Model-driven design for the visual analysis of heterogeneous data

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.     

Volatile compounds of original African black and white shea butter from Tchad and Cameroon

Shea butter is used as an edible vegetable fat in many African countries. It can be utilized as a substitute or complete replacement for cocoa butter in various applications and plays an important role in traditional African medicinal practice. Although detection of volatile compounds by solid‐phase micro‐extraction gas‐chromatography mass‐spectroscopy (SPME‐GC‐MS) is a very reliable and reproducible technique, which can be used as an important part of authenticity checking, production monitoring and contamination detection, no published data about volatile compounds of shea butter are available so far. In this investigation, the characteristic volatiles in the headspace of original African shea butter samples were identified by using SPME‐capillary‐GC coupled to a mass selective detector. Almost 100 different volatile components were identified, e.g. fatty acids, saturated and unsaturated aldehydes and ketones, terpenes, and typical Maillard reaction products such as methylfuranes and pyrazines. Furthermore, the samples have been olfactorily evaluated by a panel of professional flavorists and trained analytical chemists. It can be stated that variations in processing conditions of shea butter result in considerable differences in the composition of headspace volatiles, detected by SPME‐GC‐MS and human olfaction.     

In situ exploration of large dynamic networks

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.     

Visual methods for analyzing time-oriented data

Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.     

Microarrays for the screening of allergen-specific IgE in human serum

The described in vitro test system for allergy diagnosis is based on microscope glass slides activated with (3-glycidyloxypropyl)trimethoxysilane. Allergen solutions are immobilized as small droplets (approximately 10 nL) on the activated glass slides with a piezoelectric arrayer. In contrast to other tests for specific IgE, such as Pharmacia CAP FEIA, AlaSTAT, or FAST, only a 25-microL serum sample is needed for the screening of allergen-specific IgE against a multitude of allergens and the test can be performed in less than 1 h. Compared with multiallergen dipstick screening tests (e.g., IgEquick, CMG Immunodot) based on multiallergen-coated nitrocellulose strips, the measurement of the microarray-based system can be performed automatically. The chemiluminescence intensities are detected with a sensitive CCD camera. Allergen extracts and recombinant/purified allergens (24 preparations) have been used on the same modified surface for the screening of allergen-specific IgE. With these disposable microarray slides, it is possible to distinguish between patients with and without elevated levels of allergen-specific IgE. Repeated measurements of serum samples demonstrated a sufficient reproducibility. Detection limits (microg/L) of 0.35 (r Betvl), 0.16 (PLA2), and 1.9 (Der p1) were achieved.     

Constructing and visualizing chemical reaction networks from pi-calculus models

The π-calculus, in particular its stochastic version the stochastic π-calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π-calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequences of reaction sets, which are obtained from state trajectories, in terms of reaction networks. Our translation from model states to reaction sets is formally defined and shown to be correct, in the sense that it reflects the states and transitions as they are derived from the continuous time Markov chain-semantics of the stochastic π-calculus. Our visualization concept combines high level measures of network complexity with interactive, table-based network visualizations. It directly reflects the structures introduced in the first step and allows modelers to explore the resulting simulation traces by providing both: an overview of a network’s evolution and a detail inspection on demand.     

Addressees of performance goals.

As performance goals aim to both procure acknowledgment of one's abilities and to avoid revealing a lack of one's abilities, the authors hypothesized that students hold specific performance goals for different addressees and that there are specific correlational patterns with other motivational constructs. They analyzed a data set of 2,675 pupils (1,248 boys and 1,426 girls) attending Grades 8 and 9 (mean age=15.0, SD=0.97). The students completed a questionnaire consisting of 12 items measuring performance approach goals and 12 items measuring performance avoidance goals. In each subset, 4 groups of addressees were differentiated: parents, teachers, peers, and the acting individual him/herself. Additionally, several external criteria were measured. The authors concurrently tested theory-driven, structural equation models. Incorporating all 24 items, the best-fitting model was a multitrait-multimethod model, which posited 2 factors for approach and avoidance goals and 4 addressee factors. While performance goals addressing parents showed relationships to maladaptive motivational and learning patterns, performance goals addressing classmates and self showed relationships to adaptive motivational and learning patterns. The relationships between performance goals addressing teachers and external criteria were rather weak and unsystematic. (PsycINFO Database Record (c) 2010 APA, all rights reserved)