Web Visualization Tool Research Articles

Mapping a comprehensive assessment tool to a holistic definition of health for person-centred care planning in home care: a modified eDelphi study

BackgroundResearchers in the Netherlands proposed the Pillars for Positive Health (PPH) as a broadly encompassing health definition to support more realistic and meaningful care planning for people living with chronic disease and other life-long health conditions. The PPH was subsequently converted to the My Positive Health (MPH) spider web visualization tool. This study sought to identify opportunities for more person-centred care planning at the point of care in home care, using the MPH tool as a framework to link comprehensive assessment and dialogue-based goal-setting.MethodsA modified eDelphi method was used to conduct domain mapping with a purposively sampled expert panel (n = 25). The panel consisted of researchers, health care providers, older adults and caregivers. A two-stage eDelphi process was conducted, with each stage consisting of three survey rounds. In the first stage, participants were asked to map 201 elements of the interRAI Home Care (interRAI HC) comprehensive assessment tool to the six MPH domains or “No pillar of best fit”. The second stage focused on identifying opportunities to adapt or expand comprehensive assessment as it relates to the MPH domains.ResultsIn Stage 1, 189 of 201 elements reached consensus in domain mapping. These included: 80 elements for Bodily Functions, 32 for Daily Functioning, 32 for Mental Wellbeing, 24 for Quality of Life, 10 for Participation, and 1 for Meaningfulness. Ten elements were identified to have no pillar of best fit. The 12 elements that did not reach consensus in Stage 1 formed the basis for Stage 2, where expert panel participants proposed four new assessment elements in Meaningfulness and Participation and 11 additional descriptors across the six MPH domains. Of these, two elements and nine of the 11 descriptors reached consensus.ConclusionFindings show that elements of the interRAI HC are oriented toward the physical, functional, and mental health domains. Consequently, complementary assessment elements and/or tools may be needed to support comprehensive assessment of ‘Meaningfulness’ and ‘Participation’ in person-centred home and community care. Additional descriptors may also be needed to aid communication regarding the understanding and application of MPH domains.

CellPalmSeq: A curated RNAseq database of palmitoylating and de-palmitoylating enzyme expression in human cell types and laboratory cell lines.

The reversible lipid modification protein S-palmitoylation can dynamically modify the localization, diffusion, function, conformation and physical interactions of substrate proteins. Dysregulated S-palmitoylation is associated with a multitude of human diseases including brain and metabolic disorders, viral infection and cancer. However, the diverse expression patterns of the genes that regulate palmitoylation in the broad range of human cell types are currently unexplored, and their expression in commonly used cell lines that are the workhorse of basic and preclinical research are often overlooked when studying palmitoylation dependent processes. We therefore created CellPalmSeq (https://cellpalmseq.med.ubc.ca), a curated RNAseq database and interactive webtool for visualization of the expression patterns of the genes that regulate palmitoylation across human single cell types, bulk tissue, cancer cell lines and commonly used laboratory non-human cell lines. This resource will allow exploration of these expression patterns, revealing important insights into cellular physiology and disease, and will aid with cell line selection and the interpretation of results when studying important cellular processes that depend on protein S-palmitoylation.

A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of the COVID-19 Pandemic

Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.

Integration of Automated Vehicle Location, Fare Control, and Schedule Data for Improved Public Transport Trip Definition

This paper proposes a flexible methodology to improve the definition of each distinct trip carried out in a transport system, integrating the information provided by stop-level events from its automated vehicle location and fare collection systems, and scheduling subsystem information at the initial stop of planned trips. The data are structured; and then corrected and completed utilizing several criteria, including a probabilistic approach based on the distributions of travel and dwell times, aiming to minimize the distortions that appear due to the nature of the available sources. The case study data encompass one year of records from the automated vehicle location, fare collection, and scheduling subsystems in Santander City, Spain. The results are discussed with captures from an interactive web visualization tool that has been developed for this work.

A web visualization tool using T cell subsets as the predictor to evaluate COVID-19 patient's severity.

Wuhan, China was the epicenter of the 2019 coronavirus outbreak. As a designated hospital for COVID-19, Wuhan Pulmonary Hospital has received over 700 COVID-19 patients. With the COVID-19 becoming a pandemic all over the world, we aim to share our epidemiological and clinical findings with the global community. We studied 340 confirmed COVID-19 patients with clear clinical outcomes from Wuhan Pulmonary Hospital, including 310 discharged cases and 30 death cases. We analyzed their demographic, epidemiological, clinical and laboratory data and implemented our findings into an interactive, free access web application to evaluate COVID-19 patient’s severity level. Our results show that baseline T cell subsets results differed significantly between the discharged cases and the death cases in Mann Whitney U test: Total T cells (p < 0.001), Helper T cells (p <0.001), Suppressor T cells (p <0.001), and TH/TSC (Helper/Suppressor ratio, p<0.001). Multivariate logistic regression model with death or discharge as the outcome resulted in the following significant predictors: age (OR 1.05, 95% CI, 1.00 to 1.10), underlying disease status (OR 3.42, 95% CI, 1.30 to 9.95), Helper T cells on the log scale (OR 0.22, 95% CI, 0.12 to 0.40), and TH/TSC on the log scale (OR 4.80, 95% CI, 2.12 to 11.86). The AUC for the logistic regression model is 0.90 (95% CI, 0.84 to 0.95), suggesting the model has a very good predictive power. Our findings suggest that while age and underlying diseases are known risk factors for poor prognosis, patients with a less damaged immune system at the time of hospitalization had higher chance of recovery. Close monitoring of the T cell subsets might provide valuable information of the patient’s condition change during the treatment process. Our web visualization application can be used as a supplementary tool for the evaluation.

Machado: Open source genomics data integration framework.

BackgroundGenome projects and multiomics experiments generate huge volumes of data that must be stored, mined, and transformed into useful knowledge. All this information is supposed to be accessible and, if possible, browsable afterwards. Computational biologists have been dealing with this scenario for more than a decade and have been implementing software and databases to meet this challenge. The GMOD's (Generic Model Organism Database) biological relational database schema, known as Chado, is one of the few successful open source initiatives; it is widely adopted and many software packages are able to connect to it.FindingsWe have been developing an open source software package named Machado, a genomics data integration framework implemented in Python, to enable research groups to both store and visualize genomics data. The framework relies on the Chado database schema and, therefore, should be very intuitive for current developers to adopt it or have it running on top of already existing databases. It has several data-loading tools for genomics and transcriptomics data and also for annotation results from tools such as BLAST, InterproScan, OrthoMCL, and LSTrAP. There is an API to connect to JBrowse, and a web visualization tool is implemented using Django Views and Templates. The Haystack library integrated with the ElasticSearch engine was used to implement a Google-like search, i.e., single auto-complete search box that provides fast results and filters.ConclusionMachado aims to be a modern object-relational framework that uses the latest Python libraries to produce an effective open source resource for genomics research.

Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors

Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizens. In this article, we present an IoT pipeline for acquisition, processing, and visualization of air pollution data over the city of Antwerp, Belgium. Our system employs IoT devices mounted on vehicles as well as static reference stations to measure a variety of city parameters, such as humidity, temperature, and air pollution. Mobile measurements cover a larger area compared to static stations; however, there is a tradeoff between temporal and spatial resolution. We address this problem as a matrix completion on graphs problem and rely on variational graph autoencoders to propose a deep learning solution for the estimation of the unknown air pollution values. Our model is extended to capture the correlation among different air pollutants, leading to improved estimation. We conduct experiments at different spatial and temporal resolution and compare with state-of-the-art methods to show the efficiency of our approach. The observed and estimated air pollution values can be accessed by interested users through a Web visualization tool designed to provide an air pollution map of the city of Antwerp.

Prediction of the Clinical Outcome of COVID-19 Patients Using T Lymphocyte Subsets with 340 Cases from Wuhan, China: A Retrospective Cohort Study and a Web Visualization Tool

Background: Wuhan, China was the epicenter of the 2019 coronavirus outbreak. As a designated hospital, Wuhan Pulmonary Hospital has received over 700 COVID-19 patients. With the COVID-19 becoming a pandemic all over the world, we aim to share our epidemiological and clinical findings with the global community. Methods: In this retrospective cohort study, we studied 340 confirmed COVID-19 patients from Wuhan Pulmonary Hospital, including 310 discharged cases and 30 death cases. We analyzed their demographic, epidemiological, clinical and laboratory data and implemented our findings into an interactive, free access web application. Findings: Baseline T lymphocyte Subsets differed significantly between the discharged cases and the death cases in two-sample t-tests: Total T cells (p < 2·2e-16), Helper T cells (p < 2·2e-16), Suppressor T cells (p = 1·8-14), and TH/TS (Helper/Suppressor ratio, p = 0·0066). Multivariate logistic regression model with death or discharge as the outcome resulted in the following significant predictors: age (OR 1·05, p 0·04), underlying disease status (OR 3·42, p 0·02), Helper T cells on the log scale (OR 0·22, p 0·00), and TH/TS on the log scale (OR 4·80, p 0·00). The McFadden pseudo R-squared for the logistic regression model is 0·35, suggesting the model has a fair predictive power. Interpretation: While age and underlying diseases are known risk factors for poor prognosis, patients with a less damaged immune system at the time of hospitalization had higher chance of recovery. Close monitoring of the T lymphocyte subsets might provide valuable information of the patient’s condition change during the treatment process. Our web visualization application can be used as a supplementary tool for the evaluation. Funding Statement: The authors report no funding. Declaration of Interests: The named authors have no conflict of interest, financial or otherwise. Ethics Approval Statement: The study protocol was reviewed and approved by the Ethics committee of Wuhan Pulmonary Hospital (WPE 2020-8). Informed consents were obtained from all participants before enrollment. Patient records and information were de-identified prior to the analysis.

A Figure One Web Tool for Visualization of Experimental Designs.

This manuscript introduces a user-friendly, point and click open source and platform-independent software tool that aids the graphical representation of experimental studies. A graphical summary can give a high-level view of a study and represent in one illustration the important features of the data. Examples include sample collections, the time of each data collection, perturbations, and analysis performed. Graphical summaries can be useful in clarifying and documenting the complex relationships within an experiment by breaking down the component parts and expressing them visually. Commonly used cases for this tool include generating summary figures for presentation and publications. This tool was used either alone or in conjunction with other tools to generate schematic diagrams for talks and publications on several different on-going research projects.

Establishment of normative ranges of the healthy human immune system with comprehensive polychromatic flow cytometry profiling.

Existing normative flow cytometry data have several limitations including small sample sizes, incompletely described study populations, variable flow cytometry methodology, and limited depth for defining lymphocyte subpopulations. To overcome these issues, we defined high-dimensional flow cytometry reference ranges for the healthy human immune system using Human Immunology Project Consortium methodologies after carefully screening 127 subjects deemed healthy through clinical and laboratory testing. We enrolled subjects in the following age cohorts: 18–29 years, 30–39, 40–49, and 50–66 and enrolled cohorts to ensure an even gender distribution and at least 30% non-Caucasians. From peripheral blood mononuclear cells, flow cytometry reference ranges were defined for >50 immune subsets including T-cell (activation, maturation, T follicular helper and regulatory T cell), B-cell, and innate cells. We also developed a web tool for visualization of the dataset and download of raw data. This dataset provides the immunology community with a resource to compare and extract data from rigorously characterized healthy subjects across age groups, gender and race.

ONTOLOGY-BASED WEB TOOLS FOR RETRIEVING PHOTOGRAMMETRIC CULTURAL HERITAGE MODELS

Abstract. A key challenge in cultural heritage (CH) sites visualization is to provide models and tools that effectively integrate the content of a CH data with domain-specific knowledge so that the users can query, interpret and consume the visualized information. Moreover, it is important that the intelligent visualization systems are interoperable in the semantic web environment and thus, capable of establishing a methodology to acquire, integrate, analyze, generate and share numeric contents and associated knowledge in human and machine-readable Web. In this paper, we present a model, a methodology and a software Web-tools that support the coupling of the 2D/3D Web representation with the knowledge graph database of Xlendi shipwreck. The Web visualization tools and the knowledge-based techniques are married into a photogrammetry driven ontological model while at the same time, user-friendly web tools for querying and semantic consumption of the shipwreck information are introduced.

Corrigendum: Freeze-quenched maize mesophyll and bundle sheath separation uncovers bias in previous tissue-specific RNA-Seq data.

The high efficiency of C4 photosynthesis relies on spatial division of labor, classically with initial carbon fixation in the mesophyll and carbon reduction in the bundle sheath. By employing grinding and serial filtration over liquid nitrogen, we enriched C4 tissues along a developing leaf gradient. This method treats both C4 tissues in an integrity-preserving and consistent manner, while allowing complementary measurements of metabolite abundance and enzyme activity, thus providing a comprehensive data set. Meta-analysis of this and the previous studies highlights the strengths and weaknesses of different C4 tissue separation techniques. While the method reported here achieves the least enrichment, it is the only one that shows neither strong 3' (degradation) bias, nor different severity of 3' bias between samples. The meta-analysis highlighted previously unappreciated observations, such as an accumulation of evidence that aspartate aminotransferase is more mesophyll specific than expected from the current NADP-ME C4 cycle model, and a shift in enrichment of protein synthesis genes from bundle sheath to mesophyll during development. The full comparative dataset is available for download, and a web visualization tool (available at http://www.plant-biochemistry.hhu.de/resources.html) facilitates comparison of the the Z. mays bundle sheath and mesophyll studies, their consistencies and their conflicts.

A multi-level and multi-sensor documentation approach of the Treblinka extermination and labor camps

Abstract At Treblinka extermination camp, almost one million of people were killed during the Holocaust. A further 10,000 people are estimated to have been murdered at the nearby labor camp. Since 2007, the Centre of Archaeology of Staffordshire University (UK) has been involved in the study and research of the evidence of these atrocities in order to demonstrate how an archaeological approach to Treblinka has, and will continue to, enhance contemporary discussions about the camps and provide a richer, more accurate record of events. This study focuses on the use of digital 3D technologies and web visualization tools for the analysis of the sites, providing a digital interactive platform, which can be used both by professional users and a public audience. It proposes a pyramidal multi-level and multi-sensor approach – providing a 3D resolution spanning from a few centimetres in the landscape Digital Terrain Model to few millimetres in the layer-by-layer archaeological test trench.

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

BackgroundThe advent of “omics” science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability.ResultsHere we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB.ConclusionsCellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

Abstract 3038: Bridging the gap between NGS data and its usability: cancer gene discovery through massive-scale transcriptomic analyses and development of a powerful web-tool for dissemination of these findings

Abstract Background: In a prior transcriptomic analysis focused on lncRNA interrogation using some of these data, we discovered tens of thousands of novel lncRNAs (Iyer and Niknafs et al, Nature Genetics, 2015). Building on this analysis, we recently developed a markedly improved bioinformatics tool for novel gene/isoform discovery from massive RNA-seq datasets named TACO (Niknafs et al, Nature Methods, In press, tacorna.github.io). TACO produces high-fidelity transcript structure predictions from large RNA-seq datasets. We now set out to comprehensively leverage both TACO and the publicly available RNA-seq data for discovery of novel transcriptional cancer associations. Additionally, in order to widely disseminate these findings in an accessible manner, we have built a web-tool that provides the scientific community access to these data and analyses. Methods: We have downloaded, curated, and processed 23,623 RNA-seq samples largely from the TCGA, ICGC, GTEx, and CCLE, comprising 37 tissue types and over 35 cancer types. RNA-seq data processing was performed using STAR, Cufflinks, Kallisto, and TACO. The web tool for visualization and access to these data and analyses was built using a JavaScript-based server infrastructure (Noje.js) and a relational PostgreSQL database. Results: Generation of a consensus transcriptome from this large-scale RNA-seq dataset via TACO resulted in the discovery of tens of thousands of novel transcriptional elements, including intergenic non-coding RNAs and novel splice isoforms of known genes. Such an expansive RNA-seq cohort that includes many normal tissue samples enabled statistically powerful cancer association expression analyses that revealed a myriad of novel cancer genes, especially in tissues for which there was previously little-to-no normal tissue RNA-seq data (e.g., brain and pancreas). Many of these novel transcriptional elements discovered using TACO were also found to be cancer associated. We have built a web-tool to facilitate further analysis and discovery using these data and analyses by the scientific community. The web-tool provides a powerful and intuitive interface for researchers with little-to-no bioinformatics expertise to leverage large-scale RNA-seq datasets. Conclusion: Here we present the largest reported compendium of RNA-seq data, and reveal many novel cancer gene associations. Using a new, powerful gene discovery tool, TACO, we identify a multitude of novel transcriptional elements that are also cancer associated. Despite the abundance of publicly available RNA-seq data, necessary computing resources, data storage, and bioinformatics expertise are barriers to usability of these data by scientific community. Our RNA-seq expression web-tool bridges this gap, and enables users to powerfully interrogate cancer expression across dozens of tumor and tissue types. Citation Format: Yashar Niknafs, Nicholas Molen, Balaji Pandian, Matthew Iyer, Arul Chinnaiyan. Bridging the gap between NGS data and its usability: cancer gene discovery through massive-scale transcriptomic analyses and development of a powerful web-tool for dissemination of these findings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3038. doi:10.1158/1538-7445.AM2017-3038

Freeze-quenched maize mesophyll and bundle sheath separation uncovers bias in previous tissue-specific RNA-Seq data.

The high efficiency of C4 photosynthesis relies on spatial division of labor, classically with initial carbon fixation in the mesophyll and carbon reduction in the bundle sheath. By employing grinding and serial filtration over liquid nitrogen, we enriched C4 tissues along a developing leaf gradient. This method treats both C4 tissues in an integrity-preserving and consistent manner, while allowing complementary measurements of metabolite abundance and enzyme activity, thus providing a comprehensive data set. Meta-analysis of this and the previous studies highlights the strengths and weaknesses of different C4 tissue separation techniques. While the method reported here achieves the least enrichment, it is the only one that shows neither strong 3' (degradation) bias, nor different severity of 3' bias between samples. The meta-analysis highlighted previously unappreciated observations, such as an accumulation of evidence that aspartate aminotransferase is more mesophyll specific than expected from the current NADP-ME C4 cycle model, and a shift in enrichment of protein synthesis genes from bundle sheath to mesophyll during development. The full comparative dataset is available for download, and a web visualization tool (available at http://www.plant-biochemistry.hhu.de/resources.html) facilitates comparison of the the Z. mays bundle sheath and mesophyll studies, their consistencies and their conflicts.

Big Data Analysis for Media Production

A typical high-end film production generates several terabytes of data per day, either as footage from multiple cameras or as background information regarding the set (laser scans, spherical captures, etc). This paper presents solutions to improve the integration of the multiple data sources, and understand their quality and content, which are useful both to support creative decisions on-set (or near it) and enhance the postproduction process. The main cinema specific contributions, tested on a multisource production dataset made publicly available for research purposes, are the monitoring and quality assurance of multicamera set-ups, multisource registration and acceleration of 3-D reconstruction, anthropocentric visual analysis techniques for semantic content annotation, and integrated 2-D–3-D web visualization tools. We discuss as well improvements carried out in basic techniques for acceleration, clustering and visualization, which were necessary to deal with the very large multisource data, and can be applied to other big data problems in diverse application fields.

MyKaryoView: a light-weight client for visualization of genomic data.

The Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView's ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com.

Phage Eco-Locator: a web tool for visualization and analysis of phage genomes in metagenomic data sets

Background Bacteriophages, viruses that infect bacteria, are the most abundant biological entities on our planet, and their nucleic acids constitute a substantial proportion of total DNA in Earth’s ecosystems [1,2]. While the advent of metagenomic methods has allowed the rapid and efficient investigation of microbial and viral communities [3-5], there has not been a comprehensive comparative analysis of phage genes and genomes present in all sequenced ecosystems [6,7]. To examine the abundance and distribution of phage genes in environmental metagenomic sequences, we developed a web-based tool, Phage Eco-Locator [http://www.phantome.org/eco-locator] that screens all publicly available sequenced metagenomes for a user-defined phage genome, or all phage genomes within a user-selected metagenomic sample.

A commentary on GeoVRML: a tool for 3D representation of georeferenced data on the web

GeoVRML techniques have the potential to provide functional and transparent communication between geographical information and 3D Web visualization tools. This report outlines recommended practices and modifications to the VRML97 standard to consider pre-existing cartographic projections and georeferenced data. The concepts outlined for incorporating georeferenced coordinate systems in VRML worlds have generic applicability to 3D Web technologies like MPEG-4, Java3D and Chrome.