Interactive Focus+Context Visualization with Linked 2D/3D Scatterplots

Scatterplots in 2D and 3D are very useful tools, but also suffer from a number of problems. Overplotting hides the true number of points that are displayed, and showing point clouds in 3D is problematic both in terms of perception and interaction. We propose

Global Distribution of Human-Associated Fecal Genetic Markers in Reference Samples from Six Continents

Numerous bacterial genetic markers are available for the molecular detection of human sources of fecal pollution in environmental waters. However, widespread application is hindered by a lack of knowledge regarding geographical stability, limiting implementation to a small number of well-characterized regions. This study investigates the geographic distribution of five human-associated genetic markers (HF183/BFDrev, HF183/BacR287, BacHum-UCD, BacH, and Lachno2) in municipal wastewaters (raw and treated) from 29 urban and rural wastewater treatment plants (750-4»400»000 population equivalents) from 13 countries spanning six continents. In addition, genetic markers were tested against 280 human and nonhuman fecal samples from domesticated, agricultural and wild animal sources. Findings revealed that all genetic markers are present in consistently high concentrations in raw (median log10 7.2-8.0 marker equivalents (ME) 100 mL-1) and biologically treated wastewater samples (median log10 4.6-6.0 ME 100 mL-1) regardless of location and population. The false positive rates of the various markers in nonhuman fecal samples ranged from 5% to 47%. Results suggest that several genetic markers have considerable potential for measuring human-associated contamination in polluted environmental waters. This will be helpful in water quality monitoring, pollution modeling and health risk assessment (as demonstrated by QMRAcatch) to guide target-oriented water safety management across the globe.Fil: Mayer, René E.. Vienna University of Technology; Austria. Interuniversity Cooperation Centre for Water and Health; AustriaFil: Reischer, Georg. Vienna University of Technology; AustriaFil: Ixenmaier, Simone K.. Vienna University of Technology; Austria. Interuniversity Cooperation Centre for Water and Health; AustriaFil: Derx, Julia. Vienna University of Technology; AustriaFil: Blaschke, Alfred Paul. Vienna University of Technology; AustriaFil: Ebdon, James E.. University of Brighton; Reino UnidoFil: Linke, Rita. Vienna University of Technology; Austria. Interuniversity Cooperation Centre Water And Health; AustriaFil: Egle, Lukas. Vienna University of Technology; AustriaFil: Ahmed, Warish. Csiro Land And Water; AustraliaFil: Blanch, Anicet R.. Universidad de Barcelona; EspañaFil: Byamukama, Denis. Makerere University; UgandaFil: Savill, Marion. Affordable Water Limited;Fil: Mushi, Douglas. Sokoine University Of Agriculture; TanzaniaFil: Cristobal, Hector Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones para la Industria Química. Universidad Nacional de Salta. Facultad de Ingeniería. Instituto de Investigaciones para la Industria Química; ArgentinaFil: Edge, Thomas A.. Canada Centre for Inland Waters. Environment and Climate Change Canada; CanadáFil: Schade, Margit A.. Bavarian Environment Agency; AlemaniaFil: Aslan, Asli. Georgia Southern University; Estados UnidosFil: Brooks, Yolanda M.. Michigan State University; Estados UnidosFil: Sommer, Regina. Interuniversity Cooperation Centre Water And Health; Austria. Medizinische Universitat Wien; AustriaFil: Masago, Yoshifumi. Tohoku University; JapónFil: Sato, Maria I.. Cia. Ambiental do Estado de Sao Paulo. Departamento de Análises Ambientais; BrasilFil: Taylor, Huw D.. University of Brighton; Reino UnidoFil: Rose, Joan B.. Michigan State University; Estados UnidosFil: Wuertz, Stefan. Nanyang Technological University. Singapore Centre for Environmental Life Sciences Engineering and School of Civil and Environmental Engineering; SingapurFil: Shanks, Orin. U.S. Environmental Protection Agency; Estados UnidosFil: Piringer, Harald. Vrvis Research Center; AustriaFil: Mach, Robert L.. Vienna University of Technology; AustriaFil: Savio, Domenico. Karl Landsteiner University of Health Sciences; AustriaFil: Zessner, Matthias. Vienna University of Technology; AustriaFil: Farnleitner, Andreas. Vienna University of Technology; Austria. Interuniversity Cooperation Centre Water And Health; Austria. Karl Landsteiner University of Health Sciences; Austri

Human-Based and Automatic Feature Ideation for Time Series Data: A Comparative Study

Feature ideation is a crucial early step in the feature extraction process, where new features are extracted from raw data. For phenomena existing in time series data, this often includes the ideation of statistical parameters, representations of trends and periodicity, or other geometrical and shape-based characteristics. The strengths of automatic feature ideation methods are their generalizability, applicability, and robustness across cases, whereas human-based feature ideation is most useful in uncharted real-world applications, where incorporating domain knowledge is key. Naturally, both types of methods have proven their right to exist. The motivation for this work is our observation that for time series data, surprisingly few human-based feature ideation approaches exist. In this work, we discuss requirements for human-based feature ideation for VA applications and outline a set of characteristics to assess the goodness of feature sets. Ultimately, we present the results of a comparative study of humanbased and automated feature ideation methods, for time series data in a real-world Industry 4.0 setting. One of our results and discussion items is a call to arms for more human-based feature ideation approaches

Quantifying and comparing features in high-dimensional datasets

Linking and brushing is a proven approach to analyzing multi-dimensional datasets in the context of multiple coordinated views. Nevertheless, most of the respective visualization techniques only offer qualitative visual re-sults. Many user tasks, however, also require precise quantitative results as, for example, offered by statistical analysis. In succession of the useful Rank-by-Feature Framework, this paper describes a joint visual and statistical approach for guiding the user through a high-dimensional dataset by ranking dimensions (1D case) and pairs of dimensions (2D case) according to statistical summaries. While the original Rank-by-Feature Framework is limited to global features, the most im-portant novelty here is the concept to consider local features, i.e., data subsets defined by brushing in linked views. The ability to compare subsets to other subsets and subsets to the whole dataset in the context of a large number of dimensions significantly extends the benefits of the approach especially in later stages of an exploratory data analysis. A case study illustrates the workflow by analyzing counts of keywords for classifying e-mails as spam or no-spam