Unifying Effects of Direct and Relational Associations for Visual Communication.

People have expectations about how colors map to concepts in visualizations, and they are better at interpreting visualizations that match their expectations. Traditionally, studies on these expectations (inferred mappings) distinguished distinct factors relevant for visualizations of categorical vs. continuous information. Studies on categorical information focused on direct associations (e.g., mangos are associated with yellows) whereas studies on continuous information focused on relational associations (e.g., darker colors map to larger quantities; dark-is-more bias). We unite these two areas within a single framework of assignment inference. Assignment inference is the process by which people infer mappings between perceptual features and concepts represented in encoding systems. Observers infer globally optimal assignments by maximizing the "merit," or "goodness," of each possible assignment. Previous work on assignment inference focused on visualizations of categorical information. We extend this approach to visualizations of continuous data by (a) broadening the notion of merit to include relational associations and (b) developing a method for combining multiple (sometimes conflicting) sources of merit to predict people's inferred mappings. We developed and tested our model on data from experiments in which participants interpreted colormap data visualizations, representing fictitious data about environmental concepts (sunshine, shade, wild fire, ocean water, glacial ice). We found both direct and relational associations contribute independently to inferred mappings. These results can be used to optimize visualization design to facilitate visual communication.

Context Matters: A Theory of Semantic Discriminability for Perceptual Encoding Systems.

People's associations between colors and concepts influence their ability to interpret the meanings of colors in information visualizations. Previous work has suggested such effects are limited to concepts that have strong, specific associations with colors. However, although a concept may not be strongly associated with any colors, its mapping can be disambiguated in the context of other concepts in an encoding system. We articulate this view in semantic discriminability theory, a general framework for understanding conditions determining when people can infer meaning from perceptual features. Semantic discriminability is the degree to which observers can infer a unique mapping between visual features and concepts. Semantic discriminability theory posits that the capacity for semantic discriminability for a set of concepts is constrained by the difference between the feature-concept association distributions across the concepts in the set. We define formal properties of this theory and test its implications in two experiments. The results show that the capacity to produce semantically discriminable colors for sets of concepts was indeed constrained by the statistical distance between color-concept association distributions (Experiment 1). Moreover, people could interpret meanings of colors in bar graphs insofar as the colors were semantically discriminable, even for concepts previously considered "non-colorable" (Experiment 2). The results suggest that colors are more robust for visual communication than previously thought.

Semantic Discriminability for Visual Communication.

To interpret information visualizations, observers must determine how visual features map onto concepts. First and foremost, this ability depends on perceptual discriminability; observers must be able to see the difference between different colors for those colors to communicate different meanings. However, the ability to interpret visualizations also depends on semantic discriminability, the degree to which observers can infer a unique mapping between visual features and concepts, based on the visual features and concepts alone (i.e., without help from verbal cues such as legends or labels). Previous evidence suggested that observers were better at interpreting encoding systems that maximized semantic discriminability (maximizing association strength between assigned colors and concepts while minimizing association strength between unassigned colors and concepts), compared to a system that only maximized color-concept association strength. However, increasing semantic discriminability also resulted in increased perceptual distance, so it is unclear which factor was responsible for improved performance. In the present study, we conducted two experiments that tested for independent effects of semantic distance and perceptual distance on semantic discriminability of bar graph data visualizations. Perceptual distance was large enough to ensure colors were more than just noticeably different. We found that increasing semantic distance improved performance, independent of variation in perceptual distance, and when these two factors were uncorrelated, responses were dominated by semantic distance. These results have implications for navigating trade-offs in color palette design optimization for visual communication.

The relation between color and spatial structure for interpreting colormap data visualizations.

Interpreting colormap visualizations requires determining how dimensions of color in visualizations map onto quantities in data. People have color-based biases that influence their interpretations of colormaps, such as a dark-is-more bias-darker colors map to larger quantities. Previous studies of color-based biases focused on colormaps with weak data spatial structure, but color-based biases may not generalize to colormaps with strong data spatial structure, like "hotspots" typically found in weather maps and neuroimaging brain maps. There may be a hotspot-is-more bias to infer that colors within hotspots represent larger quantities, which may override the dark-is-more bias. We tested this possibility in four experiments. Participants saw colormaps with hotspots and a legend that specified the color-quantity mapping. Their task was to indicate which side of the colormap depicted larger quantities (left/right). We varied whether the legend specified dark-more mapping or light-more mapping across trials and operationalized a dark-is-more bias as faster response time (RT) when the legend specified dark-more mapping. Experiment 1 demonstrated robust evidence for the dark-is-more bias, without evidence for a hotspot-is-more bias. Experiments 2 to 4 suggest that a hotspot-is-more bias becomes relevant when hotspots are a statistically reliable cue to "more" (i.e., the locus of larger quantities) and when hotspots are more perceptually pronounced. Yet, comparing conditions in which the hotspots were "more," RTs were always faster for dark hotspots than light hotspots. Thus, in the presence of strong spatial cues to the locus of larger quantities, color-based biases still influenced interpretations of colormap data visualizations.

Estimating Color-Concept Associations from Image Statistics.

To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color.

Design considerations for the enhancement of human color vision by breaking binocular redundancy.

To see color, the human visual system combines the response of three types of cone cells in the retina-a compressive process that discards a significant amount of spectral information. Here, we present designs based on thin-film optical filters with the goal of enhancing human color vision by breaking its inherent binocular redundancy, providing different spectral content to each eye. We fabricated a set of optical filters that "splits" the response of the short-wavelength cone between the two eyes in individuals with typical trichromatic vision, simulating the presence of approximately four distinct cone types. Such an increase in the number of effective cone types can reduce the prevalence of metamers-pairs of distinct spectra that resolve to the same tristimulus values. This technique may result in an enhancement of spectral perception, with applications ranging from camouflage detection and anti-counterfeiting to new types of artwork and data visualization.

Color inference in visual communication: the meaning of colors in recycling.

People interpret abstract meanings from colors, which makes color a useful perceptual feature for visual communication. This process is complicated, however, because there is seldom a one-to-one correspondence between colors and meanings. One color can be associated with many different concepts (one-to-many mapping) and many colors can be associated with the same concept (many-to-one mapping). We propose that to interpret color-coding systems, people perform assignment inference to determine how colors map onto concepts. We studied assignment inference in the domain of recycling. Participants saw images of colored but unlabeled bins and were asked to indicate which bins they would use to discard different kinds of recyclables and trash. In Experiment 1, we tested two hypotheses for how people perform assignment inference. The local assignment hypothesis predicts that people simply match objects with their most strongly associated color. The global assignment hypothesis predicts that people also account for the association strengths between all other objects and colors within the scope of the color-coding system. Participants discarded objects in bins that optimized the color-object associations of the entire set, which is consistent with the global assignment hypothesis. This sometimes resulted in discarding objects in bins whose colors were weakly associated with the object, even when there was a stronger associated option available. In Experiment 2, we tested different methods for encoding color-coding systems and found that people were better at assignment inference when color sets simultaneously maximized the association strength between assigned color-object parings while minimizing associations between unassigned pairings. Our study provides an approach for designing intuitive color-coding systems that facilitate communication through visual media such as graphs, maps, signs, and artifacts.

Modeling color preference using color space metrics.

Studying color preferences provides a means to discover how perceptual experiences map onto cognitive and affective judgments. A challenge is finding a parsimonious way to describe and predict patterns of color preferences, which are complex with rich individual differences. One approach has been to model color preferences using factors from metric color spaces to establish direct correspondences between dimensions of color and preference. Prior work established that substantial, but not all, variance in color preferences could be captured by weights on color space dimensions using multiple linear regression. The question we address here is whether model fits may be improved by using different color metric specifications. We therefore conducted a large-scale analysis of color space models, and focused in-depth analysis on models that differed in color space (cone-contrast vs. CIELAB), coordinate system within the color space (Cartesian vs. cylindrical), and factor degrees (1st degree only, or 1st and 2nd degree). We used k-fold cross validation to avoid over-fitting the data and to ensure fair comparisons across models. The best model was the 2nd-harmonic Lch model ("LabC Cyl2"). Specified in CIELAB space, it included 1st and 2nd harmonics of hue (capturing opponency in hue preferences and simultaneous liking/disliking of both hues on an opponent axis, respectively), lightness, and chroma. These modeling approaches can be used to characterize and compare patterns for group averages and individuals in future datasets on color preference, or other measures in which correspondences between color appearance and cognitive or affective judgments may exist.

Post-translational regulation of the cleaved fragment of Par-4 in ovarian and endometrial cancer cells.

We recently reported the caspase3-dependent cleavage of Par-4 resulting in the accumulation of a 25kDa cleaved-Par-4 (cl-Par-4) fragment and we investigated in the present study the mechanisms regulating this fragment using cl-Par-4-expressing stable clones derived from ovarian and endometrial cancer cell lines.Cl-Par-4 protein was weakly express in all stable clones despite constitutive expression. However, upon cisplatin treatment, cl-Par-4 levels increased up to 50-fold relative to baseline conditions. Treatment of stable clones with proteasome and translation inhibitors revealed that cisplatin exposure might in fact protect cl-Par-4 from proteasome-dependent degradation. PI3K and MAPK pathways were also implicated as evidenced by an increase of cl-Par-4 in the presence of PI3K inhibitors and a decrease using MAPK inhibitors. Finally using bioinformatics resources, we found diverse datasets showing similar results to those we observed with the proteasome and cl-Par-4 further supporting our data.These new findings add to the complex mechanisms regulating Par-4 expression and activity, and justify further studies addressing the biological significance of this phenomenon in gynaecological cancer cells.

PTP1B Deficiency Enables the Ability of a High-Fat Diet to Drive the Invasive Character of PTEN-Deficient Prostate Cancers.

Diet affects the risk and progression of prostate cancer, but the interplay between diet and genetic alterations in this disease is not understood. Here we present genetic evidence in the mouse showing that prostate cancer progression driven by loss of the tumor suppressor Pten is mainly unresponsive to a high-fat diet (HFD), but that coordinate loss of the protein tyrosine phosphatase Ptpn1 (encoding PTP1B) enables a highly invasive disease. Prostate cancer in Pten(-/-)Ptpn1(-/-) mice was characterized by increased cell proliferation and Akt activation, interpreted to reflect a heightened sensitivity to IGF-1 stimulation upon HFD feeding. Prostate-specific overexpression of PTP1B was not sufficient to initiate prostate cancer, arguing that it acted as a diet-dependent modifier of prostate cancer development in Pten(-/-) mice. Our findings offer a preclinical rationale to investigate the anticancer effects of PTP1B inhibitors currently being studied clinically for diabetes treatment as a new modality for management of prostate cancer. Cancer Res; 76(11); 3130-5. ©2016 AACR.

The CASC15 Long Intergenic Noncoding RNA Locus Is Involved in Melanoma Progression and Phenotype Switching.

In recent years, considerable advances have been made in the characterization of protein-coding alterations involved in the pathogenesis of melanoma. However, despite their growing implication in cancer, little is known about the role of long noncoding RNAs in melanoma progression. We hypothesized that copy number alterations (CNAs) of intergenic nonprotein-coding domains could help identify long intergenic noncoding RNAs (lincRNAs) associated with metastatic cutaneous melanoma. Among several candidates, our approach uncovered the chromosome 6p22.3 CASC15 (cancer susceptibility candidate 15) lincRNA locus as a frequently gained genomic segment in metastatic melanoma tumors and cell lines. The locus was actively transcribed in metastatic melanoma cells, and upregulation of CASC15 expression was associated with metastatic progression to brain metastasis in a mouse xenograft model. In clinical specimens, CASC15 levels increased during melanoma progression and were independent predictors of disease recurrence in a cohort of 141 patients with AJCC (American Joint Committee on Cancer) stage III lymph node metastasis. Moreover, small interfering RNA (siRNA) knockdown experiments revealed that CASC15 regulates melanoma cell phenotype switching between proliferative and invasive states. Accordingly, CASC15 levels correlated with known gene signatures corresponding to melanoma proliferative and invasive phenotypes. These findings support a key role for CASC15 in metastatic melanoma.

Prostate cancer genetic-susceptibility locus on chromosome 20q13 is amplified and coupled to androgen receptor-regulation in metastatic tumors.

UNLABELLED: The 20q13 chromosomal region has been previously identified as the hereditary prostate cancer genetic-susceptibility locus on chromosome 20 (HPC20). In this study, the 20q13 region was shown to be frequently co-amplified with the androgen receptor (AR) in metastatic prostate cancer. Furthermore, the AR signaling axis, which plays an essential role in the pathogenesis of prostate cancer, was demonstrated to be central to the regulation of the 20q13 common amplified region (CAR). High-resolution mapping analyses revealed hot spots of AR recruitment to response elements in the vicinity of most genes located on the 20q13 CAR. Moreover, amplification of AR significantly co-occurred with CAR amplification on 20q13 and it was confirmed that the majority of AR-bound genes on the 20q13 CAR were indeed regulated by androgens. These data reveal that amplification of the AR is tightly linked to amplification of the AR-regulated CAR region on 20q13. These results suggest that the cross-talk between gene amplification and gene transcription is an important step in the development of castration-resistant metastatic disease. IMPLICATIONS: These novel results are a noteworthy example of the cross-talk between gene amplification and gene transcription in the development of advanced prostate cancer.

Large-scale independent validation of the nuclear factor-kappa B p65 prognostic biomarker in prostate cancer.

PURPOSE: Over the last decade, we and others have uncovered a robust association between the nuclear localisation of nuclear factor-kappa B (NF-κB) p65, prostate cancer (PCa) aggressiveness and biochemical recurrence (BCR). Our goal was to validate these results in a large independent cohort of PCa patients who underwent radical prostatectomy. EXPERIMENTAL DESIGN: A set of 1826 fully annotated prostate cancers treated by radical prostatectomy were analysed in a tissue microarray (TMA) format for NF-κB p65 immunohistochemistry-based protein expression. We performed standard Cox proportional hazard regression models for follow-up data, bootstrap procedure for model internal validation, Harrell's concordance index for model discrimination and graphical assessment of predicted versus actual outcomes for model calibration. RESULTS: We observed a significant association between an increase in the nuclear frequency of NF-κB p65 and Gleason score (P<0.001), overall BCR (P<0.001) and development of metastases (P=0.001). NF-κB was found to be an independent predictor of BCR (P<0.001, Cox regression). However its contribution to the predictive accuracy of a multivariate model, which included preoperative PSA, Gleason score, extraprostatic extension, lymph node invasion, seminal vesicle involvement and surgical margin status, was modest. CONCLUSIONS: Our study offers validating results linking NF-κB p65 with disease progression using a large cohort of European men. However, the contribution of NF-κB to a post-surgical predictive model appears modest. Further validating work should focus on evaluating the contribution of NF-κB p65 in pre-treatment models.

Heterogeneous epigenetic regulation of TIMP3 in prostate cancer.

Tissue inhibitor of metalloproteinase-3 (TIMP3) is a tumor suppressor gene frequently downregulated in prostate cancer. The mechanisms involved in TIMP3 transcriptional repression are not fully understood, but evidence suggests that promoter hypermethylation may not be the predominant epigenetic alteration in prostate cancer. To clarify this issue, we examined the contribution of both CpG site promoter methylation and histone modifications on TIMP3 downregulation. Using publicly available data sets, we confirmed that TIMP3 mRNA expression is decreased in prostate tumors relative to normal glands. Immunohistochemical analysis also showed decreased TIMP3 levels in high-grade primary tumors, but promoter hypermethylation was only detected in 6 of 28 (21%) high-grade specimens. Similarly, in prostate cancer cells, TIMP3 hypermethylation was only observed in DU145 cells. Treatment of DU145 cells with 5-aza-2'-deoxycytidine (5-Aza-CdR) restored TIMP3 expression, and this was significantly amplified by co-treating the cells with the HDAC inhibitor trichostatin A (TSA). Alternatively, in cells that did not exhibit aberrant TIMP3 methylation (LNCaP and PC3), TIMP3 expression could be upregulated by the combination of histone methylation inhibitor 3-Deazaneplanocin A (DZNep) and TSA. This reversal of transcriptional repression was associated with decreased H3K27me3 and increased H3K9ac histone marks at the TIMP3 promoter, as demonstrated by chromatin immunoprecipitation. Collectively, these results indicate that histone modifications can contribute to TIMP3 repression in the absence of promoter hypermethylation, and suggest that the combination of histone modifying agents could restore TIMP3 expression in prostate tumors harboring aberrant histone modifications at the TIMP3 promoter.

PTP1B is an androgen receptor-regulated phosphatase that promotes the progression of prostate cancer.

The androgen receptor (AR) signaling axis plays a key role in the pathogenesis of prostate cancer. In this study, we found that the protein tyrosine phosphatase PTP1B, a well-established regulator of metabolic signaling, was induced after androgen stimulation of AR-expressing prostate cancer cells. PTP1B induction by androgen occurred at the mRNA and protein levels to increase PTP1B activity. High-resolution chromosome mapping revealed AR recruitment to two response elements within the first intron of the PTP1B encoding gene PTPN1, correlating with an AR-mediated increase in RNA polymerase II recruitment to the PTPN1 transcriptional start site. We found that PTPN1 and AR genes were coamplified in metastatic tumors and that PTPN1 amplification was associated with a subset of high-risk primary tumors. Functionally, PTP1B depletion delayed the growth of androgen-dependent human prostate tumors and impaired androgen-induced cell migration and invasion in vitro. However, PTP1B was also required for optimal cell migration of androgen-independent cells. Collectively, our results established the AR as a transcriptional regulator of PTPN1 transcription and implicated PTP1B in a tumor-promoting role in prostate cancer. Our findings support the preclinical testing of PTP1B inhibitors for prostate cancer treatment.

The two faces of PTP1B in cancer.

PTP1B is a classical non-transmembrane protein tyrosine phosphatase that plays a key role in metabolic signaling and is a promising drug target for type 2 diabetes and obesity. Accumulating evidence also indicates that PTP1B is involved in cancer, but contrasting findings suggest that it can exert both tumor suppressing and tumor promoting effects depending on the substrate involved and the cellular context. In this review, we will discuss the diverse mechanisms by which PTP1B may influence tumorigenesis as well as recent in vivo data on the impact of PTP1B deficiency in murine cancer models. Together, these results highlight not only the great potential of PTP1B inhibitors in cancer therapy but also the need for a better understanding of PTP1B function prior to use of these compounds in human patients.

Experimental validation of single-iteration multigrid wavefront reconstruction at the Palomar Observatory.

Single-iteration multigrid (SIMG) wavefront reconstruction schemes were implemented and validated on the adaptive optics system at the Hale 5.1 m telescope at the Palomar Observatory. Results indicate that even the simplest such method produces a performance indistinguishable from that of the standard least-squares reconstructor for both bright and dim guide stars. SIMG provides a dramatic reduction in computational cost when compared to vector-matrix multiplication and can be implemented in parallel, making it the obvious choice for reconstruction in future large-scale adaptive optics systems.

Warm-started wavefront reconstruction for adaptive optics.

Future extreme adaptive optics (ExAO) systems have been suggested with up to 10(5) sensors and actuators. We analyze the computational speed of iterative reconstruction algorithms for such large systems. We compare a total of 15 different scalable methods, including multigrid, preconditioned conjugate-gradient, and several new variants of these. Simulations on a 128x128 square sensor/actuator geometry using Taylor frozen-flow dynamics are carried out using both open-loop and closed-loop measurements, and algorithms are compared on a basis of the mean squared error and floating-point multiplications required. We also investigate the use of warm starting, where the most recent estimate is used to initialize the iterative scheme. In open-loop estimation or pseudo-open-loop control, warm starting provides a significant computational speedup; almost every algorithm tested converges in one iteration. In a standard closed-loop implementation, using a single iteration per time step, most algorithms give the minimum error even in cold start, and every algorithm gives the minimum error if warm started. The best algorithm is therefore the one with the smallest computational cost per iteration, not necessarily the one with the best quasi-static performance.

Low nuclear ErbB3 predicts biochemical recurrence in patients with prostate cancer.

OBJECTIVE: To further evaluate the association between the cytoplasmic or nuclear localization of ErbB3 with biochemical recurrence (BCR) in patients with prostate cancer and positive surgical margins, as there is a greater risk of BCR for such patients after radical prostatectomy (RP). PATIENTS AND METHODS: We recently noted that ErbB3, which is normally associated with the plasma membrane, can translocate to the nucleus, an event which appears to be associated with disease progression. We evaluated ErbB3 expression and localization using immunohistochemistry on tissue samples from 55 patients with positive surgical margins after RP; 30 of these 55 (55%) had BCR after 3 years of follow-up. The relationship between ErbB3 nuclear localization and BCR (prostate-specific antigen, PSA, >0.3 ng/mL) after RP was analysed by Kaplan-Meier survival analysis and Cox regression models. RESULTS: The BCR-free survival probability at 3 years was 0.65 and 0.35 for positive and negative nuclear ErbB3, respectively (Kaplan-Meier, P = 0.029). Patients negative for nuclear ErbB3 had a 2.47-fold increase in BCR frequency in a univariate Cox model (P = 0.008) and it remained an independent prognostic marker when combined with clinical prognostic variables in a multivariate model (P = 0.023). CONCLUSION: Low nuclear localization of ErbB3 is a predictor of BCR in patients with prostate cancer and positive surgical margins after RP.