Search

We present Cell2Cell, a novel visual analytics approach for quantifying and visualizing networks of cell-cell interactions in three-dimensional (3D) multi-channel cancerous tissue data. By analyzing cellular interactions, biomedical experts can gain a more accurate understanding of the intricate relationships between cancer and immune cells. Recent methods have focused on inferring interaction based on the proximity of cells in low-resolution 2D multi-channel imaging data. By contrast, we analyze cell interactions by quantifying the presence and levels of specific proteins within a tissue sample (protein expressions) extracted from high-resolution 3D multi-channel volume data. Such analyses have a strong exploratory nature and require a tight integration of domain experts in the analysis loop to leverage their deep knowledge. We propose two complementary semi-automated approaches to cope with the increasing size and complexity of the data interactively: On the one hand, we interpret cell-to-cell interactions as edges in a cell graph and analyze the image signal (protein expressions) along those edges, using spatial as well as abstract visualizations. Complementary, we propose a cell-centered approach, enabling scientists to visually analyze polarized distributions of proteins in three dimensions, which also captures neighboring cells with biochemical and cell biological consequences. We evaluate our application in three case studies, where biologists and medical experts use Cell2Cell to investigate tumor micro-environments to identify and quantify T-cell activation in human tissue data. We confirmed that our tool can fully solve the use cases and enables a streamlined and detailed analysis of cell-cell interactions.

Data visualizations play a critical role in both communicating scientific evidence about climate change and in stimulating engagement and action. To investigate how visualizations can be better utilized to communicate the complexities of climate change to different audiences, we conducted interviews with 17 experts in the fields of climate change, data visualization, and science communication, as well as with 12 laypersons. Besides questions about climate change communication and various aspects of data visualizations, we also asked participants to share what they think is the main takeaway message for two exemplary climate change data visualizations. Through a thematic analysis, we observe differences regarding the included contents, the length and abstraction of messages, and the sensemaking process between and among the participant groups. On average, experts formulated shorter and more abstract messages, often referring to higher-level conclusions rather than specific details. We use our findings to reflect on design decisions for creating more effective visualizations, particularly in news media sources geared toward lay audiences. We hereby discuss the adaption of contents according to the needs of the audience, the trade-off between simplification and accuracy, as well as techniques to make a visualization attractive.

Fault detection is essential to ensure the proper operation of solar-thermal plants. Hence, monitoring personnel frequently analyze the data to detect unusual behavior. While visualization approaches may considerably support the monitoring of personnel during their work, no existing application can yet deal with the multivariate and time-dependent sensor data, or does not fully support the users’ workflow. Thus, this work introduces the visual framework SunScreen. It allows users to explore the sensor data, automatically detected anomalies, and system events (e.g., already detected faults and services). The feedback from the users shows that they appreciate the tool and especially its annotation functionality. However, the system-usability-scale (SUS) results indicate that it does not meet all requirements yet.

This design study presents an analysis and abstraction of temporal and spatial data, and workflows in the domain of hydrogeology and the design and development of an interactive visualization prototype. Developed in close collaboration with a group of hydrogeological researchers, the interface supports them in data exploration, selection of data for their numerical model calibration, and communication of findings to their industry partners. We highlight both pitfalls and learnings of the iterative design and validation process and explore the role of rapid prototyping. Some of the main lessons were that the ability to see their own data changed the engagement of skeptical users dramatically and that interactive rapid prototyping tools are thus powerful to unlock the advantage of visual analysis for novice users. Further, we observed that the process itself helped the domain scientists understand the potential and challenges of their data more than the final interface prototype.

This article presents different interface designs of sliders to support decision-making problems with three criteria. We present an exploration of the design space through an iterative development process with eight prototypes and the results of several evaluation studies with visualization experts and nonexperts. Our findings show three candidates for consideration: a standard ternary triangular slider, a novel circular slider, and a standard basic slider displayed three times. All three were considered intuitive and easy to use. The triangular slider is best for exploration with vague user intuition, the circular slider performs best for preference comparisons, and the parallel slider is best for direct preference setting.

In this design study, we present Uncover, an interactive tool aimed at astronomers to find previously unidentified member stars in stellar clusters. We contribute data and task abstraction in the domain of astronomy and provide an approach for the non-trivial challenge of finding a suitable hyper-parameter set for highly flexible novelty detection models. We achieve this by substituting the tedious manual trial and error process, which usually results in finding a small subset of passable models with a five-step workflow approach. We utilize ranges of a priori defined, interpretable summary statistics models have to adhere to. Our goal is to enable astronomers to use their domain expertise to quantify model goodness effectively. We attempt to change the current culture of blindly accepting a machine learning model to one where astronomers build and modify a model based on their expertise. We evaluate the tools’ usability and usefulness in a series of interviews with domain experts.

The repetitive sequence of Leishmania majorgene B protein (GBP) has previously been shown to be a useful tool in the diagnosis of cutaneous leishmaniasis (CL). Here, we have assessed enzyme-linked immunosorbent assays (ELISAs) using recombinant L. donovaniGBP (rGBP) and a peptide sequence of L. donovaniGBP (GBPP) in the diagnosis of L. donovaniinfections in Sudan. The sensitivity of the rGBP ELISA in diagnosing visceral leishmaniasis (VL) and post kala-azar dermal leishmaniasis (PKDL) was 92% and 93%, respectively. In contrast, the sensitivity of the GBPP ELISA was 55% for VL and 63% for PKDL. Plasma antibody reactivity of donors with VL and PKDL remained high for an extended period after the end of treatment. Antibody-reactivity to rGBP and GBPP was detected in 71% and 14% of plasma samples from CL patients, respectively. Plasma from healthy Sudanese donors living in an area endemic for malaria but free of leishmaniasis was negative in both assays.