Error, Bias, and Long-Branch Attraction in Data for Two Chloroplast Photosystem Genes in Seed Plants

Sequences of two chloroplast photosystem genes, psaA and psbB, together comprising about 3,500 bp, were obtained for all five major groups of extant seed plants and several outgroups among other vascular plants. Strongly supported, but significantly conflicting, phylogenetic signals were obtained in parsimony analyses from partitions of the data into first and second codon positions versus third positions. In the former, both genes agreed on a monophyletic gymnosperms, with Gnetales closely related to certain conifers. In the latter, Gnetales are inferred to be the sister group of all other seed plants, with gymnosperms paraphyletic. None of the data supported the modern ‘‘anthophyte hypothesis,’’ which places Gnetales as the sister group of flowering plants. A series of simulation studies were undertaken to examine the error rate for parsimony inference. Three kinds of errors were examined: random error, systematic bias (both properties of finite data sets), and statistical inconsistency owing to long-branch attraction (an asymptotic property). Parsimony reconstructions were extremely biased for third-position data for psbB. Regardless of the true underlying tree, a tree in which Gnetales are sister to all other seed plants was likely to be reconstructed for these data. None of the combinations of genes or partitions permits the anthophyte tree to be reconstructed with high probability. Simulations of progressively larger data sets indicate the existence of long-branch attraction (statistical inconsistency) for third-position psbB data if either the anthophyte tree or the gymnosperm tree is correct. This is also true for the anthophyte tree using either psaA third positions or psbB first and second positions. A factor contributing to bias and inconsistency is extremely short branches at the base of the seed plant radiation, coupled with extremely high rates in Gnetales and nonseed plant outgroups. M. J. Sanderson,* M. F. Wojciechowski,*† J.-M. Hu,* T. Sher Khan,* and S. G. Brady

Room for improvement : hotel giant resorts to Six Sigma to improve operations

Six Sigma provides a framework for quality improvement and business excellence. Introduced in the 1980s in manufacturing, the concept of Six Sigma has gained popularity in service organizations. After initial success in healthcare and banking, Six Sigma has gradually gained traction in other types of service industries, including hotels and lodging. Starwood Hotels and Resorts was the first hospitality giant to embrace Six Sigma. In 2001, Starwood adopted the method to develop innovative, customer-focused solutions and to transfer these solutions throughout the global organization. To analyze Starwood's use of Six Sigma, the authors collected data from articles, interviews, presentations and speeches published in magazines, newspapers and Web sites. This provided details to corroborate information, and they also made inferences from these sources. Financial metrics can explain the success of Six Sigma in any organization. There was no shortage of examples of Starwood's success resulting from Six Sigma project metrics uncovered during the research.

Stars are made of glass : children as capable and creative communicators

This booklet is the third in the Research in Practice Series, designed to complement Belonging, being & becoming: The Early Years Learning Framework for Australia (DEEWR, 2009). It focuses on Learning Outcome 5 of the Early Years Learning Framework (EYLF): Children are effective communicators (DEEWR, 2009).

Conceptualisation of articular cartilage as a giant reverse micelle: A hypothetical mechanism for joint biocushioning and lubrication

Phospholipid (PL) molecules form the main structure of the membrane that prevents the direct contact of opposing articular cartilage layers. In this paper we conceptualise articular cartilage as a giant reverse micelle (GRM) in which the highly hydrated three-dimensional network of phospholipids is electrically charged and able to resist compressive forces during joint movement, and hence loading. Using this hypothetical base, we describe a hydrophilic-hydrophilic (HL-HL) biopair model of joint lubrication by contacting cartilages, whose mechanism is reliant on lamellar cushioning. To demonstrate the viability of our concept, the electrokinetic properties of the membranous layer on the articular surface were determined by measuring via microelectrophoresis, the adsorption of ions H, OH, Na and Cl on phospholipid membrane of liposomes, leading to the calculation of the effective surface charge density. The surface charge density was found to be -0.08 ± 0.002 cm-2 (mean ± S.D.) for phospholipid membranes, in 0.155 M NaCl solution and physiological pH. This value was approximately five times less than that measured in 0.01 M NaCl. The addition of synovial fluid (SF) to the 0.155 M NaCl solution reduced the surface charge density by 30% which was attributed to the binding of synovial fluid macromolecules to the phospholipid membrane. Our experiments show that particles charge and interact strongly with the polar core of RM. We demonstrate that particles can have strong electrostatic interactions when ions and macromolecules are solubilized by reverse micelle (RM). Since ions are solubilized by reverse micelle, the surface entropy influences the change in the charge density of the phospholipid membrane on cartilage surfaces. Reverse micelles stabilize ions maintaining equilibrium, their surface charges contribute to the stability of particles, while providing additional screening for electrostatic processes. © 2008 Elsevier Ireland Ltd. All rights reserved.

Population genetic structure and patterns of dispersal in the Giant Long-Armed Prawn, Macrobrachium lar (Fabricius, 1798) (Decapoda : Palaemonidae)

The Giant Long-Armed Prawn, Macrobrachium lar is a freshwater species native to the Indo-Pacific. M. lar has a long-lived, passive, pelagic marine larval stage where larvae need to colonise freshwater within three months to complete their development. Dispersal is likely to be influenced by the extensive distances larvae must transit between small oceanic islands to find suitable freshwater habitat, and by prevailing east to west wind and ocean currents in the southern Pacific Ocean. Thus, both intrinsic and extrinsic factors are likely to influence wild population structure in this species. The present study sought to define the contemporary broad and fine-scale population genetic structure of Macrobrachium lar in the south-western Pacific Ocean. Three polymorphic microsatellite loci were used to assess patterns of genetic variation within and among 19 wild adult sample sites. Statistical procedures that partition variation implied that at both spatial scales, essentially all variation was present within sample sites and differentiation among sites was low. Any differentiation observed also was not correlated with geographical distance. Statistical approaches that measure genetic distance, at the broad-scale, showed that all south-western Pacific Islands were essentially homogeneous, with the exception of a well supported divergent Cook Islands group. These findings are likely the result of some combination of factors that may include the potential for allelic homoplasy, through to the effects of sampling regime. Based on the findings, there is most likely a divergent M. lar Cook Islands clade in the south-western Pacific Ocean, resulting from prevailing ocean currents. Confirmation of this pattern will require a more detailed analysis of nDNA variation using a larger number of loci and, where possible, use of larger population sizes.

A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals

Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.

Branch-length estimation bias misleads molecular dating for a vertebrate mitochondrial phylogeny

Despite recent methodological advances in inferring the time-scale of biological evolution from molecular data, the fundamental question of whether our substitution models are sufficiently well specified to accurately estimate branch-lengths has received little attention. I examine this implicit assumption of all molecular dating methods, on a vertebrate mitochondrial protein-coding dataset. Comparison with analyses in which the data are RY-coded (AG → R; CT → Y) suggests that even rates-across-sites maximum likelihood greatly under-compensates for multiple substitutions among the standard (ACGT) NT-coded data, which has been subject to greater phylogenetic signal erosion. Accordingly, the fossil record indicates that branch-lengths inferred from the NT-coded data translate into divergence time overestimates when calibrated from deeper in the tree. Intriguingly, RY-coding led to the opposite result. The underlying NT and RY substitution model misspecifications likely relate respectively to “hidden” rate heterogeneity and changes in substitution processes across the tree, for which I provide simulated examples. Given the magnitude of the inferred molecular dating errors, branch-length estimation biases may partly explain current conflicts with some palaeontological dating estimates.