Successful stepdown treatment of pulmonary histoplasmosis with thrice-weekly liposomal amphotericin B in a hospital-associated, outpatient infusion centre: A case report.

WHAT IS KNOWN AND OBJECTIVE: Amphotericin is the preferred treatment for pulmonary histoplasmosis during pregnancy. The long half-life of amphotericin supports less than daily administration. CASE SUMMARY: A 28-year-old pregnant woman diagnosed with recurrent pulmonary histoplasmosis was initiated on liposomal amphotericin 250 mg (4 mg/kg) intravenously daily. After 2 weeks, the patient was discharged and successfully received 250 mg thrice weekly at a hospital-associated outpatient infusion centre. After 6 weeks of outpatient treatment, a chest X-ray demonstrated no remaining disease and therapy was discontinued. WHAT IS NEW AND CONCLUSION: Administration of thrice-weekly liposomal amphotericin in a hospital-associated, outpatient infusion centre may be a promising option for stepdown treatment in patients unable to take itraconazole.

Effects of nucleotide composition bias on the success of the parsimony criterion in phylogenetic inference.

Convergence in nucleotide composition (CNC) in unrelated lineages is a factor potentially affecting the performance of most phylogeny reconstruction methods. Such convergence has deleterious effects because unrelated lineages show similarities due to similar nucleotide compositions and not shared histories. While some methods (such as the LogDet/paralinear distance measure) avoid this pitfall, the amount of convergence in nucleotide composition necessary to deceive other phylogenetic methods has never been quantified. We examined analytically the relationship between convergence in nucleotide composition and the consistency of parsimony as a phylogenetic estimator for four taxa. Our results show that rather extreme amounts of convergence are necessary before parsimony begins to prefer the incorrect tree. Ancillary observations are that (for unweighted Fitch parsimony) transition/transversion bias contributes to the impact of CNC and, for a given amount of CNC and fixed branch lengths, data sets exhibiting substantial site-to-site rate heterogeneity present fewer difficulties than data sets in which rates are homogeneous. We conclude by reexamining a data set originally used to illustrate the problems caused by CNC. Using simulations, we show that in this case the convergence in nucleotide composition alone is insufficient to cause any commonly used methods to fail, and accounting for other evolutionary factors (such as site-to-site rate heterogeneity) can give a correct inference without accounting for CNC.

A likelihood approach to estimating phylogeny from discrete morphological character data.

Evolutionary biologists have adopted simple likelihood models for purposes of estimating ancestral states and evaluating character independence on specified phylogenies; however, for purposes of estimating phylogenies by using discrete morphological data, maximum parsimony remains the only option. This paper explores the possibility of using standard, well-behaved Markov models for estimating morphological phylogenies (including branch lengths) under the likelihood criterion. An important modification of standard Markov models involves making the likelihood conditional on characters being variable, because constant characters are absent in morphological data sets. Without this modification, branch lengths are often overestimated, resulting in potentially serious biases in tree topology selection. Several new avenues of research are opened by an explicitly model-based approach to phylogenetic analysis of discrete morphological data, including combined-data likelihood analyses (morphology + sequence data), likelihood ratio tests, and Bayesian analyses.

A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data.

Phylogeny reconstruction is a difficult computational problem, because the number of possible solutions increases with the number of included taxa. For example, for only 14 taxa, there are more than seven trillion possible unrooted phylogenetic trees. For this reason, phylogenetic inference methods commonly use clustering algorithms (e.g., the neighbor-joining method) or heuristic search strategies to minimize the amount of time spent evaluating nonoptimal trees. Even heuristic searches can be painfully slow, especially when computationally intensive optimality criteria such as maximum likelihood are used. I describe here a different approach to heuristic searching (using a genetic algorithm) that can tremendously reduce the time required for maximum-likelihood phylogenetic inference, especially for data sets involving large numbers of taxa. Genetic algorithms are simulations of natural selection in which individuals are encoded solutions to the problem of interest. Here, labeled phylogenetic trees are the individuals, and differential reproduction is effected by allowing the number of offspring produced by each individual to be proportional to that individual's rank likelihood score. Natural selection increases the average likelihood in the evolving population of phylogenetic trees, and the genetic algorithm is allowed to proceed until the likelihood of the best individual ceases to improve over time. An example is presented involving rbcL sequence data for 55 taxa of green plants. The genetic algorithm described here required only 6% of the computational effort required by a conventional heuristic search using tree bisection/reconnection (TBR) branch swapping to obtain the same maximum-likelihood topology.

Success of maximum likelihood phylogeny inference in the four-taxon case.

We used simulated data to investigate a number of properties of maximum-likelihood (ML) phylogenetic tree estimation for the case of four taxa. Simulated data were generated under a broad range of conditions, including wide variation in branch lengths, differences in the ratio of transition and transversion substitutions, and the absence of presence of gamma-distributed site-to-site rate variation. Data were analyzed in the ML framework with two different substitution models, and we compared the ability of the two models to reconstruct the correct topology. Although both models were inconsistent for some branch-length combinations in the presence of site-to-site variation, the models were efficient predictors of topology under most simulation conditions. We also examined the performance of the likelihood ratio (LR) test for significant positive interior branch length. This test was found to be misleading under many simulation conditions, rejecting too often under some simulation conditions. Under the null hypothesis of zero length internal branch, LR statistics are assumed to be asymptotically distributed chi 2(1); with limited data, the distribution of LR statistics under the null hypothesis varies from chi 2(1).

Multiple-population versus hierarchical conifer breeding programs: a comparison of genetic diversity levels.

Advanced-generation domestication programs for forest-tree species has raised some concerns about the maintenance of genetic diversity in forest-tree breeding programs. Genetic diversity in natural stands was compared with two genetic conservation options for a third-generation elite Pinus taeda breeding population. The breeding population was subdivided either on the basis of geographic origin and selection goals (multiple-population or MPBS option) or stratified according to genetic value (hierarchical or HOPE option). Most allelic diversity in the natural stands of loblolly pine is present in the domesticated breeding populations. This was true at the aggregate level for both multiple-population (MPBS) and the hierarchical (HOPE) populations. Individual subpopulations within each option had less genetic diversity but it did not decline as generations of improvement increased. Genetic differentiation within the subdivided breeding populations ranged from 1 to 5%, genetic variability is within each subpopulation rather than among subpopulations for both MPBS (>95%) and the HOPE approaches (>98%). Nei's Gst estimates for amongpopulation differentiation were biased upwards relative to estimates of θ from Weir and Cockerham (1984).

Deterministic paternity exclusion using RAPD markers.

The Random Amplified Polymorphic DNA (RAPD) technique can potentially provide hundreds of polymorphic markers for use by ecologists studying mating systems in natural populations. We consider here the implications of the dominance displayed by RAPD markers for deterministic paternity assignment. Our goal was to provide a means for assessing the costs associated with such a study for ecologists who might be considering the use of RAPD markers for paternity analysis. The theoretical expected proportion of offspring for which all males except the true father can be exlucded (P(ET)) is calculated for both dominant and codominant marker systems. The ability to assign paternity unambiguously generally increases with the number of loci and the frequency of the recessive allele (but only up to a point), and decreases with increasing sample size (number of individuals surveyed). The gain in P(ET) with decreasing sample size is unexpectedly slight. Not surprisingly, the performance of dominant markers at paternity exclusion is, in general, greatly exceeded by codominant markers, with the exception of the case in which the frequency of the recessive allele is high at all loci. In this case, codominant markers perform only slightly better than do dominant markers. Thus, a researcher should expect to score more than 50 RAPD loci for each offspring for most applications of paternity exclusion analysis.(ABSTRACT TRUNCATED AT 250 WORDS)