Identification of key drivers of antimicrobial resistance in Enterococcus using machine learning.

With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML) models show promise for AMR prediction in diagnostics but require a deep understanding of internal processes to use effectively. Our study utilized AMR gene, pangenomic, and predicted plasmid features from 647 Enterococcus faecium and Enterococcus faecalis genomes across the One Health continuum, along with corresponding resistance phenotypes, to develop interpretive ML classifiers. Vancomycin resistance could be predicted with 99% accuracy with AMR gene features, 98% with pangenome features, and 96% with plasmid clusters. Top pangenome features overlapped with the resistance genes of the vanA operon, which are often laterally transmitted via plasmids. Doxycycline resistance prediction achieved approximately 92% accuracy with pangenome features, with the top feature being elements of Tn916 conjugative transposon, a tet(M) carrier. Erythromycin resistance prediction models achieved about 90% accuracy, but top features were negatively correlated with resistance due to the confounding effect of population structure. This work demonstrates the importance of reviewing ML models' features to discern biological relevance even when achieving high-performance metrics. Our workflow offers the potential to propose hypotheses for experimental testing, enhancing the understanding of AMR mechanisms, which are crucial for combating the AMR crisis.

PHA4GE quality control contextual data tags: standardized annotations for sharing public health sequence datasets with known quality issues to facilitate testing and training.

As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower quality datasets be made available for analysis and comparison alongside those of higher quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability, and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies - community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The tags can also be applied to synthetic (lab created) data. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA's GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.

A solider and a victim: Masculinity, violence, and incels celebration of December 6th.

In 1989, Marc Lépine murdered 14 women at L'École Polytechnique de Montréal. We demonstrate how involuntarily celibate ("incel") men celebrate Lépine and claim him as a member of their community. Our analysis draws on 637 comments made on incels.is, the main English-language incel forum, that explicitly mentions Marc Lépine. We argue that incels use Lépine to situate themselves in relation to masculinity and to justify violence against women. First, incels orient to both hegemonic and subordinate masculinity by arguing that feminists are waging a gender war against men. Second, incels celebrate Lépine as a methodical and efficient murderer, connecting both themselves and Lépine to hegemonic masculinity. Third, incels describe both themselves and Lépine as victims of feminists and use this perceived subordination to justify violence against women. We discuss findings in relation to theories of masculinity and policies regulating online communities.

En 1989, Marc Lépine a assassiné 14 femmes à l'École Polytechnique de Montréal. Nous montrons comment des hommes involontairement célibataires (« incel ¼) cèlébrent Lépine et le revendiquent comme membre de leur communauté. Notre analyse s'appuie sur 637 commentaires formulés sur incels.is, le principal forum incel anglophone, qui mentionnent explicitement Marc Lépine. Nous soutenons que les incels utilisent Lépine pour se situer par rapport à la masculinité et justifier les violences faites aux femmes. Premiérement, les incels s'orientent vers une masculinité à la fois hégémonique et subordonnée en soutenant que les féministes mènent une guerre de genre contre les hommes. Deuxièmement, les incels célèbrent Lépine comme un meurtrier méthodique et efficace, les liant eux-mêmes et Lépine à la masculinité hégémonique. Troisièmement, les incels se décrivent eux-mêmes et Lépine comme des victimes des féministes et utilisent cette subordination perςue pour justifier la violence contre les femmes. Nous discutons des résultats relatifs aux théories de la masculinité et aux politiques régissant les communautés en ligne.