Estimation of growth parameters from multiple-recapture data

This article develops a method for analysis of growth data with multiple recaptures when the initial ages for all individuals are unknown. The existing approaches either impute the initial ages or model them as random effects. Assumptions about the initial age are not verifiable because all the initial ages are unknown. We present an alternative approach that treats all the lengths including the length at first capture as correlated repeated measures for each individual. Optimal estimating equations are developed using the generalized estimating equations approach that only requires the first two moment assumptions. Explicit expressions for estimation of both mean growth parameters and variance components are given to minimize the computational complexity. Simulation studies indicate that the proposed method works well. Two real data sets are analyzed for illustration, one from whelks (Dicathais aegaota) and the other from southern rock lobster (Jasus edwardsii) in South Australia.

Structural determination of the K14 capsular polysaccharide from an ST25 Acinetobacter baumannii isolate, D46

The structure of the capsular polysaccharide (CPS) recovered from D46, an extensively antibiotic resistant ST25 Acinetobacter baumannii clinical isolate, was elucidated. The structure was resolved on the basis of NMR spectroscopy and chemical analyses, and was found to contain a branched neutral pentasaccharide with a backbone composed of GalpNAc and Galp residues, all d configured, and a d-Glcp side group. The KL14 gene cluster found in the D46 genome includes genes for four glycosyltransferases but no modules for synthesis of complex sugars, and this is consistent with the structure of K14. The K14 structure and KL14 sequence clarify the relationship between the structure and K locus sequence for A. nosocomialis isolate LUH5541. The identity of the first sugar of the K14 repeat unit (K unit), and the functions of the four encoded glycosyltransferases and Wzy polymerase were predicted.

Effect of individual variability on estimation of population parameters from length-frequency data

We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock when there is individual variability in the von Bertalanffy growth parameter L-infinity and investigate the possible bias in the estimates when the individual variability is ignored. Three methods are examined: (i) the regression method based on the Beverton and Holt's (1956, Rapp. P.V. Reun. Cons. Int. Explor. Mer, 140: 67-83) equation; (ii) the moment method of Powell (1979, Rapp. PV. Reun. Int. Explor. Mer, 175: 167-169); and (iii) a generalization of Powell's method that estimates the individual variability to be incorporated into the estimation. It is found that the biases in the estimates from the existing methods are, in general, substantial, even when individual variability in growth is small and recruitment is uniform, and the generalized method performs better in terms of bias but is subject to a larger variation. There is a need to develop robust and flexible methods to deal with individual variability in the analysis of length-frequency data.

An improved Fabens method for estimation of growth parameters in the vonBertalanffy model with individual asymptotes

In the analysis of tagging data, it has been found that the least-squares method, based on the increment function known as the Fabens method, produces biased estimates because individual variability in growth is not allowed for. This paper modifies the Fabens method to account for individual variability in the length asymptote. Significance tests using t-statistics or log-likelihood ratio statistics may be applied to show the level of individual variability. Simulation results indicate that the modified method reduces the biases in the estimates to negligible proportions. Tagging data from tiger prawns (Penaeus esculentus and Penaeus semisulcatus) and rock lobster (Panulirus ornatus) are analysed as an illustration.

Estimation of cosmological parameters using adaptive importance sampling

We present a Bayesian sampling algorithm called adaptive importance sampling or population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower wall-clock time for PMC. In the case of WMAP5 data, for example, the wall-clock time scale reduces from days for MCMC to hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analyzed and discussed.

Optimisation of in-cell accelerated solvent extraction technique for the determination of organochlorine pesticides in river sediments

Organochlorine pesticides (OCPs) are ubiquitous environmental contaminants with adverse impacts on aquatic biota, wildlife and human health even at low concentrations. However, conventional methods for their determination in river sediments are resource intensive. This paper presents an approach that is rapid and also reliable for the detection of OCPs. Accelerated Solvent Extraction (ASE) with in-cell silica gel clean-up followed by Triple Quadrupole Gas Chromatograph Mass Spectrometry (GCMS/MS) was used to recover OCPs from sediment samples. Variables such as temperature, solvent ratio, adsorbent mass and extraction cycle were evaluated and optimised for the extraction. With the exception of Aldrin, which was unaffected by any of the variables evaluated, the recovery of OCPs from sediment samples was largely influenced by solvent ratio and adsorbent mass and, to some extent, the number of cycles and temperature. The optimised conditions for OCPs extraction in sediment with good recoveries were determined to be 4 cycles, 4.5 g of silica gel, 105 ᴼC, and 4:3 v/v DCM: hexane mixture. With the exception of two compounds (α-BHC and Aldrin) whose recoveries were low (59.73 and 47.66 % respectively), the recovery of the other pesticides were in the range 85.35 – 117.97% with precision < 10 % RSD. The method developed significantly reduces sample preparation time, the amount of solvent used, matrix interference, and is highly sensitive and selective.

Salt in the desert: A comparison of clinical versus serum-calculated osmolality determination of dehydration

Background: Clinicians frequently use their own judgement to assess patient’s hydration status although there is limited evidence for the diagnostic utility of any individual clinical symptom. Hence, the aim of this study was to compare the diagnostic accuracy of clinically assessed dehydration in older hospital patients (using multiple symptoms), versus dehydration measured using serum-calculated osmolality (CO) as the reference standard. Method: Participants were 44 hospital patients aged ≥ 60 years. Dehydration was assessed clinically and pathologically (CO) within 24 hours of admission and at study exit. Indicators of diagnostic accuracy were calculated. Results: Clinicians identified 27% of patients as dehydrated at admission, and 19% at exit, compared to 19% and 16% using CO. Agreement between the measures was fair at admission and poor at exit. Clinical assessment showed poor sensitivity for predicting dehydration with reasonable specificity. Conclusions: Compared to the use of CO, clinical assessment of dehydration in older patients was poor. Given that failure to identify dehydration in this population may have serious consequences, we recommend that clinicians do not rely upon their own assessments without also using the reference standard.