Microsatellite markers for the cosmopolitan marine diatom Pseudo‐nitzschia pungens

Very little information is available on the genetic structure of populations of phytoplankton species. Here we report the characterization of six polymorphic microsatellite loci in the marine diatom, Pseudo‐nitzschia pungens, using clones (between 87 and 213 per locus) isolated from spring blooms in the North Sea. The number of alleles per locus ranged from five to 19 and observed heterozygosities ranged from 0.55 to 0.86.     

Hierarchical multivariate mixture generalized linear models for the analysis of spatial data: An application to disease mapping

Disease mapping of a single disease has been widely studied in the public health setup. Simultaneous modeling of related diseases can also be a valuable tool both from the epidemiological and from the statistical point of view. In particular, when we have several measurements recorded at each spatial location, we need to consider multivariate models in order to handle the dependence among the multivariate components as well as the spatial dependence between locations. It is then customary to use multivariate spatial models assuming the same distribution through the entire population density. However, in many circumstances, it is a very strong assumption to have the same distribution for all the areas of population density. To overcome this issue, we propose a hierarchical multivariate mixture generalized linear model to simultaneously analyze spatial Normal and non‐Normal outcomes. As an application of our proposed approach, esophageal and lung cancer deaths in Minnesota are used to show the outperformance of assuming different distributions for different counties of Minnesota rather than assuming a single distribution for the population density. Performance of the proposed approach is also evaluated through a simulation study.     

Hierarchical Bayesian Analysis of Correlated Zero‐inflated Count Data

This article presents two‐component hierarchical Bayesian models which incorporate both overdispersion and excess zeros. The components may be resultants of some intervention (treatment) that changes the rare event generating process. The models are also expanded to take into account any heterogeneity that may exist in the data. Details of the model fitting, checking and selecting alternative models from a Bayesian perspective are also presented. The proposed methods are applied to count data on the assessment of an efficacy of pesticides in controlling the reproduction of whitefly. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Nitrogen Utilization and Toxin Production by Two Diatoms of the Pseudo‐nitzschia pseudodelicatissima Complex: P. cuspidata and P. fryxelliana

The toxigenic diatom Pseudo‐nitzschia cuspidata, isolated from the U.S. Pacific Northwest, was examined in unialgal batch cultures to evaluate domoic acid (DA) toxicity and growth as a function of light, N substrate, and growth phase. Experiments conducted at saturating (120 μmol photons · m^?2 · s^?1) and subsaturating (40 μmol photons · m^?2 · s^?1) photosynthetic photon flux density (PPFD), demonstrate that P. cuspidata grows significantly faster at the higher PPFD on all three N substrates tested [nitrate (NO₃^?), ammonium (NH₄⁺), and urea], but neither cellular toxicity nor exponential growth rates were strongly associated with one N source over the other at high PPFD. However, at the lower PPFD, the exponential growth rates were approximately halved, and the cells were significantly more toxic regardless of N substrate. Urea supported significantly faster growth rates, and cellular toxicity varied as a function of N substrate with NO₃^?‐supported cells being significantly more toxic than both NH₄⁺‐ and urea‐supported cells at the low PPFD. Kinetic uptake parameters were determined for another member of the P. pseudodelicatissima complex, P. fryxelliana. After growth of these cells on NO₃^? they exhibited maximum specific uptake rates (V_max) of 22.7, 29.9, 8.98 × 10^?3 · h^?1, half‐saturation constants (K_s) of 1.34, 2.14, 0.28 μg‐at N · L^?1, and affinity values (α) of 17.0, 14.7, 32.5 × 10^?3 · h^?1/(μg‐at N · L^?1) for NO₃^?, NH₄⁺ and urea, respectively. These labo‐ratory results demonstrate the capability of P. cuspidata to grow and produce DA on both oxidized and reduced N substrates during both exponential and stationary growth phases, and the uptake kinetic results for the pseudo‐cryptic species, P. fryxelliana suggest that reduced N sources from coastal runoff could be important for maintenance of these small pennate diatoms in U.S. west coast blooms, especially during times of low ambient N concentrations.     

The dynamics of sexual phase in the marine diatom Pseudo‐nitzschia multistriata (Bacillariophyceae)

Sexual reproduction represents a fundamental phase in the life cycle of diatoms, linked to both the production of genotypic diversity and the formation of large‐sized initial cells. Only cells below a certain size threshold can be sexualized, but various environmental factors can modulate the success of sexual reproduction. We investigated the role of cell density and physiological conditions of parental strains in affecting the success and timing of sexual reproduction in the marine heterothallic diatom Pseudo‐nitzschia multistriata. We also studied the dynamics of the sexual phase in still conditions allowing cell sedimentation and in gently mixed conditions that keep cells in suspension. Our results showed that successful sexual reproduction can only be achieved when crossing parental strains in the exponential growth phase. Evidence was provided for the fact that sexual reproduction is a density‐dependent event and requires a threshold cell concentration to start, although this might vary considerably amongst strains. Moreover, the onset of the sexual phase was coupled to a marked reduction in growth of the vegetative parental cells. The crosses carried out in physically mixed conditions produced a significantly reduced number of sexual stages as compared to crosses in still conditions, showing that mixing impairs sexualization. The results of our experiments suggest that the signaling that triggers the sexual phase is favored when cells can accumulate, reducing the distance between them and facilitating contacts and/or the perception of chemical cues. Information on the progression of the sexual phase in laboratory conditions help understanding the conditions at which sex occurs in the natural environment.     

Saddlepoint Approximations to the Moments of Multitype Age‐Dependent Branching Processes,with Applications

Summary This article proposes saddlepoint approximations to the expectation and variance–covariance function of multitype age‐dependent branching processes. The proposed approximations are found accurate, easy to implement, and much faster to compute than by simulating the process. Multiple applications are presented, including the analyses of clonal data on the generation of oligodendrocytes from their immediate progenitor cells, and on the proliferation of Hela cells. New estimators are also constructed to analyze clonal data. The proposed methods are finally used to approximate the distribution of the generation, which has recently found several applications in cell biology.     

Small area estimation for semicontinuous skewed spatial data: An application to the grape wine production in Tuscany

Linear‐mixed models are frequently used to obtain model‐based estimators in small area estimation (SAE) problems. Such models, however, are not suitable when the target variable exhibits a point mass at zero, a highly skewed distribution of the nonzero values and a strong spatial structure. In this paper, a SAE approach for dealing with such variables is suggested. We propose a two‐part random effects SAE model that includes a correlation structure on the area random effects that appears in the two parts and incorporates a bivariate smooth function of the geographical coordinates of units. To account for the skewness of the distribution of the positive values of the response variable, a Gamma model is adopted. To fit the model, to get small area estimates and to evaluate their precision, a hierarchical Bayesian approach is used. The study is motivated by a real SAE problem. We focus on estimation of the per‐farm average grape wine production in Tuscany, at subregional level, using the Farm Structure Survey data. Results from this real data application and those obtained by a model‐based simulation experiment show a satisfactory performance of the suggested SAE approach.     

Modeling Inter‐Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model

Summary The aim of this article is to develop a spatial model for multi‐subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi‐subject data, some work on spatial modeling of single‐subject data, and some recent work on spatial modeling of multi‐subject data. However, there has been no work on spatial models that explicitly account for inter‐subject variability in activation locations. In this article, we use the idea of activation centers and model the inter‐subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass‐univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data.     

Joint generalized models for multidimensional outcomes: A case study of neuroscience data from multimodalities

This paper is motivated from the analysis of neuroscience data in a study of neural and muscular mechanisms of muscle fatigue. Multidimensional outcomes of different natures were obtained simultaneously from multiple modalities, including handgrip force, electromyography (EMG), and functional magnetic resonance imaging (fMRI). We first study individual modeling of the univariate response depending on its nature. A mixed‐effects beta model and a mixed‐effects simplex model are compared for modeling the force/EMG percentages. A mixed‐effects negative‐binomial model is proposed for modeling the fMRI counts. Then, I present a joint modeling approach to model the multidimensional outcomes together, which allows us to not only estimate the covariate effects but also to evaluate the strength of association among the multiple responses from different modalities. A simulation study is conducted to quantify the possible benefits by the new approaches in finite sample situations. Finally, the analysis of the fatigue data is illustrated with the use of the proposed methods.     

远缘杂交中不育基因的位置和效应的最大似然估计

提出了一种统计方法,利用标记位点的异常分离,来估计远缘杂交中不育基因位点的位置和效应,在回交群体中,用最大似然法对不育基因与标记位点之间的生组值和配子存活率进行估计。将表现连续分布的育性指标转化为百连续变异的遗传标记的分离,可以避免对育性直接观测所带来的重组值估计结果的不稳定,还可以同时估计雌雄配子的存活率。     

Bayesian Estimation of the Time‐Varying Sensitivity of a Diagnostic Test with Application to Mother‐to‐Child Transmission of HIV

Summary We present a Bayesian model to estimate the time‐varying sensitivity of a diagnostic assay when the assay is given repeatedly over time, disease status is changing, and the gold standard is only partially observed. The model relies on parametric assumptions for the distribution of the latent time of disease onset and the time‐varying sensitivity. Additionally, we illustrate the incorporation of historical data for constructing prior distributions. We apply the new methods to data collected in a study of mother‐to‐child transmission of HIV and include a covariate for sensitivity to assess whether two different assays have different sensitivity profiles.