Development of an evidence-based medicine mobile application for the use in medical education

BACKGROUND: Evidence-based medicine (EBM) is a methodology that is being incorporated into more medical school curricula. Boston University School of Medicine was one of early adopters of Evidence Based Medicine in the United States. A growing concern in the medical community was that the complexities of applying EBM might be lost when students enter into their clinical rotations, thus there is a need for development of a tool to help reinforce the EBM principles. METHODS: The research team in collaboration with the designers of the Finding Information Framework, a custom-made EBM finding information tool, worked to develop a mobile application to help reinforce the framework for medical students. The app was designed with both Apple and PC operating systems in mind. Key features that were identified from current literature to provide the most user-friendly mobile application. Thus, the research team specifically utilized iOS and Android platforms as both platforms have a centralized app store, possess the highest volume of medical apps available, and are most widely used in the United States by medical students. RESULTS: The Finding Information Framework was a custom-made tool developed to guide new users of EBM, and help them to apply the principles in practice. The mobile application served an added convenience by allowing easy access and fast utilization of the EBM tools. The app was designed on an Android platform first due to its open-source OS and ease in app development to new programmers. Initially, the user-friendly web-based tool, App Inventor (AI), powered by Massachusetts Institute of Technology was evaluated to program the pilot Android app. Using both the AI Component Designer and the Block Editor, several problems were encountered in AI, such as the simplicity of the program and the lack of freedom in design. This moved the project to create the app natively and with a collaborative effort with the BU's Global App Initiative club. Initially, a wireframe was built using Balsamiq. Subsequently, the Android app was built using Android SDK and the iOS app was built in XCode with Objective C; both platforms had design sections prepared in Sketch, Adobe Photoshop and Illustrator. The last and final step was to obtain Boston University branding privileges for the app. CONCLUSION: The research team identified necessary features based on research to build a user-friendly, professional mobile application of an information mastery framework that can be used off-line. The app is called FIF as it is the title of the information mastery tool designed by BUSM EBM-VIG. With a clear mobile interface, it will be beneficial to the learning and training of medical students in EBM

Visualisation of BioPAX Networks using BioLayout Express (3D).

BioLayout Express (3D) is a network analysis tool designed for the visualisation and analysis of graphs derived from biological data. It has proved to be powerful in the analysis of gene expression data, biological pathways and in a range of other applications. In version 3.2 of the tool we have introduced the ability to import, merge and display pathways and protein interaction networks available in the BioPAX Level 3 standard exchange format. A graphical interface allows users to search for pathways or interaction data stored in the Pathway Commons database. Queries using either gene/protein or pathway names are made via the cPath2 client and users can also define the source and/or species of information that they wish to examine. Data matching a query are listed and individual records may be viewed in isolation or merged using an 'Advanced' query tab. A visualisation scheme has been defined by mapping BioPAX entity types to a range of glyphs. Graphs of these data can be viewed and explored within BioLayout as 2D or 3D graph layouts, where they can be edited and/or exported for visualisation and editing within other tools

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies.

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future

Integration of tools for binding archetypes to SNOMED CT

Background The Archetype formalism and the associated Archetype Definition Language have been proposed as an ISO standard for specifying models of components of electronic healthcare records as a means of achieving interoperability between clinical systems. This paper presents an archetype editor with support for manual or semi-automatic creation of bindings between archetypes and terminology systems. Methods Lexical and semantic methods are applied in order to obtain automatic mapping suggestions. Information visualisation methods are also used to assist the user in exploration and selection of mappings. Results An integrated tool for archetype authoring, semi-automatic SNOMED CT terminology binding assistance and terminology visualization was created and released as open source. Conclusion Finding the right terms to bind is a difficult task but the effort to achieve terminology bindings may be reduced with the help of the described approach. The methods and tools presented are general, but here only bindings between SNOMED CT and archetypes based on the openEHR reference model are presented in detail. Background The Archetype formalism and the associated Archetype Definition Language have been proposed as an ISO standard for specifying models of components of electronic healthcare records as a means of achieving interoperability between clinical systems. This paper presents an archetype editor with support for manual or semi-automatic creation of bindings between archetypes and terminology systems. Methods Lexical and semantic methods are applied in order to obtain automatic mapping suggestions. Information visualisation methods are also used to assist the user in exploration and selection of mappings. Results An integrated tool for archetype authoring, semi-automatic SNOMED CT terminology binding assistance and terminology visualization was created and released as open source. Conclusion Finding the right terms to bind is a difficult task but the effort to achieve terminology bindings may be reduced with the help of the described approach. The methods and tools presented are general, but here only bindings between SNOMED CT and archetypes based on the openEHR reference model are presented in detail.Original Publication: Erik Sundvall, Rahil Qamar, Mikael Nyström, Mattias Forss, Håkan Petersson, Hans Åhlfeldt and Alan Rector, Integration of Tools for Binding Archetypes to SNOMED CT, 2008, BMC Medical Informatics and Decision Making, (8), S7. http://dx.doi.org/10.1186/1472-6947-8-S1-S7 Licensee: BioMed Central http://www.biomedcentral.com/</p

Semi-Autonomous Behaviour Tree-Based Framework for Sorting Electric Vehicle Batteries Components

The process of recycling electric vehicle (EV) batteries currently represents a significant challenge to the waste management automation industry. One example of it is the necessity of removing and sorting dismantled components from EV battery pack. This paper proposes a novel framework to semi-automate the process of removing and sorting different objects from an EV battery pack using a mobile manipulator. The work exploits the Behaviour Trees model for cognitive task execution and monitoring, which links different robot capabilities such as navigation, object tracking and motion planning in a modular fashion. The framework was tested in simulation, in both static and dynamic environments, and it was evaluated based on task time and the number of objects that the robot successfully placed in the respective containers. Results suggested that the robot’s success rate in accomplishing the task of sorting the battery components was 95% and 82% in static and dynamic environments, respectively

Development and application of software and algorithms for network approaches to proteomics data analysis

The cells making up all living organisms integrate external and internal signals to carry out the functions of life. Dysregulation of signaling can lead to a variety of grave diseases, including cancer [Slamon et al., 1987]. In order to understand signal transduction, one has to identify and characterize the main constituents of cellular signaling cascades. Proteins are involved in most cellular processes and form the major class of biomolecules responsible for signal transduction. Post-translational modifications (PTMs) of proteins can modulate their enzymatic activity and their protein-protein interactions (PPIs) which in turn can ultimately lead to changes in protein expression. Classical biochemistry has approached the study of proteins, PTMs and interaction from a reductionist view. The abundance, stability and localization of proteins was studied one protein at a time, following the one gene-one protein-one function paradigm [Beadle and Tatum, 1941]. Pathways were considered to be linear, where signals would be transmitted from a gene to proteins, eventually resulting in a specific phenotype. Establishing the crucial link between genotype and phenotype remains challenging despite great advances in omics technologies, such as liquid chromatography (LC)-mass spectrometry (MS) that allow for the system-wide interrogation of proteins. Systems and network biology [Barabási and Oltvai, 2004, Bensimon et al., 2012, Jørgensen and Locard-Paulet, 2012, Choudhary and Mann, 2010] aims to transform modern biology by utilizing omics technologies to understand and uncover the various complex networks that govern the cell. The first detected large-scale biological networks have been found to be highly structured and non-random [Albert and Barabási, 2002]. Furthermore, these are assembled from functional and topological modules. The smallest topological modules are formed by the direct physical interactions within protein-protein and protein-RNA complexes. These molecular machines are able to perform a diverse array of cellular functions, such as transcription and degradation [Alberts, 1998]. Members of functional modules are not required to have a direct physical interaction. Instead, such modules also include proteins with temporal co-regulation throughout the cell cycle [Olsen et al., 2010], or following the circadian day-night rhythm [Robles et al., 2014]. The signaling pathways that make up the cellular network [Jordan et al., 2000] are assembled from a hierarchy of these smaller modules [Barabási and Oltvai, 2004]. The regulation of these modules through dynamic rewiring enables the cell to respond to internal an external stimuli. The main challenge in network biology is to develop techniques to probe the topology of various biological networks, to identify topological and functional modules, and to understand their assembly and dynamic rewiring. LC-MS has become a powerful experimental platform that addresses all these challenges directly [Bensimon et al., 2012], and has long been used to study a wide range of biomolecules that participate in the cellular network. The field of proteomics in particular, which is concerned with the identification and characterization of the proteins in the cell, has been revolutionized by recent technological advances in MS. Proteomics experiments are used not only to quantify peptides and proteins, but also to uncover the edges of the cellular network, by screening for physical PPIs in a global [Hein et al., 2015] or condition specific manner [Kloet et al., 2016]. Crucial for the interpretation of the large-scale data generated by MS experiments is the development of software tools that aid researchers in translating raw measurements into biological insights. The MaxQuant and Perseus platforms were designed for this exact purpose. The aim of this thesis was to develop software tools for the analysis of MS-based proteomics data with a focus on network biology and apply the developed tools to study cellular signaling. The first step was the extension of the Perseus software with network data structures and activities. The new network module allows for the sideby-side analysis of matrices and networks inside an interactive workflow and is described in article 1. We subsequently apply the newly developed software to study the circadian phosphoproteome of cortical synapses (see article 2). In parallel we aimed to improve the analysis of large datasets by adapting the previously Windows-only MaxQuant software to the Linux operating system, which is more prevalent in high performance computing environments (see article 3)

Integration of large datasets for plant model organisms

This dissertation is concerned with bioinformatics data integration. The first chapter illustrates the current state of biological pathway databases in general, and in particular, plant pathway databases. Key studies are cited to illustrate the potential benefits that may come from further research into integration methods. Different models are explored to interface with the various stakeholders of biological data repositories. A public website (http://www.metnetonline.org) was built to address the role of a bioinformatics data warehouse as a server for external third parties. A dedicated API (MetNetAPI: http://www.metnetonline.org/api) accommodates bioinformaticians (and software developers in general) who wish to build advanced applications on top of MetNet. The API (implemented as .NET and Java libraries) was designed to be as user-friendly to programmers, as the public website is to end-users. Finally, a hybrid model is examined: the use of XML as a repository for information integration, downstream processing, and data manipulation. An overview of the use of XML in biological applications is included. MetNetAPI functions according to certain principles; a subset of the API is abstracted and implemented to interface with a range of other public databases. This results in a new bioinformatics toolkit that can be used to mix and match data from heterogeneous sources in a transparent manner. An example would be the grafting of protein-protein interaction data on top of araCyc pathways. Biological network data is often distributed over a variety of independently modeled databases. This dissertation makes two contributions to the field of bioinformatics: A new service - MetNet Online - is now operating which offers access to the earlier created and integrated MetNetDB data repository. The service is geared toward end-users, students and researchers alike, as well as seasoned bioinformatics software developers who wish to build their own applications on top of an already integrated datasource. Furthermore, integrated databases are only useful when they can be synchronized with their respective external sources. Thus, a framework was created that allows for a systematic approach to such integration efforts. In closing, this work provides a roadmap to maintain current as well as prepare for future integrated biological database projects