Visualization Tools Research Articles

MAPPING GLIOMAS: A RADIOGRAPHIC AND SPATIAL ANALYSIS ACCORDING TO THE 2021 WHO CLASSIfiCATION

Abstract AIMS The spatial positioning of gliomas within the brain significantly influences prognosis, potentially mirroring the genetic landscape of their precursor cells. Furthermore, the association between glioma subtypes and their spatial distribution underscores a complex interplay that may illuminate underlying pathogenetic mechanisms. This study explores the intricate relationship between the location of gliomas and their classification according to the 2021 WHO guidelines, aiming to unravel the spatial and genetic nuances that define glioma heterogeneity. METHOD In this study, preoperative anatomical MRI data from 242 glioma patients were meticulously analyzed to elucidate spatial distribution patterns in relation to tumor genetics. The imaging data underwent registration to the MNI-152 standard brain space, facilitating uniform analysis. Subsequent tumor segmentation and division of the brain into 84 distinct subregions allowed for a detailed examination of tumor locations. A statistical analysis was conducted to explore the stereospecific frequency of glioma occurrence across these subregions, correlating findings with tumor grade and pivotal genetic markers, including MGMT promoter methylation, IDH1 mutation, 1p/19q co-deletion, and CDKN2A/B homozygous deletion. RESULTS We developed 21 distinct tumor frequency atlases based on subtypes (Glioblastoma, Oligodendroglioma, Astrocytoma), biomarker status (IDH, 1p19q, MGMT, CDKN2A/B), and grade (2, 3, 4). Employing probability vectors for statistical analysis revealed a significant correlation among different subtypes, locations and overall survival. Furthermore, we used a Visualization tool to build a 3D heatmap for each tumor subtype, more intuitively illustrating spatial correlations. CONCLUSION Our study underscores the prognostic value of tumor location and its correlation with glioma subtypes and patient outcomes, suggesting a potential pathway to personalized treatments. Future efforts will focus on incorporating multi-omics data into spatial radiographic analyses.

DTI fiber-tracking parameters adjacent to gliomas: the role of tract irregularity value in operative planning, resection, and outcome.

The goal of glioma surgery is maximal tumor resection associated with minimal post-operative morbidity. Diffusion tensor imaging-tractography/fiber tracking (DTI-FT) is a valuable white-matter (WM) visualization tool for diagnosis and surgical planning. Still, it assumes a descriptive role since the main DTI metrics and parameters showed several limitations in clinical use. New applications and quantitative measurements were recently applied to describe WM architecture that surround the tumor area. The brain adjacent tumor area (BAT) is defined as the region adjacent to the gross tumor volume, which contains signal abnormalities on T2-weighted or FLAIR sequences. The DTI-FT analysis of the BAT can be adopted as predictive values and a guide for safe tumor resection. This is an observational prospective study on an extensive series of glioma patients who performed magnetic resonance imaging (MRI) with pre-operative DTI-FT analyzed on the BAT by two different software. We examined DTI parameters of Fractional anisotropy (FA mean, min-max), Mean diffusivity (MD), and the shape-metric "tract irregularity" (TI) grade, comparing it with the surgical series' clinical, radiological, and outcome data. The population consisted of 118 patients, with a mean age of 60.6 years. 82 patients suffering from high-grade gliomas (69.5%), and 36 from low-grade gliomas (30.5%). A significant inverse relationship exists between the FA mean value and grading (p = 0.001). The relationship appears directly proportional regarding MD values (p = 0.003) and TI values (p = 0.005). FA mean and MD values are susceptible to significant variations with tumor and edema volume (p = 0.05). TI showed an independent relationship with grading regardless of tumor radiological features and dimensions, with a direct relationship with grading, ki67% (p = 0,05), PFS (p < 0.001), and EOR (p < 0.01). FA, MD, and TI are useful predictive measures of the clinical behavior of glioma, and TI could be helpful for tumor grading identification and surgical planning.

Environmental Design Teaching with the Integration of Traditional and Modern Techniques

Background: Environmental management considers the architecture in design, planning, programming, or manufacturing. It intends to create places that recover the cultural, social, ecological, and physical architecture of certain locations. However, traditional teaching methods are indeed employed. Modern technologies serve as supplemental tools. The current method of instruction is a step toward contemporary technology.Objective: Examining environmental design training using conventional and contemporary techniques was the aim of the study. Method:We collected survey data for this investigation. 400 students completed and submitted the questionnaire. Statistical Package for Social Sciences (SPSS) software was used to present the data. The research examines well-known visualization tools and traditional contemporary presentation techniques.Result: According to the study, architecture students rated modern technologies more satisfactory than traditional methods, indicating higher quality facilities and services. Modern technology in Environmental Design Teaching enhances learning experiences by reducing time consumption (65%), increasing efficiency (85%), offering flexibility (65%) and improved visualization (90%), and facilitating better documentation (80%), leading to higher student satisfaction (87%).Conclusion: Integrating traditional and modern architectural design techniques enhances learning experiences and efficiency, resulting in more effective and innovative design solutions through advanced tools and methods.

Patient perceptions of an electronic-health-record-based rheumatoid arthritis outcomes dashboard: a mixed-methods study

BackgroundOutcome measures are crucial to support a treat-to-target approach to rheumatoid arthritis (RA) care, yet their integration into clinical practice remains inconsistent. We developed an Electronic Heath Record-integrated, patient-facing side-car application to display RA outcomes (disease activity, functional status, pain scores), medications, and lab results during clinical visits (“RA PRO Dashboard”). The study aimed to evaluate patient perceptions and attitudes towards the implementation of a novel patient-facing dashboard during clinical visits using a mixed-methods approach.MethodsRA patients whose clinicians used the dashboard at least once during their clinical visit were invited to complete a survey regarding its usefulness in care. We also conducted semi-structured interviews with a subset of patients to assess their perceptions of the dashboard. The interviews were transcribed verbatim and analyzed thematically using deductive and inductive techniques. Emerging themes and subthemes were organized into four domains of the Ecological Model of Health.ResultsOut of 173 survey respondents, 79% were interested in seeing the dashboard again at a future visit, 71% felt it improved their understanding of their disease, and 65% believed it helped with decision-making about their RA care. Many patients reported that the dashboard helped them discuss their RA symptoms (76%) and medications (72%) with their clinician. Interviews with 29 RA patients revealed 10 key themes: the dashboard was perceived as a valuable visual tool that improved patients’ understanding of RA outcome measures, enhanced their involvement in care, and increased their trust in clinicians and the clinic. Common reported limitations included concerns about reliability of RA outcome questionnaires for some RA patients and inconsistent collection and explanation of these measures by clinicians.ConclusionsIn both the quantitative and qualitative components of the study, patients reported that the dashboard improved their understanding of their RA, enhanced patient-clinician communication, supported shared decision-making, and increased patient engagement in care. These findings support the use of dashboards or similar data visualization tools in RA care and can be used in future interventions to address challenges in data collection and patient education.

Predictive Gaze Analytics: A Comparative Case Study of the Foretelling Signs of User Performance during Interaction with Visualizations of Ontology Class Hierarchies

The current research landscape in ontology visualization has largely focused on tool development, yielding an extensive array of visualization tools. Although many existing solutions provide multiple ontology visualization layouts, there is limited research in adapting to an individual user’s performance, despite successful applications of adaptive technologies in related fields, including information visualization. In an effort to innovate beyond traditional one-size-fits-all visualizations, this paper contributes one step towards realizing user adaptive visualization by recognizing timely moments when users may potentially need intervention, as real-time adaptation can only occur if it is possible to correctly predict user success and failure during an interaction in the first place. In addition, an open-source, reusable, and extensible software: Beach Environment for the Analytics of Human Gaze (BEACH-Gaze) is made available to the broader scientific community interested in descriptive and predictive gaze analytics. Building on a wealth of research in eye tracking, this paper compares four approaches to predictive gaze analytics through a series of experiments that utilize scheduled gaze digests, irregular gaze events, the last known gaze status, as well as all gaze captured for a user at a given moment in time. The results from a set of experimental trials suggest that irregular gaze events are most informative of early predictions of user performance, whereas cognitive workload appears to be most indicative of overall user performance in the task scenario presented in this paper. These empirical findings highlight the importance of an analytical approach to gaze on user predictions and indicate careful consideration when applying.

How Does Virtual Reality Technology Affect Suicidal Ideation in Society?

Mental health issues such as anxiety and depression are on the rise in communities. Ignoring severe mental health issues can lead to suicide, which is a global public health issue. The use of advanced tools and methods to prevent suicide can help save human lives. Visual tools and virtual technologies have multiple applications in the medical and educational fields. The aim of this study was to investigate the effect of using virtual reality (VR) technology on suicidal thoughts. A quasi-experimental study was conducted, in which 189 individuals who had a history of suicide were tested. These individuals were divided into three groups: one control group and two intervention groups that received traditional and VR-based training for 90 days. The Beck Scale for Suicide Ideation was used. The posttest results after the training period showed that conducting the training course had a statistically significant effect on individuals' suicidal behaviour and those who participated in the VR-based training course had better improvements in suicidal thoughts. Therefore, the use of 3D simulation and visualisation tools can have a significant impact on individuals' thoughts and perceptions. The results of this study have practical implications for hospital managers and counsellors in healthcare centres to use VR technology in counselling and training courses to improve the behaviour of individuals with a history of suicide.

Potential of mobile virtual reality as a tool for supporting students’ motivation in school geography

The purpose of this study is to present the possibilities of implementing mobile virtual reality (VR) in school geography and to examine its benefits and risks. A total of 163 students from various elementary schools in Slovakia participated in this study. We used qualitative and quantitative data analysis from students’ feedback and observations of the VR activities. The Intrinsic Motivation Inventory questionnaire and researcher observation sheet were used as research tools. The study showed that the implementation of mobile VR is possible in the environment of Slovak schools and has positive effects on several areas of students’ internal motivation. The best results were achieved in the areas of interest/pleasure, understanding of the activity, and perception of the value and usefulness of the activity. We also found that the gender or age of the students in our research sample did not have a significant impact on students’ internal motivation when working with VR. Our work not only provides evidence to support the wider adoption of mobile VR in education but also clarifies several possibilities for its implementation in school geography. In conclusion, we identify the most significant benefits and risks of using mobile virtual reality in geography education. The results of testing mobile VR show the great usefulness of this technology, which serves as a motivational tool for 3D visualization of various locations and phenomena on Earth.

Behavioral Economics Strategies to Improve Medication Adherence in Diabetes

Nowadays, diabetes has become an international challenge. In spite of the great technological advances in human society compared to a few decades ago, a cure for this disease has never been discovered. Hundreds of millions of patients are suffering from it, and in order to enable diabetics to keep their condition as stable as possible, this paper examines how to enhance patients' medication adherence from the perspective of behavioral economics. After reclassification of the influencing factors through tables such as pivot tables, among the data of 1127 patients selected who required long-term medication, 559 were affected by Convergence and Avoidance of Harm and were reluctant to take medication. Another 231 participants suffered from cognitive bias. A further 165 patients made irrational calculations about whether or not to take their medication. The remaining patients were influenced by some objective factors. Based on the analysis of the distinct causes of medication non-adherence in diabetic patients, it was found that this could be tackled by allowing patients to be more informed about their condition, improving access to education in order to break down information barriers, increasing the presence of family and friends, and illustrating the cost of treatment with the help of visualization tools. Beyond financial and dietary approaches, these facilitation methods could nudge as much to stabilize patients' medication-taking behaviors.

Insights into turbulent airflow structures in blind headings under different ventilation duct distances

The paper explores the 3D stationary vortex structure of turbulent airflow near the dead-end face of a blind heading, ventilated via a forcing ventilation system. Despite its significance in blind heading ventilation, previous studies primarily focused on temporal dynamics of harmful impurities, overlooking flow structure details crucial for mass transfer processes. Our study delves into the ventilation flow structure across diverse parameters of the auxiliary ventilation system. Alongside standard flow visualization tools, we introduce three dimensionless indicators to comprehensively characterize flow structure, facilitating analysis of its variations with parameter changes and quantitative evaluation of system efficiency. Analysis revealed the formation of a single large-scale vortex within the entire range of considered ventilation system parameters in the heading. This vortex induces a significant increase in air circulation, approximately 2.5–3.5 times greater than airflow emerging from the ventilation duct’s end, thus intensifying mass transfer processes within the heading. We found that ventilation efficiency of the dead-end face zone in a blind heading with a 29.2 m² cross-section decreases linearly with increasing distance between the ventilation duct’s end and the dead-end face. However, compensating for this distance by increasing duct velocity is feasible, bearing significant implications for mine ventilation safety, particularly in ventilating blind headings with distant ducts from the dead-end face.

Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks

This study introduces a novel method that addresses the challenge of visualizing and quantifying detection coverage areas in wireless sensor networks. The method involves projecting a network of range-free sensors and pre-existing transmitters, located within a predefined area of interest, onto a global coordinate system. Detection areas are defined as those covered by the sensing range of at least three sensors. Pre-existing transmitters located within the detection range of the sensors are assumed to degrade the networks’ performance by causing coverage gaps. Interactive satellite maps facilitate the dynamic exploration of coverage via the calculation and visualization of the resulting detection areas. The algorithmic structure of the proposed tool is explained in detail, and four example scenarios demonstrate the tool’s capabilities, as well as its flexibility, adaptability, and effectiveness in identifying the triangulated detection areas. Designed primarily as a geometry calculation and visualization tool that allows for the adjustment of sensor parameters such as locations, ranges, and angular ranges of detection, the proposed tool has the potential to enhance decision-making in sensor network configuration, prior to final sensor placement, across a wide range of applications.

What is in your cookie box? Explaining ingredients of web cookies with knowledge graphs

The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies being unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie “disappears”, and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific Internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine-readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation approach and tool help to increase users’ awareness of cookies and data sharing.

Differentiating Reactive Oxygen Species with DNA Framework Monitors.

The lifespan, oxidizing properties, bonding behaviors, and reactivity of reactive oxygen species (ROS) produced during photocatalytic activation can vary significantly due to the differences in electron configurations of ROS, which are dependent on their generation mechanisms: energy transfer or charge transfer. Hence, identifying and differentiating ROS of different mechanisms can improve our understanding of redox reactions and related diseases, providing a basis for the prevention and treatment of related diseases. Here, we have developed a DNA framework monitor (DFM) based on dynamic DNA structural changes to effectively distinguish the two types of ROS produced in photocatalytic activation of O2. This DFM provides a visualization tool for observing the reaction kinetics of ROS with DNA, not only distinguishing two types of ROS with different mechanisms but also serving as a universal system for evaluating the efficacy and performance of nanomaterials for ROS regulation.

Microperimetry Sensitivity Correlates to Structural Macular Changes in Adolescents with Achromatopsia Unlike Other Visual Function Tests.

Objectives: Achromatopsia (ACHM) is a rare autosomal, recessively inherited disease that is characterized by cone dysfunction, for which several gene therapies are currently on trial. The aim of this study was to find correlations between the morphological macular changes identified using optical coherence tomography (OCT) and some visual functional parameters. Visual acuity (VA), contrast sensitivity (CS), and macular sensitivity obtained by means of microperimetry were assessed. Methods: Adolescents with ACHM underwent macular microperimetry (S-MAIA device) in mesopic condition, macular OCT, best corrected visual acuity (BCVA), low luminance visual acuity (LLVA), near vision acuity (NVA), and CS measurement. Results: Eight patients (15 eyes) with ACHM were analyzed. The mean age was 17 ± 2.7 years, and genetic variants involved the CNGA3 gene (37.5%) and CNGB3 gene (62.5%). OCT staging significantly correlated with microperimetry sensitivity parameters, namely the sensitivity of the central foveal point (p = 0.0286) and of the first and second perifoveal rings (p = 0.0008 and p = 0.0014, respectively). No correlations were found between OCT staging and VA measurements, nor with CS value. Conclusions: Among the extensive evaluated visual function tests, only microperimetry sensitivity showed a correlation with morphological macular changes identified at OCT. Microperimetry sensitivity may thus represent a useful visual function tool in natural ACHM history studies considering the upcoming research on gene therapies for the treatment of ACHM.

GP-Plotter: Flexible Spectral Visualization for Proteomics Data with Emphasis on Glycoproteomics Analysis.

Identification evaluation and result dissemination are essential components in mass spectrometry-based proteomics analysis. The visualization of fragment ions in mass spectrum provides strong evidence for peptide identification and modification localization. Here, we present an easy-to-use tool, named GP-Plotter, for ion annotation of tandem mass spectra and corresponding image output. Identification result files of common searching tools in the community and user-customized files are supported as input of GP-Plotter. Multiple display modes and parameter customization can be achieved in GP-Plotter to present annotated spectra of interest. Different image formats, especially vector graphic formats, are available for image generation which is favorable for data publication. Notably, GP-Plotter is also well-suited for the visualization and evaluation of glycopeptide spectrum assignments with comprehensive annotation of glycan fragment ions. With a user-friendly graphical interface, GP-Plotter is expected to be a universal visualization tool for the community. GP-Plotter has been implemented in the latest version of Glyco-Decipher (v1.0.4) and the standalone GP-Plotter software is also freely available at https://github.com/DICP-1809.

BoReMi: Bokeh-based jupyter-interface for registering spatio-molecular data to related microscopy images.

Spatio-molecular data and microscopy images provide complementary information, essential to study structure and function of spatially organised multicellular systems such as healthy or diseased tissues. However, aligning these two types of data can be challenging due to distortions and differences in resolution, orientation, and position. Manual registration is tedious but may be necessary for challenging samples as well as for the generation of ground-truth data sets that enable benchmarking of existing and emerging automated alignment tools. To make the process of manual registration more convenient, efficient, and integrated, we created BoReMi, a python-based, Jupyter-integrated, visual tool that offers all the relevant functionalities for aligning and registering spatio-molecular data and associated microscopy images. We showcase BoReMi's utility using publicly available data and images and make BoReMi as well as an interactive demo available on GitHub.

A taxonomic guide to diffusion MRI tractography visualization tools.

Visualizing neuroimaging data is a key step in evaluating data quality, interpreting results, and communicating findings. This survey focuses on diffusion MRI tractography, which has been widely used in both research and clinical domains within the neuroimaging community. With an increasing number of tractography tools and software, navigating this landscape poses a challenge, especially for newcomers. A systematic exploration of a diverse range of features is proposed across 27 research tools, delving into their main purpose and examining the presence or absence of prevalent visualization and interactive techniques. The findings are structured within a proposed taxonomy, providing a comprehensive overview. Insights derived from this analysis will help (novice) researchers, clinicians, and developers in identifying knowledge gaps and navigating the landscape of tractography visualization tools.

CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.

Development of a culturally informed tool to assess chronic pain for Māori: the Pictorial Representation of Illness and Self Measure-Aotearoa (PRISM-A)

Chronic pain is one of the most common long-term conditions in Aotearoa (New Zealand), and there is a higher prevalence and greater impact of pain for Māori (Indigenous People of New Zealand). Māori tend to have a more holistic interpretation of pain compared with western views; therefore, current assessments may not capture the full meaning and impact of pain for Māori. This study developed a holistic assessment tool for chronic pain in collaboration with Māori clinicians and Māori with chronic pain. The Pictorial Representation of Illness and Self Measure–Aotearoa (PRISM-A) is a verbal and visual assessment tool that is based on Te Ao Māori (Māori world), imbeds whakawhanaungatanga (relationship formation) between the patient and clinician, and incorporates Te Whare Tapa Whā, a Māori model of health. The tool is likely to provide a more meaningful assessment of pain and facilitate better outcomes for Māori with chronic pain.

The ECP ALPINE project: In situ and post hoc visualization infrastructure and analysis capabilities for exascale

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent.

A Comprehensive Guide to Generating SDTM Demographic Datasets Using R in Clinical Trials

R is a programming language widely used for statistical analysis and data visualization, offering a flexible and interactive environment supported by various packages for data cleaning, tidying, and analysis. It is particularly relevant for professionals in mathematics and statistics, including biostatisticians and programmers in the pharmaceutical and biotech industries. R provides a robust array of user-developed packages that can efficiently manipulate complex datasets, such as those based on the Study Data Tabulation Model (SDTM). The popularity of R in data-related fields has surged exponentially over the past decade due to its open-source nature, powerful statistical capabilities, and advanced visualization tools. In this paper, we demonstrate a step-by-step approach to generating an SDTM Demographic (DM) dataset using R. The process leverages R packages such as sas7bdat, tidyverse, haven, parsedate, dplyr, tidyr, and Hmisc. We also provide a detailed procedure for setting up the R environment required for this process. While R has been extensively used for exploratory analysis in the pharmaceutical and biotech industries, its application in creating and analyzing clinical trial datasets, such as SDTM, has been limited. Traditionally, SAS® has been the preferred tool for generating clinical trial datasets. This paper explores R’s potential as a viable alternative, offering enhanced flexibility and cost- effectiveness in clinical trial.