Project D04

The emerging field of Immersive Analytics investigates how novel display and interaction technologies can enable people to better explore and analyse data and complex information. Collaboration is a crucial aspect of Immersive Analytics. In this paper we present ContextuWall, a system for interactive local and remote collaboration using touch and mobile devices as well as displays of various sizes. The system enables groups of users located on different sites to share content to a jointly used virtual desktop which is accessible over a secured network. This virtual desktop can be shown on different large displays simultaneously, taking advantage of their high resolution. To enable users to intuitively share, arrange as well as annotate image content, a purpose-built client software has been developed and can easily be adapted with plug-ins for existing data analytics software. We show exemplary use cases and describe the system architecture and its implementation.

Background Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. Findings A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Conclusions Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

We propose Graph Thumbnails, small icon-like visualisations of the high-level structure of network data. Graph Thumbnails are designed to be legible in small multiples to support rapid browsing within large graph corpora. Compared to existing graph-visualisation techniques our representation has several advantages: (1) the visualisation can be computed in linear time; (2) it is canonical in the sense that isomorphic graphs will always have identical thumbnails; and (3) it provides precise information about the graph structure. We report the results of two user studies. The first study compares Graph Thumbnails to node-link and matrix views for identifying similar graphs. The second study investigates the comprehensibility of the different representations. We demonstrate the usefulness of this representation for summarising the evolution of protein-protein interaction networks across a range of species.

Immersive Analytics is a new research initiative that aims to remove barriers between people, their data and the tools they use for analysis and decision making. Here the aims of immersive analytics research are clarified, its opportunities and historical context, as well as providing a broad research agenda for the field. In addition, it is reviewed how the term immersion has been used to refer to both technological and psychological immersion, both of which are central to immersive analytics research.

Functional magnetic resonance imaging (fMRI) is a 4D medical imaging modality that depicts a proxy of neuronal activity in a series of temporal scans. Statistical processing of the modality shows promise in uncovering insights about the functioning of the brain, such as the default mode network, and characteristics of mental disorders. Current statistical processing generally summarises the temporal signals between brain regions into a single data point to represent the 'coactivation' of the regions. That is, how similar are their temporal patterns over the scans. However, the potential of such processing is limited by issues of possible data misrepresentation due to uncertainties, e.g. noise in the data. Moreover, it has been shown that brain signals are characterised by brief traces of coactivation, which are lost in the single value representations. To alleviate the issues, alternate statistical processes have been used, however creating effective techniques has proven difficult due to problems, e.g. issues with noise, which often require user input to uncover. Visual analytics, therefore, through its ability to interactively exploit human expertise, presents itself as an interesting approach of benefit to the domain. In this work, we present the conceptual design behind TemporalTracks, our visual analytics system for exploration of 4D fMRI time-series coactivation data, utilising a visual metaphor to effectively present coactivation data for easier understanding. We describe our design with a case study visually analysing Human Connectome Project data, demonstrating that TemporalTracks can uncover temporal events that would otherwise be hidden in standard analysis.

Recent advances in miniaturising sensor tags allow to obtain high-resolution bird trajectories, presenting an opportunity for immersive close-up observation of individual and group behaviour in mid-air. The combination of geographical, environmental, and movement data is well suited for investigation in immersive analytics environments. We explore the benefits and requirements of a wide range of such environments, and illustrate a multi-platform immersive analytics solution, based on a tiled 3D display wall and head-mounted displays (Google Cardboard, HTC Vive and Microsoft Hololens). Tailored to biologists studying bird movement data, the immersive environment provides a novel interactive mode to explore the geolocational time-series data. This paper aims to inform the 3D visualisation research community about design considerations obtained from a real world data set in different 3D immersive environments. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the planet-level scale, thereby capturing the public awareness of environmental issues.

Immersive Analytics is an emerging research thrust investigating how new interaction and display technologies can be used to support analytical reasoning and decision making. The aim is to provide multi-sensory interfaces that support collaboration and allow users to immerse themselves in their data in a way that supports real-world analytics tasks. Immersive Analytics builds on technologies such as large touch surfaces, immersive virtual and augmented reality environments, sensor devices and other, rapidly evolving, natural user interface devices. While there is a great deal of past and current work on improving the display technologies themselves, our focus in this position paper is on bringing attention to the higher-level usability and design issues in creating effective user interfaces for data analytics in immersive environments.

Here, we have characterized the spatial heterogeneity of the cereal grain’s metabolism and demonstrated how, by integrating a distinct set of metabolic strategies, the grain has evolved to become an almost perfect entity for carbon storage. In vivo imaging revealed light-induced cycles in assimilate supply toward the ear/grain of barley (Hordeum vulgare) and wheat (Triticum aestivum). In silico modeling predicted that, in the two grain storage organs (the endosperm and embryo), the light-induced shift in solute influx does cause adjustment in metabolic flux without changes in pathway utilization patterns. The enveloping, leaf-like pericarp, in contrast, shows major shifts in flux distribution (starch metabolism, photosynthesis, remobilization, and tricarboxylic acid cycle activity) allow to refix 79% of the CO2 released by the endosperm and embryo, allowing the grain to achieve an extraordinary high carbon conversion efficiency of 95%. Shading experiments demonstrated that ears are autonomously able to raise the influx of solutes in response to light, but with little effect on the steady-state levels of metabolites or transcripts or on the pattern of sugar distribution within the grain. The finding suggests the presence of a mechanism(s) able to ensure metabolic homeostasis in the face of short-term environmental fluctuation. The proposed multicomponent modeling approach is informative for predicting the metabolic effects of either an altered level of incident light or a momentary change in the supply of sucrose. It is therefore of potential value for assessing the impact of either breeding and/or biotechnological interventions aimed at increasing grain yield

Background Experimental datasets are becoming larger and increasingly complex, spanning different data domains, thereby expanding the requirements for respective tool support for their analysis. Networks provide a basis for the integration, analysis and visualization of multi-omics experimental datasets. Results Here we present Vanted (version 2), a framework for systems biology applications, which comprises a comprehensive set of seven main tasks. These range from network reconstruction, data visualization, integration of various data types, network simulation to data exploration combined with a manifold support of systems biology standards for visualization and data exchange. The offered set of functionalities is instantiated by combining several tasks in order to enable users to view and explore a comprehensive dataset from different perspectives. We describe the system as well as an exemplary workflow. Conclusions Vanted is a stand-alone framework which supports scientists during the data analysis and interpretation phase. It is available as a Java open source tool from http://www.vanted.org