scholarly journals RADAR-base: An Open Source mHealth Platform for Collecting, Monitoring and Analyzing Data Using Sensors, Wearables, and Mobile Devices (Preprint)

2018 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Maximilian Kerz ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
User Interfaces ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
Multiple Sources ◽  
High Resolution Data ◽  
Wide Range

BACKGROUND With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable and extensible platform is of high interest to the open source mHealth community. The EU IMI RADAR-CNS program is an exemplar project with the requirements to support collection of high resolution data at scale; as such, the RADAR-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. OBJECTIVE Wide-bandwidth networks, smartphone penetrance and wearable sensors offer new possibilities for collecting (near) real-time high resolution datasets from large numbers of participants. We aimed to build a platform that would cater for large scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security and privacy. METHODS RADAR-base is developed as a modular application, the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides two main mobile apps for data collection, a Passive App and an Active App. Other 3rd Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. RESULTS General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy and Depression cohorts. CONCLUSIONS RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

scholarly journals RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

10.2196/11734 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e11734 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Pauline Conde ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
Mobile Health ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
The European Union ◽  
Wide Range ◽  
Remote Assessment

Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

scholarly journals Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

2021 ◽  
pp. 47-59
Author(s):  
Sacha J. van Albada ◽  
Jari Pronold ◽  
Alexander van Meegen ◽  
Markus Diesmann
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Network Models ◽  
Macaque Monkey ◽  
Source Model ◽  
Model Specification ◽  
Data Sets ◽  
Neural Network Models ◽  
Wide Range ◽  
Ict Infrastructure

AbstractWe are entering an age of ‘big’ computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other’s work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of an ICT infrastructure for neuroscience.

scholarly journals Dependence of Model Energy Spectra on Vertical Resolution

2016 ◽  
Vol 144 (4) ◽  
pp. 1407-1421 ◽  
Author(s):  
Michael L. Waite
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Vertical Mixing ◽  
Energy Spectra ◽  
Small Scale ◽  
Rossby Number ◽  
Vertical Resolution ◽  
Wide Range ◽  
Qualitative Shape ◽  
Vertical Grid

Abstract Many high-resolution atmospheric models can reproduce the qualitative shape of the atmospheric kinetic energy spectrum, which has a power-law slope of −3 at large horizontal scales that shallows to approximately −5/3 in the mesoscale. This paper investigates the possible dependence of model energy spectra on the vertical grid resolution. Idealized simulations forced by relaxation to a baroclinically unstable jet are performed for a wide range of vertical grid spacings Δz. Energy spectra are converged for Δz 200 m but are very sensitive to resolution with 500 m ≤ Δz ≤ 2 km. The nature of this sensitivity depends on the vertical mixing scheme. With no vertical mixing or with weak, stability-dependent mixing, the mesoscale spectra are artificially amplified by low resolution: they are shallower and extend to larger scales than in the converged simulations. By contrast, vertical hyperviscosity with fixed grid-scale damping rate has the opposite effect: underresolved spectra are spuriously steepened. High-resolution spectra are converged except for the stability-dependent mixing case, which are damped by excessive mixing due to enhanced shear over a wide range of horizontal scales. It is shown that converged spectra require resolution of all vertical scales associated with the resolved horizontal structures: these include quasigeostrophic scales for large-scale motions with small Rossby number and the buoyancy scale for small-scale motions at large Rossby number. It is speculated that some model energy spectra may be contaminated by low vertical resolution, and it is recommended that vertical-resolution sensitivity tests always be performed.

3D Printing Complex Structures Using Modeling and Simulation

2015 ◽  
Author(s):  
Hammad Mazhar
Keyword(s):  
3D Printing ◽  
Open Source ◽  
Data Structures ◽  
Large Scale ◽  
Selective Layer ◽  
Parallel Data ◽  
Wide Range ◽  
Data Structures And Algorithms ◽  
Multi Body ◽  
Dynamics Problems

This paper describes an open source parallel simulation framework capable of simulating large-scale granular and multi-body dynamics problems. This framework, called Chrono::Parallel, builds upon the modeling capabilities of Chrono::Engine, another open source simulation package, and leverages parallel data structures to enable scalable simulation of large problems. Chrono::Parallel is somewhat unique in that it was designed from the ground up to leverage parallel data structures and algorithms so that it scales across a wide range of computer architectures and yet has a rich modeling capability for simulating many different types of problems. The modeling capabilities of Chrono::Parallel will be demonstrated in the context of additive manufacturing and 3D printing by modeling the Selective Layer Sintering layering process and simulating large complex interlocking structures which require compression and folding to fit into a 3D printer’s build volume.

scholarly journals Short communication: A semi-automated method for rapid fault slip analysis from topographic scarp profiles

2019 ◽  
Author(s):  
Franklin D. Wolfe ◽  
Timothy A. Stahl ◽  
Pilar Villamor ◽  
Biljana Lukovic
Keyword(s):  
Monte Carlo ◽  
High Resolution ◽  
Open Source ◽  
Hanging Wall ◽  
Temporal Analysis ◽  
Fault Scarp ◽  
Fault Slip ◽  
Source Characterization ◽  
Automated Method ◽  
Wide Range

Abstract. Here, we introduce an open source, semi-automated, Python-based graphical user interface (GUI) called the Monte Carlo Slip Statistics Toolkit (MCSST) for estimating dip slip on individual or bulk fault datasets. Using this toolkit, profiles are defined across fault scarps in high-resolution digital elevation models (DEMs) and then relevant fault scarp components are interactively identified (e.g., footwall, hanging wall, and scarp). Displacement statistics are calculated automatically using Monte Carlo simulation and can be conveniently visualized in Geographic Information Systems (GIS) for spatial analysis. Fault slip rates can also be calculated when ages of footwall and hanging wall surfaces are known, allowing for temporal analysis. This method allows for rapid analysis of tens to hundreds of faults in rapid succession within GIS and a Python coding environment. Application of this method may contribute to a wide range of regional and local earthquake geology studies with adequate high-resolution DEM coverage, both regional fault source characterization for seismic hazard and/or estimating geologic slip and strain rates, including creating long-term deformation maps. ArcGIS versions of these functions are available, as well ones that utilize free, open source Quantum GIS (QGIS) and Jupyter Notebook Python software.

scholarly journals Platanus_B: an accurate de novo assembler for bacterial genomes using an iterative error-removal process

DNA Research ◽  
2020 ◽  
Vol 27 (3) ◽  
Author(s):  
Rei Kajitani ◽  
Dai Yoshimura ◽  
Yoshitoshi Ogura ◽  
Yasuhiro Gotoh ◽  
Tetsuya Hayashi ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
De Novo ◽  
Bacterial Genomes ◽  
Long Reads ◽  
Wide Range ◽  
Long Read ◽  
Phylogenomic Analyses ◽  
Error Removal ◽  
Iterative Error

Abstract De novo assembly of short DNA reads remains an essential technology, especially for large-scale projects and high-resolution variant analyses in epidemiology. However, the existing tools often lack sufficient accuracy required to compare closely related strains. To facilitate such studies on bacterial genomes, we developed Platanus_B, a de novo assembler that employs iterations of multiple error-removal algorithms. The benchmarks demonstrated the superior accuracy and high contiguity of Platanus_B, in addition to its ability to enhance the hybrid assembly of both short and nanopore long reads. Although the hybrid strategies for short and long reads were effective in achieving near full-length genomes, we found that short-read-only assemblies generated with Platanus_B were sufficient to obtain ≥90% of exact coding sequences in most cases. In addition, while nanopore long-read-only assemblies lacked fine-scale accuracies, inclusion of short reads was effective in improving the accuracies. Platanus_B can, therefore, be used for comprehensive genomic surveillances of bacterial pathogens and high-resolution phylogenomic analyses of a wide range of bacteria.

scholarly journals Electronic Data Management for Vaccine Trials in Low Resource Settings: Upgrades, Scalability, and Impact of ODK

2021 ◽  
Vol 9 ◽  
Author(s):  
Michael Marks ◽  
Sham Lal ◽  
Hannah Brindle ◽  
Pierre-Stéphane Gsell ◽  
Matthew MacGregor ◽  
...  
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Data Management ◽  
Open Source ◽  
Large Scale ◽  
Democratic Republic Of Congo ◽  
World Health ◽  
Electronic Data ◽  
Diverse Range ◽  
Electronic Data Collection

Background: ODK provides software and standards that are popular solutions for off-grid electronic data collection and has substantial code overlap and interoperability with a number of related software products including CommCare, Enketo, Ona, SurveyCTO, and KoBoToolbox. These tools provide open-source options for off-grid use in public health data collection, management, analysis, and reporting. During the 2018–2020 Ebola epidemic in the North Kivu and Ituri regions of Democratic Republic of Congo, we used these tools to support the DRC Ministère de la Santé RDC and World Health Organization in their efforts to administer an experimental vaccine (VSV-Zebov-GP) as part of their strategy to control the transmission of infection.Method: New functions were developed to facilitate the use of ODK, Enketo and R in large scale data collection, aggregation, monitoring, and near-real-time analysis during clinical research in health emergencies. We present enhancements to ODK that include a built-in audit-trail, a framework and companion app for biometric registration of ISO/IEC 19794-2 fingerprint templates, enhanced performance features, better scalability for studies featuring millions of data form submissions, increased options for parallelization of research projects, and pipelines for automated management and analysis of data. We also developed novel encryption protocols for enhanced web-form security in Enketo.Results: Against the backdrop of a complex and challenging epidemic response, our enhanced platform of open tools was used to collect and manage data from more than 280,000 eligible study participants who received VSV-Zebov-GP under informed consent. These data were used to determine whether the VSV-Zebov-GP was safe and effective and to guide daily field operations.Conclusions: We present open-source developments that make electronic data management during clinical research and health emergencies more viable and robust. These developments will also enhance and expand the functionality of a diverse range of data collection platforms that are based on the ODK software and standards.

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