Incorporating morphological data into modern phylogenies allows integration of fossil evidence, facilitating divergence dating and macroevolutionary inferences. Improvements in the phylogenetic utility of morphological data have been sought via Procrustes-based geometric morphometrics (GMM), but with mixed success and little clarity over what anatomical areas are most suitable. Here, we assess GMM-based phylogenetic reconstructions in a heavily sampled source of discrete characters for mammalian phylogenetics-the basicranium-in 57 species of marsupial mammals, compared with the remainder of the cranium. We show less phylogenetic signal in the basicranium compared with a 'Rest of Cranium' partition, using diverse metrics of phylogenetic signal (, phylogenetically aligned principal components analysis, comparisons of UPGMA/neighbour-joining/parsimony trees and cophenetic distances to a reference phylogeny) for scaled, Procrustes-aligned landmarks and allometry-corrected residuals. Surprisingly, a similar pattern emerged from parsimony-based analyses of discrete cranial characters. The consistent results across methods suggest that easily computed metrics such as can provide good guidance on phylogenetic information in a landmarking configuration. In addition, GMM data may be less informative for intricate but conservative anatomical regions such as the basicranium, while better-but not necessarily novel-phylogenetic information can be expected for broadly characterized shapes such as entire bones. This article is part of the theme issue 'The mammalian skull: development, structure and function'.
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http://dx.doi.org/10.1098/rstb.2022.0085 | DOI Listing |
PLoS One
January 2025
Department of Animal Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia.
Lumpy skin disease (LSD) is an emerging, highly contagious transboundary disease of bovines caused by the Lumpy skin disease virus (LSDV), responsible for substantial economic losses to the dairy, meat, and leather industries in Pakistan as well as various countries around the world. Epidemiological information on LSD is scarce in Punjab, Pakistan. Therefore, a molecular epidemiological study was conducted in two agro-ecologically diverse districts (Bhakkar and Jhang) of Punjab, Pakistan.
View Article and Find Full Text PDFInt J Syst Evol Microbiol
January 2025
School of Life Science, Nanyang Normal University, Nanyang 473061, PR China.
Two novel yeast strains, NYNU 236247 and NYNU 23523, were isolated from the leaves of Hance, collected in the Tianchi Mountain National Forest Park, Henan Province, central China. Phylogenetic analysis of the D1/D2 domain of the large subunit rRNA gene and the internal transcribed spacer (ITS) region revealed the closest relatives of the strains are three described species: , and . The novel species differed from the type strains of these three species by 12 to 22 nucleotide substitutions and 1 gap (~2.
View Article and Find Full Text PDFInt J Syst Evol Microbiol
January 2025
Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Small, obligately anaerobic strains 13CB8C, 13CB11C, 13CB18C and 13GAM1G were isolated from a faecal sample in a patient with Parkinson's disease with a history of duodenal resection. After conducting a comprehensive polyphasic taxonomic analysis including genomic analysis, we propose the establishment of one new genus and four new species. The novel bacteria are sp.
View Article and Find Full Text PDFMol Biol Rep
January 2025
Division of Animal Biotechnology, Faculty of Veterinary Sciences & Animal Husbandry, SKUAST-K, Srinagar, India.
Background: The identification of helminth parasites in Schizothorax spp. from Kashmir, including Schyzocotyle acheilognathi, Pomphorhynchus kashmirensis, and Adenoscolex oreini, is hindered by morphological limitations and high intraspecific variation. While previous studies have relied on morphological diagnosis, a comprehensive molecular characterization is lacking.
View Article and Find Full Text PDFJ Gen Virol
January 2025
Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more.
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