Environmental factors associated with the prevalence of ESBL/AmpC-producing Escherichia coli in wild boar (Sus scrofa)

Antimicrobial resistances (AMR) in bacteria, such as ESBL/AmpC-producing E. coli, are a burden to human and animal health. This burden is mainly driven by the consumption and release of antimicrobial substances into the environment. The pollution and contamination of habitats by AMR in bacteria and antimicrobial substances can lead to the transmission of bacterial AMR to wildlife. Therefore, it is necessary to understand the transmission cycle of antibiotics and resistant bacteria between humans, and animals as well as their occurrences in the environment. Environmental factors associated with the occurrence of bacterial AMR in wildlife can lead to a better understanding of the distribution of bacterial AMR in humans and animals using One Health approaches. Here, we analyzed data gathered in the framework of the German zoonoses monitoring program in 2016 and 2020 using spatiotemporal statistics to identify relevant environmental factors (e.g., livestock density, climatic variables, and human density) in association with the spatial distribution of ESBL/AmpC-producing E. coli. For this purpose, we developed a generic data integration and analysis pipeline to link spatially explicit environmental factors to the monitoring data. Finally, we built a binomial generalized linear mixed model (GLMM) to determine the factors associated with the spatial distribution of ESBL/AmpC-producing E. coli. In 2016 and 2020, 807 fecal samples from hunted wild boar (Sus scrofa L.) were randomly taken in 13 federal states and selectively analyzed for ESBL/AmpC-producing E. coli. Forty-eight isolates were identified in 12 German federal states, with an overall prevalence of 6%. We observed an almost three times higher probability of ESBL/AmpC-producing E. coli isolates in wild boar in counties with high cattle densities (OR = 2.57, p ≤ 0.01). Furthermore, we identified a seasonal effect in areas with high precipitation during the off-hunting seasons (OR = 2.78, p = 0.025) and low precipitation throughout the years (OR = 0.42, p = 0.025). However, due to the low amount of identified isolates, confidence intervals were wide, indicating a high level of uncertainty. This suggests that further studies on smaller scales need to be conducted with multiannual data and improved metadata, e.g., on the location, the hunting procedure, and species characteristics to be collected during field sampling

Making Linked Data accessible for One Health Surveillance with the "One Health Linked Data Toolbox"

In times of emerging diseases, data sharing and data integration are of particular relevance for One Health Surveillance (OHS) and decision support. Furthermore, there is an increasing demand to provide governmental data in compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Semantic web technologies are key facilitators for providing data interoperability, as they allow explicit annotation of data with their meaning, enabling reuse without loss of the data collection context. Among these, we highlight ontologies as a tool for modeling knowledge in a field, which simplify the interpretation and mapping of datasets in a computer readable medium; and the Resource Description Format (RDF), which allows data to be shared among human and computer agents following this knowledge model. Despite their potential for enabling cross-sectoral interoperability and data linkage, the use and application of these technologies is often hindered by their complexity and the lack of easy-to-use software applications.To overcome these challenges the OHEJP Project ORION developed the Health Surveillance Ontology (HSO). This knowledge model forms a foundation for semantic interoperability in the domain of One Health Surveillance. It provides a solution to add data from the target sectors (public health, animal health and food safety) in compliance with the FAIR principles of findability, accessibility, interoperability, and reusability, supporting interdisciplinary data exchange and usage. To provide use cases and facilitate the accessibility to HSO, we developed the One Health Linked Data Toolbox (OHLDT), which consists of three new and custom-developed web applications with specific functionalities. The first web application allows users to convert surveillance data available in Excel files online into HSO-RDF and vice versa. The web application demonstrates that data provided in well-established data formats can be automatically translated in the linked data format HSO-RDF. The second application is a demonstrator of the usage of HSO-RDF in a HSO triplestore database. In the user interface of this application, the user can select HSO concepts based on which to search and filter among surveillance datasets stored in a HSO triplestore database. The service then provides automatically generated dashboards based on the context of the data. The third web application demonstrates the use of data interoperability  in the OHS context by using HSO-RDF to annotate meta-data, and in this way link datasets across sectors. The web application provides a dashboard to compare public data on zoonosis surveillance provided by EFSA and ECDC.The first solution enables linked data production, while the second and third provide examples of linked data consumption, and their value in enabling data interoperability across sectors. All described solutions are based on the open-source software KNIME and are deployed as web service via a KNIME Server hosted at the German Federal Institute for Risk Assessment. The semantic web extension of KNIME, which is based on the Apache Jena Framework, allowed a rapid an easy development within the project. The underlying open source KNIME workflows are freely available and can be easily customized by interested end users.With our applications, we demonstrate that the use of linked data has a great potential strengthening the use of FAIR data in OHS and interdisciplinary data exchange

Effect of Sodium Chloride, Sodium Nitrite and Sodium Nitrate on the Infectivity of Hepatitis E Virus

Hepatitis E virus (HEV) infection can cause acute and chronic hepatitis in humans. The zoonotic HEV genotype 3, which is highly prevalent in Europe, is mainly transmitted by consumption of raw meat and raw meat products produced from infected pigs or wild boars. High salt concentrations represent an important measure to preserve meat products and to inactivate foodborne pathogens. Here, an HEV preparation in phosphate-buffered saline (PBS) was subjected to different salt concentrations and the remaining infectivity was measured in a cell culture assay. Treatments with up to 20% sodium chloride for 24 h at 23 °C, with and without addition of 0.015% sodium nitrite or 0.03% sodium nitrate, did not lead to virus inactivation as compared to PBS only. Conditions usually applied for short-term and long-term fermented raw sausages were simulated by incubation at 22 °C for up to 6 days and at 16 °C for up to 8 weeks, respectively. Only 2% sodium chloride with 0.015% sodium nitrite showed a weak (< 1 log₁₀), but significant, infectivity reduction after 2 and 4 days as compared to PBS only. Addition of 2% sodium chloride and 0.03% sodium nitrate showed a slight, but not significant, decrease in infectivity after 2 and 8 weeks as compared to PBS only. In conclusion, HEV is highly stable at high salt concentrations and at salt conditions usually applied to preserve raw meat products

The Glossaryfication Web Service – an automated glossary creation tool to support One Health communication

Efficient communication and collaboration across sectors is an important precondition for true One Health Surveillance (OHS) activities. Despite the overall willingness to embrace the One Health paradigm, it is still challenging to accomplish this in day-to-day practice due to the differences in terminology and interpretation of sector-specific terms. In this sense, simple interventions like the inclusion of integrative glossaries in OHS documents (e.g. reports, research papers and guidelines) would help to reduce misunderstandings and could significantly improve the written communication in OHS. Here, we present the Glossaryfication Web Service that generates a document-specific glossary for any text file provided by the user. The web service automatically adds the available definitions with their corresponding references for the words in the document that match with terms in the user-selected glossaries.The Glossaryfication Web Service was developed to provide added value to the OHEJP Glossary that was developed within the OHEJP project ORION. The OHEJP Glossary improves the communication and collaboration among OH sectors by providing an online resource that lists relevant OH terms and sector-specific definitions. The Glossaryfication Web Service supports the practical use of the curated OHEJP Glossary and can also source information from other glossaries relevant for OH professionals (currently supporting the online CDC, WHO and EFSA glossaries).The Glossaryfication Web Service was created using the open-source software KNIME and the KNIME Text Processing extension (https://www.knime.com/knime-text-processing). The Glossaryfication KNIME workflow is deployed on BfR’s KNIME Server infrastructure providing an easy-to-use web interface where the users can upload their documents (any text-type file e.g. PDF, Word, Excel) and select the desired glossary to compare with. The Glossaryfication KNIME workflow reads in the document provided via the web interface and applies natural language processing (e.g. text cleaning, stemming), transforming (bag-of-words generation) and information retrieval methods to identify the matching terms in the selected glossaries.The Glossaryfication Web Service generates as an output a table containing all the terms that match with the selected glossaries. It also provides the available definitions, corresponding references and additional meta-information, e.g. the term frequency, i.e., how often each term appears in the given text, and the sectoral classification (only for the OHEJP Glossary terms). Furthermore, the workflow generates a tag cloud where the terms are categorized as: (i) exact match when the term in the text matches exactly with the entry of this term in the glossary; (ii) inexact match when the term appears in the text slightly modified (e.g. plural forms or suffixes) and (iii) non-matching that corresponds to all the other words appearing in the text that do not match with any glossary term. Through the user interface, the users can then choose if they want to download the whole list of terms, select only the exact/inexact matching terms, or just choose those terms and definitions that match with the meaning intended for this term in the user-provided document. The resulting table of terms can be downloaded as an Excel file and added to the user’s document as a document-specific glossary.The Glossaryfication Web Service provides an easy-to-adopt solution to enrich documents and reports with more comprehensive and unambiguous glossaries. Furthermore, it improves the referentiality of terms and definitions from different OH sectors. An additional feature provided by the Glossaryfication Web Service is the possibility of extending its use to other glossaries from other national or international institutions allowing the user to customize this glossary creation service.

The Glossaryfication Web Service: an automated glossary creation tool to support the One Health community

In many interdisciplinary research domains, the creation of a shared understanding of relevant terms is considered the foundation for efficient cross-sector communication and interpretation of data and information. This is also true for the domain of One Health (OH) where many One Health Surveillance (OHS) documents rarely contain glossaries with a list of terms for which their specific meaning in the context of the given document is defined Cornelia et al. 2018, Buschhardt et al. 2021. The absence of glossaries within these documents may lead to misinterpretation of surveillance results due to the wrong interpretation of terminology specifically when term definitions differ across OH sectors. Under the One Health EJP project ORION, the OHEJP Glossary was recently created. The OHEJP Glossary is a tool to improve communication and collaboration among OH sectors by providing an easy-to-use online resource that lists relevant OH terms and sector-specific definitions. To improve the accessibility of content from the OHEJP Glossary and support the creation of integrative glossaries in future OHS-related documents, the OHEJP Glossaryfication Web Service was created. This service can support the practical use of the OHEJP Glossary and other relevant online glossaries by OH professionals.The Glossaryfication Web Service (GWS) is an application that automatically identifies terms in any uploaded text-based document and creates a document-specific list of matching definitions in selected online glossaries. This auto-generated document-specific glossary can easily be adjusted by the user, e.g. by selecting the desired definition in case multiple definitions were found for a specific term. The document-specific glossary could then be downloaded, manually adjusted, and finally included into the original document where it supports the correct interpretation of terminology used within the document. Especially in sector-specific reports such as from animal health or public health authorities, this can be beneficial to ensure the correct interpretation by other OH sectors in the future. The GWS developed with the open-source desktop software KNIME Analytics Platform and runs as a web service on a KNIME Web Server infrastructure. The core data processing functionality in the GWS is based on KNIME’s Textprocessing extension. KNIME's JavaScript nodes provided the basis for an interactive user interface where users can easily upload their files and select between different reference glossaries such as the OHEJP Glossary, the CDC Glossary, the WHO Glossary, or the EFSA Glossary. After retrieval of the user input settings, the GWS tags words within the provided document and maps these tagged words with matching entries in the selected glossaries. As the main output, the user receives a downloadable list of matching terms with their corresponding definitions, sectorial assignments, and references, which can then be added by the user to the original document. The GWS is freely accessible via this link as well as the underlying KNIME workflow

A one health glossary to support communication and information exchange between the human health, animal health and food safety sectors.

OHEJP Project: ORIO