subjective feedback., 13 pages, submitted to Computers & Graphics Journal

The application potential of Visual Analytics (VA), with its supporting interactive 2D and 3D visualization techniques, in the environmental domain is unparalleled. Such advanced systems may enable an in-depth interactive exploration of multifaceted geospatial and temporal changes in very large and complex datasets. This is facilitated by a unique synergy of modules for simulation, analysis, and visualization, offering instantaneous visual feedback of transformative changes in the underlying data. However, even if the resulting knowledge holds great potential for supporting decision-making in the environmental domain, the consideration of such techniques still have to find their way to daily practice. To advance these developments, we demonstrate four case studies that portray different opportunities in data visualization and VA in the context of climate research and natural disaster management. Firstly, we focus on 2D data visualization and explorative analysis for climate change detection and urban microclimate development through a comprehensive time series analysis. Secondly, we focus on the combination of 2D and 3D representations and investigations for flood and storm water management through comprehensive flood and heavy rain simulations. These examples are by no means exhaustive, but serve to demonstrate how a VA framework may apply to practical research.

Geological analysis of 3D Digital Outcrop Models (DOMs) for reconstruction of ancient habitable environments is a key aspect of the upcoming ESA ExoMars 2022 Rosalind Franklin Rover and the NASA 2020 Rover Perseverance missions in seeking signs of past life on Mars. Geologists measure and interpret 3D DOMs, create sedimentary logs and combine them in `correlation panels' to map the extents of key geological horizons, and build a stratigraphic model to understand their position in the ancient landscape. Currently, the creation of correlation panels is completely manual and therefore time-consuming, and inflexible. With InCorr we present a visualization solution that encompasses a 3D logging tool and an interactive data-driven correlation panel that evolves with the stratigraphic analysis. For the creation of InCorr we closely cooperated with leading planetary geologists in the form of a design study. We verify our results by recreating an existing correlation analysis with InCorr and validate our correlation panel against a manually created illustration. Further, we conducted a user-study with a wider circle of geologists. Our evaluation shows that InCorr efficiently supports the domain experts in tackling their research questions and that it has the potential to significantly impact how geologists work with digital outcrop representations in general.

In this paper, we propose a novel approach for outlier detection, called local projections, which is based on concepts of Local Outlier Factor (LOF) (Breunig et al., 2000) and RobPCA (Hubert et al., 2005). By using aspects of both methods, our algorithm is robust towards noise variables and is capable of performing outlier detection in multi-group situations. We are further not reliant on a specific underlying data distribution. For each observation of a dataset, we identify a local group of dense nearby observations, which we call a core, based on a modification of the k-nearest neighbours algorithm. By projecting the dataset onto the space spanned by those observations, two aspects are revealed. First, we can analyze the distance from an observation to the center of the core within the projection space in order to provide a measure of quality of description of the observation by the projection. Second, we consider the distance of the observation to the projection space in order to assess the suitability of the core for describing the outlyingness of the observation. These novel interpretations lead to a univariate measure of outlyingness based on aggregations over all local projections, which outperforms LOF and RobPCA as well as other popular methods like PCOut (Filzmoser et al., 2008) and subspace-based outlier detection (Kriegel et al., 2009) in our simulation setups. Experiments in the context of real-word applications employing datasets of various dimensionality demonstrate the advantages of local projections.

International audience; Temporal fluctuations of wind strength and direction can influence aeolian bedform morphology and orientation, which can be encoded into the architecture of aeolian deposits. These strata represent a direct record of atmospheric processes and can be used to understand ancient Martian atmospheric processes as well as those on Earth. The strata can: give insight to ancient atmospheric circulation, how the atmosphere evolved in response to global changes in habitability, and how ancient processes differ from modern processes. The Stimson formation at the Greenheugh pediment (Gale crater) records evidence of fluctuating wind across multiple temporal scales. The strata can be subdivided into three intervals-Gleann Beag, Ladder, and Edinburgh intervals. Internally, the intervals record changes of dune morphology and orientation, correlatable to wind fluctuations at multiple temporal scales. The basal Gleann Beag interval comprises compound cross-strata, deposited by oblique compound dunes. These dunes record a bimodal wind regime, resulting in net sediment transport toward the north. The Ladder interval records a reversal of sediment transport to the south, where straight-crested simple-dunes shaped by a seasonally variable winds formed. Finally, the Edinburgh interval records sediment transport to the west, where a unimodal wind formed sinuous-crested simple dunes. These observations demonstrate active and variable atmospheric circulation in Gale crater during the accumulation of the Stimson dune field, at multiple temporal scales from seasonally driven winds to much longer time-frames, during the Hesperian. These observations can be used to further understand ancient atmospheric conditions and processes, at a high temporal resolution on Mars.

Modern and ancient fluvial-deltaic systems on Earth contain highly diverse ecosystems in all terrestrial climates. Fluvial and lacustrine deposits have been discovered on Mars by the NASA Mars Science Laboratory rover Curiosity, and may be present in Oxia Planum, where the ESA/ROSCOSMOS ExoMars rover Rosalind Franklin is set to land in 2023. The primary aim of the ExoMars mission is to search for signs of past and present life on Mars. Whilst fluvio-deltaic-lacustrine sandstones and mudstones are high priority targets for sampling and drilling, it is important to obtain information on the palaeoenvironmental context of these deposits during mission exploration. The geometries of sedimentary structures and distribution of sedimentary facies within fluvial deposits provide information which can be used to reconstruct the geometries and flow parameters of these ancient systems. This provides us with quantitative means with which to make inferences on the ancient climate of Mars, and aids decision making with regards to rover science operations. Here we present a detailed quantitative 3D analysis of fluvial sedimentary architecture on Mars using rover image data. We used the 3D visualization software tool PRo3D1 to render the Shaler outcrop, observed at Yellowknife Bay by the NASA Mars Science Laboratory Rover, Curiosity2, as a scaled 3D textured model using the PRoViP 3D vision processing software3, and to map out key sedimentological features in order to characterize their geometry and dimensions, following existing facies descriptions4 . Mastcam data taken from different rover locations was processed into 3D surfaces and spatially matched to Navcam stereo-panoramas to create a digital outcrop model (DOM). The Shaler DOM was constructed using 17 Mastcam stereo-panoramas taken on Sols 120-121 and 309-324. A 30 m x 13 m area of the NE-SW trending outcrop was analysed. Sedimentary facies, key bounding surfaces and sedimentary structures were mapped out on the DOM (Fig. 1) and the dip and strike of lithological boundaries, key bounding surfaces, and cross-laminations were measured directly from the DOM. Apparent widths and thicknesses of the layers and cross-lamination sets were measured. Regularly spaced, sedimentary logs were collected and matched to illustrate the detailed internal structures of the outcrops analysed. Four types of sedimentary structures were identified; low-angle cross strata dipping to the SE (Fig. 2), ~ 50 cm thick; convex up, sub-parallel undulating laminations forming structures with 20-40 cm amplitude and 2 m wavelength; single sets of trough cross-laminations and compound, stacked cosets of trough cross-laminations, with thicknesses on average 9 cm, and yielding a common palaeoflow direction to the NE and SW. These data allow us to reconstruct the internal architecture of a fluvial bar-form which forms the Shaler outcrop, and quantify the key geometries and their spatial relationships in three-dimensions. These data are highly useful in providing context and relative timings for environmental reconstruction. Figure 1. Line interpretation of Shaler in PRo3D, showing the lithological boundaries (pink lines), facies assocation boundaries (green lines), accretion surfaces (yellow lines), layer contacts (white lines), cross-lamina sets (blue lines) and undulatory convex up bedforms (green lines). Figure 2. Detailed view of stacked sets of cross-laminations in the southwestern part of the Shaler outcrop. 1. Barnes et al., 2018, Geological Analysis of Martian Rover‐Derived Digital Outcrop Models Using the 3‐D Visualization Tool, Planetary Robotics 3‐D Viewer—PRo3D. Earth and Space Science. 2. Grotzinger et al., 2014, A Habitable Fluvio-Lacustrine Environment at Yellowknife Bay, Gale Crater, Mars. Science 3. Paar et al., 2015, PRoViDE: Planetary Robotics Vision Data Processing and Fusion. European Planetary Science Congress 2015. 4. Edgar et al., 2017, Shaler: in situ analysis of a fluvial sedimentary deposit on Mars. Sedimentology.

MURFI was financially supported in part by the UK Space Agency, and by the following grants: Balme: UK SA grants ST/L00643X/1 and ST/R001413/1; Bridges: UK SA grant ST/R00143X/1; Butcher and Wright: STFC studentship grant ST/N50421X/1; Coates and Griffiths: UK SA grant ST/R002223/1; Cousins: Royal Society of Edinburgh Research Fellowship and UK SA grant ST/P001297/1; Davis: UK STFC grant ST/K502388/1; Grindrod: UK SA and STFC grants ST/J005215/1, ST/L006456/1, ST/N000528/1; Gunn: UK SA grants ST/P001408/1, ST/P001394/1, ST/N006410/1; Muller: UK SA grant ST/P002145/1; Preston: UK SA grant ST/P001254/1; Stabbins: UK SA studentship grant ST/N002377/1. We thank the UK Harwell Campus Satellite Applications Catapult for providing access to the control room that became the MOC, and the STFC Rutherford Appleton Laboratory for other ROC support. The 2016 Mars Utah Rover Field Investigation (MURFI) was a Mars rover field trial run by the UK Space Agency in association with the Canadian Space Agency's 2015/2016 Mars Sample Return Analogue Deployment mission. MURFI had over 50 participants from 15 different institutions around the UK and abroad. The objectives of MURFI were to develop experience and leadership within the UK in running future rover field trials; to prepare the UK planetary community for involvement in the European Space Agency/Roscosmos ExoMars 2020 rover mission; and to assess how ExoMars operations may differ from previous rover missions. Hence, the wider MURFI trial included a ten-day (or ten-‘sol’) ExoMars rover-like simulation. This comprised an operations team and control centre in the UK, and a rover platform in Utah, equipped with instruments to emulate the ExoMars rovers remote sensing and analytical suite. The operations team operated in ‘blind mode’, where the only available data came from the rover instruments, and daily tactical planning was performed under strict time constraints to simulate real communications windows. The designated science goal of the MURFI ExoMars rover-like simulation was to locate in-situ bedrock, at a site suitable for sub-surface core-sampling, in order to detect signs of ancient life. Prior to “landing”, the only information available to the operations team was Mars-equivalent satellite remote sensing data, which were used for both geologic and hazard (e.g., slopes, loose soil) characterisation of the area. During each sol of the mission, the operations team sent driving instructions and imaging/analysis targeting commands, which were then enacted by the field team and rover-controllers in Utah. During the ten-sol mission, the rover drove over 100 m and obtained hundreds of images and supporting observations, allowing the operations team to build up geologic hypotheses for the local area and select possible drilling locations. On sol 9, the team obtained a subsurface core sample that was then analyzed by the Raman spectrometer. Following the conclusion of the ExoMars-like component of MURFI, the operations and field team came together to evaluate the successes and failures of the mission, and discuss lessons learnt for ExoMars rover and future field trials. Key outcomes relevant to ExoMars rover included a key recognition of the importance of field trials for (i) understanding how to operate the ExoMars rover instruments as a suite, (ii) building an operations planning team that can work well together under strict time-limited pressure, (iii) developing new processes and workflows relevant to the ExoMars rover, (iv) understanding the limits and benefits of satellite mapping and (v) practicing efficient geological interpretation of outcrops and landscapes from rover-based data, by comparing the outcomes of the simulated mission with post-trial, in-situ field observations. In addition, MURFI was perceived by all who participated as a vital learning experience, especially for early and mid-career members of the team, and also demonstrated the UK capability of implementing a large rover field trial. The lessons learnt from MURFI are therefore relevant both to ExoMars rover, and to future rover field trials. Postprint

A powerful data transformation method named guided projections is proposed creating new possibilities to reveal the group structure of high-dimensional data in the presence of noise variables. Using projections onto a space spanned by a selection of a small number of observations allows measuring the similarity of other observations to the selection based on orthogonal and score distances. Observations are iteratively exchanged from the selection creating a nonrandom sequence of projections, which we call guided projections. In contrast to conventional projection pursuit methods, which typically identify a low-dimensional projection revealing some interesting features contained in the data, guided projections generate a series of projections that serve as a basis not just for diagnostic plots but to directly investigate the group structure in data. Based on simulated data, we identify the strengths and limitations of guided projections in comparison to commonly employed data transformation methods...

Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups differ so much from the remaining data that it may be worthy to look at them in more detail. In contrast, media with popular content appear in larger groups. Identifying groups of varying sizes is addressed by clustering of imbalanced data. Clustering highly imbalanced media groups is additionally challenged by the high dimensionality of the underlying features. In this paper, we present the imbalanced clustering (IClust) algorithm designed to reveal group structures in high-dimensional media data. IClust employs an existing clustering method in order to find an initial set of a large number of potentially highly pure clusters which are then successively merged. The main advantage of IClust is that the number of clusters does not have to be pre-specified and that no specific assumptions about the cluster or data characteristics need to be made. Experiments on real-world media data demonstrate that in comparison to existing methods, IClust is able to better identify media groups, especially groups of small sizes.

We introduce a robust k-means-based clustering method for high-dimensional data where not only outliers but also a large number of noise variables are very likely to be present [4]. Although Kondo et al. [2] already addressed such an application scenario, our approach goes even further. Firstly, the introduced method is designed to identify clusters, informative variables, and outliers simultaneously. Secondly, the proposed clustering technique additionally aims at optimizing required parameters, e.g. the number of clusters. This is a great advantage over most existing methods. Moreover, the robustness aspect is achieved through a robust initialization [3] and a proposed weighting function using the Local Outlier Factor [1]. The weighting function provides a valuable source of information about the outlyingness of each observation for a subsequent outlier detection. In order to reveal both clusters and informative variables properly, the approach uses a lasso-type penalty [5]. The method has thoroughly been tested on simulated as well as on real high-dimensional datasets. The conducted experiments demonstrated a great ability of the clustering method to identify clusters, outliers, and informative variables.

The visual analysis of surface cracks plays an essential role in tunnel maintenance when assessing the condition of a tunnel. To identify patterns of cracks, which endanger the structural integrity of its concrete surface, analysts need an integrated solution for visual analysis of geometric and multivariate data to decide if issuing a repair project is necessary. The primary contribution of this work is a design study, supporting tunnel crack analysis by tightly integrating geometric and attribute views to allow users a holistic visual analysis of geometric representations and multivariate attributes. Our secondary contribution is Visual Analytics and Rendering, a methodological approach which addresses challenges and recurring design questions in integrated systems. We evaluated the tunnel crack analysis solution in informal feedback sessions with experts from tunnel maintenance and surveying. We substantiated the derived methodology by providing guidelines and linking it to examples from the literature.

Panoramic camera systems on robots exploring the surface of Mars are used to collect images of terrain and rock outcrops which they encounter along their traverse. Image mosaics from these cameras are essential in mapping the surface geology and selecting locations for analysis by other instruments on the rover's payload. 2‐D images do not truly portray the depth of field of features within an image, nor their 3‐D geometry. This paper describes a new 3‐D visualization software tool for geological analysis of Martian rover‐derived Digital Outcrop Models created using photogrammetric processing of stereo‐images using the Planetary Robotics Vision Processing tool developed for 3‐D vision processing of ExoMars PanCam and Mars 2020 Mastcam‐Z data. Digital Outcrop Models are rendered in real time in the Planetary Robotics 3‐D Viewer PRo3D, allowing scientists to roam outcrops as in a terrestrial field campaign. Digitization of point, line, and polyline features is used for measuring the physical dimensions of geological features and communicating interpretations. Dip and strike of bedding and fractures is measured by digitizing a polyline along the contact or fracture trace, through which a best fit plane is plotted. The attitude of this plane is calculated in the software. Here we apply these tools to analysis of sedimentary rock outcrops and quantification of the geometry of fracture systems encountered by the science teams of NASA's Mars Exploration Rover Opportunity and Mars Science Laboratory rover Curiosity. We show the benefits PRo3D allows for visualization and collection of geological interpretations and analyses from rover‐derived stereo‐images., Earth and Space Science, 5 (7), ISSN:2333-5084

Recent aeolian bedforms comprising loose sand are common on the martian surface and provide a mobility hazard to Mars rovers. The ExoMars rover will launch in 2020 to one of two candidate sites: Mawrth Vallis or Oxia Planum. Both sites contain numerous aeolian bedforms with simple ripple-like morphologies. The larger examples are ‘Transverse Aeolian Ridges’ (TARs), which stereo imaging analyses have shown to be a few metres high and up to a few tens of metres across. Where they occur, TARs therefore present a serious, but recognized and avoidable, rover mobility hazard. There also exists a population of smaller bedforms of similar morphology, but it is unknown whether these bedforms will be traversable by the ExoMars rover. We informally refer to these bedforms as “mini-TARs”, as they are about an order of magnitude smaller than most TARs observed to date. They are more abundant than TARs in the Oxia Planum site, and can be pervasive in areas. The aim of this paper is to estimate the heights of these features, which are too small to measured using High Resolution Imaging Science Experiment (HiRISE) Digital Elevation Models (DEMs), from orbital data alone. Thereby, we aim to increase our knowledge of the hazards in the proposed ExoMars landing sites. We propose a methodology to infer the height of these mini-TARs based on comparisons with similar features observed by previous Mars rovers. We use rover-based stereo imaging from the NASA Mars Exploration Rover (MER) Opportunity and PRo3D software, a 3D visualisation and analysis tool, to measure the size and height of mini-TARs in the Meridiani Planum region of Mars. These are good analogues for the smaller bedforms at the ExoMars rover candidate landing sites. We show that bedform height scales linearly with length (as measured across the bedform, perpendicular to the crest ridge) with a ratio of about 1:15. We also measured the lengths of many of the smaller aeolian bedforms in the ExoMars rover Oxia Planum candidate landing site, and find that they are similar to those of the Meridiani Planum mini-TARs. Assuming that the Oxia Planum bedforms have the same length/height ratio as the MER Opportunity mini-TARs, we combine these data to provide a probabilistic method of inferring the heights of bedforms at the Oxia Planum site. These data can then be used to explore the likely traversability of this site. For example, our method suggests that most of the bedforms studied in Oxia Planum have ridge crests higher than 15 cm, but lower than 25 cm. Hence, if the tallest bedforms the ExoMars rover will be able to safely cross are only 15 cm high, then the Oxia Planum sites studied here contain mostly impassable bedforms. However, if the rover can safely traverse 25 cm high bedforms, then most bedforms here will be smaller than this threshold. As an additional outcome, our results show that the mini-TARs have length/height ratios similar to TARs in general. Hence, these bedforms could probably be classified simply as “small TARs”, rather than forming a discrete population or sub-type of aeolian bedforms.

State-of-the-art lighting design is based on physically accurate lighting simulations of scenes such as offices. The simulation results support lighting designers in the creation of lighting configurations, which must meet contradicting customer objectives regarding quality and price while conforming to industry standards. However, current tools for lighting design impede rapid feedback cycles. On the one side, they decouple analysis and simulation specification. On the other side, they lack capabilities for a detailed comparison of multiple configurations. The primary contribution of this paper is a design study of LiteVis, a system for efficient decision support in lighting design. LiteVis tightly integrates global illumination-based lighting simulation, a spatial representation of the scene, and non-spatial visualizations of parameters and result indicators. This enables an efficient iterative cycle of simulation parametrization and analysis. Specifically, a novel visualization supports decision making by ranking simulated lighting configurations with regard to a weight-based prioritization of objectives that considers both spatial and non-spatial characteristics. In the spatial domain, novel concepts support a detailed comparison of illumination scenarios. We demonstrate LiteVis using a real-world use case and report qualitative feedback of lighting designers. This feedback indicates that LiteVis successfully supports lighting designers to achieve key tasks more efficiently and with greater certainty.

Testing for structural changes is a well studied field. Classical tests for breakpoint detection utilize F-statistics which depend on independent and identically normal distributed residuals. In general, this condition is not satisfied which leads to distorted test results when the p-values of classical tests are close to the significance level. Thus, permutation tests are used to properly estimate the critical values. Based on the accounting data of Austrian hospitals, collected by the Austrian social insurance institutions, specific observations (hospital stays) which are connected to pre-defined diseases are analysed. For those groups of observations we use characteristic factors, to test for structural changes from different perspectives. The first test analyses the temporal trend and identifies breakpoints, caused by changes in the underlying system between years. The second analysis focuses on identifying differences between hospitals. Both implemented tests ensure the often ignored aspect of a homogeneous data base for further analysis.

Percutaneous retrograde screw fixation for acetabular fractures is a demanding procedure due to the complex anatomy of the pelvis and the varying narrow safe bony corridors. Limited information is available on optimal screw placement and the geometry of safe zones for screw insertion in the pelvis. Three-dimensional reconstructions of 50 consecutive CT scans of polytrauma patients (35 males, 15 females) were used to introduce three virtual CAD bolts (representing screws) into the anterior column (superior ramus of the pubic bone), posterior column (the ischial bone) and the supraacetabular region, as performed during percutaneous screw fixation. The three-dimensional (3D) position of these screws was evaluated with a computer software (MIMICS) after virtual optimal insertion. The 3D position, the narrowest zone and the distance to the hip joint of the two columns and the supraacetabular region were defined. The mean maximal screw length for the three virtual screws measured between 107.4 and 148 ± 18.7 mm. The narrowest zone of the pelvic bone (superior pubic ramus) had a width of 9.2 ± 2.4 mm. The average distances between the bolts and the hip joint were 3.9 and 19.4 ± 7.4 mm. For the anterior column (superior pubic ramus) screw, the mean lateral angle to the sagittal midline plane was 39.0 ± 3.2° and the mean posterior angle to the transversal midline plane was 15.1 ± 4.0°. The mean supraacetabular screw angles measured 22.4 ± 3.4° (medial), 35.3 ± 4.6° (cranial) and the mean angles for the ischial screw were 12.0 ± 5.4° (posterior) and 18.4 ± 4.0° (lateral). The zones for safe screw positioning are very narrow, making percutaneous screw fixation of the acetabulum a challenging procedure. The predefined angles for the most frequently positioned percutaneous screws may aid in preoperative planning, decrease operative and radiation times and help to increase safe insertion of screws.

3D visibility analysis plays a key role in urban planning for assessing the visual impact of proposed buildings on the cityscape. A call for proposals typically yields around 30 candidate buildings that need to be evaluated with respect to selected viewpoints. Current visibility analysis methods are very time-consuming and limited to a small number of viewpoints. Further, analysts neither have measures to evaluate candidates quantitatively, nor to compare them efficiently. The primary contribution of this work is the design study of Vis-A-Ware, a visualization system to qualitatively and quantitatively evaluate, rank, and compare visibility data of candidate buildings with respect to a large number of viewpoints. Vis-A-Ware features a 3D spatial view of an urban scene and non-spatial views of data derived from visibility evaluations, which are tightly integrated by linked interaction. To enable a quantitative evaluation we developed four metrics in accordance with experts from urban planning. We illustrate the applicability of Vis-A-Ware on the basis of a use case scenario and present results from informal feedback sessions with domain experts from urban planning and development. This feedback suggests that Vis-A-Ware is a valuable tool for visibility analysis allowing analysts to answer complex questions more efficiently and objectively.

In der Sozialversicherung treffen zwei grundlegend verschiedene Ansichten aufeinander. Auf der einen Seite stehen Patienten und Arzte fur die bestmogliche Behandlung von Krankheiten im Zentrum. Der Kostenfaktor spielt durch den praktisch nicht vorhandenen Selbstbehalt keine Rolle, naturlich unter der Annahme, dass nur Sozialversicherungsleistungen konsumiert werden. Fur das Management der Sozialversicherung ist hingegen der Weg der Behandlung nicht relevant, sondern rein die Finanzierung. Da das Gesamtbudget im Allgemeinen nie ausreichen kann, um jedem Patienten eine optimale Behandlung zu bieten, muss ein Gleichgewicht zwischen den Kosten und der Effektivitat einer Behandlung gefunden werden.

In Abschnitt 1 wurde ein Uberblick uber die vorhandenen Daten und die Problemstellung gegeben. Insbesondere wurde in Abschnitt 1.2 ein Datensatz erstellt, auf dessen Basis die multivariaten Analysemethoden angewandt werden sollen. Das Ziel ist es, die Kosten von Patienten fur Antipsychotika uber Regressionsmethoden zu schatzen. Als beschreibende Variablen verwenden wir dabei das Geburtsjahr, die Kosten fur Allgemeinmediziner, die Kosten fur Facharzte, die Kosten fur sonstige Heilmittel und die Anzahl an verordneten DDDs. Alle Grosen sind dabei pro Versorgungstag zu verstehen.

Background FTY720 (Fingolimod) is a novel immunosuppressive drug investigated in clinical trials for organ transplantation and multiple sclerosis. It acts as a functional sphingosine-1-phosphate (S1P) receptor antagonist, thereby inhibiting the egress of lymphocytes from secondary lymphoid organs. As S1P is able to prevent IL-1beta induced cartilage degradation, we examined the direct impact of FTY720 on cytokine induced cartilage destruction. Methods Bovine chondrocytes were treated with the bioactive phosphorylated form of FTY720 (FTY720-P) in combination with IL-1beta or TNF-alpha. Expression of MMP-1,-3.-13, iNOS and ADAMTS-4,-5 and COX-2 was evaluated using quantitative real-time PCR and western blot. Glycosaminoglycan depletion from cartilage explants was determined using a 1,9-dimethylene blue assay and safranin O staining. Results FTY720-P significantly reduced IL-1beta and TNF-alpha induced expression of iNOS. In contrast FTY720-P increased MMP-3 and ADAMTS-5 mRNA expression. Furthermore depletion of glycosaminoglycan from cartilage explants by IL-1beta and TNF-alpha was significantly enhanced by FTY720-P in an MMP-3 dependent manner. Conclusions Our results suggest that FTY720 may enhance cartilage degradation in pro-inflammatory environment.