The concept of clipping planes is well known in computer graphics and can be used to create cut-away views. But clipping against just analytical defined planes is not always suitable for communicating every aspect of such visualization. For example, in hand-drawn technical illustrations, artists tend to communicate the difference between a cut and a model feature by using non-regular, sketchy cut lines instead of straight ones. To enable this functionality in computer graphics, this paper presents a technique for applying 2.5D clip-surfaces in real-time. Therefore, the clip plane equation is extended with an additional offset map, which can be represented by a texture map that contains height values. Clipping is then performed by varying the clip plane equation with respect to such an offset map. Further, a capping technique is proposed that enables the rendering of caps onto the clipped area to convey the impression of solid material. It avoids a re-meshing of a solid polygonal mesh after clipping is performed. Our approach is pixel precise, applicable in real-time, and takes fully advantage of graphics accelerators.

New acquisition methods have increased the availability of surface property data that capture location-dependent data on feature surfaces. However, these data are not supported as fully in the geovisualization of the Digital City as established data categories such as feature attributes, 2D rasters, or geometry. Consequently, 3D surface properties are largely excluded from the information extraction and knowledge creation process of geovisualization despite their potential for being an effective tool in many such tasks. To overcome this situation, this paper examines the benefits of a better integration into geovisualization systems in terms of two examples and discusses technological foundations for surface property support. The main contribution is the identification of computer graphics techniques as a suitable basis for such support. This way, the processing of surface property data fits well into existing visualization systems. This finding is demonstrated through an interactive prototypic visualization system that extends an existing system with surface property support. While this prototype concentrates on technology and neglects user-related and task-related aspects, the paper includes a discussion on challenges for making surface properties accessible to a wider audience.

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

BACKGROUND: Machine learning promises to support the diagnosis of dementia and Alzheimer's Disease, but may not perform well in new settings. We present a framework to assess the transportability of models predicting cognitive impairment in external settings with different demographics. METHODS: We mapped and quantified relationships between variables associated with cognitive impairment using causal graphs, structural equation models, and data from the ADNI study. These estimates generated datasets for training and validating prediction models. We measured transportability to external settings with interventions on age, APOE e4, and sex, using calibration metric differences. RESULTS: Models predicting with causes of the outcome were 1.3-12.8 times more transportable than those predicting with consequences. Logistic and lasso models had better calibration in internal validation settings than random forest and boosted models. DISCUSSION: Applying a framework considering causal relationships is crucial to assess transportability. Future research could investigate more interventions and methods to quantify causal relationships in risk prediction.

Monitoring fatigue during resistance training is essential to avoid injuries caused by overtraining. Fatigue can be comprehensively quantified by the external and internal load, where the external load is the work done by the athlete, and the internal load is the psychological and physiological response to the external load. This paper proposes a computer vision method to continuously monitor fatigue during resistance training by predicting external and internal parameters, namely the generated power and the rating of perceived exertion. We utilize the human pose estimation from two Microsoft Azure Kinect cameras to capture the movement of athletes while performing stationary exercises. Our method processes the obtained kinematic data, computes skeleton features to train traditional machine learning algorithms, and constructs feature maps to train convolutional neural network-based models to predict the load parameters. For evaluation, we recorded a dataset of 16 subjects who performed squat exercises on a Flywheel and rated their perceived exertion after each set. A measuring unit integrated into the Flywheel provided power readings for each repetition. The results show that our method achieves good results in predicting both parameters. Gradient Boosting Regression Trees best predicted perceived exertion with a mean absolute percentage error of 8.08% and a Spearman’s ρ=0.74. Multi-layer Perceptron performed best in predicting power with a mean absolute error of 23.13 Watts and ρ=0.79. Our findings show that our approach delivers promising external and internal load quantifications for fatigue, with great potential to provide external feedback to coaches or athletes.

We present CARDIO:DE, the first freely available and distributable large German clinical corpus from the cardiovascular domain. CARDIO:DE encompasses 500 clinical routine German doctor's letters from Heidelberg University Hospital, which were manually annotated. Our prospective study design complies well with current data protection regulations and allows us to keep the original structure of clinical documents consistent. In order to ease access to our corpus, we manually de-identified all letters. To enable various information extraction tasks the temporal information in the documents was preserved. We added two high-quality manual annotation layers to CARDIO:DE, (1) medication information and (2) CDA-compliant section classes. To the best of our knowledge, CARDIO:DE is the first freely available and distributable German clinical corpus in the cardiovascular domain. In summary, our corpus offers unique opportunities for collaborative and reproducible research on natural language processing models for German clinical texts.

There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary. Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines’ instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30% for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.

Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.

\textcopyright 2015 American Academy of Allergy, Asthma \& Immunology. Background Brain-derived neurotrophic factor (BDNF) is a secretory protein that has been implicated in the pathogenesis of allergic rhinitis (AR), atopic asthma, and eczema, but it is currently unknown whether BDNF polymorphisms influence susceptibility to moderate-to-severe AR. Objective We sought to identify disease associations and the functional effect of BDNF genetic variants in patients with moderate-to-severe AR. Methods Tagging single nucleotide polymorphisms (SNPs) spanning the BDNF gene were selected from the human HapMap Han Chinese from Beijing (CHB) data set, and associations with moderate-to-severe AR were assessed in 2 independent cohorts of Chinese patients (2216 from Shandong province and 1239 living in Singapore). The functional effects of the BDNF genetic variants were determined by using both in vitro and ex vivo assays. Results The tagging SNP rs10767664 was significantly associated with the risk of moderate-to-severe AR in both Singapore Chinese (P =.0017; odds ratio, 1.324) and Shandong Chinese populations (P =.039; odds ratio, 1.180). The coding nonsynonymous SNP rs6265 was in perfect linkage with rs10767664 and conferred increased BDNF protein secretion by a human cell line in vitro. Subjects bearing the AA genotype of rs10767664 exhibited increased risk of moderate-to-severe AR and displayed increased BDNF protein and total IgE levels in plasma. Using a large-scale expression quantitative trait locus study, we demonstrated that BDNF SNPs are significantly associated with altered BDNF concentrations in peripheral blood. Conclusion A common genetic variant of the BDNF gene is associated with increased risk of moderate-to-severe AR, and the AA genotype is associated with increased BDNF mRNA levels in peripheral blood. Together, these data indicate that functional BDNF gene variants increase the risk of moderate-to-severe AR.

We describe the software architecture of a rendering system that follows a pragmatic approach to integrating and bundling the power of different lowlevel rendering systems within an object-oriented framework. The generic rendering system provides higher-level abstractions to existing rendering systems and serves as a framework for developing new rendering techniques. It wraps the functionality of several, widely used rendering systems, defines a unified, object-oriented application programming interface, and provides an extensible, customizable apparatus for evaluating and interpreting hierarchical scene information. As a fundamental property, individual features of a specific rendering system can be integrated into the generic rendering system in a transparent way. The system is based on a state machine, called engine, which operates on rendering components. Four major categories of rendering components constitute the generic rendering system: shapes represent geometries, attributes specify properties assigned to geometries and scenes, handlers encapsulate rendering algorithms, and techniques represent evaluation strategies for rendering components. As a proof of concept, we have implemented the described software architecture by the Virtual Rendering System which currently wraps OpenGL, Radiance, POV Ray, and RenderMan.