Feeding ecology of the fish ectoparasite Gnathia sp. (Crustacea: Isopoda) from the Great Barrier Reef, and its implications for fish cleaning behaviour

The feeding rate of a parasitic gnathiid isopod on fish was examined. Individual fish, Hemigymnus melapterus, were exposed to gnathiid larvae and sampled after 5, 10, 30, 60, and 240 min. I recorded whether larvae had an engorged gut, an engorged gut containing red material, or had dropped off the fish after having completed engorgement; variation among sampling times and larval stages was analyzed using generalized linear mixed model analyses. The likelihood that larvae had an engorged gut increased with time and varied with larval stage. First stage (1.45 mm) larvae. After 30 min, however, most (>93%) larvae had an engorged gut regardless of their larval stage. The likelihood of red material in the gut of third stage larvae increased over time (46% after 30 min, 70% after 60 min, and 86% after 240 min) while that of first and second stage larvae remained relatively low (

Sampling times for monitoring tacrolimus in stable adult liver transplant recipients

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R-2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

Ventilatory changes following head-up tilt and standing in healthy subjects

Passive tilting increases ventilation in healthy subjects; however, controversy surrounds the proposed mechanism. This study is aimed to evaluate the possible mechanism for changes to ventilation following passive head-up tilt (HUT) and active standing by comparison of a range of ventilatory, metabolic and mechanical parameters. Ventilatory parameters (V (T), V (E), V (E)/VO2, V (E)/VCO2, f and PetCO(2)), functional residual capacity (FRC), respiratory mechanics with impulse oscillometry; oxygen consumption (VO2) and carbon dioxide production (VCO2) were measured in 20 healthy male subjects whilst supine, following HUT to 70 degrees and unsupported standing. Data were analysed using a linear mixed model. HUT to 70 degrees from supine increased minute ventilation (V (E)) (P < 0.001), tidal volume (V (T)) (P=0.001), ventilatory equivalent for O-2 (V (E)/VO2) (P=0.020) and the ventilatory equivalent for CO2 (V (E)/VCO2) (P < 0.001) with no change in f (P=0.488). HUT also increased FRC (P < 0.001) and respiratory system reactance (X5Hz) (P < 0.001) with reduced respiratory system resistance (R5Hz) (P=0.004) and end-tidal carbon dioxide (PetCO(2)) (P < 0.001) compared to supine. Standing increased V (E) (P < 0.001), V (T) (P < 0.001) and V (E)/VCO2 (P=0.020) with no change in respiratory rate (f) (P=0.065), V (E)/VO2 (P=0.543). Similar changes in FRC (P < 0.001), R5Hz (P=0.013), X5Hz (P < 0.001) and PetCO(2) (P < 0.001) compared to HUT were found. In contrast to HUT, standing increased VO2 (P=0.002) and VCO2 (P=0.048). The greater increase in V (E) in standing compared to HUT appears to be related to increased VO2 and VCO2 associated with increased muscle activity in the unsupported standing position. This has implications for exercise prescription and rehabilitation of critically ill patients who have reduced cardiovascular and respiratory reserve.

The rellationship between specific features of aphasia-friendly written material and comprehension of written material for people with aphasia: An exploratory study

Background: Written material is often inaccessible fro people with aphasia. The format of written material needs to be adapted to enable people with aphasia to read with understanding. Aims: This study aimed to further explore some issues raised in Rose, Worrall, and MacKenna (2003) concerning the effects of aphasia-friendly formats on the reading comprehension of people with aphasia. It was hypothesised that people with aphasia would comprehend significantly more paragraphs that were formatted in an aphasia-friendly manner than control paragraphs. This study also aimed to investigate if each single aspect of aphasia-friendly formatting (i.e., simplified vocabulary and syntax, large print, increased white spacem and pictures) used in isolation would result in increased comprehension compared to control paragraphs. Other aims were to compare the effect of aphasia-friendly fromatting with the effects of each single adaptation, and to investigate if the effects of aphasia-friendly formates were related to aphasia severity. Methods & Procedures: Participants with mild to moderately severe aphasia (N = 9) read a battery of 90 paragraphs and selected the best word of phrase from a choice of four to complete each paragraph. A linear mixed model (p < .05) was used to analyse the differences in reading comprehension with each paragraph fromat across three reading grade levels. Outcomes & Results: People with aphasia comprehended significantly more aphasia-friendly paragraphs than control paragraphs. They also comprehended significantly more paragraphs with each of the following single adaptations: simplified vocabulary and syntax, large ptint, and increased white spaces. Although people with aphasia tended to comprehend more paragraphs with pictures added than control paragraphs, this difference was not significant. No significant correlation between aphasia severity and the effect of aphasia-friendly formatting was found. Conclusion: This study supports the idea that aphasia-friendly formats increase the reading comprehension of people with aphasia. It suggests that adding pictures, particularly Clip Art pictures, may not significantly improve the reading the reading comprehension of people with aphasia. These findings have implications for all written communication with people with aphasia, both in the clinical setting and in the wider community. Applying these findings may enable people with aphasia to have equal access to written information and to participate in society.

A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants

The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.

Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

A randomized controlled trial of the effect of thyroxine replacement on cognitive function in community-living elderly subjects with subclinical hypothyroidism: the Birmingham elderly thyroid study

Context: Subclinical hypothyroidism (SCH) and cognitive dysfunction are both common in the elderly and have been linked. It is important to determine whether T4 replacement therapy in SCH confers cognitive benefit. Objective: Our objective was to determine whether administration of T4 replacement to achieve biochemical euthyroidism in subjects with SCH improves cognitive function. Design and Setting: We conducted a double-blind placebo-controlled randomized controlled trial in the context of United Kingdom primary care. Patients: Ninety-four subjects aged 65 yr and over (57 females, 37 males) with SCH were recruited from a population of 147 identified by screening. Intervention: T4 or placebo was given at an initial dosage of one tablet of either placebo or 25 µg T4 per day for 12 months. Thyroid function tests were performed at 8-weekly intervals with dosage adjusted in one-tablet increments to achieve TSH within the reference range for subjects in treatment arm. Fifty-two subjects received T4 (31 females, 21 males; mean age 73.5 yr, range 65–94 yr); 42 subjects received placebo (26 females, 16 males; mean age 74.2 yr, 66–84 yr). Main Outcome Measures: Mini-Mental State Examination, Middlesex Elderly Assessment of Mental State (covering orientation, learning, memory, numeracy, perception, attention, and language skills), and Trail-Making A and B were administered. Results: Eighty-two percent and 84% in the T4 group achieved euthyroidism at 6- and 12-month intervals, respectively. Cognitive function scores at baseline and 6 and 12 months were as follows: Mini-Mental State Examination T4 group, 28.26, 28.9, and 28.28, and placebo group, 28.17, 27.82, and 28.25 [not significant (NS)]; Middlesex Elderly Assessment of Mental State T4 group, 11.72, 11.67, and 11.78, and placebo group, 11.21, 11.47, and 11.44 (NS); Trail-Making A T4 group, 45.72, 47.65, and 44.52, and placebo group, 50.29, 49.00, and 46.97 (NS); and Trail-Making B T4 group, 110.57, 106.61, and 96.67, and placebo group, 131.46, 119.13, and 108.38 (NS). Linear mixed-model analysis demonstrated no significant changes in any of the measures of cognitive function over time and no between-group difference in cognitive scores at 6 and 12 months. Conclusions: This RCT provides no evidence for treating elderly subjects with SCH with T4 replacement therapy to improve cognitive function.

Population pharmacokinetic and pharmacogenetic analysis of tacrolimus in paediatric liver transplant patients

Aims - To build a population pharmacokinetic model that describes the apparent clearance of tacrolimus and the potential demographic, clinical and genetically controlled factors that could lead to inter-patient pharmacokinetic variability within children following liver transplantation. Methods - The present study retrospectively examined tacrolimus whole blood pre-dose concentrations (n = 628) of 43 children during their first year post-liver transplantation. Population pharmacokinetic analysis was performed using the non-linear mixed effects modelling program (nonmem) to determine the population mean parameter estimate of clearance and influential covariates. Results - The final model identified time post-transplantation and CYP3A5*1 allele as influential covariates on tacrolimus apparent clearance according to the following equation: TVCL = 12.9 x (Weight/13.2)0.35 x EXP (-0.0058 x TPT) x EXP (0.428 x CYP3A5) where TVCL is the typical value for apparent clearance, TPT is time post-transplantation in days and the CYP3A5 is 1 where *1 allele is present and 0 otherwise. The population estimate and inter-individual variability (%CV) of tacrolimus apparent clearance were found to be 0.977 l h−1 kg−1 (95% CI 0.958, 0.996) and 40.0%, respectively, while the residual variability between the observed and predicted concentrations was 35.4%. Conclusion Tacrolimus apparent clearance was influenced by time post-transplantation and CYP3A5 genotypes. The results of this study, once confirmed by a large scale prospective study, can be used in conjunction with therapeutic drug monitoring to recommend tacrolimus dose adjustments that take into account not only body weight but also genetic and time-related changes in tacrolimus clearance.

Population pharmacokinetics of single-dose intravenous paracetamol in children

Background To determine the pharmacokinetics (PK) of a new i.v. formulation of paracetamol (Perfalgan) in children ≤15 yr of age. Methods After obtaining written informed consent, children under 16 yr of age were recruited to this study. Blood samples were obtained at 0, 15, 30 min, 1, 2, 4, 6, and 8 h after administration of a weight-dependent dose of i.v. paracetamol. Paracetamol concentration was measured using a validated high-performance liquid chromatographic assay with ultraviolet detection method, with a lower limit of quantification (LLOQ) of 900 pg on column and an intra-day coefficient of variation of 14.3% at the LLOQ. Population PK analysis was performed by non-linear mixed-effect modelling using NONMEM. Results One hundred and fifty-nine blood samples from 33 children aged 1.8–15 yr, weight 13.7–56 kg, were analysed. Data were best described by a two-compartment model. Only body weight as a covariate significantly improved the goodness of fit of the model. The final population models for paracetamol clearance (CL), V1 (central volume of distribution), Q (inter-compartmental clearance), and V2 (peripheral volume of distribution) were: 16.51×(WT/70)0.75, 28.4×(WT/70), 11.32×(WT/70)0.75, and 13.26×(WT/70), respectively (CL, Q in litres per hour, WT in kilograms, and V1 and V2 in litres). Conclusions In children aged 1.8–15 yr, the PK parameters for i.v. paracetamol were not influenced directly by age but were by total body weight and, using allometric size scaling, significantly affected the clearances (CL, Q) and volumes of distribution (V1, V2).

Fuel supplier selection for large scale UK bioenergy schemes

This article presents a potential method to assist developers of future bioenergy schemes when selecting from available suppliers of biomass materials. The method aims to allow tacit requirements made on biomass suppliers to be considered at the design stage of new developments. The method used is a combination of the Analytical Hierarchy Process and the Quality Function Deployment methods (AHP-QFD). The output of the method is a ranking and relative weighting of the available suppliers which could be used to improve optimization algorithms such as linear and goal programming. The paper is at a conceptual stage and no results have been obtained. The aim is to use the AHP-QFD method to bridge the gap between treatment of explicit and tacit requirements of bioenergy schemes; allowing decision makers to identify the most successful supply strategy available.

Fully probabilistic control design in an adaptive critic framework

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem. In particular very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic contro algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this short paper.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

The enantioselective population pharmacokinetics of intravenous ketorolac in children using a stereoselective assay suitable for microanalysis

Objective: To describe the effect of age and body size on enantiomer selective pharmacokinetic (PK) of intravenous ketorolac in children using a microanalytical assay. Methods: Blood samples were obtained at 0, 15 and 30 min and at 1, 2, 4, 6, 8 and 12 h after a weight-dependent dose of ketorolac. Enantiomer concentration was measured using a liquid chromatography tandem mass spectrometry method. Non-linear mixed-effect modelling was used to assess PK parameters. Key findings: Data from 11 children (1.7–15.6 years, weight 10.7–67.4 kg) were best described by a two-compartment model for R(+), S(−) and racemic ketorolac. Only weight (WT) significantly improved the goodness of fit. The final population models were CL = 1.5 × (WT/46)0.75, V1 = 8.2 × (WT/46), Q = 3.4 × (WT/46)0.75, V2 = 7.9 × (WT/46), CL = 2.98 × (WT/46), V1 = 13.2 × (WT/46), Q = 2.8 × (WT/46)0.75, V2 = 51.5 × (WT/46), and CL = 1.1 × (WT/46)0.75, V1 = 4.9 × (WT/46), Q = 1.7 × (WT/46)0.75 and V2 = 6.3 × (WT/46)for R(+), S(−) and racemic ketorolac. Conclusions: Only body weight influenced the PK parameters for R(+) and S(−) ketorolac. Using allometric size scaling significantly affected the clearances (CL, Q) and volumes of distribution (V1, V2).