A Staining Method for Assessing the Viability of Esteya vermicola Conidia

The viability of conidia of Esteya vermicola, a potentially important biocontrol agent against the pinewood nematode Bursaphelenchus xylophilus, is usually determined by cultivation for 18–48 h in culture medium. As an alternative to this labor-intensive method, we have developed a rapid, simple, and low-cost staining method for assessing E vermicola conidia survival rates. A mixture of neutral red and methylene blue was found to be the most optimal among several stains that also included safranin O and Janus green B. This mixture stained nonviable conidia blue, in contrast to viable conidia, which were stained red in the cytoplasm and blue in the cell wall. This method may be particularly useful for traditional research laboratories, as it provides rapid results using common, relatively inexpensive laboratory equipment.     

Combining counts and incidence data: an efficient approach for estimating the log-normal species abundance distribution and diversity indices

Obtaining accurate estimates of diversity indices is difficult because the number of species encountered in a sample increases with sampling intensity. We introduce a novel method that requires that the presence of species in a sample to be assessed while the counts of the number of individuals per species are only required for just a small part of the sample. To account for species included as incidence data in the species abundance distribution, we modify the likelihood function of the classical Poisson log-normal distribution. Using simulated community assemblages, we contrast diversity estimates based on a community sample, a subsample randomly extracted from the community sample, and a mixture sample where incidence data are added to a subsample. We show that the mixture sampling approach provides more accurate estimates than the subsample and at little extra cost. Diversity indices estimated from a freshwater zooplankton community sampled using the mixture approach show the same pattern of results as the simulation study. Our method efficiently increases the accuracy of diversity estimates and comprehension of the left tail of the species abundance distribution. We show how to choose the scale of sample size needed for a compromise between information gained, accuracy of the estimates and cost expended when assessing biological diversity. The sample size estimates are obtained from key community characteristics, such as the expected number of species in the community, the expected number of individuals in a sample and the evenness of the community.     

Coarse-grained molecular dynamics simulations of phase transitions in mixed lipid systems containing LPA, DOPA, and DOPE lipids

The mechanisms that mediate biomembrane shape transformations are of considerable interest in cell biology. Recent in vitro experiments show that the chemical transformation of minor membrane lipids can induce dramatic shape changes in biomembranes. Specifically, it was observed that the addition of DOPA to DOPE has no effect on the stability of the bilayer structure of the membrane. In contrast, the addition of LPA to DOPE stabilizes the bilayer phase of DOPE, increasing the temperature of a phase transition from the bilayer to the inverted hexagonal phase. This result suggests that the chemical conversion of DOPA to LPA is sufficient for triggering a dramatic change in the shape of biomembranes. The LPA/DOPA/DOPE mixture of lipids provides a simple model system for understanding the molecular events driving the shape change. In this work, we used coarse-grained molecular dynamics simulations to study the phase transitions of this lipid mixture. We show that despite the simplicity of the coarse-grained model, it reproduces the experimentally observed phase changes of: 1), pure LPA and DOPA with respect to changes in the concentration of cations; and 2), LPA/DOPE and DOPA/DOPE mixtures with respect to temperature. The good agreement between the model and experiments suggests that the computationally inexpensive coarse-grained approach can be used to infer macroscopic membrane properties. Furthermore, analysis of the shape of the lipid molecules demonstrates that the phase behavior of single-lipid systems is consistent with molecular packing theory. However, the phase stability of mixed lipid systems exhibits significant deviations from this theory, which suggests that the elastic energy of the lipids, neglected in the packing theory, plays an important role.     

A simple method for the elimination of chemiluminescence in the liquid scintillation counting of alkaline protein digests.

A liquid scintillation mixture is described with which chemiluminescence is virtually absent ten minutes or less after mixing with alkaline digests of blood serum. The mixture provides a clear colourless solution for counting, and was found to be equally effective with three commercial solubilizers. If the digest contains hydrogen peroxide it must be heated for a period of time before the addition of the mixture.     

Silver fir stand productivity is enhanced when mixed with Norway spruce: evidence based on large‐scale inventory data and a generic modelling approach

Questions: How to evaluate the mixture effect on basal area increment in two‐species forest stands? Is a mixed Norway spruce–silver fir stand more productive than pure adjacent stands of either species? How to develop generic modelling approaches to assess mixture effects in forest stands? Location: In addition to a case study on Norway spruce–silver fir stands in French mountain forests, the generic approach used goes beyond local applications. Methods: We took advantage of National Forest Inventory data to develop a unique stand basal‐area‐increment model for pure and mixed stands of Norway spruce and silver fir that responds to ecological site conditions. The database was made up of 284 pure Norway spruce stands, 196 pure silver fir stands, and 323 mixed stands of these species. Results: Pure silver fir basal area increment is strongly influenced by spring climatic conditions, whereas pure Norway spruce is more influenced by soil conditions. The mixture of these species has a positive effect on silver fir, which decreases as the proportion of fir increases. In contrast, the mixture has no noticeable effect on Norway spruce. Conclusion: We developed a stand basal‐area‐increment model evidencing an advantage of the mixture on silver fir basal area increment, but not on Norway spruce. The mathematical formulation of the model developed is generic and can be used in all two‐species mixture situations. It also makes it possible to compare different mixture situations with each other.     

Improved stem cell MR detectability in animal models by modification of the inhalation gas

In vivo monitoring of cells labeled with paramagnetic iron oxide particles by magnetic resonance imaging (MRI) is complicated by intrinsic contrast of blood vessels. Distinction between T2* effects caused by blood vessels from those caused by labeled cells was so far only possible after carefully following the location of hypointense regions through subsequent slices of T2*-weighted 3-D MRI datasets, a procedure that is time consuming and not always reliable in the case of smaller blood vessels. Here, we demonstrate that the modification of the inhalation gas mixture from the routinely used composition 35% O2 and 65% N2O to a mixture containing 95% O2 and 5% CO2 results in a contrast suppression of most small blood vessels and reduces the intrinsic T2* effect of large vessels dramatically in an animal model. This change in protocol of physiological conditions was well tolerated by all studied animals, even over prolonged experimental times. The changed inhalation gas mixture thus provides a more reliable identification method for small clusters of iron oxide labeled cells in vivo.     

Combining gene annotations and gene expression data in model-based clustering: weighted method

It has been increasingly recognized that incorporating prior knowledge into cluster analysis can result in more reliable and meaningful clusters. In contrast to the standard modelbased clustering with a global mixture model, which does not use any prior information, a stratified mixture model was recently proposed to incorporate gene functions or biological pathways as priors in model-based clustering of gene expression profiles: various gene functional groups form the strata in a stratified mixture model. Albeit useful, the stratified method may be less efficient than the global analysis if the strata are non-informative to clustering. We propose a weighted method that aims to strike a balance between a stratified analysis and a global analysis: it weights between the clustering results of the stratified analysis and that of the global analysis; the weight is determined by data. More generally, the weighted method can take advantage of the hierarchical structure of most existing gene functional annotation systems, such as MIPS and Gene Ontology (GO), and facilitate choosing appropriate gene functional groups as priors. We use simulated data and real data to demonstrate the feasibility and advantages of the proposed method.     

Segmentation and intensity estimation of microarray images using a gamma-t mixture model

MOTIVATION: We present a new approach to the analysis of images for complementary DNA microarray experiments. The image segmentation and intensity estimation are performed simultaneously by adopting a two-component mixture model. One component of this mixture corresponds to the distribution of the background intensity, while the other corresponds to the distribution of the foreground intensity. The intensity measurement is a bivariate vector consisting of red and green intensities. The background intensity component is modeled by the bivariate gamma distribution, whose marginal densities for the red and green intensities are independent three-parameter gamma distributions with different parameters. The foreground intensity component is taken to be the bivariate t distribution, with the constraint that the mean of the foreground is greater than that of the background for each of the two colors. The degrees of freedom of this t distribution are inferred from the data but they could be specified in advance to reduce the computation time. Also, the covariance matrix is not restricted to being diagonal and so it allows for nonzero correlation between R and G foreground intensities. This gamma-t mixture model is fitted by maximum likelihood via the EM algorithm. A final step is executed whereby nonparametric (kernel) smoothing is undertaken of the posterior probabilities of component membership. The main advantages of this approach are: (1) it enjoys the well-known strengths of a mixture model, namely flexibility and adaptability to the data; (2) it considers the segmentation and intensity simultaneously and not separately as in commonly used existing software, and it also works with the red and green intensities in a bivariate framework as opposed to their separate estimation via univariate methods; (3) the use of the three-parameter gamma distribution for the background red and green intensities provides a much better fit than the normal (log normal) or t distributions; (4) the use of the bivariate t distribution for the foreground intensity provides a model that is less sensitive to extreme observations; (5) as a consequence of the aforementioned properties, it allows segmentation to be undertaken for a wide range of spot shapes, including doughnut, sickle shape and artifacts. RESULTS: We apply our method for gridding, segmentation and estimation to cDNA microarray real images and artificial data. Our method provides better segmentation results in spot shapes as well as intensity estimation than Spot and spotSegmentation R language softwares. It detected blank spots as well as bright artifact for the real data, and estimated spot intensities with high-accuracy for the synthetic data. AVAILABILITY: The algorithms were implemented in Matlab. The Matlab codes implementing both the gridding and segmentation/estimation are available upon request. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.     

Efficient nonparametric estimation of causal effects in randomized trials with noncompliance

Causal approaches based on the potential outcome framework providea useful tool for addressing noncompliance problems in randomizedtrials. We propose a new estimator of causal treatment effectsin randomized clinical trials with noncompliance. We use theempirical likelihood approach to construct a profile randomsieve likelihood and take into account the mixture structurein outcome distributions, so that our estimator is robust toparametric distribution assumptions and provides substantialfinite-sample efficiency gains over the standard instrumentalvariable estimator. Our estimator is asymptotically equivalentto the standard instrumental variable estimator, and it canbe applied to outcome variables with a continuous, ordinal orbinary scale. We apply our method to data from a randomizedtrial of an intervention to improve the treatment of depressionamong depressed elderly patients in primary care practices.     

Mixture models for detecting differentially expressed genes in microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.     

Protein synthesis by pure translation systems

We have developed a partially recombinant, cell-free, protein-synthesis system reconstituted solely from those essential elements of the Escherichia coli translation system, termed protein synthesis using recombinant elements (PURE). It provides higher reaction controllability in comparison to crude cell-free protein-synthesis systems for translation studies and biotechnology applications. The PURE system stands out among translation methods in that it provides not only a simple and unique "reverse" purification method of separating the synthesized protein from reaction mixture, but also that the system can be tailor-made according to individual protein requirements. In this paper, two new approaches to obtaining active proteins are described: the use of molecular chaperones, and modification of the reaction conditions. Several possible applications of the PURE system are also discussed.     

Measurement of lysophospholipid acyltransferase activities using substrate competition

Lysophospholipid acyltransferases (LPATs) incorporate fatty acyl chains into phospholipids via a CoA-dependent mechanism and are important in remodeling phospholipids to generate the molecular species of phospholipids found in cells. These enzymes use one lysophospholipid and one acyl-CoA ester as substrates. Traditional enzyme activity assays engage a single substrate pair, whereas in vivo multiple molecular species exist. We describe here an alternative biochemical assay that provides a mixture of substrates presented to the microsomal extracts. Microsomal preparations from RAW 264.7 cells were used to compare traditional LPAT assays with data obtained using a dual substrate choice assay using six different lysophospholipids and eight different acyl-CoA esters. The complex mixture of newly synthesized phospholipid products was analyzed using LC-MS/MS. Both types of assays provided similar results, but the dual choice assay provided information about multiple fatty acyl chain incorporation into various phospholipid classes in a single reaction. Engineered suppression of LPCAT3 activity in RAW 264.7 cells was easily detected by the dual choice method. These findings demonstrate that this assay is both specific and sensitive and that it provides much richer biochemical detail than traditional assays.     

Detecting differential gene expression with a semiparametric hierarchical mixture method

Mixture modeling provides an effective approach to the differential expression problem in microarray data analysis. Methods based on fully parametric mixture models are available, but lack of fit in some examples indicates that more flexible models may be beneficial. Existing, more flexible, mixture models work at the level of one-dimensional gene-specific summary statistics, and so when there are relatively few measurements per gene these methods may not provide sensitive detectors of differential expression. We propose a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data. EM-based algorithms are used to fit both parametric and semiparametric versions of the model. We restrict attention to the two-sample comparison problem; an experiment involving Affymetrix microarrays and yeast translation provides the motivating case study. Gene-specific posterior probabilities of differential expression form the basis of statistical inference; they define short gene lists and false discovery rates. Compared to several competing methodologies, the proposed methodology exhibits good operating characteristics in a simulation study, on the analysis of spike-in data, and in a cross-validation calculation.     

Mixture models for estimating the size of a closed population when capture rates vary among individuals

We develop a parameterization of the beta-binomial mixture that provides sensible inferences about the size of a closed population when probabilities of capture or detection vary among individuals. Three classes of mixture models (beta-binomial, logistic-normal, and latent-class) are fitted to recaptures of snowshoe hares for estimating abundance and to counts of bird species for estimating species richness. In both sets of data, rates of detection appear to vary more among individuals (animals or species) than among sampling occasions or locations. The estimates of population size and species richness are sensitive to model-specific assumptions about the latent distribution of individual rates of detection. We demonstrate using simulation experiments that conventional diagnostics for assessing model adequacy, such as deviance, cannot be relied on for selecting classes of mixture models that produce valid inferences about population size. Prior knowledge about sources of individual heterogeneity in detection rates, if available, should be used to help select among classes of mixture models that are to be used for inference.     

Spatial guilds in the Serengeti food web revealed by a Bayesian group model

Food webs, networks of feeding relationships in an ecosystem, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. A standard approach in food-web analysis, and network analysis in general, has been to identify compartments, or modules, defined by many links within compartments and few links between them. This approach can identify large habitat boundaries in the network but may fail to identify other important structures. Empirical analyses of food webs have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure using a flexible definition that can describe both functional trophic roles and standard compartments. We apply this method to a newly compiled plant-mammal food web from the Serengeti ecosystem that includes high taxonomic resolution at the plant level, allowing a simultaneous examination of the signature of both habitat and trophic roles in network structure. We find that groups at the plant level reflect habitat structure, coupled at higher trophic levels by groups of herbivores, which are in turn coupled by carnivore groups. Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial pattern, in contrast to the standard compartments typically identified. The network topology supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence. Furthermore, our Bayesian approach provides a powerful, flexible framework for the study of network structure, and we believe it will prove instrumental in a variety of biological contexts.     

Assessing variation in life‐history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists

Mixed models are now well‐established methods in ecology and evolution because they allow accounting for and quantifying within‐ and between‐individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi‐modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life‐history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life‐history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long‐term studies of large mammals to illustrate the potential of using mixture models for assessing within‐population variation in life‐history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life‐history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users.     

Quantitative analysis of a tetraribonucleotide mixture by ultraviolet spectrophotometry

Using the least squares method we have calculated the proportions of each nucleotide of a mixture of AMP, CMP, GMP and UMP, after measuring the absorbance of the mixture every ten nanometers from 230 to 290 nm at pH 12.7 and 2. The method is very simple and rapid (the calculations are made in less than one minute), does not require a highly sensitive spectrophotometer to obtain reasonably precise results and, in contrast to the other methods of the literature, it can be applied to quantities as little as 50 micrograms of nucleotides.     

Clines in clock genes: fine-tuning circadian rhythms to the environment

The dissection of the circadian clock into its molecular components represents the most striking and well-studied example of a gene regulatory network underlying a complex behavioural trait. By contrast, the evolutionary analysis of the clock has developed more slowly. Here we review studies that have surveyed intraspecific clock gene variation over large geographical areas and have discovered latitudinal clines in gene frequencies. Such spatial patterns traditionally suggest that natural selection shapes genetic variation, but it is equally possible that population history, or a mixture of demography and selection, could contribute to the clines. We discuss how population genetics, together with functional assays, can illuminate these possible cases of natural selection in Drosophila clock genes.