Myosin light chain isoforms retain their species-specific electrophoretic mobility after processing, which enables differentiation between six species: 2DE analysis of minced meat and meat products made from beef, pork and poultry

Investigation of protein changes as well as authentication of meat is particularly difficult in processed meat products due to their different composition, complexity and very often inhomogeneity. The aim of this study was to check if the inter-species differences in the expression of myosin light chain (MLC) isoforms observed in raw meat were retained in meat products. MLCs from mixtures of minced meat (16 variants), frankfurters and sausages (15 products) made from cattle, pig, chicken, turkey, duck and goose were analysed by 2DE. Species-specific patterns of MLC isoforms were observed in all the mixtures and processed meat products. Relatively small degradation was observed in the MLCs after processing. Image analysis enabled species identification of the meat in all samples when the content of meat of one species was not lower than 10%. However, it was impossible to differentiate between all the six species under investigation on the basis of individual isoform. It was possible when the combination of all the three isoforms (myosin light chain 1 fast, myosin light chain 2 fast and myosin light chain 3 fast) was analysed. The results evidenced that MLCs have potential to be used as markers in authentication of meat products made from the analysed six species.     

First evidence for the salt-dependent folding and activity of an esterase from the halophilic archaea Haloarcula marismortui

A gene encoding an esterase from Haloarcula marismortui, a halophilic archaea from the Dead Sea, was cloned, expressed in Escherichia coli, and the recombinant protein (Hm EST) was biochemically characterized. The enzymatic activity of Hm EST was shown to exhibit salt dependence through salt-dependent folding. Hm EST exhibits a preference for short chain fatty acids and monoesters. It is inhibited by phenylmethylsulfonyl fluoride, diethyl-p-nitrophenyl phosphate, and 5-methoxy-3-(4-phenoxyphenyl)-3H-[1,3,4]oxadiazol-2-one, confirming the conclusion from sequence alignments that Hm EST is a serine carboxylesterase belonging to the hormone-sensitive lipase family. The activity of Hm EST is optimum in the presence of 3 M KCl and no activity was detected in the absence of salts. Far–UV circular dichroism showed that Hm EST is totally unfolded in salt-free medium and secondary structure appears in the presence of 0.25–0.5 M KCl. After salt depletion, the protein was able to recover 60% of its initial activity when 2 M KCl was added. A 3D model of Hm EST was built and its surface properties were analyzed, pointing to an enrichment in acidic residues paralleled by a depletion in basic residues. This peculiar charge repartition at the protein surface supports a better stability of the protein in a high salt environment.     

Persistence and heterogeneity in habitat selection studies using radio telemetry

Biologists attach radio transmitters to animals so that the animals' movements through their preferred habitats can be followed. To analyze the resulting sequences of visited habitat classes, McCracken, Manly, and Vander Heyden (1998, Journal of Agricultural, Biological, and Environmental Statistics 3(3), 268-279) proposed an independent multinomial selections (IMS) model. Two issues that arise when using this approach are: (i) serial dependence possibly affects measures of uncertainty; and (ii) individual animals from the population studied may exhibit heterogeneity in their selection patterns. We develop two single-parameter extensions of the IMS model to address these issues. A Markov chain model allows for persistence in the habitat class previously visited. Heterogeneity is modeled by assuming the population of animal selection patterns follows a Dirichlet distribution, from which the study animals are a random sample. We show that these persistence and heterogeneity characteristics are present in the study by McCracken et al. (1998) of bear movements. Simulations demonstrate that failure to account for persistence or heterogeneity when either is present can seriously misrepresent measures of uncertainty.     

Bayesian deconvolution analysis of pulsatile hormone concentration profiles

Many hormones are secreted into the circulatory system in a pulsatile manner and are cleared exponentially. The most common method of analyzing these systems is to deconvolve the hormone concentration into a secretion function and a clearance function. Accurate estimation of the model parameters depends on the number and location of the secretion pulses. To date, deconvolution analysis assumes the number and approximate location of these pulses are known a priori. In this article, we present a novel Bayesian approach to deconvolution that jointly models the number of pulses along with all other model parameters. Our method stochastically searches for the secretion pulses. This is accomplished by viewing the set of parameters that define the pulses as a point process. Pulses are determined by a birth-death process which is embedded in Markov chain Monte Carlo algorithm. This idea originated with Stephens (2000, Annals of Statistics 28, 40-74) in the context of finite mixture model density estimation, where the number of mixture components is unknown. There are several advantages that our model enjoys over the traditional frequentist approaches. These advantages are highlighted with four datasets consisting of serum concentration levels of luteinizing hormone obtained from ovariectomized ewes.     

In silico prediction of the structure of membrane proteins: is it feasible?

In the 'omic' era, hundreds of genomes are available for protein sequence analysis, and some 30 per cent of all sequences are of membrane proteins. Unlike globular proteins, a 3D model for membrane proteins can hardly be computed starting from the sequence. Why is this so? What can we really compute and with what reliability? These and other matters are outlined.     

RDOCK: refinement of rigid-body protein docking predictions

We present a simple and effective algorithm RDOCK for refining unbound predictions generated by a rigid-body docking algorithm ZDOCK, which has been developed earlier by our group. The main component of RDOCK is a three-stage energy minimization scheme, followed by the evaluation of electrostatic and desolvation energies. Ionic side chains are kept neutral in the first two stages of minimization, and reverted to their full charge states in the last stage of brief minimization. Without side chain conformational search or filtering/clustering of resulting structures, RDOCK represents the simplest approach toward refining unbound docking predictions. Despite its simplicity, RDOCK makes substantial improvement upon the top predictions by ZDOCK with all three scoring functions and the improvement is observed across all three categories of test cases in a large benchmark of 49 non-redundant unbound test cases. RDOCK makes the most powerful combination with ZDOCK2.1, which uses pairwise shape complementarity as the scoring function. Collectively, they rank a near-native structure as the number-one prediction for 18 test cases (37% of the benchmark), and within the top 4 predictions for 24 test cases (49% of the benchmark). To various degrees, funnel-like energy landscapes are observed for these 24 test cases. To the best of our knowledge, this is the first report of binding funnels starting from global searches for a broad range of test cases. These results are particularly exciting, given that we have not used any biological information that is specific to individual test cases and the whole process is entirely automated. Among three categories of test cases, the best results are seen for enzyme/inhibitor, with a near-native structure ranked as the number-one prediction for 48% test cases, and within the top 10 predictions for 78% test cases. RDOCK is freely available to academic users at http://zlab.bu.edu/ approximately rong/dock.     

Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests

Many analyses of results from multiple diagnostic tests assume the tests are statistically independent conditional on the true disease status of the subject. This assumption may be violated in practice, especially in situations where none of the tests is a perfectly accurate gold standard. Classical inference for models accounting for the conditional dependence between tests requires that results from at least four different tests be used in order to obtain an identifiable solution, but it is not always feasible to have results from this many tests. We use a Bayesian approach to draw inferences about the disease prevalence and test properties while adjusting for the possibility of conditional dependence between tests, particularly when we have only two tests. We propose both fixed and random effects models. Since with fewer than four tests the problem is nonidentifiable, the posterior distributions are strongly dependent on the prior information about the test properties and the disease prevalence, even with large sample sizes. If the degree of correlation between the tests is known a priori with high precision, then our methods adjust for the dependence between the tests. Otherwise, our methods provide adjusted inferences that incorporate all of the uncertainty inherent in the problem, typically resulting in wider interval estimates. We illustrate our methods using data from a study on the prevalence of Strongyloides infection among Cambodian refugees to Canada.     

Quality-adjusted survival estimation with periodic observations

Quality-adjusted survival is a measure that integrates both longevity and quality-of-life information. The analysis of quality-adjusted survival in a clinical study with data collected at periodic intervals encounters difficulties due to incomplete information. Based on observed time points, the time axis is partitioned into a set of disjoint time intervals, and under a Markovian assumption on patient's health status, the expected quality-adjusted survival is estimated as the summed product of the quality of life and its mean sojourn time of each health state within partitioned intervals. It is shown that the estimator is asymptotically normal with a simple variance calculation. A simulation study is conducted to investigate the behavior of the estimator, and a stroke study illustrates the use of the estimator.     

Inference for an epidemic when susceptibility varies

A stochastic epidemic model featuring fixed-length latent periods, gamma-distributed infectious periods and randomly varying heterogeneity among susceptibles is considered. A Markov chain Monte Carlo algorithm is developed for performing Bayesian inference for the parameters governing the infectious-period length and the hyper-parameters governing the heterogeneity of susceptibility. This method of analysis applies to a wider class of diseases than methods proposed previously. An application to smallpox data confirms results about heterogeneity suggested by an earlier analysis that relied on less realistic assumptions.