Revisiting the phylogeny of royal ferns (Osmundales) through the lens of character dependence and restudied fossil taxa questions existing family and subfamily concepts.

The royal ferns (Osmundales) are a morphologically diverse group of leptosporangiate ferns, the fossil record of which dates back to the Permian. Despite there being numerous described permineralized species, the phylogenetic relationships between extinct species remain contentious. Although several analytical approaches have been applied to infer well-resolved phylogenetic hypotheses-even methods that are arguably conceived to be better at dealing with data conflict and uncertainty, many taxa have not been assigned to specific taxonomic categories.

Corrigendum: Health status and self-perception of health among homeless people in Spain: A mixed-methods study.

[This corrects the article DOI: 10.3389/fpubh.2024.

Adenovirus 41 diversity in Arizona (USA) using wastewater-based epidemiology, long-range PCR, and pathogen sequencing between October 2019 and March 2020.

By coupling long-range polymerase chain reaction, wastewater-based epidemiology, and pathogen sequencing, we show that adenovirus type 41 hexon-sequence lineages, described in children with hepatitis of unknown origin in the United States in 2021, were already circulating within the country in 2019. We also observed other lineages in the wastewater, whose complete genomes have yet to be documented from clinical samples.

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.

Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy.

Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancements in computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities.