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- Padamsee, Mahajabeen, et al.
(författare)
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The genome of the xerotolerant mold Wallemia sebi reveals adaptations to osmotic stress and suggests cryptic sexual reproduction
- 2012
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Ingår i: Fungal Genetics and Biology. - : Elsevier BV. - 1087-1845 .- 1096-0937. ; 49:3, s. 217-226
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Tidskriftsartikel (refereegranskat)abstract
- Wallemia (Wallemiales, Wallemiomycetes) is a genus of xerophilic Fungi of uncertain phylogenetic position within Basidiomycota. Most commonly found as food contaminants, species of Wallemia have also been isolated from hypersaline environments. The ability to tolerate environments with reduced water activity is rare in Basidiomycota. We sequenced the genome of W. sebi in order to understand its adaptations for surviving in osmotically challenging environments, and we performed phylogenomic and ultrastructural analyses to address its systematic placement and reproductive biology. W. sebi has a compact genome (9.8Mb), with few repeats and the largest fraction of genes with functional domains compared with other Basidiomycota. We applied several approaches to searching for osmotic stress-related proteins. In silico analyses identified 93 putative osmotic stress proteins; homology searches showed the HOG (High Osmolarity Glycerol) pathway to be mostly conserved. Despite the seemingly reduced genome, several gene family expansions and a high number of transporters (549) were found that also provide clues to the ability of W. sebi to colonize harsh environments. Phylogenetic analyses of a 71-protein dataset support the position of Wallemia as the earliest diverging lineage of Agaricomycotina, which is confirmed by septal pore ultrastructure that shows the septal pore apparatus as a variant of the Tremella-type. Mating type gene homologs were identified although we found no evidence of meiosis during conidiogenesis, suggesting there may be aspects of the life cycle of W. sebi that remain cryptic.
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| 2. |
- Mejia, A. F., et al.
(författare)
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A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
- 2020
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Ingår i: Journal of the American Statistical Association. - : Informa UK Limited. - 0162-1459 .- 1537-274X.
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Tidskriftsartikel (refereegranskat)abstract
- Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a "massive univariate" approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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