A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

Management and analysis of high-throughput sequence data for infectious animal diseases.

Advances in technology and decreasing costs have accelerated the use of high-throughput sequencing (HTS) for both diagnosis and characterisation of infectious animal diseases. High-throughput sequencing offers several advantages over previous techniques, including rapid turnaround times and the ability to resolve single nucleotide changes among samples, both of which are important for epidemiological investigations of outbreaks. However, due to the plethora of genetic data being routinely generated, the storage and analysis of these data are proving challenging in their own right. In this article, the authors provide insight into the aspects of data management and analysis that should be considered before adopting HTS for routine animal health diagnostics. These elements fall largely into three interrelated categories: data storage, data analysis and quality assurance. Each has numerous complexities and may need to be adapted as HTS evolves. Making appropriate strategic decisions about bioinformatic sequence analysis early on in project development will help to avert major issues in the long term.

Les avancées technologiques dans le domaine du séquençage à haut débit (SHD) et la diminution des coûts liés à cette technique en ont accéléré l'utilisation à des fins de diagnostic et de caractérisation des maladies animales infectieuses. Le séquençage à haut débit offre plusieurs avantages par rapport aux techniques antérieures, en particulier la rapidité de son exécution et une résolution de l'ordre d'un seul changement de nucléotide parmi plusieurs échantillons, ce qui présente un grand intérêt lors des enquêtes épidémiologiques sur les foyers. Néanmoins, la pléthore de données génétiques générées en routine par le SHD devient un véritable problème en termes de stockage et d'analyse de ces données. Les auteurs apportent un éclairage sur les aspects de la gestion et de l'analyse des données qu'il convient de prendre en compte avant d'adopter le SHD pour le diagnostic de routine en santé animale. Ces éléments relèvent de trois catégories étroitement reliées : le stockage de données, l'analyse de données et l'assurance qualité. Chacun de ces aspects présente de nombreuses complexités et nécessitera sans doute d'être adapté à mesure que le SHD évolue. Lorsqu'elles sont prises dès la phase initiale d'un projet, des décisions stratégiques appropriées en matière d'analyse bio-informatique de séquences peuvent contribuer à éviter des problèmes majeurs sur le long terme.

Los avances tecnológicos y la reducción de los costos han acelerado el uso de la secuenciación de alto rendimiento (SAR) con fines de diagnóstico y caracterización de enfermedades animales infecciosas. La secuenciación de alto rendimiento presenta varias ventajas en comparación con otras técnicas anteriores, en particular ciclos más rápidos y una resolución que permite detectar diferencias de un solo nucleótido entre las muestras, aspectos ambos de gran importancia para el estudio epidemiológico de brotes infecciosos. Sin embargo, debido al sinnúmero de datos genéticos que constantemente se generan, no es de extrañar que esté resultando problemático almacenar y analizar los datos obtenidos. Los autores arrojan luz sobre los aspectos de la gestión y el análisis de datos que conviene tener en cuenta antes de aplicar la SAR a las labores sistemáticas de diagnóstico en sanidad animal. Estos elementos corresponden a grandes líneas a tres categorías relacionadas entre sí: el almacenamiento de datos; el análisis de datos; y la garantía de calidad. Cada una de ellas presenta multitud de complicaciones y exige un proceso permanente de adaptación a medida que la técnica de secuenciación va evolucionando. El hecho de adoptar las buenas decisiones estratégicas sobre el análisis bioinformático de secuencias en los primeros momentos de la concepción de un proyecto ayudará a evitar importantes problemas a largo plazo.

Informatics progress of the Global Burden of Animal Diseases programme towards data for One Health.

The Global Burden of Animal Diseases (GBADs) programme will provide data-driven evidence that policy-makers can use to evaluate options, inform decisions, and measure the success of animal health and welfare interventions. The GBADs' Informatics team is developing a transparent process for identifying, analysing, visualising and sharing data to calculate livestock disease burdens and drive models and dashboards. These data can be combined with data on other global burdens (human health, crop loss, foodborne diseases) to provide a comprehensive range of information on One Health, required to address such issues as antimicrobial resistance and climate change. The programme began by gathering open data from international organisations (which are undergoing their own digital transformations). Efforts to achieve an accurate estimate of livestock numbers revealed problems in finding, accessing and reconciling data from different sources over time. Ontologies and graph databases are being developed to bridge data silos and improve the findability and interoperability of data. Dashboards, data stories, a documentation website and a Data Governance Handbook explain GBADs data, now available through an application programming interface. Sharing data quality assessments builds trust in such data, encouraging their application to livestock and One Health issues. Animal welfare data present a particular challenge, as much of this information is held privately and discussions continue regarding which data are the most relevant. Accurate livestock numbers are an essential input for calculating biomass, which subsequently feeds into calculations of antimicrobial use and climate change. The GBADs data are also essential to at least eight of the United Nations Sustainable Development Goals.

Le programme " Impact mondial des maladies animales " (GBADs) a pour but de réunir des éléments probants axés sur des données, qui soient exploitables par les décideurs politiques pour évaluer les solutions envisagées, fonder leurs décisions et mesurer le succès des interventions dans les domaines de la santé et du bien-être des animaux. L'équipe informatique du GBADs a conçu un processus transparent pour l'identification, l'analyse, la visualisation et le partage des données, grâce auquel il sera possible d'estimer l'impact des maladies du bétail et de réaliser des modèles et des tableaux de bord sur le sujet. Les données ainsi réunies peuvent être combinées avec celles couvrant d'autres problématiques ayant un impact mondial (santé humaine, pertes de récoltes, maladies d'origine alimentaire) afin de fournir l'éventail complet d'informations Une seule santé requis pour faire face à des enjeux tels que la résistance aux agents antimicrobiens ou le changement climatique. La première phase du programme a consisté à recueillir des données ouvertes auprès de diverses organisations internationales (qui procèdent également à leur propre transformation numérique). Les efforts déployés pour parvenir à une estimation précise des effectifs des cheptels ont mis en lumière les difficultés à trouver les données détenues par différentes sources, à y accéder et à les recouper au fil du temps. Des ontologies et des bases de données graphiques sont en cours d'élaboration pour résoudre le problème des silos de données et pour améliorer la facilité de recherche et l'interopérabilité des données. Les données du GBADs sont désormais expliquées sous forme de tableaux de bord, de récits construits à partir des données, ainsi que dans un site web documentaire et un Manuel de gouvernance des données, tous disponibles via une interface de programmation d'applications. Le partage des évaluations de la qualité des données renforce la confiance dans ces dernières et encourage à les appliquer pour traiter les problématiques affectant l'élevage ou relevant de l'approche Une seule santé. Les données relatives au bien-être animal présentent une difficulté particulière : elles sont, pour l'essentiel, détenues à titre privé et la question de savoir quelles sont les données les plus pertinentes est toujours en discussion. Les effectifs des cheptels doivent avoir été déterminés de manière précise afin de calculer la biomasse animale, élément qui entre par la suite dans le calcul des quantités d'agents antimicrobiens utilisés et des indicateurs du changement climatique. Les données du programme GBADs sont également essentielles au regard d'au moins huit des objectifs de développement durable des Nations Unies.

El programa sobre el Impacto Global de las Enfermedades Animales (GBADs) proporcionará información contrastada y basada en el uso de datos de la que luego puedan servirse los planificadores de políticas para valorar distintas opciones, decidir con conocimiento de causa y medir la eficacia de una u otra intervención en materia de sanidad y bienestar animales. El equipo informático encargado del GBADs está preparando un proceso transparente destinado a seleccionar, analizar, visualizar y poner en común datos que ayuden a calcular la carga de enfermedades del ganado y a guiar la elaboración de modelos y paneles de control. Estos datos pueden ser combinados con datos referidos a otros grandes problemas planetarios (salud humana, pérdida de cultivos, enfermedades de transmisión alimentaria) para obtener el repertorio completo de información en clave de Una sola salud que se necesita para abordar problemáticas como la resistencia a los antimicrobianos o el cambio climático. El programa empezó por reunir datos abiertos procedentes de organizaciones internacionales (inmersas, por otra parte, en su propio proceso de transformación digital). La labor emprendida para estimar con exactitud las cifras de ejemplares del mundo pecuario reveló ciertos problemas a la hora de encontrar, obtener y conciliar datos de distintas fuentes a lo largo del tiempo. Ahora se están elaborando ontologías y bases de datos gráficos para crear conexiones entre los "silos de datos" y lograr que los datos sean a la vez más compatibles entre sí y más fáciles de localizar. Paneles de control, interpretaciones narrativas de los datos ("data stories"), un sitio web de documentación y un manual de gestión de datos ayudan a explicar y aprehender los datos del GBADs, accesibles ahora por medio de una interfaz de programación de aplicaciones. El hecho de poner en común las evaluaciones de la calidad de los datos genera mayor confianza en esta información, promoviendo con ello su aplicación en temas de ganadería y de Una sola salud. Los datos de bienestar animal plantean una particular dificultad, pues gran parte de esta información está en manos privadas y todavía no está claro cuáles son los datos de mayor interés. Disponer de cifras exactas sobre el número de cabezas de ganado es fundamental para efectuar los cálculos de biomasa que después se utilizan para hacer otros cómputos referidos al uso de antimicrobianos y al cambio climático. Los datos del GBADs son asimismo esenciales para al menos ocho de los Objetivos de Desarrollo Sostenible de las Naciones Unidas.

SecProMTB: Support Vector Machine-Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis.

Secretory proteins of Mycobacterium tuberculosis have created more concern, given their dominant immunogenicity and role in pathogenesis. In view of expensive and time-consuming traditional biochemical experiments, an advanced support vector machine model named SecProMTB is constructed in this study and the proteins are identified by a bioinformatic approach. First, an improved pseudo-amino acid composition (PseAAC) algorithm is used to extract features from all entities. Second, a novel imbalanced-data strategy is proposed and adopted to divide the original data set into train set and test set. Third, to overcome the overfitting problem, feature-ranking algorithms are applied with an increment feature selection. Finally, the model is trained and optimized. Consequently, a model is obtained with an area under the curve of 0.862 and average accuracy of 86% in the independent test. For the convenience of users, SecProMTB and related data are openly accessible at http://server.malab.cn/SecProMTB/index.jsp.

Design and Implementation of a New System for Large Bridge Monitoring-GeoSHM.

Structural Health Monitoring (SHM) is a relatively new branch of civil engineering that focuses on assessing the health status of infrastructure, such as long-span bridges. Using a broad range of in-situ monitoring instruments, the purpose of the SHM is to help engineers understand the behaviour of structures, ensuring their structural integrity and the safety of the public. Under the Integrated Applications Promotion (IAP) scheme of the European Space Agency (ESA), a feasibility study (FS) project that used the Global Navigation Satellite Systems (GNSS) and Earth Observation (EO) for Structural Health Monitoring of Long-span Bridges (GeoSHM) was initiated in 2013. The GeoSHM FS Project was led by University of Nottingham and the Forth Road Bridge (Scotland, UK), which is a 2.5 km long suspension bridge across the Firth of Forth connecting Edinburgh and the Northern part of Scotland, was selected as the test structure for the GeoSHM FS project. Initial results have shown the significant potential of the GNSS and EO technologies. With these successes, the FS project was further extended to the demonstration stage, which is called the GeoSHM Demo project where two other long-span bridges in China were included as test structures. Led by UbiPOS UK Ltd. (Nottingham, UK), a Nottingham Hi-tech company, this stage focuses on addressing limitations identified during the feasibility study and developing an innovative data strategy to process, store, and interpret monitoring data. This paper will present an overview of the motivation and challenges of the GeoSHM Demo Project, a description of the software and hardware architecture and a discussion of some primary results that were obtained in the last three years.

Digital twins in sustainable transition: exploring the role of EU data governance.

Introduction: Digital twins can accelerate sustainable development by leveraging big data and artificial intelligence to simulate state, reactions and potential developments of physical systems. In doing so, they can create a comprehensive basis for data-driven policy decisions. One of the purposes of digital twins is to facilitate the implementation of the EU's Green Deal-in line with internationally binding climate and environmental targets. One prerequisite for the success of digital twins is a comprehensive, high-quality database. This requires a suitable legal framework that ensures access to such data. Methods: Applying a qualitative governance analysis, the following article examines if the EU's strategies and legal acts on data governance are paving the way for digital twin projects which promote sustainability. Results: Results show important starting points for open and fair data use within the growing field of EU digital law. However, there is still a lot of progress to be made to legally link the use of digital twins with binding sustainability objectives.

Bringing Communities Together: Mapping the Investigation-Study-Assay-Model (ISA) to Fast Healthcare Interoperability Resources (FHIR).

Adhering to FAIR principles (findability, accessibility, interoperability, reusability) ensures sustainability and reliable exchange of data and metadata. Research communities need common infrastructures and information models to collect, store, manage and work with data and metadata. The German initiative NFDI4Health created a metadata schema and an infrastructure integrating existing platforms based on different information models and standards. To ensure system compatibility and enhance data integration possibilities, we mapped the Investigation-Study-Assay (ISA) model to Fast Healthcare Interoperability Resources (FHIR). We present the mapping in FHIR logical models, a resulting FHIR resources' network and challenges that we encountered. Challenges mainly related to ISA's genericness, and to different structures and datatypes used in ISA and FHIR. Mapping ISA to FHIR is feasible but requires further analyses of example data and adaptations to better specify target FHIR elements, and enable possible automatized conversions from ISA to FHIR.

A national evaluation analysis and expert interview study of real-world data sources for research and healthcare decision-making.

Real-world data (RWD) can provide intel (real-world evidence, RWE) for research and development, as well as policy and regulatory decision-making along the full spectrum of health care. Despite calls from global regulators for international collaborations to integrate RWE into regulatory decision-making and to bridge knowledge gaps, some challenges remain. In this work, we performed an evaluation of Austrian RWD sources using a multilateral query approach, crosschecked against previously published RWD criteria and conducted direct interviews with representative RWD source samples. This article provides an overview of 73 out of 104 RWD sources in a national legislative setting where major attempts are made to enable secondary use of RWD (e.g. law on the organisation of research, "Forschungsorganisationsgesetz"). We were able to detect omnipresent challenges associated with data silos, variable standardisation efforts and governance issues. Our findings suggest a strong need for a national health data strategy and data governance framework, which should inform researchers, as well as policy- and decision-makers, to improve RWD-based research in the healthcare sector to ultimately support actual regulatory decision-making and provide strategic information for governmental health data policies.

Evolving Clinical Data Strategies and Tactics in Response to Digital Transformation.

BACKGROUND: Contending with a continuously expanding volume and variety of clinical data poses challenges and opportunities for the industry and clinical data management organizations. METHODS: Tufts CSDD conducted an online survey aimed at further quantifying and understanding the magnitude and impact that expanded data volume, sources and diversity are having on clinical trials. The survey was distributed between October and December 2019. Responses from a total of 149 individuals were included in the final analysis. RESULTS: The survey found that companies use or pilot from one to six different data sources with the majority of respondents using or piloting 3-4 different sources of data in their clinical trials. The results showed that average times to database lock have increased an average 5 days compared to a 2017 study, possibly as a result of managing an even larger number of data sources. Finally, three key mitigation strategies surfaced as techniques respondents used to tackle expanding data volume, sources, and diversity: the creation of a formalized data strategy, investment in new analytics tools and more sophisticated data technology infrastructures, and the development of new data science disciplines. CONCLUSION: Without further investments into infrastructure and developments of additional mitigation techniques in this area, database lock cycle times are likely to continue to increase as more and more data supporting a clinical trial are coming from nontraditional, CRF sources. Further research must be done into organizations who are handling these challenges appropriately.

Artificial Intelligence: A Private Practice Perspective.

Artificial intelligence (AI) is an exciting technology that can transform the practice of radiology. However, radiology AI is still immature with limited adopters, dominated by academic institutions, and few use cases in general practice. With scale and a focus on innovation, our practice has had the opportunity to be an early adopter of AI technology. We have gained experience identifying use cases that provide value for our patients and practice; selecting AI products and vendors; piloting vendors' AI algorithms; creating our own AI algorithms; implementing, optimizing, and maintaining these algorithms; garnering radiologist acceptance of these tools; and integrating AI into our radiologists' daily workflow. With this experience, our practice has both managed challenges and identified unexpected benefits of AI. To ensure a successful and scalable AI implementation, multiple steps are required, including preparing the data, systems, and radiologists. This article reviews our experience with AI and describes why each step is important.

Establishing data-intensive healthcare: the case of Hospital Electronic Prescribing and Medicines Administration systems in Scotland.

BACKGROUND: Creating learning health systems, characterised by the use and repeated reuse of demographic, process and clinical data to improve the safety, quality and efficiency of care, is a key aim in realising the potential benefits and efficiency savings associated with the implementation of health information technology. OBJECTIVES: We sought to investigate stakeholder perspectives on and experiences of the implementation of hospital electronic prescribing and medicines administration (HEPMA) systems in Scotland and use these to inform political decisions on approaches to promoting the use and reuse of digitised prescribing and medication administration data in order to improve care processes and outcomes.Methods We identified and recruited key national stakeholders involved in implementing and/or using HEPMA data from generic and specialty systems. These included representatives from healthcare settings (i.e. doctors, pharmacists and nurses), managers of existing national databases, policy makers, healthcare analytics companies, system suppliers and patient representatives. We conducted multi-disciplinary focus group discussions, audio-recorded these, transcribed data verbatim and thematically analysed the transcripts with the help of NVivo10. In analysing the data, we drew on theoretical and previous empirical work on information infrastructures. RESULTS: We identified the following key themes: 1) micro-factors - usability of systems and motivating users to input data; 2) meso-factors - developing technical and organisational infrastructures to facilitate the aggregation of data; and 3) macro-factors - facilitating interoperability and data reuse at larger scales to ensure that data are effectively generated and used. CONCLUSIONS: This work is relevant not only to countries in the early stages of data strategy development but also to countries aiming to aggregate data at national levels. An overall shared vision of a learning health system at individual, organisational and national levels can help to catalyse such data-intensive transformational efforts.