To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g., lightness, hue) map onto quantities of a given measure (e.g., brain activity, correlation magnitude). This process is easier when the encoded mappings in the visualization match people's predictions of how visual features will map onto concepts, their inferred mappings. To harness this principle in visualization design, it is necessary to understand what factors determine people's inferred mappings. In this study, we investigated how inferred color-quantity mappings for colormap data visualizations were influenced by the background color. Prior literature presents seemingly conflicting accounts of how the background color affects inferred color-quantity mappings. The present results help resolve those conflicts, demonstrating that sometimes the background has an effect and sometimes it does not, depending on whether the colormap appears to vary in opacity. When there is no apparent variation in opacity, participants infer that darker colors map to larger quantities (dark-is-more bias). As apparent variation in opacity increases, participants become biased toward inferring that more opaque colors map to larger quantities (opaque-is-more bias). These biases work together on light backgrounds and conflict on dark backgrounds. Under such conflicts, the opaque-is-more bias can negate, or even supersede the dark-is-more bias. The results suggest that if a design goal is to produce colormaps that match people's inferred mappings and are robust to changes in background color, it is beneficial to use colormaps that will not appear to vary in opacity on any background color, and to encode larger quantities in darker colors.

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Objectives Sjӧgren’s disease (SjD) is a systemic autoimmune disease characterized by focal lymphocytic infiltrate of salivary glands (SGs) and high SG IFNγ, both of which are associated with elevated lymphoma risk. IFNγ is also biologically relevant to mesenchymal stromal cells (MSCs), a SG resident cell with unique niche regenerative and immunoregulatory capacities. In contrast to the role of IFNγ in SjD, IFNγ promotes an anti-inflammatory MSC phenotype in other diseases. The objective of this study was to define the immunobiology of IFNγ-exposed SG-MSCs with and without the JAK1 & 2 inhibitor, ruxolitinib. Methods SG-MSCs were isolated from SjD and controls human subjects. SG-MSCs were treated with 10 ng/ml IFNγ +/- 1000 nM ruxolitinib. Experimental methods included flow cytometry, RNA-sequencing, chemokine array, ELISA, and transwell chemotaxis experiments. Results We found that IFNγ promoted expression of SG-MSC immunomodulatory markers, including HLA-DR, and this expression was inhibited by ruxolitinib. We confirmed the differential expression of CXCL9, CXCL10, CXCL11, CCL2, and CCL7, initially identified with RNA-sequencing. SG-MSCs promoted CD4+ T cell chemotaxis when pre-stimulated with IFNγ. Ruxolitinib blocks chemotaxis through inhibition of SG-MSC production of CXCL9, CXCL10, and CXCL11. Conclusions These findings establish that ruxolitinib inhibits IFNγ-induced expression of SG-MSC immunomodulatory markers and chemokines. Ruxolitinib also reverses IFNγ-induced CD4+ T cell chemotaxis, through inhibition of CXCL9, -10, and -11. Because IFNγ is higher in SjD than control SGs, we have identified SG-MSCs as a plausible pathogenic cell type in SjD. We provide proof of concept supporting further study of ruxolitinib to treat SjD.

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