Ayan Biswas

An Information-Aware Framework for Exploring Multivariate Data Sets

Information theory provides a theoretical framework for measuring information content for an observed variable, and has attracted much attention from visualization researchers for its ability to quantify saliency and similarity among variables. In this paper, we present a new approach towards building an exploration framework based on information theory to guide the users through the multivariate data exploration process. In our framework, we compute the total entropy of the multivariate data set and identify the contribution of individual variables to the total entropy. The variables are classified into groups based on a novel graph model where a node represents a variable and the links encode the mutual information shared between the variables. The variables inside the groups are analyzed for their representativeness and an information based importance is assigned. We exploit specific information metrics to analyze the relationship between the variables and use the metrics to choose isocontours of selected variables. For a chosen group of points, parallel coordinates plots (PCP) are used to show the states of the variables and provide an interface for the user to select values of interest. Experiments with different data sets reveal the effectiveness of our proposed framework in depicting the interesting regions of the data sets taking into account the interaction among the variables.

Dicer Knockdown Inhibits Endothelial Cell Tumor Growth via MicroRNA 21a-3p Targeting of Nox-4

Background: Endothelial cell tumors are the most common soft tissue tumor in infants. Results: Dicer knockdown up-regulated miR-21a-3p that targeted nox-4 mRNA preventing endothelial cell tumor formation in vivo. Conclusion: Nox-4 silencing inhibits endothelial cell tumor formation. Dicer knockdown up-regulates miR 21a-3p, which targets the Nox-4 3′-UTR. Significance: Novel drivers of endothelial cell tumor formation are reported. MicroRNAs (miR) are emerging as biomarkers and potential therapeutic targets in tumor management. Endothelial cell tumors are the most common soft tissue tumors in infants, yet little is known about the significance of miR in regulating their growth. A validated mouse endothelial cell (EOMA) tumor model was used to demonstrate that post-transcriptional gene silencing of dicer, the enzyme that converts pre-miR to mature miR, can prevent tumor formation in vivo. Tumors were formed in eight of eight mice injected with EOMA cells transfected with control shRNA but formed in only four of ten mice injected with EOMA cells transfected with dicer shRNA. Tumors that formed in the dicer shRNA group were significantly smaller than tumors in the control group. This response to dicer knockdown was mediated by up-regulated miR 21a-3p activity targeting the nox-4 3′-UTR. EOMA cells were transfected with miR 21a-3p mimic and luciferase reporter plasmids containing either intact nox-4 3′-UTR or with mutation of the proposed 3′-UTR miR21a-3p binding sites. Mean luciferase activity was decreased by 85% in the intact compared with the site mutated vectors (p < 0.01). Attenuated Nox-4 activity resulted in decreased cellular hydrogen peroxide production and decreased production of oxidant-inducible monocyte chemoattractant protein-1, which we have previously shown to be critically required for endothelial cell tumor formation. These findings provide the first evidence establishing the significance of dicer and microRNA in promoting endothelial cell tumor growth in vivo.

Uncertainty modeling and error reduction for pathline computation in time-varying flow fields

When the spatial and temporal resolutions of a time-varying simulation become very high, it is not possible to process or store data from every time step due to the high computation and storage cost. Although using uniformly down-sampled data for visualization is a common practice, important information in the un-stored data can be lost. Currently, linear interpolation is a popular method used to approximate data between the stored time steps. For pathline computation, however, errors from the interpolated velocity in the time dimension can accumulate quickly and make the trajectories rather unreliable. To inform the scientist the error involved in the visualization, it is important to quantify and display the uncertainty, and more importantly, to reduce the error whenever possible. In this paper, we present an algorithm to model temporal interpolation error, and an error reduction scheme to improve the data accuracy for temporally down-sampled data. We show that it is possible to compute polynomial regression and measure the interpolation errors incrementally with one sequential scan of the time-varying flow field. We also show empirically that when the data sequence is fitted with least-squares regression, the errors can be approximated with a Gaussian distribution. With the end positions of particle traces stored, we show that our error modeling scheme can better estimate the intermediate particle trajectories between the stored time steps based on a maximum likelihood method that utilizes forward and backward particle traces.

Endothelial cell tumor growth is Ape/ref-1 dependent

Tumor-forming endothelial cells have highly elevated levels of Nox-4 that release H2O2 into the nucleus, which is generally not compatible with cell survival. We sought to identify compensatory mechanisms that enable tumor-forming endothelial cells to survive and proliferate under these conditions. Ape-1/ref-1 (Apex-1) is a multifunctional protein that promotes DNA binding of redox-sensitive transcription factors, such as AP-1, and repairs oxidative DNA damage. A validated mouse endothelial cell (EOMA) tumor model was used to demonstrate that Nox-4-derived H2O2 causes DNA oxidation that induces Apex-1 expression. Apex-1 functions as a chaperone to keep transcription factors in a reduced state. In EOMA cells Apex-1 enables AP-1 binding to the monocyte chemoattractant protein-1 (mcp-1) promoter and expression of that protein is required for endothelial cell tumor formation. Intraperitoneal injection of the small molecule inhibitor E3330, which specifically targets Apex-1 redox-sensitive functions, resulted in a 50% decrease in tumor volume compared with mice injected with vehicle control (n = 6 per group), indicating that endothelial cell tumor proliferation is dependent on Apex-1 expression. These are the first reported results to establish Nox-4 induction of Apex-1 as a mechanism promoting endothelial cell tumor formation.

An uncertainty-driven approach to vortex analysis using oracle consensus and spatial proximity

Although vortex analysis and detection have been extensively investigated in the past, none of the existing techniques are able to provide fully robust and reliable identification results. Local vortex detection methods are popular as they are efficient and easy to implement, and produce binary outputs based on a user-specified, hard threshold. However, vortices are global features, which present challenges for local detectors. On the other hand, global detectors are computationally intensive and require considerable user input. In this work, we propose a consensus-based uncertainty model and introduce spatial proximity to enhance vortex detection results obtained using point-based methods. We use four existing local vortex detectors and convert their outputs into fuzzy possibility values using a sigmoid-based soft-thresholding approach. We apply a majority voting scheme that enables us to identify candidate vortex regions with a higher degree of confidence. Then, we introduce spatial proximity- based analysis to discern the final vortical regions. Thus, by using spatial proximity coupled with fuzzy inputs, we propose a novel uncertainty analysis approach for vortex detection. We use experts input to better estimate the system parameters and results from two real-world data sets demonstrate the efficacy of our method.

Efficient local histogram searching via bitmap indexing

Representing features by local histograms is a proven technique in several volume analysis and visualization applications including feature tracking and transfer function design. The efficiency of these applications, however, is hampered by the high computational complexity of local histogram computation and matching. In this paper, we propose a novel algorithm to accelerate local histogram search by leveraging bitmap indexing. Our method avoids exhaustive searching of all voxels in the spatial domain by examining only the voxels whose values fall within the value range of user‐defined local features and their neighborhood. Based on the idea that the value range of local features is in general much smaller than the dynamic range of the entire dataset, we propose a local voting scheme to construct the local histograms so that only a small number of voxels need to be examined. Experimental results show that our method can reduce much computational workload compared to the conventional approaches. To demonstrate the utility of our method, an interactive interface was developed to assist users in defining target features as local histograms and identify the locations of these features in the dataset.

Visualization of Time-Varying Weather Ensembles across Multiple Resolutions

Uncertainty quantification in climate ensembles is an important topic for the domain scientists, especially for decision making in the real-world scenarios. With powerful computers, simulations now produce time-varying and multi-resolution ensemble data sets. It is of extreme importance to understand the model sensitivity given the input parameters such that more computation power can be allocated to the parameters with higher influence on the output. Also, when ensemble data is produced at different resolutions, understanding the accuracy of different resolutions helps the total time required to produce a desired quality solution with improved storage and computation cost. In this work, we propose to tackle these non-trivial problems on the Weather Research and Forecasting (WRF) model output. We employ a moment independent sensitivity measure to quantify and analyze parameter sensitivity across spatial regions and time domain. A comparison of clustering structures across three resolutions enables the users to investigate the sensitivity variation over the spatial regions of the five input parameters. The temporal trend in the sensitivity values is explored via an MDS view linked with a line chart for interactive brushing. The spatial and temporal views are connected to provide a full exploration system for complete spatio-temporal sensitivity analysis. To analyze the accuracy across varying resolutions, we formulate a Bayesian approach to identify which regions are better predicted at which resolutions compared to the observed precipitation. This information is aggregated over the time domain and finally encoded in an output image through a custom color map that guides the domain experts towards an adaptive grid implementation given a cost model. Users can select and further analyze the spatial and temporal error patterns for multi-resolution accuracy analysis via brushing and linking on the produced image. In this work, we collaborate with a domain expert whose feedback shows the effectiveness of our proposed exploration work-flow.

MRP-1 dependent GSSG efflux as a critical survival factor for oxidant-enriched tumorigenic endothelial cells

Abstract Endothelial cell tumors are the most common soft tissue tumors in infants. Tumor forming endothelial (EOMA) cells are able to escape cell death fate despite excessive nuclear oxidant burden. Our previous work recognized peri-nuclear Nox-4 as a key contributor to EOMA growth. The objective of this work was to characterize the mechanisms by which EOMA cells evade oxidant toxicity and thrive. In EOMA, compared to that in the cytosol, nuclear GSSG/GSH ratio was five-fold higher. Compared to those in healthy murine arterial endothelial cells (MAE), GSSG/GSH was over twice in EOMA in a situation. Multidrug resistance-associated protein-1 (MRP-1), an active GSSG efflux mechanism, showed two-fold increased activity in EOMA compared to MAE. Hyperactive YB-1 and Ape/Ref-1 were responsible for high MRP-1 expression in EOMA. Proximity ligand assay demonstrated MRP-1 and YB-1 binding. Such binding enabled the nuclear targeting of MRP-1 in EOMA in a leptomycin-B sensitive manner. MRP-1 inhibition as well as knockdown trapped nuclear GSSG causing cell death of EOMA. Disulfide loading of cells by inhibition of GSSG reductase (bischoloronitrosourea) or thioredoxin reductase (auranofin) was effective in causing EOMA death as well. In sum, EOMA cells survive a heavy oxidant burden by rapid efflux of GSSG, which is lethal if trapped within the cell. A hyperactive MRP-1 system for GSSG efflux acts as a critical survival factor for these cells making it a potential target for EOMA therapeutics.Endothelial cell tumors are the most common soft tissue tumors in infants. Tumor-forming endothelial (EOMA) cells are able to escape cell death fate despite excessive nuclear oxidant burden. Our previous work recognized perinuclear Nox-4 as a key contributor to EOMA growth. The objective of this work was to characterize the mechanisms by which EOMA cells evade oxidant toxicity and thrive. In EOMA cells, compared with in the cytosol, the nuclear GSSG/GSH ratio was 5-fold higher. Compared to the ratio observed in healthy murine aortic endothelial (MAE) cells, GSSG/GSH was over twice as high in EOMA cells. Multidrug resistance-associated protein-1 (MRP-1), an active GSSG efflux mechanism, showed 2-fold increased activity in EOMA compared with MAE cells. Hyperactive YB-1 and Ape/Ref-1 were responsible for high MRP-1 expression in EOMA. Proximity ligand assay demonstrated MRP-1 and YB-1 binding. Such binding enabled the nuclear targeting of MRP-1 in EOMA in a leptomycin-B-sensitive manner. MRP-1 inhibition as well as knockdown trapped nuclear GSSG, causing cell death of EOMA. Disulfide loading of cells by inhibition of GSSG reductase (bischoloronitrosourea) or thioredoxin reductase (auranofin) was effective in causing EOMA death as well. In sum, EOMA cells survive a heavy oxidant burden by rapid efflux of GSSG, which is lethal if trapped within the cell. A hyperactive MRP-1 system for GSSG efflux acts as a critical survival factor for these cells, making it a potential target for EOMA therapeutics.

Supporting correlation analysis on scientific datasets in parallel and distributed settings

With growing computational capabilities of parallel machines, scientific simulations are being performed at finer spatial and temporal scales, leading to a data explosion. Careful analysis of this data holds much promise for future scientific discoveries. Particularly, correlation analysis, which focuses on studying the potential relationships among multiple variables, is becoming a useful method for scientific analysis. This paper focuses on the problem of correlation analysis across large-scale simulation datasets, including 1) accelerating this analysis with the use of bitmap indexing as a representative summary of the data, 2) developing efficient algorithms for parallel execution, 3) performing analysis in distributed environments, i.e., for cases where different attributes are stored in geographically distributed repositories, and 4) combining sampling with correlation analysis. These algorithms have been implemented in a system that provides a high-level API for specification of the analyses, including allowing correlation analysis on specified value-based and dimension-based subsets of the data, and supports interactive and incremental analysis. We have extensively evaluated our framework for efficiency, and have also carried out case studies with domain scientists to establish how it can aid data-driven discovery process.

Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.