The microbial consortium involved in anaerobic digestion has not yet been precisely characterized and this process remains a 'black box' with limited efficiency. In this study, seven anaerobic sludge digesters were selected based on technology, type of sludge, process and water quality. The prokaryotic community of these digesters was examined by constructing and analysing a total of 9890 16S rRNA gene clones. Libraries were constructed using primers specific for the Bacteria and Archaea domains for each digester, respectively. After phylogenetic affiliation, the libraries were compared using statistical tools to determine the similarities or differences among the seven digesters. Results show that the prokaryotic community of an anaerobic digester is composed of phylotypes commonly found in all anaerobic digesters sampled and also of specific phylotypes. The Archaea community is represented by an equilibrium among a restricted number of operational taxonomic units (OTUs). These OTUs are affiliated with Methanosarcinales, Methanomicrobiales and Arc I phylogenetic groups. Statistical analysis revealed that the Bacteria community can be described as a three component model: one-third making up a core group of phylotypes common to most of the digesters, one-third are phylotypes shared among a few digesters and another one-third are specific phylotypes. The core group is composed of only six OTUs affiliated with Chloroflexi, Betaproteobacteria, Bacteroidetes and Synergistetes. Its role in anaerobic degradation appears critical to investigate. This comparison of anaerobic digester populations is a first step towards a future understanding of the relationship among biodiversity, operating conditions and digester efficiency.

Harmonized terms, concepts and metadata for microbiological risk assessment models: the basis for knowledge integration and exchange, Microbial Risk Analysis (2018Analysis ( ), doi: 10.1016Analysis ( /j.mran.2018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Author names and affiliations Leticia Corresponding authorsLaurent Guillier; Maarten Nauta; Matthias Filter Short Title or Running HeadHarmonization for microbiological risk assessment modelling AbstractIn the last decades the microbial food safety community has developed a variety of valuable knowledge (e.g., mathematical models and data) and resources (e.g., databases and software tools)in the areas of quantitative microbial risk assessment (QMRA) and predictive microbiology.However, the reusability of this knowledge and the exchange of information between resources are currently difficult and time consuming. This problem has increased over time due to the lack of harmonized data format and rules for knowledge annotation. It includes the lack of a common understanding of basic terms and concepts and of a harmonized information exchange format to describe and annotate knowledge. The existence of ambiguities and inconsistencies in the use of terms and concepts in the QMRA and predictive microbial (PM) modelling necessitates a consensus on their refinement, which will allow a harmonized exchange of information within these areas.Therefore, this work aims to harmonize terms and concepts used in QMRA and PM modelling spanning from high level concepts as defined by Codex Alimentarius, Food and Agriculture Organization (FAO) and World Health Organization (WHO), up to terms generally used in statistics or data and software science. As a result, a harmonized schema for metadata that allows consistent annotation of data and models from these two domains is proposed. This metadata schema is also a . This platform will facilitate the sharing and execution of curated QMRA and PM models using the foundation of the proposed harmonized metadata schema and information exchange format. Furthermore, it will also provide access to related open source software libraries, converter tools and software-specific import and export functions that promote the adoption of FSK-ML by the microbial food safety community. In the future, these resources will hopefully promote both the knowledge reusability and the highquality information exchange between stakeholders within the areas of QMRA and PM modelling worldwide.

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