Systems analysis and controlled malaria infection in Europeans and Africans elucidate naturally acquired immunity.

Controlled human infections provide opportunities to study the interaction between the immune system and malaria parasites, which is essential for vaccine development. Here, we compared immune signatures of malaria-naive Europeans and of Africans with lifelong malaria exposure using mass cytometry, RNA sequencing and data integration, before and 5 and 11 days after venous inoculation with Plasmodium falciparum sporozoites. We observed differences in immune cell populations, antigen-specific responses and gene expression profiles between Europeans and Africans and among Africans with differing degrees of immunity. Before inoculation, an activated/differentiated state of both innate and adaptive cells, including elevated CD161+CD4+ T cells and interferon-γ production, predicted Africans capable of controlling parasitemia. After inoculation, the rapidity of the transcriptional response and clusters of CD4+ T cells, plasmacytoid dendritic cells and innate T cells were among the features distinguishing Africans capable of controlling parasitemia from susceptible individuals. These findings can guide the development of a vaccine effective in malaria-endemic regions.

Hypertensive panesophageal pressurization in type II achalasia.

We present a case-report about a patient with type II achalasia. In the high-resolution esophageal manometry (HRM), an atypical hypertensive panesophageal pressurizations were observed. Until now, the presence of hypertensive panesophageal pressurizations in type II achalasia was described in only one case-report. A POEM was performed. After the treatment, the patient presents a complete resolution of the symptoms. Control HRM showed a partial recovery of esophageal motility and the hypotonia of the gastro-esophageal junction.

Diffusion MRI visualization.

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.

BrainScope: interactive visual exploration of the spatial and temporal human brain transcriptome.

Spatial and temporal brain transcriptomics has recently emerged as an invaluable data source for molecular neuroscience. The complexity of such data poses considerable challenges for analysis and visualization. We present BrainScope: a web portal for fast, interactive visual exploration of the Allen Atlases of the adult and developing human brain transcriptome. Through a novel methodology to explore high-dimensional data (dual t-SNE), BrainScope enables the linked, all-in-one visualization of genes and samples across the whole brain and genome, and across developmental stages. We show that densities in t-SNE scatter plots of the spatial samples coincide with anatomical regions, and that densities in t-SNE scatter plots of the genes represent gene co-expression modules that are significantly enriched for biological functions. We also show that the topography of the gene t-SNE maps reflect brain region-specific gene functions, enabling hypothesis and data driven research. We demonstrate the discovery potential of BrainScope through three examples: (i) analysis of cell type specific gene sets, (ii) analysis of a set of stable gene co-expression modules across the adult human donors and (iii) analysis of the evolution of co-expression of oligodendrocyte specific genes over developmental stages. BrainScope is publicly accessible at www.brainscope.nl.

Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution.

Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment.

Class-Constrained t-SNE: Combining Data Features and Class Probabilities.

Data features and class probabilities are two main perspectives when, e.g., evaluating model results and identifying problematic items. Class probabilities represent the likelihood that each instance belongs to a particular class, which can be produced by probabilistic classifiers or even human labeling with uncertainty. Since both perspectives are multi-dimensional data, dimensionality reduction (DR) techniques are commonly used to extract informative characteristics from them. However, existing methods either focus solely on the data feature perspective or rely on class probability estimates to guide the DR process. In contrast to previous work where separate views are linked to conduct the analysis, we propose a novel approach, class-constrained t-SNE, that combines data features and class probabilities in the same DR result. Specifically, we combine them by balancing two corresponding components in a cost function to optimize the positions of data points and iconic representation of classes - class landmarks. Furthermore, an interactive user-adjustable parameter balances these two components so that users can focus on the weighted perspectives of interest and also empowers a smooth visual transition between varying perspectives to preserve the mental map. We illustrate its application potential in model evaluation and visual-interactive labeling. A comparative analysis is performed to evaluate the DR results.

Inclusion Depth for Contour Ensembles.

Ensembles of contours arise in various applications like simulation, computer-aided design, and semantic segmentation. Uncovering ensemble patterns and analyzing individual members is a challenging task that suffers from clutter. Ensemble statistical summarization can alleviate this issue by permitting analyzing ensembles' distributional components like the mean and median, confidence intervals, and outliers. Contour boxplots, powered by Contour Band Depth (CBD), are a popular non-parametric ensemble summarization method that benefits from CBD's generality, robustness, and theoretical properties. In this work, we introduce Inclusion Depth (ID), a new notion of contour depth with three defining characteristics. First, ID is a generalization of functional Half-Region Depth, which offers several theoretical guarantees. Second, ID relies on a simple principle: the inside/outside relationships between contours. This facilitates implementing ID and understanding its results. Third, the computational complexity of ID scales quadratically in the number of members of the ensemble, improving CBD's cubic complexity. This also in practice speeds up the computation enabling the use of ID for exploring large contour ensembles or in contexts requiring multiple depth evaluations like clustering. In a series of experiments on synthetic data and case studies with meteorological and segmentation data, we evaluate ID's performance and demonstrate its capabilities for the visual analysis of contour ensembles.

Strategies towards Fully Recyclable Commercial Epoxy Resins: Diels-Alder Structures in Sustainable Composites.

The Diels-Alder equilibrium is a widely known process in chemistry that can be used to provide a thermoset structure with recyclability and reprocessability mechanisms. In this study, a commercial epoxy resin is modified through the integration of functional groups into the network structure to provide superior performance. The present study has demonstrated that it is possible to adapt the curing process to efficiently incorporate these moieties in the final structure of commercial epoxy-based resins. It also evaluates the impact that they have on the final properties of the cured composites. In addition, different approaches have been studied for the incorporation of the functional group, adjusting and adapting the stoichiometry of the system components due to the differences in reactivity caused by the presence of the incorporated reactive groups, with the objective of maintaining comparable ratios of epoxy/amine groups in the formulation. Finally, it has been demonstrated that although the Diels-Alder equilibrium responds under external conditions, such as temperature, different sets of parameters and behaviors are to be expected as the structures are integrated into the thermoset, generating new equilibrium temperatures. In this way, the present research has explored sustainable strategies to enable the recyclability of commercial thermoset systems through crosslinking control and its modification.

ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data.

Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at osf.io/9k6jw, and source code at github.com/ManiVaultStudio.

Calculation of reference intervals for the concentrations of α-tocopherol and retinol in serum using indirect data-mining procedures.

BACKGROUND: Establishing adequate reference intervals (RIs) for vitamins A and E is essential for diagnosing and preventing deficiencies. Due to the current boom in data mining and its easy applicability, more laboratories are establishing RIs using indirect methods. Our study aims to obtain RIs using four indirect data-mining procedures (Bhattacharya, Hoffmann, Kosmic, and RefineR) for vitamins A and E. MATERIAL AND METHODS: 8943 individuals were collected to establish the RIs. After using different data cleaning steps and checking whether these data should be divided according to age and gender based on multiple linear regression and variance component analyses, indirect RIs were calculated using specific Excel spreadsheets or R-packages software. RESULTS: A total of 2004 records were eligible. For vitamin A, the RIs obtained were (1.11 - 2.68) µmol/L, (1.13 - 2.70) µmol/L, (1.13 - 2.71) µmol/L, and (1.17 - 2.66) µmol/L using the Bhattacharya, Hoffmann, Kosmic and RefineR approaches, respectively. For vitamin E, these intervals were (17.3 - 49.9) µmol/L (Bhattacharya), (17.3 - 48.9) µmol/L (Hoffmann), (19.6 - 50.3) µmol/L (Kosmic), and (19.4 - 50.9) µmol/L (RefineR). In all cases, the RIs were comparable. CONCLUSIONS: Suitable RIs for vitamins A and E were calculated using four indirect methods that are suitable and adapted to our population's demographic characteristics.

ProactiV: Studying Deep Learning Model Behavior under Input Transformations.

Deep learning (DL) models have shown performance benefits across many applications, from classification to image-to-image translation. However, low interpretability often leads to unexpected model behavior once deployed in the real world. Usually, this unexpected behavior is because the training data domain does not reflect the deployment data domain. Identifying a model's breaking points under input conditions and domain shifts, i.e., input transformations, is essential to improve models. Although visual analytics (VA) has shown promise in studying the behavior of model outputs under continually varying inputs, existing methods mainly focus on per-class or instance-level analysis. We aim to generalize beyond classification where classes do not exist and provide a global view of model behavior under co-occurring input transformations. We present a DL model-agnostic VA method (ProactiV) to help model developers proactively study output behavior under input transformations to identify and verify breaking points. ProactiV relies on a proposed input optimization method to determine the changes to a given transformed input to achieve the desired output. The data from this optimization process allows the study of global and local model behavior under input transformations at scale. Additionally, the optimization method provides insights into the input characteristics that result in desired outputs and helps recognize model biases. We highlight how ProactiV effectively supports studying model behavior with example classification and image-to-image translation tasks.

PanVA: Pangenomic Variant Analysis.

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.

The Transform-and-Perform framework: Explainable deep learning beyond classification.

In recent years, visual analytics (VA) has shown promise in alleviating the challenges of interpreting black-box deep learning (DL) models. While the focus of VA for explainable DL has been mainly on classification problems, DL is gaining popularity in high-dimensional-to-high-dimensional (H-H) problems such as image-to-image translation. In contrast to classification, H-H problems have no explicit instance groups or classes to study. Each output is continuous, high-dimensional, and changes in an unknown non-linear manner with changes in the input. These unknown relations between the input, model and output necessitate the user to analyze them in conjunction, leveraging symmetries between them. Since classification tasks do not exhibit some of these challenges, most existing VA systems and frameworks allow limited control of the components required to analyze models beyond classification. Hence, we identify the need for and present a unified conceptual framework, the Transform-and-Perform framework (T&P), to facilitate the design of VA systems for DL model analysis focusing on H-H problems. T&P provides a checklist to structure and identify workflows and analysis strategies to design new VA systems, and understand existing ones to uncover potential gaps for improvements. The goal is to aid the creation of effective VA systems that support the structuring of model understanding and identifying actionable insights for model improvements. We highlight the growing need for new frameworks like T&P with a real-world image-to-image translation application. We illustrate how T&P effectively supports the understanding and identification of potential gaps in existing VA systems.

Harassment disparities and risk profile within lesbian, gay, bisexual and transgender Spanish adult population: Comparisons by age, gender identity, sexual orientation, and perpetration context.

Lesbian, Gay, Bisexual and Transgender (LGBT) harassment disparities have become a public health issue due to discrimination and the effects on these people's health and wellbeing. The purpose was to compare harassment disparities within the Spanish adult LGBT population according to age, gender identity, sexual orientation and the context of perpetration and to describe the harassment risk profile. A sample of 1,051 LGBT adults participated in a cross-sectional study. Frequencies, percentages and Chi-square tests of independence for stablishing significant differences (p < 0.05) were calculated. The corrected standardized residuals allowed to identify the categories in which significant differences emerged. Binomial logistic regression was used to define the probability of the main LGBT groups of suffering harassment. Results show that 54.4% of the participants had experienced harassment. Young adults presented a higher prevalence than the older group. There were significant harassment differences between transgender (67.2%) and cisgender (52.7%) groups, and also between the subgroup of trans women (75.8%) and the subgroups of cis men (60.2%) and cis women (42.9%). The main disparities according to sexual orientation emerged between lesbian trans and the other LGB groups. Most harassment occurred in educational contexts and public spaces. Trans-women and trans non-binary reported a higher rate of harassment than cis LGB persons in all contexts. Trans people with different orientations (especially lesbian and gay trans) differed in harassment from LGB cis in four of the six contexts analyzed. Harassment is likely to diminish between 2 and 3% each year as LGBTs get older in educational contexts and public spaces but increases 1.07 times in the workplace. Trans women, trans non-binary, lesbian cis and trans-men were more likely to suffer harassment than bisexual cis persons. Trans women present the highest risk of harassment in three contexts (workplace, family and public spaces) and trans non-binary in the other three contexts (education, health and sport). Harassment is a serious problem for LGBT adults in Spain, especially among trans people, which differ in characteristics from those of the sexual minorities mainstream. Programs and policies targeted for improving health should therefore consider the differences that came to light in this study.

Stabilization and visual analysis of video-recorded sailing sessions.

One common way to aid coaching and seek to improve athletes' performance is by recording training sessions for posterior analysis. In the case of sailing, coaches record videos from another boat, but usually rely on handheld devices, which may lead to issues with the footage and missing important moments. On the other hand, by autonomously recording the entire session with a fixed camera, the analysis becomes challenging owing to the length of the video and possible stabilization issues. In this work, we aim to facilitate the analysis of such full-session videos by automatically extracting maneuvers and providing a visualization framework to readily locate interesting moments. Moreover, we address issues related to image stability. Finally, an evaluation of the framework points to the benefits of video stabilization in this scenario and an appropriate accuracy of the maneuver detection method.

Ten Open Challenges in Medical Visualization.

The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization.

Illustrative volume visualization using GPU-based particle systems.

Illustrative techniques are generally applied to produce stylized renderings. Various illustrative styles have been applied to volumetric data sets, producing clearer images and effectively conveying visual information. We adopt particle systems to produce user-configurable stylized renderings from the volume data, imitating traditional pen-and-ink drawings. In the following, we present an interactive GPU-based illustrative volume rendering framework, called VolFliesGPU. In this framework, isosurfaces are sampled by evenly distributed particle sets, delineating surface shape by illustrative styles. The appearance of these styles is based on locally-measured surface properties. For instance, hatches convey surface shape by orientation and shape characteristics are enhanced by color, mapped using a curvature-based transfer function. Hidden-surfaces are generally removed to avoid visual clutter, after that a combination of styles is applied per isosurface. Multiple surfaces and styles can be explored interactively, exploiting parallelism in both graphics hardware and particle systems. We achieve real-time interaction and prompt parametrization of the illustrative styles, using an intuitive GPGPU paradigm that delivers the computational power to drive our particle system and visualization algorithms.

Exploration of 4D MRI blood flow using stylistic visualization.

Insight into the dynamics of blood-flow considerably improves the understanding of the complex cardiovascular system and its pathologies. Advances in MRI technology enable acquisition of 4D blood-flow data, providing quantitative blood-flow velocities over time. The currently typical slice-by-slice analysis requires a full mental reconstruction of the unsteady blood-flow field, which is a tedious and highly challenging task, even for skilled physicians. We endeavor to alleviate this task by means of comprehensive visualization and interaction techniques. In this paper we present a framework for pre-clinical cardiovascular research, providing tools to both interactively explore the 4D blood-flow data and depict the essential blood-flow characteristics. The framework encompasses a variety of visualization styles, comprising illustrative techniques as well as improved methods from the established field of flow visualization. Each of the incorporated styles, including exploded planar reformats, flow-direction highlights, and arrow-trails, locally captures the blood-flow dynamics and may be initiated by an interactively probed vessel cross-section. Additionally, we present the results of an evaluation with domain experts, measuring the value of each of the visualization styles and related rendering parameters.

Living Life Through Sport: The Transition of Elite Spanish Student-Athletes to a University Degree in Physical Activity and Sports Sciences.

Interest in studying the different transitions faced by elite athletes throughout their careers has grown significantly in recent years. While transition from secondary school to university is an important research area in Europe, there is a void of studies on how student-athletes experience the transition to specific degrees. One of the most sought-after university degrees among elite athletes in Spain is a degree in Physical Activity and Sport Sciences (PASS). The first aim of this study was to investigate the main demands, barriers, and resources perceived by elite student-athletes in various phases of dual career transition to a university degree in PASS. The second aim was to identify the transition pathways pursued depending on the subjective importance they attached to sport and education. Eleven elite student-athletes (M age = 20.7, SD = 1.6 years) who were in their second and third year of the degree in PASS participated in semi-structured interviews. Deductive-inductive thematic analysis of the interview transcripts revealed three main themes: (a) general university transition issues, (b) PASS-specific transition issues, and (c) transition pathways. Our results show that the close link between sport and the content of the degree was perceived by the elite student-athletes as their main resource. This link, however, was also perceived as a major barrier as the compulsory practical subjects entailed a risk of injury or overtraining that could affect both athletic and academic development. We noticed how the importance they attached to sport or studies varied at different moments of the transition period, a phenomenon we termed "fluid transition pathways." Dual career promotion for elite athletes is an important part of European sports policy, and our findings provide new knowledge that could help Spanish PASS faculties develop specific assistance programs to support transitioning student-athletes.

GPGPU Linear Complexity t-SNE Optimization.

In recent years the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm has become one of the most used and insightful techniques for exploratory data analysis of high-dimensional data. It reveals clusters of high-dimensional data points at different scales while only requiring minimal tuning of its parameters. However, the computational complexity of the algorithm limits its application to relatively small datasets. To address this problem, several evolutions of t-SNE have been developed in recent years, mainly focusing on the scalability of the similarity computations between data points. However, these contributions are insufficient to achieve interactive rates when visualizing the evolution of the t-SNE embedding for large datasets. In this work, we present a novel approach to the minimization of the t-SNE objective function that heavily relies on graphics hardware and has linear computational complexity. Our technique decreases the computational cost of running t-SNE on datasets by orders of magnitude and retains or improves on the accuracy of past approximated techniques. We propose to approximate the repulsive forces between data points by splatting kernel textures for each data point. This approximation allows us to reformulate the t-SNE minimization problem as a series of tensor operations that can be efficiently executed on the graphics card. An efficient implementation of our technique is integrated and available for use in the widely used Google TensorFlow.js, and an open-source C++ library.