Specifying and Generating Editing Environments for Interactive Animated Visual Models

The behavior of a dynamic system is most easily understood if it is illustrated by a visual model that is animated over time. Graphs are a widely accepted approach for representing such dynamic models in an abstract way. System behavior and, therefore, model behavior corresponds to modifications of its representing graph over time. Graph transformations are an obvious choice for specifying these graph modifications and, hence, model behavior. Existing approaches use a graph to represent the static state of a model whereas modifications of this graph are described by graph transformations that happen instantaneously, but whose durations are stretched over time in order to allow for smooth animations. However, long-running and simultaneous animations of different parts of a model as well as interactions during animations are difficult to specify and realize that way. This paper describes a different approach. A graph does not necessarily represent the static aspect of a model, but rather represents the currently changing model. Graph transformations, when triggered at specific points of time, modify such graphs and thus start, change, or stop animations. Several concurrent animations may simultaneously take place in a model. Graph transformations can easily describe interactions within the model or between user and model, too. This approach has been integrated into the DiaMeta framework that now allows for specifying and generating editing environments for interactive animated visual models. The approach is demonstrated using the game Avalanche where many parallel and interacting movements take place

Integrated optical sensors for disposable microfluidics

Optical chemical sensors are established process monitoring tools in industry and research laboratories. Optical chemical sensors basically comprise of luminescent indicator dye based in a host polymer. They are easy to integrate, non-invasive, do not need any reference element and can be read-out contactless from outside. However, to fully exploit the potential in microfluidic or organ-on- chip devices, the sensors have to fulfil several demands including high brightness, capability to be applied as thin film, excellent photo-stability, cheap and accurate read-out systems, ease in use (simple calibration and drift free), simple mass production compatible preparation steps, compatibility with the chip materials, resistance towards γ-sterilisation and no toxicity. We present sensors for oxygen and pH fulfilling these demands. Our sensors can be excited with red-light and emit light in the near infra-red range (\u3c700 nm). This suppresses background fluorescence and scattering from biological material. Sensor layers or spots are deposited with inkjet-based micro-dispensing or air-brush spraying with good adherence on glass or polymeric materials. A modified miniaturized phase-fluorimeter in a foot-print of a memory stick enables the read-out of sensor sizes below 100 micrometers. The sensor enable dynamic cell culturing and monitoring of cell metabolism in a microfluidic environment. We will give examples of oxygen sensors in a organ-on-chip model and pH sensors in cell cultures. Please click Additional Files below to see the full abstract

From the Behavior Model of an Animated Visual Language to its Editing Environment Based on Graph Transformation

Animated visual models are a reasonable means for illustrating system behavior. However, implementing animated visual languages and their editing environments is difficult. Therefore, guidelines, specification methods, and tool support are necessary. A flexible approach for specifying model states and behavior is to use graphs and graph transformations. Thereby, a graph can also represent dynamic aspects of a model, like animations, and graph transformations are triggered over time to control the behavior, like starting, modifying, and stopping animations or adding and removing elements. These concepts had already been added to Dia-Meta, a framework for generating editing environments, but they provide only low-level support for specifying and implementing animated visual languages; specifying complex dynamic languages was still a challenging task. This paper proposes the Animation Modeling Language (AML), which allows to model behavior and animations on a higher level of abstraction. AML models are then translated into low-level specifications based on graph transformations. The approach is demonstrated using a traffic simulation

Monitoring of metabolic parameters of mammal cells cultures in microfluidic devices using integrated optical chemical sensors

Optical chemical sensors are well established in the chemical industry, life science, biotechnology and research laboratories. They are operate non-invasive, do not need any reference elements and can be read-out via contactless measurement. Moreover, it is possible to miniaturize and integrate them into microfluidic systems. Due to their simple composition, optical sensors can be produced at low price and therefore represent a good alternative compared to electrochemical sensors for their application in disposable microfluidics. The various possibilities of integrated optical oxygen sensors have already shown their potential in different microfluidic applications [1]. However, monitoring of further metabolic parameters is important for a better understanding of biological processes. Therefore, our group develops, next to oxygen sensors, also optical sensors for monitoring pH, glucose, CO2, ammonia and various ions. Still, integration in a Lab-on-a-chip format is a challenging task due to the state-of-the-art performances in terms of signal brightness, response times, optoelectronic read-out systems, fabrication and integration. Please click Additional Files below to see the full abstrac

Emotional experience in patients with clinically isolated syndrome and early multiple sclerosis

Background and purpose: Evidence suggests that there are changes in the processing of emotional information (EP) in people with multiple sclerosis (MS). It is unclear which functional domains of EP are affected, whether these changes are secondary to other MS-related neuropsychological or psychiatric symptoms and if EP changes are present in early MS. The aim of the study was to investigate EP in patients with early MS (clinically isolated syndrome and early relapsing/remitting MS) and healthy controls (HCs). Methods: A total of 29 patients without neuropsychological or psychiatric deficits and 29 matched HCs were presented with pictures from the International Affective Picture System with negative, positive or neutral content. Participants rated the induced emotion regarding valence and arousal using nine-level Likert scales. A speeded recognition test assessed memory for the emotional stimuli and for the emotional modulation of response time. A subgroup of participants was tested during a magnetic resonance imaging (MRI) session. Results: Patients in the MRI subgroup rated the experience induced by pictures with positive or negative emotional content significantly more weakly than HCs. Further, these patients were significantly less aroused when watching the pictures from the International Affective Picture System. There were no effects in the non-MRI subgroup or effects on emotional memory or response times. Conclusions: Emotional processing changes may be present in early MS in the form of flattened emotional experience on both the valence and arousal dimensions. These changes do not appear to be secondary to neuropsychological or psychiatric deficits. The fact that emotional flattening was only found in the MRI setting suggests that EP changes may be unmasked within stressful environments and points to the potential yet underestimated impact of the MRI setting on behavioral outcomes

Bias in random forest variable importance measures: Illustrations, sources and a solution

BACKGROUND: Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale of measurement or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types, for example when predictors include both sequence data and continuous variables such as folding energy, or when amino acid sequence data show different numbers of categories. RESULTS: Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. CONCLUSION: We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale of measurement or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analyzing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research

A versatile optode system for oxygen, carbon dioxide, and pH measurements in seawater with integrated battery and logger

Herein, we present a small and versatile optode system with integrated battery and logger for monitoring of O-2, pH, and pCO(2) in seawater. Three sensing materials designed for seawater measurements are optimized with respect to dynamic measurement range and long-term stability. The spectral properties of the sensing materials were tailored to be compatible with a commercially available laboratory oxygen logger that was fitted into a pressure housing. Interchangeable sensor caps with appropriate "sensing chemistry" are conveniently attached to the end of the optical fiber. This approach allows using the same instrument for multiple analytes, which offers great flexibility and minimizes hardware costs. Applications of the new optode system were demonstrated by recording depth profiles for the three parameters during a research cruise in the Baltic Sea and by measuring surface water transects of pH. The optode was furthermore used to monitor the concentration of dissolved oxygen in a seagrass meadow in the Limfjord, Denmark, and sensor packages consisting of pO(2), pH, and pCO(2) were deployed in the harbors of Kiel, Germany, and Southampton, England, for 6 d. The measurements revealed that the system can resolve typical patterns in seawater chemistry related to spatial heterogeneities as well as temporal changes caused by biological and tidal activity