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As in other areas of experimental science, operation of electrophysiological laboratory, design and performance of electrophysiological experiments, collection, storage and sharing of experimental data and metadata, analysis and interpretation of these data, and publication of results are time consuming activities. If these activities are well organized and supported by a suitable infrastructure, work efficiency of researchers increases significantly. This article deals with the main concepts, design, and development of software and hardware infrastructure for research in electrophysiology. The described infrastructure has been primarily developed for the needs of neuroinformatics laboratory at the University of West Bohemia, the Czech Republic. However, from the beginning it has been also designed and developed to be open and applicable in laboratories that do similar research. After introducing the laboratory and the whole architectural concept the individual parts of the infrastructure are described. The central element of the software infrastructure is a web-based portal that enables community researchers to store, share, download and search data and metadata from electrophysiological experiments. The data model, domain ontology and usage of semantic web languages and technologies are described. Current data publication policy used in the portal is briefly introduced. The registration of the portal within Neuroscience Information Framework is described. Then the methods used for processing of electrophysiological signals are presented. The specific modifications of these methods introduced by laboratory researches are summarized; the methods are organized into a laboratory workflow. Other parts of the software infrastructure include mobile and offline solutions for data/metadata storing and a hardware stimulator communicating with an EEG amplifier and recording software.

Objective The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. Materials and Methods Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers. Results We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD). Conclusion Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.

Objective The COVID-19 pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDM) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500,000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. Materials and methods We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHR) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. Results We identified 502,505 participants (3,086 with COVID-19) and transformed 690 fields (1,373,239,555 rows) to the OMOP CDM using eight different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported non-cancer illnesses 946,053 (83.91% of all source entries), cancers 37,802 (70.81%), medications 1,218,935 (88.25%), and prescriptions 864,788 (86.96%). In EHR, we transformed 1,3028,182 (99.95%) hospital diagnoses, 6,465,399 (89.2%) procedures, 337,896,333 primary care diagnoses (CTV3, SNOMED-CT), 139,966,587 (98.74%) prescriptions (dm+d) and 77,127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. Discussion and conclusion Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.

This paper deals with the description, measurement, and use of electromagnetic properties of ferromagnetic fibres used as dispersed fibre reinforcement in composite mixtures. Firstly, the fibres’ magnetic properties are shown, and a method of measuring the hysteresis loop of fibres is proposed. The results from the measurements are presented and a discussion of the influence of measured parameters on the fibres’ orientation in a magnetic field is performed. Furthermore, methods of non-destructive estimation, of their amount and orientation in the composite specimens, are discussed. The main experimental goal of this paper is to show the relationship between this non-destructive method’s results and the destructive flexural strength measurements. The method is sensitive enough to provide information related to fibre reinforcement.

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Abstract-Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.

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