A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling.

An important approach for scientific inquiry across many disciplines involves using observational time series data to understand the relationships between key variables to gain mechanistic insights into the underlying rules that govern the given system. In real systems, such as those found in ecology, the relationships between time series variables are generally not static; instead, these relationships are dynamical and change in a nonlinear or state-dependent manner. To further understand such systems, we investigate integrating methods that appropriately characterize these dynamics (i.e., methods that measure interactions as they change with time-varying system states) with visualization techniques that can help analyze the behavior of the system. Here, we focus on empirical dynamic modeling (EDM) as a state-of-the-art method that specifically identifies causal variables and measures changing state-dependent relationships between time series variables. Instead of using approaches centered on parametric equations, EDM is an equation-free approach that studies systems based on their dynamic attractors. We propose a visual analytics system to support the identification and mechanistic interpretation of system states using an EDM-constructed dynamic graph. This work, as detailed in four analysis tasks and demonstrated with a GUI, provides a novel synthesis of EDM and visualization techniques such as brush-link visualization and visual summarization to interpret dynamic graphs representing ecosystem dynamics. We applied our proposed system to ecological simulation data and real data from a marine mesocosm study as two key use cases. Our case studies show that our visual analytics tools support the identification and interpretation of the system state by the user, and enable us to discover both confirmatory and new findings in ecosystem dynamics. Overall, we demonstrated that our system can facilitate an understanding of how systems function beyond the intuitive analysis of high-dimensional information based on specific domain knowledge.

Visualization of JOV abstracts.

ABSTRACT: Since the abstract can be found at the beginning of most scientific articles and is an essential part of the article, several attempts have been made to explore the rhetorical moves of abstracts in various research fields. These studies dealt only with accepted articles since they can be easily accessed. Although the findings of such works have some pedagogical implications for academic writing courses for young researchers who are relatively new to their fields, they do not contribute enough to the transparency of the peer review processes conducted in research fields. Increasing transparency requires considering rejected articles since they help to clarify the decision criteria in the peer review. Based on 591 abstracts of accepted or rejected articles submitted to Journal of Visualization (JOV), the present study aimed at exploring the differences between the accepted and rejected abstracts. The results show that there are significant differences in the structures of the abstracts. Since we also successfully develop a classification model for the decision using a machine-learning technique, the findings of this study have some implications for developing a semi-automatic reviewing system that can reduce the reviewer's burden and increase the review quality.

Layered Graph Drawing for Visualizing Evaluation Structures.

An evaluation structure is a hierarchical structure of human cognition extracted from interviews based on the evaluation grid method. An evaluation structure can be defined as a directed acyclic graph (DAG). The authors propose a layer-assignment method that is part of the Sugiyama framework, a popular method for drawing DAGs, to satisfy the requirements for drawing evaluation structures. Their evaluations demonstrate that the layered graph drawing produced by the proposed layer-assignment method is preferred by users and aids in the understanding of evaluation structures.

Dysregulation of a potassium channel, THIK-1, targeted by caspase-8 accelerates cell shrinkage.

Activation of caspases is crucial for the execution of apoptosis. Although the caspase cascade associated with activation of the initiator caspase-8 (CASP8) has been investigated in molecular and biochemical detail, the physiological role of CASP8 is not fully understood. Here, we identified a two-pore domain potassium channel, tandem-pore domain halothane-inhibited K+ channel 1 (THIK-1), as a novel CASP8 substrate. The intracellular region of THIK-1 was cleaved by CASP8 in apoptotic cells. Overexpression of THIK-1, but not its mutant lacking the CASP8-target sequence in the intracellular portion, accelerated cell shrinkage in response to apoptotic stimuli. In contrast, knockdown of endogenous THIK-1 by RNA interference resulted in delayed shrinkage and potassium efflux. Furthermore, a truncated THIK-1 mutant lacking the intracellular region, which mimics the form cleaved by CASP8, led to a decrease of cell volume of cultured cells without apoptotic stimulation and excessively promoted irregular development of Xenopus embryos. Taken together, these results indicate that THIK-1 is involved in the acceleration of cell shrinkage. Thus, we have demonstrated a novel physiological role of CASP8: creating a cascade that advances the cell to the next stage in the apoptotic process.

Minimizing the Number of Edges via Edge Concentration in Dense Layered Graphs.

Edge concentration in dense bipartite graphs is a technique for reducing the numbers of edges and edge crossings in graph drawings. The conventional method proposed by Newbery is designed to reduce the number of edge crossings; however, it does not always reduce the number of edges. Reducing the number of edges is also an important factor for improving the readability of graphs. However, no edge concentration method with the explicit purpose of minimizing the number of edges has previously been studied. In this study, we propose a novel, efficient heuristic method for minimizing the number of edges during edge concentration. We demonstrate the efficiency of the proposed method via a comparison using randomly generated graphs. We find that Newbery's method fails to reduce the number of edges when the number of vertices is large. By contrast, the proposed method achieves an average compression ratio of 47 to 82 percent for all generated graph groups. We also present a real-world application of the proposed method using a causality network of biological data.

In vivo imaging of hierarchical spatiotemporal activation of caspase-8 during apoptosis.

BACKGROUND: Activation of caspases is crucial for the execution of apoptosis. Although the caspase cascade associated with activation of the initiator caspase-8 (CASP8) has been investigated in molecular and biochemical detail, the dynamics of CASP8 activation are not fully understood. METHODOLOGY/PRINCIPAL FINDINGS: We have established a biosensor based on fluorescence resonance energy transfer (FRET) for visualizing apoptotic signals associated with CASP8 activation at the single-cell level. Our dual FRET (dual-FRET) system, comprising a triple fusion fluorescent protein, enabled us to simultaneously monitor the activation of CASP8 and its downstream effector, caspase-3 (CASP3) in single live cells. With the dual-FRET-based biosensor, we detected distinct activation patterns of CASP8 and CASP3 in response to various apoptotic stimuli in mammalian cells, resulting in the positive feedback amplification of CASP8 activation. We reproduced these observations by in vitro reconstitution of the cascade, with a recombinant protein mixture that included procaspases. Furthermore, using a plasma membrane-bound FRET-based biosensor, we captured the spatiotemporal dynamics of CASP8 activation by the diffusion process, suggesting the focal activation of CASP8 is sufficient to propagate apoptotic signals through death receptors. CONCLUSIONS: Our new FRET-based system visualized the activation process of both initiator and effector caspases in a single apoptotic cell and also elucidated the necessity of an amplification loop for full activation of CASP8.

Framework for creating new discriminats for detecting DTI properties: DTI mapper.

We have commonly employed medical images, not including X-ray photography, which have their image enhanced by taking advantage of certain characteristics of the human body to look for some valuable medical information. In general, the medical imaging modalities have employed an image acquisition method which enhances a certain feature of the seat of a disease for later processing of the acquired images. Typical examples of the feature enhancement for medical images are the active area mapping on fMRI using Statistical Parametric Mapping (SPM), and the diffusivity mapping on Diffusion Tensor Imaging (DTI) using Fractional Anisotropy (FA), Apparent Diffusion Coefficient (ABC) or Relative Anisotropy (RA). Especially in DTI, many researchers have been trying to reveal the current state of a disease without any invasion of the body by using some variable discriminants. In this paper, we propose a framework, which supports and promotes the creation of new discriminants for DTI. The proposed system enables the users to create new discriminants by using eigen values from the voxels of DTI data, and to search for important clinical information applying discriminant mapping to the DTI slice images.

Improved hierarchical parameter optimization technique: application for a cardiac myocyte model.

We propose a hierarchical parameter optimization technique which further enhances our original technique for an accurate biological cell simulation. Our original technique generates a k-d tree and uses the coefficient of multiple determination (R2) for tree-branching, however it requires a huge computation time since it processes the response surface at every leaf node of the k-d tree, including those that do not have optimum parameters. In our parameter optimization problem, the objective function is defined as the difference between the measured and calculated waveform of action potentials in a cardiac myocyte. The function value is always non-negative, and is equal to zero if and only if the best optimized parameter is included in the leaf node. To minimize the computational cost problem, our proposed technique takes advantage of the aforementioned conditions and only processes a leaf node if the corresponding Hessian matrix of the objective function is found to be a positive definite matrix. We confirmed the effectiveness of the proposed parameter optimization technique by searching for some pre-determined parameters.