Visual Workflow Research Articles

Effects of sequence features on machine-learned enzyme classification fidelity

Assigning enzyme commission (EC) numbers using sequence information alone has been the subject of recent classification algorithms where statistics, homology and machine-learning based methods are used. This work benchmarks performance of a few of these algorithms as a function of sequence features such as chain length and amino acid composition (AAC). This enables determination of optimal classification windows for de novo sequence generation and enzyme design. Parallelization and visualization workflows are developed to observe the performance of the classifier over changing enzyme length, main EC class and AAC. We applied these workflows to the entire SwissProt database to date (n = 565928) using two, locally installable classifiers, ECpred and DeepEC, and collecting results from two other webserver-based tools, Deepre and BENZ-ws. All the classifiers exhibit peak performance in the range of 300–450 amino acids in length. Classifiers were most accurate at predicting translocases (EC-6) and were least accurate in determining hydrolases (EC-3) and oxidoreductases (EC-1). We also identified AAC ranges that are most common in the annotated enzymes and found that all classifiers work best in this common range. Among the four classifiers, ECpred showed the best consistency in changing feature space. These workflows can be used to benchmark new algorithms as they are developed and find optimum design spaces for the generation of new, synthetic enzymes.

Continuous Emulation and Multiscale Visualization of Traffic Flow Using Stationary Roadside Sensor Data

With the advent of the next-generation traffic monitoring systems, there has been a significant increase in the spatial-temporal resolution of vehicle mobility data in many cities. Effective analysis and visualization of such data can provide transportation planners with data-driven insights, which can facilitate the understanding of multiscale traffic dynamics. In this paper, we present a web-based traffic emulator for emulating and visualizing near-real-time and historical traffic flows on highways using data from road-side sensors. To construct a continuous traffic flow, the emulator adopts an analytical pipeline that can (a) integrate traffic data collected from discrete road-side radar detection sensors, (b) interpolate traffic conditions (vehicle speed and volume) on unmeasured road segments based on traffic flow theory, and (c) generate lane-specific vehicle trajectories and movements using a mathematically optimized representation of the road network. Our app also provides an integrated visual workflow that allows users to explore the interconnected traffic dynamics using an appropriate traffic flow visualization selected based on the level of detail. We devise two innovative geo-visualization techniques that utilize an animated strips-network representation and a lane usage matrix to visualize lane performances. To ensure a smooth emulation of large-scale traffic flow in an easy-to-access web environment, we implement the emulator using client-side GPU-accelerated techniques. Finally, we close with a case study that visualizes traffic dynamics of two scenarios – an afternoon peak hour and a traffic accident – in Chattanooga, Tennessee. Our app visualizes the responses of traffic dynamics during different traffic conditions, and to the presence of the traffic accident at different spatial scales.

Integrating computational fluid dynamics data into medical image visualization workflows via DICOM.

Communicating complex blood flow patterns generated from computational fluid dynamics (CFD) simulations to clinical audiences for the purposes of risk assessment or treatment planning is an ongoing challenge. While attempts have been made to develop new software tools for such clinical visualization of CFD data, these often overlook established medical imaging/visualization practice and data infrastructures. Here, leveraging the clinical ubiquity of the DICOM file format, we present techniques for the translation of CFD data to DICOM series, facilitating interactive visualization in standard radiological software. Unstructured CFD data (volumetric fields of velocity magnitude, Q-criterion, and pathlines) are resampled to structured grids. Novel raster-based techniques that simulate experimental optical blurring are presented for bringing simulated pathlines into structured image volumes. DICOM series are created by strategically encoding these data into the file's PixelArray tag. Lumen surface information is also strategically encoded into a different range of pixel intensities, allowing hemodynamics and morphology to be co-visualized in a single volume using opacity-based rendering transfer functions. We show that 3D temporal CFD data represented as structured DICOM series can be rendered interactively in Horos, a widely-used medical imaging/radiology software. Our transfer function-based approach allows for representations of scalar isosurfaces, volumetric rendering, and tubular pathlines to be modified in real-time, resembling conventional unstructured visualizations. Careful selection of voxelization ROIs helps to ensure that data are kept lightweight for real-time rendering and minimal storage. While our approach inherently sacrifices some of the advanced visualization capabilities of specialized software tools, we believe our closer consideration of standardization can help to facilitate meaningful clinical interaction. This work opens up possibilities for the complete integration of measured and simulated data in established radiological software environments and workflows from PACS storage to 3D/4D visualization.

Riboviz 2: a flexible and robust ribosome profiling data analysis and visualization workflow.

MotivationRibosome profiling, or Ribo-seq, is the state-of-the-art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses.ResultsWe present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system for end-to-end processing of Ribo-seq data. riboviz 2 has been extensively tested on diverse species and library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline.Availability and implementationriboviz 2 is freely available at github.com/riboviz/riboviz.

Federated horizontally partitioned principal component analysis for biomedical applications.

Federated learning enables privacy-preserving machine learning in the medical domain because the sensitive patient data remain with the owner and only parameters are exchanged between the data holders. The federated scenario introduces specific challenges related to the decentralized nature of the data, such as batch effects and differences in study population between the sites. Here, we investigate the challenges of moving classical analysis methods to the federated domain, specifically principal component analysis (PCA), a versatile and widely used tool, often serving as an initial step in machine learning and visualization workflows. We provide implementations of different federated PCA algorithms and evaluate them regarding their accuracy for high-dimensional biological data using realistic sample distributions over multiple data sites, and their ability to preserve downstream analyses. Federated subspace iteration converges to the centralized solution even for unfavorable data distributions, while approximate methods introduce error. Larger sample sizes at the study sites lead to better accuracy of the approximate methods. Approximate methods may be sufficient for coarse data visualization, but are vulnerable to outliers and batch effects. Before the analysis, the PCA algorithm, as well as the number of eigenvectors should be considered carefully to avoid unnecessary communication overhead. Simulation code and notebooks for federated PCA can be found at https://gitlab.com/roettgerlab/federatedPCA; the code for the federated app is available at https://github.com/AnneHartebrodt/fc-federated-pca. Supplementary data are available at Bioinformatics Advances online.

Direct R-Loop Visualization on Genomic DNA by Native Automated Electron Microscopy.

R-loop are physiologically present on genomic DNA of different organisms and play important roles in genome regulation. However, an increase in their abundance and/or size has been suggested to interfere with the DNA replication process, contributing to genome instability. Most available approaches to monitor R-loops are based on antibodies/enzymes that cannot effectively distinguish R-loops from DNA-RNA hybrids and assess R-loop size and frequency in a population of molecules. Electron microscopy has successfully allowed single-molecule visualization of DNA replication and repair intermediates, uncovering key architectural modifications in DNA, induced by genotoxic stress or by the associated cellular response. Here, we describe recent modifications of this visualization workflow to implement partial automation of image acquisition and analysis. Coupling this refined workflow with sample preparation procedures that protect R-loop stability allows for direct visualization of R-loop structures on genomic DNA, independently from probes. Combining single-molecule information and DNA content assessment, this approach provides direct estimations of R-loop frequency, size, and burden on genomic DNA.

BeerMIMS: Exploring the Use of Membrane-Inlet Mass Spectrometry (MIMS) Coupled to KNIME for the Characterization of Danish Beers.

Beer is a complex mix of more than 7700 compounds, around 800 of which are volatile. While GC-MS has been actively employed in the analysis of the volatome of beer, this method is challenged by the complex nature of the sample. Herein, we explored the possible of using membrane-inlet mass spectrometry (MIMS) coupled to KNIME to characterize local Danish beers. KNIME stands for Konstanz Information Miner and is a free open-source data processing software which comes with several prebuilt nodes, that, when organized, result in data processing workflows allowing swift analysis of data with outputs that can be visualized in the desired format. KNIME has been shown to be promising in automation of large datasets and requires very little computing power. In fact, most of the computations can be carried out on a regular PC. Herein, we have utilized a KNIME workflow for data visualization of MIMS data to understand the global volatome of beers. Feature identification was not possible as of now but with a combination of MIMS and a KNIME workflow, we were able to distinguish beers from different micro-breweries located in Denmark, laying the foundation for the use of MIMS in future analysis of the beer volatome.

SBGNview: towards data analysis, integration and visualization on all pathways.

SummaryPathway analysis is widely used in genomics and omics research, but the data visualization has been highly limited in function, pathway coverage and data format. Here, we develop SBGNview a comprehensive R package to address these needs. By adopting the standard SBGN format, SBGNview greatly extend the coverage of pathway-based analysis and data visualization to essentially all major pathway databases beyond KEGG, including 5200 reference pathways and over 3000 species. In addition, SBGNview substantially extends or exceeds current tools (esp. Pathview) in both design and function, including standard input format (SBGN), high-quality output graphics (SVG format) convenient for both interpretation and further update, and flexible and open-end workflow for iterative editing and interactive visualization (Highlighter module). In addition to pathway analysis and data visualization, SBGNview provides essential infrastructure for SBGN data manipulation and processing.Availability and implementationThe data underlying this article are available as part of the SBGNview package is available on both GitHub and Bioconductor: https://github.com/datapplab/SBGNview, https://bioconductor.org/packages/SBGNview.Supplementary information Supplementary data are available at Bioinformatics online.

Visualization and interpretation of geochemical exploration data using GIS and machine learning methods

Geochemical exploration has provided significant clues for mineral exploration and has helped discover many mineral deposits. Although various methods, including classic statistics, multivariate statistics, geostatistics, fractal/multifractal models, and machine learning algorithms, have been successfully employed to process geochemical exploration data, efficient interpretation and visualization of geochemical exploration data in support of the discovery of mineral deposits remain challenging. In this study, a workflow for intelligent interpretation and visualization of geochemical exploration data, defined as processing geochemical survey data with support of a geographical information system (GIS) and machine learning algorithms, was proposed. The effectiveness of the intelligent interpretation and visualization of geochemical exploration data supported by GIS and machine learning algorithms was demonstrated using a case study of processing a regional-scale geochemical survey dataset collected from Sichuan Province, China. Future research should add more advanced mathematical and statistical models, such as deep learning algorithms, into GIS to support the intelligent interpretation and visualization of geochemical exploration data.

Visualization Workflow for Quantifying Parameter Sensitivities and Uncertainties for Hydrologic Models

AbstractFrom regional to continental scales, hydrologic processes are represented by modular modeling frameworks dependent on input datasets and parameter sets representing physical attributes. The hydrologic community needs a common procedure to evaluate model output based on parameter sensitivities and uncertainties compared to performance metrics (i.e., objective functions), especially for large parameter sets. We developed a reproducible workflow for evaluating hydrologic models to objectively analyze model outputs as a function of parameter choice using numerical and visualization techniques. Our workflow was implemented on three separate case studies, each with a different hydrologic model, and the results can be reproduced and visualized from a community github code repository. Model parameter sensitivity was evaluated using several global sensitivity indices and Bayesian theory. Uncertainty in parameter spaces was quantified to highlight the impact of unreliable input data on model output. Model parameter sensitivities and uncertainties were evaluated numerically and visually to provide a comprehensive perspective on their impacts on model output. For each case study, we provided a summary and interpretation of the workflow results. Our workflow can be integrated into hydrologic modeling frameworks for objective modular model and parameter set evaluations based on a data‐driven approach appropriate for model selection.

A Data Centre Configurable Data Mining Document Management Information System

Data extraction is often a dynamic process that can be easily modelled as a workflow for data processing. When massive collections of data have to be evaluated and/or sophisticated data mining algorithms have to be performed, it can take very long to execute data analysis workflows. Effective technologies are also needed to incorporate flexible data collection workflows through the use of cloud-based storage platforms, where data is stored even more regularly. The paper attempts to show how cloud infrastructure is implemented to introduce an optimised framework in which scalable data analyzation workflows can be planned and performed. We explain how the Data Mining Cloud Architecture is built and applied and a data analytics method that incorporates visual workflow vocabulary, parallel to the Virtualized environment. DMCF is developed with a view to simplifying the creation of applications for data mining associated with generic system monitoring schemes that are not created especially for this area, in view of the specifications of actual data mining applications. The effects are a high-level environment that minimises the programming effort with an optimised visual workflow language, allowing the implementation of typical patterns meant to generate and execute data mining application in parallel simple to professional developers. The wall mounted of the workflow, device design and mechanisms of the DMCF are shown. We also address many DMCF-developed data mining business processes and the scalability achieved by running business processes in a cloud environment.

Online XR presentation of dicephalic parapagus full‐term conjoined twins collected by Dr. Jacob Henle

Anatomical analysis of conjoined twins provides important information concerning embryological development. Museum specimens provide a unique sample that can be analyzed non-intrusively using advanced biomedical imaging and displayed online which is currently important due to limited in-person learning opportunities as a result of the COVID-19 pandemic. The purpose of this study is to create an extended reality (XR) workflow for visualization of dicephalic parapagus full-term conjoined twins obtained by Dr. Jacob Henle sometime between 1844-1852 for use in anatomy education. The workflow comprised image capture, segmentation, and visualization. The cadaver twins were curated at the University of Heidelberg and were subjected to CT and MR imaging. Relevant bones and soft tissues were manually segmented to create XR models, post-processed for visualization using Unity-based systems. A learning module was created and posted to Rad3d.com for presentation to students including visualization on Z-space computers (zspace.com) and sketchfab.com online. Osteology analysis showed commonality in the upper limb and shared ribs. Two vertebral columns were identified, and a single pelvic girdle was present with a single set of lower limbs. Duplicated supradiaphragmatic structures included two hearts (one with situs inversus) and four lungs, but single subdiaphragmatic visceral organs were observed. In particular, the gut was continuous on the right, but terminated at the distal esophagus on the left. One large liver occupied the abdomen with one large spleen located on the left. These observations suggest zygote fission was blocked near the yolk sac during midgut formation, but with secondary fusion of midline upper extremities and ribs. A radiology report was developed and presented to medical students as an embryology clinical correlation. This application indicates that university based museum specimens are useful as supplemental instructional subjects and, in this case, almost 170 years after arriving in the Department of Anatomy at the University of Heidelberg.

HPC-cloud native framework for concurrent simulation, analysis and visualization of CFD workflows

Analysis and rendering of high-resolution computational fluid dynamics (CFD) simulations often requires execution of multiple parallel data-processing pipelines. In this paper we present a hybrid cloud solution for efficient simulation and analysis of drop dispersions. The simulation component runs on a high-performance computing (HPC) cluster and cloud native framework performs the processing of CFD outputs. To facilitate some of the cloud features, we broke down the CFD data analysis pipeline into small microservice-like processes. In this way, a given resource can be set up and shared between different simulations, codes and locations to minimize idle times and dynamically adjust the capacity to the workload while producing results on the fly, independent of the simulation engine. A combination of HPC and cloud technologies allows us to create agile and highly scalable solutions for CFD purposes. We focus on HPC-cloud infrastructure; however, other arrangements such as cloud–cloud or desktop–cloud can be easily adapted depending on the needs. This enables a framework for centralizing services for collaborative use between different users as well as for automatically providing data from different sources to machine learning algorithms. We describe a proof of concept implementation of the proposed framework and provide detailed analysis of its performance applied to a real two-phase flow application.

TaxonTableTools - A comprehensive, platform-independent graphical user interface software to explore and visualise DNA metabarcoding data

DNA-based identification methods, such as DNA metabarcoding, are increasingly used as biodiversity assessment tools in research and environmental management. Although powerful analysis software exists to process raw data, the translation of sequence read data into biological information and downstream analyses may be difficult for end users with limited expertise in bioinformatics. Thus, the need for easy-to-use, graphical user interface (GUI) software to analyze and visualize DNA metabarcoding data is growing. Here we present TaxonTableTools (TTT), a new platform-independent GUI that aims to fill this gap by providing simple, reproducible analysis and visualization workflows. The input format of TTT is a so-called "TaXon table". This data format can easily be generated within TTT from two common file formats that can be obtained using various published DNA metabarcoding pipelines: a read table and a taxonomy table. TTT offers a wide range of processing, filtering and analysis modules. The user can analyze and visualize basic statistics, such as read proportion per taxon, as well as more sophisticated visualizations such as interactive Krona charts for taxonomic data exploration, or complex parallel category diagrams to assess species distribution patterns. Venn diagrams can be calculated to compare taxon overlap among replicates, samples, or analysis methods. Various ecological analyses can be produced directly, including alpha or beta diversity estimates, rarefaction analyses, and principal coordinate or non-metric multidimensional scaling plots. The taxonomy of a data set can be validated via the Global Biodiversity Information Facility (GBIF) API to check for synonyms and spelling mistakes. Furthermore, geographical distribution data can be automatically downloaded from GBIF. Additionally, TTT offers a conversion tool for DNA metabarcoding data into formats required for traditional, taxonomy-based analyses performed by regulatory bioassessment programs. Beyond that, TTT is able to produce fully interactive html-based graphics that can be analyzed in any web browser. The software comes with a manual and tutorial, is free and publicly available through GitHub (https://github.com/TillMacher/TaxonTableTools) or the Python package index (https://pypi.org/project/taxontabletools/).

An ANN-based identification of geological features using multi-attributes: a case study from Krishna-Godavari basin, India

Artificial neural network (ANN)–based workflow has been applied to the multi-channel seismic reflection data from the Krishna-Godavari (K-G) basin on the Eastern continental margin of India for delineation of prominent structural features such as chimneys and faults. To eliminate the noise and enhance the data quality, the seismic data is conditioned and filtered by data-conditioning techniques such as dip-steered and structural filters. A single attribute is not sufficient for the delineation of structures such as faults and chimneys; hence, an amalgamation of multiple attributes is necessary for the enhancement of laterally continuous seismic events. Hence, multi-attributes such as dip variance, curvature, coherency, energy, similarity, instantaneous frequency, and frequency washout ratio are extracted from the conditioned data which were combined and fed as inputs for the ANN for the chimney and fault detection. The neural network is trained at the identified chimney/non-chimney and fault/non-fault locations, which generates a final output, namely chimney probability attribute and a fault probability attribute, revealing an improved visibility of faults and chimneys in the seismic data. This multi-attribute approach shows more reliability in comparison with individual attribute responses, which helps in better structural interpretation. The study presents a workflow for better visualization of the subsurface structural features which thereby helps in detailed structural interpretation of the seismic data.

MRI-Based 3-Dimensional Visualization Workflow for the Preoperative Planning of Nephron-Sparing Surgery in Wilms’ Tumor Surgery: A Pilot Study

Purpose. Due to the size and localization of Wilms’ tumor (WT), nephron-sparing surgery (NSS) is only possible in a limited number of cases. When NSS is considered, the surgeon preoperatively requires a thorough understanding of the patient-specific anatomy to prevent positive surgical margins and other complications. Through a collaboration between the radiology and pediatric surgery departments and 3D imaging specialists, a 3D visualization workflow was developed to improve preoperative planning of NSS for WT patients. Methods. The 3D visualization workflow combines a MRA sequence, a segmentation protocol, and augmented reality (AR) visualization, additional to in-house 3D printing. A noncontrast-enhanced MRA scan was added to the MRI protocol. MRI sequences were segmented with a segmentation protocol in an open-source software package. The resulting 3D models were visualized in AR with a HoloLens and 3D print. Results. In a pilot study, five WT patients eligible for NSS were preoperatively planned through the 3D visualization workflow. AR visualization software was fast and free to use and allowed adequate handling of the 3D holograms. The 3D printed models were considered convenient and practical for intraoperative orientation. The patient-friendly, fast, and low-cost 3D visualization workflow was easily implemented and appeared to be valuable for the preparation of NSS. Conclusion. This pilot study demonstrates how a strong collaboration between the pediatric surgery and radiology departments and 3D imaging specialists will help to shape the future of pediatric oncological surgery. This 3D visualization workflow aims to prepare pediatric oncological surgeons for nephron-sparing surgery in patients with Wilms’ tumors.

A modular and scalable computational framework for interactive immersion into imaging data with a holographic augmented reality interface

Background and objectiveModern imaging scanners produce an ever-growing body of 3D/4D multimodal data requiring image analytics and visualization of fused images, segmentations, and information. For the latter, augmented reality (AR) with head-mounted displays (HMDs) has shown potential. This work describes a framework (FI3D) for interactive immersion with data, integration of image processing and analytics, and rendering and fusion with an AR interface. MethodsThe FI3D was designed and endowed with modules to communicate with peripherals, including imaging scanners and HMDs, and to provide computational power for data acquisition and processing. The core of FI3D is deployed to a dedicated computational unit that performs the computationally demanding processes in real-time, and the HMD is used as a display output peripheral and an input peripheral through gestures and voice commands. FI3D offers user-made processing and analysis dedicated modules. Users can customize and optimize these for a particular workflow while incorporating current or future libraries. ResultsThe FI3D framework was used to develop a workflow for processing, rendering, and visualization of CINE MRI cardiac sets. In this version, the data were loaded from a remote database, and the endocardium and epicardium of the left ventricle (LV) were segmented using a machine learning model and transmitted to a HoloLens HMD to be visualized in 4D. Performance results show that the system is capable of maintaining an image stream of one image per second with a resolution of 512 × 512. Also, it can modify visual properties of the holograms at 1 update per 16 milliseconds (62.5 Hz) while providing enough resources for the segmentation and surface reconstruction tasks without hindering the HMD. ConclusionsWe provide a system design and framework to be used as a foundation for medical applications that benefit from AR visualization, removing several technical challenges from the developmental pipeline.

From Ta-Kesh to Ta-Kush: The affordances of digital, haptic visualisation for heritage accessibility

This paper describes the 3D facial depiction of a 2700-year-old mummy, Ta-Kush, stewarded by Maidstone Museum, UK, informed by new scientific and visual analysis which demanded a complete re-evaluation of her biography and presentation. The digital haptic reconstruction and visualisation workflow used to reconstruct her facial morphology is described, in the context of the multimodal and participatory approach taken by the museum in the complete redesign of the galleries in which the mummy is displayed. Informed by contemporary approaches to working with human remains in heritage spaces, we suggest that our virtual modelling methodology finds a logical conclusion in the presentation of the depiction both as a touch-object as well as a digital animation, and that this ‘digital unshelving’ enables the further rehumanization of Ta-Kush. Finally, we present and reflect upon visitor feedback, which suggests that audiences respond well to interpretive material in museums that utilizes cutting-edge, multimedia technologies.

Overcoming Resistance to Big Data and Operational Changes Through Interactive Data Visualization.

Research has shown that the use of big data can modify operational processes in organizations. However, little research has been conducted on overcoming resistance to the process changes needed for adoption of big data technologies. In this article, we address this gap in the literature by investigating the impact of interactive data visualization on decision-making around operational process changes with big data. Our goal is to demonstrate how the choice of visualization of workflow and operational processes impacts decisions to embrace real-time, big data technology. To do so, we conduct a case study of patient/provider interactions in a large health care practice and compare the initial state with a revised workflow using a big data, real-time analytics platform. We then investigate the impact of the data visualization strategy on decision-making to implement operational changes caused by big data. The study demonstrates that interactive data visualization of operational processes can be an enabler in overcoming organizational resistance to big data technologies in a change-resistant organization. The concomitant benefit is that big data analytics is placed directly into the hands of primary decision makers.

Visual Workflow Process Modeling and Simulation Approach Based on Non-Functional Properties of Resources

With the emergence of big data and cloud technologies, companies now evolve in complex IT environments. This situation requires good practices for data process automation to be adopted to ensure system interoperability. Visual computing helps companies to describe and organize the ways in which information systems, devices, and people must interact. It incorporates a number of fields including modeling and simulation (M&S). This paper focused on M&S of data workflow processes that are key steps to bridging the gap between business views and goals on the one side, and operational implementations on the other side. Simulation adds a dynamic view to static modeling; it increases understanding of the behavior of process mechanisms and the identification of weak areas in data flow. Several research projects have been focused on control flow and data flow, but less attention has been paid to resource characteristics. This work is based on the MDSEA approach and the eBPMN language, and proposes an approach that aims to distinguish the types of resources carrying out process tasks. Furthermore, it introduces a new composite resource made from the relationship between a user (human resource) and a task form (IT resource). Moreover, it proposes a resource aggregation based on process performance combination in order to run and display a global performance measurement of a process path.