The depiction of evolutionary relationships within phylum Ascomycota is still controversial because of unresolved branching orders in the radiation of major taxa. Here we generated a dataset of 166 small subunit (18S) rDNA sequences, representative of all groups of Fungi and used as input in a Bayesian phylogenetic analysis. This phylogeny suggests that Discomycetes are a basal group of filamentous Ascomycetes and probably maintain ancestor characters since their representatives are intermingled among other filamentous fungi. Also, we show that the evolutionary rate heterogeneity within Ascomycota precludes the assumption of a global molecular clock. Accordingly, we used the penalized likelihood method, and for calibration we included a 400 million-year-old Pyrenomycete fossil considering two distinct scenarios found in the literature, one with an estimated date of 1576 Myr for the plant-animal-fungus split and the other with an estimated date of 965 Myr for the animal-fungus split. Our data show that the current classification of the fossil as a Pyrenomycete is not compatible with the second scenario. Estimates under the first scenario are older than dates proposed in previous studies based on small subunit rDNA sequences but support estimates based on multiprotein analysis, suggesting that the radiation of the major Ascomycota groups occurred into the Proterozoic era.
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http://dx.doi.org/10.1007/s00239-004-0164-y | DOI Listing |
PLoS One
January 2025
Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States of America.
The genetic basis of complex traits involves the function of many genes with small effects as well as complex gene-gene and gene-environment interactions. As one of the major players in complex diseases, the role of gene-environment interactions has been increasingly recognized. Motivated by epidemiology studies to evaluate the joint effect of environmental mixtures, we developed a functional varying-index coefficient model (FVICM) to assess the combined effect of environmental mixtures and their interactions with genes, under a longitudinal design with quantitative traits.
View Article and Find Full Text PDFStat Med
February 2025
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
There is a growing number of Phase I dose-finding studies that use a model-based approach, such as the CRM or the EWOC method to estimate the dose-toxicity relationship. It is common to assume that all patients will have similar toxicity risk given the dose regardless of patients' individual characteristics. In many trials, however, some patients' covariates (e.
View Article and Find Full Text PDFClin Orthop Relat Res
January 2025
Department of Orthopedics, First Affiliated Hospital of Xinjiang Medical University, Urumqi, PR China.
Background: Bacteremia is sometimes observed in patients with prosthetic joint infection (PJI), and it is associated with a lower likelihood of infection control. However, the prevalence and association of bacteremia in chronic PJI remain unknown.
Questions/purposes: (1) What percentage of patients are diagnosed with bacteremia at the time of hospital admission and before surgery for chronic PJI? (2) What clinical factors are associated with positive blood cultures? (3) To what degree are positive blood cultures associated with infection-free implant survival in patients with chronic PJI?
Methods: This prospective study was conducted at a single academic institution from June 2021 to August 2022.
BMJ Open
December 2024
Department of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham, UK.
Introduction: Ewing sarcoma is a rare paediatric cancer. Currently, there is no way of accurately predicting these patients' survival at diagnosis. Disease type (ie, localised disease, lung/pleuropulmonary metastases and other metastases) is used to guide treatment decisions, with metastatic patients generally having worse outcomes than localised disease patients.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data.
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