Inferring population history from microsatellite and enzyme data in serially introduced cane toads, Bufo marinus

Much progress has been made on inferring population history from molecular data. However, complex demographic scenarios have been considered rarely or have proved intractable. The serial introduction of the South-Central American cane Load Bufo marinas in various Caribbean and Pacific islands involves four major phases: a possible genetic admixture during the first introduction, a bottleneck associated with founding, a transitory, population boom, and finally, a demographic stabilization. A large amount of historical and demographic information is available for those introductions and can be combined profitably with molecular data. We used a Bayesian approach to combine this information With microsatellite (10 loci) and enzyme (22 loci) data and used a rejection algorithm to simultaneously estimate the demographic parameters describing the four major phases of the introduction history,. The general historical trends supported by microsatellites and enzymes were similar. However, there was a stronger support for a larger bottleneck at introductions for microsatellites than enzymes and for a more balanced genetic admixture for enzymes than for microsatellites. Verb, little information was obtained from either marker about the transitory population boom observed after each introduction. Possible explanations for differences in resolution of demographic events and discrepancies between results obtained with microsatellites and enzymes were explored. Limits Of Our model and method for the analysis of nonequilibrium populations were discussed.

Detection and description of non-linear interdependence in normal multichannel human EEG data

Objectives: This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. Methods: Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. Results: Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings; one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. Conclusions: Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Estimating Two-Stage Models for Genetic Influences on Alcohol, Tobacco or Drug Use Initiation and Dependence Vulnerability in Twin and Family Data

Genetic research on risk of alcohol, tobacco or drug dependence must make allowance for the partial overlap of risk-factors for initiation of use, and risk-factors for dependence or other outcomes in users. Except in the extreme cases where genetic and environmental risk-factors for initiation and dependence overlap completely or are uncorrelated, there is no consensus about how best to estimate the magnitude of genetic or environmental correlations between Initiation and Dependence in twin and family data. We explore by computer simulation the biases to estimates of genetic and environmental parameters caused by model misspecification when Initiation can only be defined as a binary variable. For plausible simulated parameter values, the two-stage genetic models that we consider yield estimates of genetic and environmental variances for Dependence that, although biased, are not very discrepant from the true values. However, estimates of genetic (or environmental) correlations between Initiation and Dependence may be seriously biased, and may differ markedly under different two-stage models. Such estimates may have little credibility unless external data favor selection of one particular model. These problems can be avoided if Initiation can be assessed as a multiple-category variable (e.g. never versus early-onset versus later onset user), with at least two categories measurable in users at risk for dependence. Under these conditions, under certain distributional assumptions., recovery of simulated genetic and environmental correlations becomes possible, Illustrative application of the model to Australian twin data on smoking confirmed substantial heritability of smoking persistence (42%) with minimal overlap with genetic influences on initiation.

Testing insect movement theory: empirical analysis of pest data routinely collected from agricultural crops

Observations of an insect's movement lead to theory on the insect's flight behaviour and the role of movement in the species' population dynamics. This theory leads to predictions of the way the population changes in time under different conditions. If a hypothesis on movement predicts a specific change in the population, then the hypothesis can be tested against observations of population change. Routine pest monitoring of agricultural crops provides a convenient source of data for studying movement into a region and among fields within a region. Examples of the use of statistical and computational methods for testing hypotheses with such data are presented. The types of questions that can be addressed with these methods and the limitations of pest monitoring data when used for this purpose are discussed. (C) 2002 Elsevier Science B.V. All rights reserved.

Physiological and symptomatic responses to cycling and walking in intermittent claudication

To shed light on the potential efficacy of cycling as a resting modality in the treatment of intermittent claudication (IC), this study compared physiological and symptomatic responses to graded walking and cycling tests in claudicants. Sixteen subjects with peripheral arterial disease (resting ankle:brachial index (ABI) < 0.9) and IC completed a maximal graded treadmill walking (T) and cycle (C) Lest after three familiarization tests on each mode. During cacti test, symptoms, oxygen uptake (VO2), minute ventilation (V-E), (respiratory exchange ratio) (RER) and heart rate (HR) were measured, and for 10 min after each Lest the brachial and ankle systolic pressures were recorded, All but One subject experienced calf pain as the primary limiting symptom during T whereas the symptoms were more varied during C and included thigh pain, calf pain and dyspnoea, Although maximal exercise time was significantly longer on C than T (690 +/- 67 vs, 495 +/- 57 s), peak VO2, peak, V-E and peak heart rate during C and T were not different; whereas peak RER was higher during C. These responses during C and T were also positively 1, (P < 0.05) with each other, with the exception of RER. The postexercise systolic pressures were also not different between C and T. However, the peak decline ill ankle pressures from resting values after C and T were not correlated with each other. Thew data demonstrate that cycling and walking induce a similar level of metabolic and cardiovascular strain, but that the primary limiting symptoms and haemodynamic response in an individual's extremity, measured after exercise, can differ substantially between these two modes.

Outcome of stroke patients admitted to intensive care: Experience from an Australian teaching hospital

The objective of this study was to determine the mortality rate and the functional outcomes of stroke patients admitted to the intensive care unit (ICU) and to identify predictors of poor outcome in this population. The records of all patients admitted to the ICU with the diagnosis of stroke between January 1994 and December 1999 were reviewed. Patients with subarachnoid haemorrhage were excluded. Data were collected on clinical and biological variables, risk factors for stroke and the presence of comorbidities. Mortality (ICU, in-hospital and three-month) and functional outcome were used as end-points. In the six-year-period, 61 patients were admitted to the ICU with either haemorrhagic or ischaemic stroke. Medical records were available for only 58 patients. There were 23 ischaemic and 35 haemorrhagic strokes. The ICU, in-hospital and three-month mortality rates were 36%, 47% and 52% respectively. There were no significant differences in the prevalence of premorbid risk factors between survivors and non-survivors. The mean Barthel score was significantly different between the independent and dependent survivors (94 +/- 6 vs 45 +/- 26, P < 0.001). A substantial number of patients with good functional outcomes had lower Rankin scores (92% vs 11%, P < 0.001). Only 46% of those who were alive at three months were functionally independent. Intensive care admission was associated with a high mortality rate and a high likelihood of dependent lifestyle after hospital discharge. Haemorrhagic stroke, fixed dilated pupil(s) and GCS < 10 during assessment were associated with increased mortality and poor functional outcome.

Survival following mechanical ventilation of recipients of bone marrow transplants and peripheral blood stem cell transplants

Survival of bone marrow transplant recipients requiting mechanical ventilation is poor but improving. This study reports a retrospective audit of all haematopoietic stem cell transplant (HSCT) recipients requiring mechanical ventilation at an Australian institution over a period spanning 11 years from 1988 to 1998. Recipients of autologous transplants are significantly less likely to require mechanical ventilation than recipients of allogeneic transplants. Of 50 patients requiring mechanical ventilation, 28% survived to discharge from the intensive care unit, 20% to 30 days post-ventilation, 18% to discharge from hospital and 12% to six months post-ventilation. Risk factors for mortality in the HSCT recipient requiting mechanical ventilation include renal, hepatic and cardiovascular insufficiency and greater severity of illness. Mechanical ventilation of HSCT recipients should not be regarded as futile therapy.