A dual system for naming prokaryotes is currently in place based on the well-established International Code of Nomenclature of Prokaryotes (ICNP) and the newly created Code of Nomenclature of Prokaryotes Described from Sequence Data (SeqCode). Whilst recent creation of the SeqCode opened an avenue to accelerate the naming of uncultured taxa, the existence of two codes increases the risk of species being assigned multiple validly published names. In this work we present a workflow that aims to limit conflicts by firstly naming novel cultured taxa under the SeqCode, and secondly under the ICNP, enhancing the traceability of the taxa across the two codes. To exemplify this workflow, we describe four novel taxa isolated from the intestine of pigs: Intestinicryptomonas porci gen. nov., sp. nov. (strain CLA-KB-P66, genome accession GCA_033971905.1) within a novel family, Intestinicryptomonaceae; Grylomicrobium aquisgranensis gen. nov., sp. nov. (CLA-KB-P133, GCA_033971865.1); Absicoccus intestinalis sp. nov. (CLA-KB-P134, GCA_033971885.1); and Mesosutterella porci sp. nov. (oilRF-744- wt-GAM-9, GCF_022134585.1).
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http://dx.doi.org/10.1016/j.syapm.2024.126543 | DOI Listing |
Int J Syst Evol Microbiol
January 2025
Institute of Life Sciences, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, 9190401 Jerusalem, Israel.
Following a proposal for further integration of names into the International Code of Nomenclature of Prokaryotes, I here report the outcome of the ballot on this proposal by the members of the International Committee on Systematics of Prokaryotes.
View Article and Find Full Text PDFInt J Syst Evol Microbiol
January 2025
Institute of Life Sciences, The Hebrew University of Jerusalem, The Edmond J. Safra Campus, 9190401 Jerusalem, Israel.
Following a proposal to emend Recommendation 6(7), Rule 64 and Appendix 9, Section D of the International Code of Nomenclature of Prokaryotes to regulate the formation of prokaryote names from personal names, I hereby report the outcome of the ballot on this proposal by the members of the International Committee on Systematics of Prokaryotes.
View Article and Find Full Text PDFAMA J Ethics
January 2025
Data scientist at the National Health Service in England.
Coded health care data from patients' health records are used in epidemiological research, especially on incidence or prevalence of disease; for drug safety monitoring or long-term cohort tracking; and to inform policy making. This article briefly summarizes the evolution of internationally recognized coding ontologies and nomenclature and describes applications of coded electronic health record (EHR) data in day-to-day health care operations, research, auditing, and policy development. This article also illuminates how errors can occur when EHR information is coded, considers errors' consequences, and suggests strategies for mitigating errors and improving overall use of coded EHR data.
View Article and Find Full Text PDFJ Cheminform
December 2024
Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany.
Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained cell types and establish standardized annotations at the cluster level, hindering comprehensive understanding of the brain's cellular composition. To facilitate data integration across multiple models and datasets, we designed BrainCellR.
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