Data Visualization Platform Research Articles

Surgical Outcomes and Quality Assessment of Trabeculectomy: Leveraging Electronic Health Records for Clinical Data Visualization.

The purpose of this study was to develop an interactive data visualization platform for evaluating trabeculectomy outcomes and monitoring clinical care quality. This is a retrospective electronic health record study in a glaucoma division of an academic medical center. We included 633 patients who underwent trabeculectomy between July 2016 and December 2018. Data were collected for intraocular pressure (IOP), vision loss, number of follow-up visits, and return to the operating room within the initial 3 months. Regression analyses were used to assess factors associated with these study outcomes. Risk of IOP ≤5 mm Hg was assessed using survival analysis. An interactive data visualization platform was created. A total of 429 trabeculectomy and 204 combined surgeries were analyzed, with preoperative IOP of 21.9±9.4 and 19.4±6.9 mm Hg, respectively. Mean pressure declined by 53% and 41% (P=0.001) by 3 months. Risk of pressure ≤5 mm Hg was higher in white patients and those who underwent trabeculectomy alone. At 3 months, patients on average had 5 clinic visits; 5.5% required additional surgical procedures, and 18% experienced loss of at least 3 lines of best-recorded (not best-corrected) vision. Returning to the operating room was significantly associated with an increased number of clinic visits (coefficient=3.94) and higher odds of vision loss (odds ratio=11.12). Over the initial 3 months after trabeculectomy, additional surgeries are rare, while a significant proportion of patients lost vision, and this was strongly associated with a need to return to the operating room. Interactive electronic health record data visualization can be a useful tool for monitoring clinical outcomes.

Wind Farm NWP Data Preprocessing Method Based on t-SNE

The operation prediction of wind farms will be accompanied by the need for massive data processing, especially the preprocessing of wind farm meteorological data or numerical weather prediction (NWP). Because NWP data are strongly correlated with wind farm operation, proper processing of NWP data could not only reduce data volume but also improve the correlations of wind farm operation predictions. For this purpose, this paper proposes a data preprocessing algorithm based on t-distributed stochastic neighbor embedding (t-SNE). Firstly, the data collected were normalized to eliminate the influence caused by different dimensions. The t-SNE algorithm is then used to reduce the dimensionality of the NWP data related to wind farm operation. Finally, the wind farm data visualization platform is established. In this paper, 22 index variables in NWP data were taken as objects. The t-SNE method was used to preprocess the NWP historical data of a wind farm, and the results were compared with the results of the principal component analysis (PCA) algorithm. It outperformed PCA in error precision; in addition, t-SNE dimension reduction preprocessing also had a visual effect, which could be applied to big data visualization platforms. A long short-term memory network (LSTM) was used to predict the operation of the wind farm by combining the preprocessed NWP data and the operation data. The simulation results proved that the effect of the preprocessed NWP data based on t-SNE on the wind power prediction was significantly improved.

A Generic Future Mobility Sensing System for Travel Data Collection, Management, Fusion, and Visualization

In studies of human mobility, there is a need for a holistic system for collection of sensing data, management of data flows, fusion of multiple data sources, and visualization of integrated data to better understand travel behavior. We have designed and implemented a generic Future Mobility Sensing (FMS) system to serve these purposes. FMS harnesses various sensing technologies, heterogeneous multi-source data and analytical functionalities with three dedicated platforms, namely 1) the FMS Data Collection Platform, which intertwines sensing objects, machine learning algorithms and user verifications to collect high resolution, multi-day travel data; 2) the FMS Data Management Platform, which provides standardized APIs to access data stored in an interconnected data model; and 3) the FMS Data Fusion and Visualization Platform, which consolidates multi-source data to be interpreted and presented. Together, the three platforms form a mobility sensing flow to facilitate data-driven analysis and decision-making. With FMS, heterogeneous multi-source data are suitably integrated for analysis, and multi-dimensional knowledge is extracted and presented in intuitive and interactive analytical dashboards. The system is intended to be generic to support different requirements of mobility studies, such as travel surveys, as its function modules are reusable and can be customized to support a unified data collection, management, fusion and visualization process. This paper introduces the overall architecture of the FMS system and summarizes various applications that it can support. Specifically, we present a study of commercial vehicle parking in Singapore to demonstrate the capability of the system.

Tersect: a set theoretical utility for exploring sequence variant data.

Comparing genomic features among a large panel of individuals across the same species is considered nowadays a core part of the bioinformatics analyses. This typically involves a series of complex theoretical expressions to compare, intersect, extract symmetric differences between individuals within a large set of genotypes. Several publically available tools are capable of performing such tasks; however, due to the sheer size of variants being queried, such tasks can be computationally expensive with a runtime ranging from few minutes up to several hours depending on the dataset size. This makes existing tools unsuitable for interactive data query or as part of genomic data visualization platforms such as genome browsers. Tersect is a lightweight, high-performance command-line utility which interprets and applies flexible set theoretical expressions to sets of sequence variant data. It can be used both for interactive data exploration and as part of a larger pipeline thanks to its highly optimized storage and indexing algorithms for variant data. Tersect was implemented in C and released under the MIT license. Tersect is freely available at https://github.com/tomkurowski/tersect. Supplementary data are available at Bioinformatics online.

A 3d platform for energy data visualization of building assets

With an exemplary role, the improvement of energy efficiency in public buildings is in the forefront of the European policies for smart and sustainable growth. However, very often the sector is characterized by large and old constructions that may also be marked by historical and cultural value and whose energy consumption is hard to be reduced, due to specific constraints. In order to operate in this field, the definition of a solid knowledge framework on the built environment appears to be the only viable starting point. Therefore, the analysis of the delicate balance between conservation and transformation should be investigated with a multi-scalar approach able to move from the city to the building elements. For this reason, it is extremely important to provide tools for monitoring and analysing the energy behaviour of the public building stocks to those actors that are involved in their management. The research here presented proposes a workflow to implement a web platform based on a three-dimensional GIS (Geographic Information System) interoperable with BIM (Building Information Modeling) and able to store, handle and display information on building assets and their energy consumption. With the aim of defining a repeatable model, the process starts from easily retrievable data on the built environment and uses standard data models and classification systems. The three-dimensional model is built in a semi-automated way from the combination of the two-dimensional GIS cartography of the municipality and from the point cloud resulting from a LiDAR (Light Detection And Ranging) national survey campaign. The set of thermal properties and energy data can be retrieved from the energy performance certificate of the buildings. In order to test and validate the process, an application on the building stock owned by the University of Pavia (Italy) is presented. Nine complexes distribute inside the historical centre of the city and heterogeneously dated from the X to the XX century are considered. After the definition of the model and its representation inside the web environment, an example of use is displayed with reference to a comparative energy analysis of different buildings.

Abstract 2130: Development of a biomarker-driven patient selection strategy for AsiDNA treatment

Abstract Purpose: Accurate evaluation and prediction of response to anti-cancer treatment remain a great challenge. Stratification biomarkers are of great value to identify responders or non-responders to a specific drug, or even to distinguish between early and delayed responses. In this study, we identified a gene signature to predict AsiDNA treatment efficacy in patients. Experimental design: The first step of this study consisted on the analysis of gene expression profile in a set of 12 Breast cancer cell lines. Genes retrieved from correlation analysis between sensitivity to AsiDNA and gene expression were used to create a gene signature predicated on the upregulation of positively correlated genes and the down regulation of negatively correlated genes (the AsiDNA sensitivity signature - AsiSS) using the UCSC Xena platform for data visualization. The AsiSS was used to sort the TCGA Breast Cancer (BRCA) patient cohort, DNA repair gene expression and cell lines from the CCLE (1100 cell lines) into rank orders, in order to retrieve a gene set highly predictive of response to AsiDNA. Results: A list of genes the most strongly correlated with survival to AsiDNA was compiled. 39 genes showed expression profiles positively correlated with survival to AsiDNA treatment (Spearman r > 0.67, P<0.005) and 35 genes showed a strong negative correlation with survival (Spearman r > -0.63, P<0.005). Based on these correlated genes, an AsiSS was created. In the BRCA cohort the top 25% AsiDNA sensitivity scoring patients were identified, and DNA repair gene expression levels of these patients were rank ordered and sorted according to AsiSS. The top 7 genes with significantly lower expression in the 25% top AsiDNA sensitive patient group were retrieved (XRCC2, MRE11A, POLQ, BRCA2, NBN, FANCA, RAD54B; 10-14 < p value < 10-9). To validate the predictive value of these genes, cell lines from the CCLE were rank ordered according to the AsiSS, and response to AsiDNA of 20 cell lines (10 predicted sensitive; 10 predicted resistant) as well as the expression level of the top 7 genes were analyzed. Importantly, cells predicted resistant show significantly higher survival (mean survival = 80%) compared to cells predicted sensitive (mean survival = 40%; p < 0.01). Among the 7 retrieved genes, gene expression analysis revealed a correlation between sensitivity to AsiDNA and the expression of BRCA2 (Pearson r: 0.75), NBN (r: 0.62), MRE11A (r: 0.6), RAD54B (r: 0.54), FANCA (r: 0.6) and XRCC2 (r: 0.55) genes (0.001< p <0.05). This set of genes could be used to stratify patients for future clinical trials involving AsiDNA. Conclusion: Overall, our results highlight the interest of retrospective analysis based on patient databases to retrieve gene biomarkers predictive of drug outcome. As AsiDNA is being currently tested in a clinical trial, a potential exist for a rapid validation of our gene set in the aim to develop a Biomarker-driven patient selection strategy for AsiDNA treatment. Citation Format: Wael Jdey, Donogh O'Brien, Richard Tripelon, Séverine Rochas, Marie Dutreix, Françoise Bono. Development of a biomarker-driven patient selection strategy for AsiDNA treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2130.

ClickGene: an open cloud-based platform for big pan-cancer data genome-wide association study, visualization and exploration

Tremendous amount of whole-genome sequencing data have been provided by large consortium projects such as TCGA (The Cancer Genome Atlas), COSMIC and so on, which creates incredible opportunities for functional gene research and cancer associated mechanism uncovering. While the existing web servers are valuable and widely used, many whole genome analysis functions urgently needed by experimental biologists are still not adequately addressed. A cloud-based platform, named CG (ClickGene), therefore, was developed for DIY analyzing of user’s private in-house data or public genome data without any requirement of software installation or system configuration. CG platform provides key interactive and customized functions including Bee-swarm plot, linear regression analyses, Mountain plot, Directional Manhattan plot, Deflection plot and Volcano plot. Using these tools, global profiling or individual gene distributions for expression and copy number variation (CNV) analyses can be generated by only mouse button clicking. The easy accessibility of such comprehensive pan-cancer genome analysis greatly facilitates data mining in wide research areas, such as therapeutic discovery process. Therefore, it fills in the gaps between big cancer genomics data and the delivery of integrated knowledge to end-users, thus helping unleash the value of the current data resources. More importantly, unlike other R-based web platforms, Dubbo, a cloud distributed service governance framework for ‘big data’ stream global transferring, was used to develop CG platform. After being developed, CG is run on an independent cloud-server, which ensures its steady global accessibility. More than 2 years running history of CG proved that advanced plots for hundreds of whole-genome data can be created through it within seconds by end-users anytime and anywhere. CG is available at http://www.clickgenome.org/.

A context-driven platform using Internet of things and data stream processing for heating, ventilation and air conditioning systems control

Control approaches of heating, ventilation and air conditioning systems in buildings have been proposed in the past years for minimizing energy consumption and maintaining occupants’ comfort. However, recent studies have shown that context-driven control approaches using Internet of things and data stream processing technologies could further improve energy saving in heating, ventilation and air conditioning systems. In this article, an intelligent control approach using a state feedback technique is introduced to regulate the heating, ventilation and air conditioning system according to the actual context. The proposed thermal state feedback control was then implemented and deployed in our EEBLab to study its effectiveness in a real-setting scenario. The performance of the proposed control was evaluated in a real test-site by deploying a control card that links the controller with the heating, ventilation and air conditioning system. A smart mobile application for real feedback control was also developed and deployed to dynamically adapt the controller to context’s changes. The mobile application and the heating, ventilation and air conditioning system communicate and exchange data under a data acquisition and visualization platform. In this article, a holistic platform that combines Internet of things and data stream processing technologies was developed and deployed in a real-setting scenario. Experiments have been performed, and results are reported to demonstrate the effectiveness and usefulness of the proposed approach in terms of energy saving while maintaining a comfortable room temperature. The proposed state feedback control outperforms the proportional–integral–derivative and ON/OFF approaches in terms of energy consumption while providing acceptable thermal comfort by allowing a neutral thermal sensation with ± 0.30 of predictive mean vote and less than 7% of predicted percentage of dissatisfaction.

Abstract 237: An Analytic Approach to and Creation of a Data Visualization Platform for National Cardiovascular Surveillance Data

Background: Data visualization (DV) is a creative method to depict data and convey the underlying meaning of analytical results. Effective and interactive DV includes integrating, unifying, and standardizing data from different sources, allowing users to directly modify the style and outcome of the results to create visual access of big data in simple ingestible pieces. Such freedom to users brings substantial enhancements in productivity and practicality of complex surveillance data. Well-designed interactive DV also increases value gained from data and analytics. The American Heart Association’s (AHA) Center for Heath Metrics and Evaluation (CHME) has developed resources and tools for DV of surveillance data. The purpose of this study is to illustrate the processes of data analyses and visualizations of national surveillance data. Methods: This study introduces the different surveillance data, describes the concept, analytic approaches, and demonstrates the visualization platforms. The National Health and Nutrition Examination Survey (NHANES) data from 1999 - 2016 and Compressed Mortality data from the CDC (Centers for Disease Control and Prevention) WONDER, which included mortality by 15 underlying causes of cardiovascular diseases using ICD-10 from 1999 to 2017, were used to apply DV methods and tools for programming and creating interactive dashboards depicting trends in key cardiovascular health indices. Data were extracted, cleaned, and merged using SAS 9.4. Final estimates were calculated using SUDAAN 11 for weighting, imputing, and analyzing. Tableau 18.2 was used to share an interactive visualization of the analyzed data. Results: The CHME DV resources offer a variety of options to help parse and understand the significance of key health indicator data by placing it in a visual context. Using the NHANES and CDC Wonder datasets we developed interactive dashboards and a visualization platform depicting trends over time in cardiovascular metrics such as AHA’s Life’s Simple 7 indices of Blood Pressure, Blood Sugar, Cholesterol, Physical Activity, Diet, Body Weight, and Smoking. Trends in cardiovascular disease mortality in the United States were also developed. A visualization platform has been imbedded into the AHA CHME website for a wide variety of stakeholders, such as consumers and researchers, to view dashboards and customize products based on their specific needs. Conclusion: Patterns, trends, and associations that may otherwise go undetected or overlooked can be clearly exposed and communicated with an effective DV approach. DV tools comprise an array of resources from simple infographics to interactive dashboards. More interactive visualizations using other data sources, innovative methodologies and tools are needed and being developed by the CHME to present data more interactively enabling users to further understand and leverage data and trends.

BioVR: a platform for virtual reality assisted biological data integration and visualization

BackgroundFunctional characterization of single nucleotide variants (SNVs) involves two steps, the first step is to convert DNA to protein and the second step is to visualize protein sequences with their structures. As massively parallel sequencing has emerged as a leading technology in genomics, resulting in a significant increase in data volume, direct visualization of SNVs together with associated protein sequences/structures in a new user interface (UI) would be a more effective way to assess their potential effects on protein function.ResultsWe have developed BioVR, an easy-to-use interactive, virtual reality (VR)-assisted platform for integrated visual analysis of DNA/RNA/protein sequences and protein structures using Unity3D and the C# programming language. It utilizes the cutting-edge Oculus Rift, and Leap Motion hand detection, resulting in intuitive navigation and exploration of various types of biological data. Using Gria2 and its associated gene product as an example, we present this proof-of-concept software to integrate protein and nucleic acid data. For any amino acid or nucleotide of interest in the Gria2 sequence, it can be quickly linked to its corresponding location on Gria2 protein structure and visualized within VR.ConclusionsUsing innovative 3D techniques, we provide a VR-based platform for visualization of DNA/RNA sequences and protein structures in aggregate, which can be extended to view omics data.

Nationwide Children's Hospital BMT Smart Forms

Background Integration of patient information across medical institutions remains a major challenge in American healthcare. In particular, meaningful data compilation, extraction and analysis are impeded by separate information technology systems, thereby limiting collaborative efforts addressing translational and patient-centered outcomes research. Furthermore, incorporating predictive models to ensure favorable patient outcomes are critically needed, especially for high-complexity, high-resource utilizing patient populations like blood and marrow transplant (BMT) recipients. To this end, the Nationwide Children's Hospital (NCH) BMT Smart Forms were created with the ultimate goal of developing an analytics-integrated, EMR (electronic medical record)-based smart form for BMT and cell therapy patients, which would have the potential to improve patient outcomes by assimilating clinical data across transplant and cell therapy centers. Smart Form Design and Functionality Originally created in 2013 at Nationwide Children's Hospital, the pre- and post-BMT Smart Forms (SFs) were designed by a collaborative team including members of the BMT Program, Information Services and Technology, Electronic Data Warehouse, and Research Information Solutions and Innovation (RISI). Specifically, the BMT SFs were designed using the Epic® platform to enable clinical and quality reporting and research data capture (Figure 1). In this regard, the SFs contain minimal free text fields to minimize data entry error and standard data elements (SDEs), which facilitate data mapping and routing. Once completed, the SFs can be transformed into clinical documentation for clinical management purposes. By design and through a data visualization platform (QlikView®), the BMT SFs also facilitate internal and external data reporting for transplant- and cell therapy-related agencies including the CIBMTR (Center of International Blood and Marrow Transplant Research), FACT (Federation for Accreditation of Cellular Therapy), and NMDP (National Marrow Donor Program). Clinical data entered into the BMT SFs route into a database with the capability for automated data transfer from within and outside NCH, thereby creating the potential for a combined, multi-institutional database through which data can be easily queried (Figure 2). Lastly, the database enables predictive analytic modeling for common transplant-related metrics (e.g., engraftment) and complications (e.g., infection). Reiterations of the BMT SFs have occurred over time, reflecting new technologies and approaches to transplant care as well as future steps for on-boarding additional transplant centers.

GRACC: GRid ACcounting Collector

The OSG has long maintained a central accounting system called Gratia. It uses small probes on each computing and storage resource in order to collect resource usage. The probes report to a central collector which stores the usage in a database. The database is then queried to generate reports. As the OSG aged, the size of the database grew very large. It became too large for the database technology to efficiently query to generate detailed reports. The design of a replacement requires data storage that could be queried efficiently to generate multi-year reports. Additionally, it requires flexibilityto add new attributes to the collected data. In this paper we will describe updates to the GRACC architecture in the last 18 months. GRACC uses modern web technologies that were designed for large data storage, query, and visualization. That includes the open source database Elasticsearch, message broker software RabbitMQ, and Grafana and Kibana as data visualization platforms. It uses multiple agents that perform operations on the data to transform it for easier querying and summarization.

Development of the Texas A&M Superfund Research Program Computational Platform for Data Integration, Visualization, and Analysis

The National Institute of Environmental Health Sciences (NIEHS) Superfund Research Program (SRP) aims to support university-based multidisciplinary research on human health and environmental issues related to hazardous substances and pollutants. The Texas A&M Superfund Research Program comprehensively evaluates the complexities of hazardous chemical mixtures and their potential adverse health impacts due to exposure through a number of multi-disciplinary projects and cores. One of the essential components of the Texas A&M Superfund Research Center is the Data Science Core, which serves as the basis for translating the data produced by the multi-disciplinary research projects into useful knowledge for the community via data collection, quality control, analysis, and model generation. In this work, we demonstrate the Texas A&M Superfund Research Program computational platform, which houses and integrates large-scale, diverse datasets generated across the Center, provides basic visualization service to facilitate interpretation, monitors data quality, and finally implements a variety of state-of-the-art statistical analysis for model/tool development. The platform is aimed to facilitate effective integration and collaboration across the Center and acts as an enabler for the dissemination of comprehensive ad-hoc tools and models developed to address the environmental and health effects of chemical mixture exposure during environmental emergency-related contamination events.

Predicting seasonal fluctuation in ovine haemonchosis in contrasting eco-climatic zones using time series analysis

Event Abstract Back to Event Predicting seasonal fluctuation in ovine haemonchosis in contrasting eco-climatic zones using time series analysis Eric Morgan1 and Muhammad-Bashir Bolajoko2* 1 The Institute for Global Food Security, Faculty of Medicine, Health and Life Sciences, Queen's University Belfast, United Kingdom 2 National Veterinary Research Institute (NVRI), Nigeria Infection with H. contortus remains one of the most pervasive challenges to sheep production worldwide, particularly in the tropics and sub-tropical regions. Development and survival of the free-living stages of H. contortus depend strongly on the prevailing temperature and water availability. Hypothetically, seasonal patterns and incidence of haemonchosis should be predictable from information on key climatic variables both in time and space. Statistical models are useful to determine the seasonal influence of climate in the pattern of disease incidence without full understanding of the mechanism behind them. These models have been used to successfully deduce the relationships between climatic variables and disease incidence to predict disease incidence in space and time. Predictive models for haemonchosis are needed to underpin an integrated-sustainable control. This study sets forth to determine if variations in climatic variables are able to consistently predict incidence of haemonchosis in different eco-climatic areas, with contrasting prevailing climate. Our ultimate aim was to develop reliable and stable regional models capable of predicting haemonchosis occurrence from climatic conditions. We used time series analyses to assess the seasonal forcing influence of climate (mainly rainfall and temperature) on the pattern and incidence of haemonchosis in UK (2004-2011), North Yorkshire (2001-2006) and in a South African farm (1997-2000). This was to find out if statistical approach can be used to predict the pattern and risk of haemonchosis across different eco-climatic zones. Our results indicate that time series analysis was able to identify predictors of seasonal pattern of haemonchosis incidence for each of the three locations. However, the main predictors and the relationships between them and haemonchosis incidence varied from zone to zone; thus one model could not be used in all locations. This suggests that if this statistical approach is to be used on a previously unstudied farm, a new time series analysis has to be performed, which critically depends on the availability of quality data on both haemonchosis incidence and current climatic situation. If this is the case, time series modelling appears to be a promising starting-point approach to examine local or regional climate-haemonchosis links. Another implication of our study is that predicting patterns and incidence of haemonchosis may benefit from the inclusion of outputs of climate-based mechanistic models of Haemonchus contortus and non-climatic factors such as husbandry systems. Keywords: climate, Haemonchosis, Model-validation, Time-series-analysis, South Africa, United Kindgom Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019. Presentation Type: Regular oral presentation Topic: Real-time field data collection and visualization platforms Citation: Morgan E and Bolajoko M (2019). Predicting seasonal fluctuation in ovine haemonchosis in contrasting eco-climatic zones using time series analysis. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00091 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Jun 2019; Published Online: 27 Sep 2019. * Correspondence: Dr. Muhammad-Bashir Bolajoko, National Veterinary Research Institute (NVRI), Vom, Nigeria, mbbolajoko@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Eric Morgan Muhammad-Bashir Bolajoko Google Eric Morgan Muhammad-Bashir Bolajoko Google Scholar Eric Morgan Muhammad-Bashir Bolajoko PubMed Eric Morgan Muhammad-Bashir Bolajoko Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Zoonotic Rift Valley fever virus prevalence and risk factors in the Lake Zone of Tanzania

Zoonotic Rift Valley fever virus prevalence and risk factors in the Lake Zone of Tanzania

Difference in the factors of effect on reproductive rate of feral raccoon (Procyon lotor) between high- (2008-2010) and low- (2015-2017) density terms in Kanagawa Prefecture, Japan

Difference in the factors of effect on reproductive rate of feral raccoon (Procyon lotor) between high- (2008-2010) and low- (2015-2017) density terms in Kanagawa Prefecture, Japan

AgriSeqDB: an online RNA-Seq database for functional studies of agriculturally relevant plant species

BackgroundThe genome-wide expression profile of genes in different tissues/cell types and developmental stages is a vital component of many functional genomic studies. Transcriptome data obtained by RNA-sequencing (RNA-Seq) is often deposited in public databases that are made available via data portals. Data visualization is one of the first steps in assessment and hypothesis generation. However, these databases do not typically include visualization tools and establishing one is not trivial for users who are not computational experts. This, as well as the various formats in which data is commonly deposited, makes the processes of data access, sharing and utility more difficult. Our goal was to provide a simple and user-friendly repository that meets these needs for data-sets from major agricultural crops.DescriptionAgriSeqDB (https://expression.latrobe.edu.au/agriseqdb) is a database for viewing, analysing and interpreting developmental and tissue/cell-specific transcriptome data from several species, including major agricultural crops such as wheat, rice, maize, barley and tomato. The disparate manner in which public transcriptome data is often warehoused and the challenge of visualizing raw data are both major hurdles to data reuse. The popular eFP browser does an excellent job of presenting transcriptome data in an easily interpretable view, but previous implementation has been mostly on a case-by-case basis. Here we present an integrated visualisation database of transcriptome data-sets from six species that did not previously have public-facing visualisations. We combine the eFP browser, for gene-by-gene investigation, with the Degust browser, which enables visualisation of all transcripts across multiple samples. The two visualisation interfaces launch from the same point, enabling users to easily switch between analysis modes. The tools allow users, even those without bioinformatics expertise, to mine into data-sets and understand the behaviour of transcripts of interest across samples and time. We have also incorporated an additional graphic download option to simplify incorporation into presentations or publications.ConclusionPowered by eFP and Degust browsers, AgriSeqDB is a quick and easy-to-use platform for data analysis and visualization in five crops and Arabidopsis. Furthermore, it provides a tool that makes it easy for researchers to share their data-sets, promoting research collaborations and data-set reuse.

Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments

The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.

Xi-cam: a versatile interface for data visualization and analysis.

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

Developing an Interactive Data Visualization Tool to Assess the Impact of Decision Support on Clinical Operations.

Due to mandates from recent legislation, clinical decision support (CDS) software is being adopted by radiology practices across the country. This software provides imaging study decision support for referring providers at the point of order entry. CDS systems produce a large volume of data, providing opportunities for research and quality improvement. In order to better visualize and analyze trends in this data, an interactive data visualization dashboard was created using a commercially available data visualization platform. Following the integration of a commercially available clinical decision support product into the electronic health record, a dashboard was created using a commercially available data visualization platform (Tableau, Seattle, WA). Data generated by the CDS were exported from the data warehouse, where they were stored, into the platform. This allowed for real-time visualization of the data generated by the decision support software. The creation of the dashboard allowed the output from the CDS platform to be more easily analyzed and facilitated hypothesis generation. Integrating data visualization tools into clinical decision support tools allows for easier data analysis and can streamline research and quality improvement efforts.