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Little J, Chikina M, Clark NL. Evolutionary rate covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions. eLife 2024; 12:RP93333. [PMID: 38415754 PMCID: PMC10942632 DOI: 10.7554/elife.93333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Abstract
Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and nonphysical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than nonphysical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.
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Affiliation(s)
- Jordan Little
- Department of Human Genetics, University of UtahSalt Lake CityUnited States
| | - Maria Chikina
- Department of Computational Biology, University of PittsburghPittsburghUnited States
| | - Nathan L Clark
- Department of Human Genetics, University of UtahSalt Lake CityUnited States
- Department of Biological Sciences, University of PittsburghPittsburghUnited States
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Giuseppe A, Raffaella EM. The First Genome-Wide Mildew Locus O Genes Characterization in the Lamiaceae Plant Family. Int J Mol Sci 2023; 24:13627. [PMID: 37686433 PMCID: PMC10487521 DOI: 10.3390/ijms241713627] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Powdery mildew (PM) is a widespread plant disease that causes significant economic losses in thousands crops of temperate climates, including Lamiaceae species. Multiple scientific studies describe a peculiar form of PM-resistance associated at the inactivation of specific members of the Mildew Locus O (MLO) gene family, referred to as mlo-resistance. The characterization of Lamiaceae MLO genes, at the genomic level, would be a first step toward their potential use in breeding programs. We carried out a genome-wide characterization of the MLO gene family in 11 Lamiaceae species, providing a manual curated catalog of 324 MLO proteins. Evolutionary history and phylogenetic relationships were studied through maximum likelihood analysis and motif patter reconstruction. Our approach highlighted seven different clades diversified starting from an ancestral MLO domain pattern organized in 18 highly conserved motifs. In addition, 74 Lamiaceae putative PM susceptibility genes, clustering in clade V, were identified. Finally, we performed a codon-based evolutionary analysis, revealing a general high level of purifying selection in the eleven Lamiaceae MLO gene families, and the occurrence of few regions under diversifying selection in candidate susceptibility factors. The results of this work may help to address further biological questions concerning MLOs involved in PM susceptibility. In follow-up studies, it could be investigated whether the silencing or loss-of-function mutations in one or more of these candidate genes may lead to PM resistance.
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Affiliation(s)
- Andolfo Giuseppe
- Department of Agricultural Sciences, University of Naples “Federico II”, Via Università 100, Portici, 80055 Naples, Italy
| | - Ercolano Maria Raffaella
- Department of Agricultural Sciences, University of Naples “Federico II”, Via Università 100, Portici, 80055 Naples, Italy
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Liu Z, Guo T, Yin Z, Zeng Y, Liu H, Yin H. Functional inference of long non-coding RNAs through exploration of highly conserved regions. Front Genet 2023; 14:1177259. [PMID: 37260771 PMCID: PMC10229068 DOI: 10.3389/fgene.2023.1177259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 06/02/2023] Open
Abstract
Background: Long non-coding RNAs (lncRNAs), which are generally less functionally characterized or less annotated, evolve more rapidly than mRNAs and substantially possess fewer sequence conservation patterns than protein-coding genes across divergent species. People assume that the functional inference could be conducted on the evolutionarily conserved long non-coding RNAs as they are most likely to be functional. In the past decades, substantial progress has been made in discussions on the evolutionary conservation of non-coding genomic regions from multiple perspectives. However, understanding their conservation and the functions associated with sequence conservation in relation to further corresponding phenotypic variability or disorders still remains incomplete. Results: Accordingly, we determined a highly conserved region (HCR) to verify the sequence conservation among long non-coding RNAs and systematically profiled homologous long non-coding RNA clusters in humans and mice based on the detection of highly conserved regions. Moreover, according to homolog clustering, we explored the potential function inference via highly conserved regions on representative long non-coding RNAs. On lncRNA XACT, we investigated the potential functional competence between XACT and lncRNA XIST by recruiting miRNA-29a, regulating the downstream target genes. In addition, on lncRNA LINC00461, we examined the interaction relationship between LINC00461 and SND1. This interaction or association may be perturbed during the progression of glioma. In addition, we have constructed a website with user-friendly web interfaces for searching, analyzing, and downloading to present the homologous clusters of humans and mice. Conclusion: Collectively, homolog clustering via the highly conserved region definition and detection on long non-coding RNAs, as well as the functional explorations on representative sequences in our research, would provide new evidence for the potential function of long non-coding RNAs. Our results on the remarkable roles of long non-coding RNAs would presumably provide a new theoretical basis and candidate diagnostic indicators for tumors.
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Affiliation(s)
- Zhongpeng Liu
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Tianbin Guo
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Zhuoda Yin
- TJ-YZ School of Network Science, Haikou University of Economics, Haikou, China
| | - Yanluo Zeng
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Haiwen Liu
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Hongyan Yin
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
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Mayoral-Peña Z, Lázaro-Vidal V, Fornoni J, Álvarez-Martínez R, Garrido E. Studying Plant-Insect Interactions through the Analyses of the Diversity, Composition, and Functional Inference of Their Bacteriomes. Microorganisms 2022; 11. [PMID: 36677331 DOI: 10.3390/microorganisms11010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
As with many other trophic interactions, the interchange of microorganisms between plants and their herbivorous insects is unavoidable. To test the hypothesis that the composition and diversity of the insect bacteriome are driven by the bacteriome of the plant, the bacteriomes of both the plant Datura inoxia and its specialist insect Lema daturaphila were characterised using 16S sRNA gene amplicon sequencing. Specifically, the bacteriomes associated with seeds, leaves, eggs, guts, and frass were described and compared. Then, the functions of the most abundant bacterial lineages found in the samples were inferred. Finally, the patterns of co-abundance among both bacteriomes were determined following a multilayer network approach. In accordance with our hypothesis, most genera were shared between plants and insects, but their abundances differed significantly within the samples collected. In the insect tissues, the most abundant genera were Pseudomonas (24.64%) in the eggs, Serratia (88.46%) in the gut, and Pseudomonas (36.27%) in the frass. In contrast, the most abundant ones in the plant were Serratia (40%) in seeds, Serratia (67%) in foliar endophytes, and Hymenobacter (12.85%) in foliar epiphytes. Indeed, PERMANOVA analysis showed that the composition of the bacteriomes was clustered by sample type (F = 9.36, p < 0.001). Functional inferences relevant to the interaction showed that in the plant samples, the category of Biosynthesis of secondary metabolites was significantly abundant (1.4%). In turn, the category of Xenobiotics degradation and metabolism was significantly present (2.5%) in the insect samples. Finally, the phyla Proteobacteria and Actinobacteriota showed a pattern of co-abundance in the insect but not in the plant, suggesting that the co-abundance and not the presence−absence patterns might be more important when studying ecological interactions.
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Djemiel C, Maron PA, Terrat S, Dequiedt S, Cottin A, Ranjard L. Inferring microbiota functions from taxonomic genes: a review. Gigascience 2022; 11:giab090. [PMID: 35022702 PMCID: PMC8756179 DOI: 10.1093/gigascience/giab090] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/02/2021] [Accepted: 12/02/2021] [Indexed: 12/13/2022] Open
Abstract
Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.
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Affiliation(s)
- Christophe Djemiel
- Agroécologie, AgroSup Dijon, INRAE, Université de Bourgogne, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Pierre-Alain Maron
- Agroécologie, AgroSup Dijon, INRAE, Université de Bourgogne, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Sébastien Terrat
- Agroécologie, AgroSup Dijon, INRAE, Université de Bourgogne, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Samuel Dequiedt
- Agroécologie, AgroSup Dijon, INRAE, Université de Bourgogne, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Aurélien Cottin
- Agroécologie, AgroSup Dijon, INRAE, Université de Bourgogne, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Lionel Ranjard
- Agroécologie, AgroSup Dijon, INRAE, Université de Bourgogne, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
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Mehta S, Kumar P, Crane M, Johnson JE, Sajulga R, Nguyen DDA, McGowan T, Arntzen MØ, Griffin TJ, Jagtap PD. Updates on metaQuantome Software for Quantitative Metaproteomics. J Proteome Res 2021; 20:2130-2137. [PMID: 33683127 DOI: 10.1021/acs.jproteome.0c00960] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Marie Crane
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Ray Sajulga
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Dinh Duy An Nguyen
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Magnus Ø Arntzen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås 1432, Norway
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455, United States
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Abstract
BACKGROUND To explore the features and function of gut microbiota in necrotizing enterocolitis patients over 28 gestational age weeks through a case-control study. METHODS Fecal samples from patients with NEC over 28 gestational week age and matched control cases were collected. DNA of the fecal samples was extracted for 16 s rRNA sequencing to estimate the composition of the microbiota. Functional inference analyses were conducted through PICRUSt based on the sequencing raw data. RESULTS There was no significant difference in the total diversity of microbiota between the fecal samples from the patients with NEC and the controls (P = .40). Propionibacterium was more abundant in the NEC cases than in the controls. Conversely, Lactobacillus, Phascolarctobacterium, and Streptococcus_salivarius were found to be more plentiful in the controls through LEfSe analysis. Functional inference analysis revealed that the xenobiotic biodegradation and metabolic activity was lower in the NEC cases than in the controls (P < .05). CONCLUSION The NEC cohort with a gestational age of over 28 weeks has a different pattern of microbiota compared with the controls. Functional inference analysis indicated that the potential function of the microbiota may also differ between these groups.
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Affiliation(s)
- JinXing Feng
- Department of Neonatology, Shenzhen Children's Hospital, Shenzhen
| | - Yu He
- Department of Neonatology, Children's Hospital, Chongqing Medical University, Chongqing
| | - Dong Liu
- Department of Neonatology, Shenzhen People's Hospital, Shenzhen
| | - Luquan Li
- Department of Neonatology, Children's Hospital, Chongqing Medical University, Chongqing
| | - Jingyu Chen
- Department of Ultrasonography, Children's Hospital, Chongqing Medical University, Chongqing
| | - Jialin Yu
- Department of Neonatology, Shenzhen University General Hospital, China
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Abstract
The chordate proteome history database (http://ioda.univ-provence.fr) comprises some 20,000 evolutionary analyses of proteins from chordate species. Our main objective was to characterize and study the evolutionary histories of the chordate proteome, and in particular to detect genomic events and automatic functional searches. Firstly, phylogenetic analyses based on high quality multiple sequence alignments and a robust phylogenetic pipeline were performed for the whole protein and for each individual domain. Novel approaches were developed to identify orthologs/paralogs, and predict gene duplication/gain/loss events and the occurrence of new protein architectures (domain gains, losses and shuffling). These important genetic events were localized on the phylogenetic trees and on the genomic sequence. Secondly, the phylogenetic trees were enhanced by the creation of phylogroups, whereby groups of orthologous sequences created using OrthoMCL were corrected based on the phylogenetic trees; gene family size and gene gain/loss in a given lineage could be deduced from the phylogroups. For each ortholog group obtained from the phylogenetic or the phylogroup analysis, functional information and expression data can be retrieved. Database searches can be performed easily using biological objects: protein identifier, keyword or domain, but can also be based on events, eg, domain exchange events can be retrieved. To our knowledge, this is the first database that links group clustering, phylogeny and automatic functional searches along with the detection of important events occurring during genome evolution, such as the appearance of a new domain architecture.
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Affiliation(s)
- Anthony Levasseur
- INRA, UMR1163 Biotechnologie des Champignons Filamenteux, Aix Marseille Université, ESIL Polytech, 163 avenue de Luminy, CP 925, 13288 Marseille Cedex 09, France
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O'Sullivan F, Muzi M, Spence AM, Mankoff DM, O'Sullivan JN, Fitzgerald N, Newman GC, Krohn KA. Nonparametric Residue Analysis of Dynamic PET Data With Application to Cerebral FDG Studies in Normals. J Am Stat Assoc 2009; 104:556-571. [PMID: 19830267 PMCID: PMC2760850 DOI: 10.1198/jasa.2009.0021] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Kinetic analysis is used to extract metabolic information from dynamic positron emission tomography (PET) uptake data. The theory of indicator dilutions, developed in the seminal work of Meier and Zierler (1954), provides a probabilistic framework for representation of PET tracer uptake data in terms of a convolution between an arterial input function and a tissue residue. The residue is a scaled survival function associated with tracer residence in the tissue. Nonparametric inference for the residue, a deconvolution problem, provides a novel approach to kinetic analysis-critically one that is not reliant on specific compartmental modeling assumptions. A practical computational technique based on regularized cubic B-spline approximation of the residence time distribution is proposed. Nonparametric residue analysis allows formal statistical evaluation of specific parametric models to be considered. This analysis needs to properly account for the increased flexibility of the nonparametric estimator. The methodology is illustrated using data from a series of cerebral studies with PET and fluorodeoxyglucose (FDG) in normal subjects. Comparisons are made between key functionals of the residue, tracer flux, flow, etc., resulting from a parametric (the standard two-compartment of Phelps et al. 1979) and a nonparametric analysis. Strong statistical evidence against the compartment model is found. Primarily these differences relate to the representation of the early temporal structure of the tracer residence-largely a function of the vascular supply network. There are convincing physiological arguments against the representations implied by the compartmental approach but this is the first time that a rigorous statistical confirmation using PET data has been reported. The compartmental analysis produces suspect values for flow but, notably, the impact on the metabolic flux, though statistically significant, is limited to deviations on the order of 3%-4%. The general advantage of the nonparametric residue analysis is the ability to provide a valid kinetic quantitation in the context of studies where there may be heterogeneity or other uncertainty about the accuracy of a compartmental model approximation of the tissue residue.
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Affiliation(s)
- Finbarr O'Sullivan
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
| | - Mark Muzi
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
| | - Alexander M. Spence
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
| | - David M. Mankoff
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
| | - Janet N. O'Sullivan
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
| | - Niall Fitzgerald
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
| | - George C. Newman
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
| | - Kenneth A. Krohn
- Finbarr O'Sullivan is Professor of Statistics, University College Cork, Ireland and Affiliate Professor of Radiology, University of Washington, Seattle, WA 98195 (E-mail: ). Mark Muzi is Director of Image Analysis, Department of Radiology, University of Washington, Seattle, WA 98195. Alexander M. Spence is Professor of Neurology, University of Washington, Seattle, WA 98195. David M. Mankoff is Professor of Radiology, University of Washington, Seattle, WA 98195. Janet N. O'Sullivan is Research Scientist, University College Cork, Ireland. Niall Fitzgerald is Ph.D. student, University College Cork, Ireland. George C. Newman is Chair of Neurosensory Sciences, Albert Einstein Medical Center, Philadelphia, PA. Kenneth A. Krohn is Professor of Radiology, University of Washington, Seattle, WA 98195
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