webTDat: A Web-Based, Real-Time, 3D Visualization Framework for Mesoscopic Whole-Brain Images

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data.

Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization

We present a cloud-based approach to transform arbitrarily large terrain data to a hierarchical level-of-detail structure that is optimized for web visualization. Our approach is based on a divide-and-conquer strategy. The input data is split into tiles that are distributed to individual workers in the cloud. These workers apply a Delaunay triangulation with a maximum number of points and a maximum geometric error. They merge the results and triangulate them again to generate less detailed tiles. The process repeats until a hierarchical tree of different levels of detail has been created. This tree can be used to stream the data to the web browser. We have implemented this approach in the frameworks Apache Spark and GeoTrellis. Our paper includes an evaluation of our approach and the implementation. We focus on scalability and runtime but also investigate bottlenecks, possible reasons for them, as well as options for mitigation. The results of our evaluation show that our approach and implementation are scalable and that we are able to process massive terrain data.

LiveSankey: Advanced Web Visualization in Data Intelligence Multi Domain Contexts

In the last years, the growing volumes and sources of data has made Big Data technologies to become mainstream. In that sense, techniques like Data Visualization are being used more and more to group large amounts of data in order to transform them into useful information. Nevertheless, these techniques are currently included in Business Intelligence approaches to provide companies and public organizations with helpful tools for making decisions based on evidences instead of intuition. The Sankey diagram is an example of those complex visualization tools allowing the user to graphically trace meaningful relationships in large volumes of data. However, this type of diagram is usually static so they must be continuously and manually rebuilt on top of massive multivariable environments whenever decision makers need to evaluate different options and they do not allow to establish conditions over the data shown. This paper presents LiveSankey, an approach to automatically generate dynamic Sankey Diagrams allowing users to filter the data shown. As a result, multiple conditions may be established over the data used and the corresponding diagram can be dynamically rebuilt.

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

BackgroundWuhan, 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.MethodsIn 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.FindingsBaseline 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.InterpretationWhile 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.FundingThe authors report no funding.