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An Overview of Parasite Diversity
Published in Eric S. Loker, Bruce V. Hofkin, Parasitology, 2015
Eric S. Loker, Bruce V. Hofkin
Another similar example is provided by monogenean flukes of the family Capsalidae. Flukes of this family live on the external surfaces of marine fishes (see monogeneans). The existing taxonomic scheme recognizing ~180 species is based on relatively few morphological characters. A phylogenetic study based on three genes suggested that of the four subfamilies within the Capsalidae, three were not monophyletic. A monophyletic group is one that includes all the taxa derived from the most recent hypothetical common ancestor of that group. Such a related group of organisms is said to be mono-phyletic or to exhibit monophyly. Because the taxonomy of the Capsalidae did not reflect natural related evolutionary groups, it was concluded that some of the morphological traits used to order the taxonomic scheme exhibited homoplasy. Homoplasy is likely to be a common outcome in parasite evolution because parasites often experience similar hosts and microhabitats within those hosts that favor the evolution of similar traits.
Leveraging Genome Sequencing Strategies for Basic and Applied Algal Research, Exemplified by Case Studies
Published in Gokare A. Ravishankar, Ranga Rao Ambati, Handbook of Algal Technologies and Phytochemicals, 2019
Ariana A. Vasconcelos, Vitor H. Pomin
Turmel and coworkers (2006) have sequenced the chloroplast genome of Chara vulgaris and compared the sequence with previously known Mesostigma (Mesostigmatales), Chlorokybus (Chlorokybales), Staurastrum and Zygnema (Zygnematales), Chaetosphaeridium (Coleochaetales) and some terrestrial plants. It was inferred that chloroplast genome remained largely unchanged in terms of genetic content, gene order and intron composition during the transition from charophyte green algae to terrestrial plants, indicating thus an upcoming evolutionary relationship between these two plants. From the analyses of inversion-based genomic rearrangements, no changes were observed in the order of genes during the transition from charophyte green algae to terrestrial plants (Turmel et al. 2006). In order to elucidate the evolutionary history of the main characteristics of algae and terrestrial plants, a group led by Bhattacharya et al. (2013) sequenced the 70 million-base pair nuclear genome of the unicellular algal Cyanophora paradoxa CCMP329 (Pringsheim strain). Price et al. (2012) have shown that plastids studied could be used to trace their origin to a single ancestral, supporting thus the hypothesis of monophyly in Plantae. This has helped to clarify the permanent question in eukaryotic evolution about monophyly in the Plantae kingdom (McFadden and van Dooren 2004; Rodríguez-Ezpeleta et al. 2005; Chan et al. 2011). With this study, it became clear that the ancestral Plantae contained many of the major innovations that can serve as an initial element to constitute the plant genomes and terrestrial algae.
Bayes Factor–Based Test Statistics
Published in Albert Vexler, Alan D. Hutson, Xiwei Chen, Statistical Testing Strategies in the Health Sciences, 2017
Albert Vexler, Alan D. Hutson, Xiwei Chen
In the context of comparison among two or more competing hypotheses, Bayes factors are frequently estimated by constructing a Markov chain Monte Carlo (MCMC) sampler to explore the joint space of the hypotheses. Müller and Parmigiani (1995) fitted a model to estimate a smooth function of individual points using MCMC. Gelman and Rubin (1992a, 1992b) fitted an analysis of variance (ANOVA) model to the output of multiple MCMC chains and recommended adjusting final inferences for the uncertainty from parameter estimation and computation uncertainty. Brooks and Roberts (1998) updated Gelman and Rubin’s model to incorporate the time series dependence of the MCMC output. To efficiently estimate the Bayes factor in support of H0 against the alternative H1, Carlin and Chib (1995) recommended adjusting the prior odds in favor of H0 such that the posterior probability is approximately 1/2, and then solving for the Bayes factor as the posterior odds divided by the prior odds. This method minimizes the standard error of estimation, while preserving the length of the MCMC chain to a computationally practical amount. Compared to the approach that uses prior odds based on scientific input or a commonly used flat prior π(H0) = 1/2, Carlin and Chib’s method results in a more accurate Bayes factor estimate. However, it should be noted that Carlin and Chib’s method suffers from the fact that several independent MCMC chains may be produced, but only one is actually used in the estimation. In general, Carlin and Chib’s method applies equally well to problems where only one model is contemplated, but its proper size is not known at the outset, for example, problems involving integer-valued parameters, multiple change points (see Chapter 12), or finite mixture distributions. To illustrate, the authors applied their method on the following examples: (a) fitting two plausible straight line models to the data set of Williams and Williams (1959) and (b) analyzing a two-component normal mixture model under a noninformative prior in the context of finite mixture models (Evans et al. 1992). Suchard et al. (2005) extended the method of Carlin and Chib (1995) to incorporate the output from multiple chains. The authors proposed three statistical models that allow for the estimation of the uncertainty in the calculation of the Bayes factor and the use of several different MCMC chains even when the prior odds of the hypotheses vary from chain to chain. The first model assumes independent sampler draws and uses logistic regression to model the hypothesis indicator function for various choices of the prior odds, and the other two, more complex models relax the independence assumption by allowing for higher lag dependence within the MCMC output. The authors illustrated the use of these models to calculate Bayes factors for tests of monophyly with the following phylogenetic examples: (a) employing a Bayesian phylogenetic reconstruction model as a medical diagnostic tool on a study exploring the relationship of an unknown pathogen to a set of known pathogens, and (b) employing a Bayesian phylogenetic reconstruction model to examine the evidence in favor of intragenic recombination between different HIV-1 subtype strains.
Clonal evolution of Candida albicans, Candida glabrata and Candida dubliniensis at oral niche level in health and disease
Published in Journal of Oral Microbiology, 2021
Alexander J. Moorhouse, Rosa Moreno-Lopez, Neil A.R. Gow, Karolin Hijazi
C. glabrata and C. dubliniensis population structure at oral niches. C. dubliniensis sequence types were also distinct between participants, two distinct sequence types were identified from both P10 and P8, and a single sequence type identified from P15 (Figure 1). Currently, C. dubliniensis is without a centrally curated MLST database. Thus, no sequence type numbering system is applicable. Previous studies have implemented varied primer combinations usually including several regions included in the closely related C. albicans MLST scheme and the addition of RPN2 to reinforce discriminatory power [9,40,51]. The six core MLST regions shared directly with the C. albicans MLST scheme (ACC1a, ADP1, MP1b, SYA, VPS13 and at the ZWF1 region) identified 26 SNPs that distinguished 5 sequence types. Nine SNPs separated two P8 sequence types, and 5 SNPs separated two P10 sequence types – interestingly in both cases, only one SNP was a heterozygous/homozygous mutation. Phylogenetic analysis confirmed within-patient monophyly for micro-variant C. dubliniensis isolates from P8 and P10 (Figure 4).
Hepatic effects of low-dose rate radiation in natural mouse populations (Apodemus uralensis and Apodemus agrarius): comparative interspecific analysis
Published in International Journal of Radiation Biology, 2020
Objects of study [pygmy wood mouse (Apodemus uralensis Pallas, 1811) and striped field mouse (Apodemus agrarius Pallas, 1771)] are two sympatric and closely related small rodent species. According to (Musser & Carleton, 2005) they are considered as species belonging to the same genus (Apodemus Kaup), but to different subgenera (Apodemus Kaup, 1829 and Sylvaemus Ognev et Worobiew, 1923, respectively). However, it should be noted that the current molecular phylogeny and taxonomy results both provide support for the monophyly of the genus Apodemus, and contrariwise, allow with the hypothesis of paraphyly for this genus (Macholán et al. 2001; Michaux et al. 2002; Filippucci et al. 2002; Liu et al. 2004; Suzuki et al. 2008). The A. agrarius, compared to A. uralensis, is evolutionarily young, being at the initial stage of intraspecific differentiation and shaping (Chelomina 2005).