Jackknife and bootstrap tests of the composition vector trees

Composition vector trees(CVTrees)are inferred from whole-genome data by an alignment-free and parameter-free method.The agreement of these trees with the corresponding taxonomy provides an objective justification of the inferred phylogeny.In this work,we show the stability and self-consistency of CVTrees by performing bootstrap and jackknife re-sampling tests adapted to this alignment-free approach.Our ultimate goal is to advocate the viewpoint that time-consuming statistical re-sampling tests can be avoided at all in using this alignment-free approach.Agreement with taxonomy should be taken as a major criterion to estimate prokaryotic phylogenetic trees.     

A method for inferring the rate of evolution of homologous characters that can potentially improve phylogenetic inference, resolve deep divergence and correct systematic biases

Current phylogenetic methods attempt to account for evolutionary rate variation across characters in a matrix. This is generally achieved by the use of sophisticated evolutionary models, combined with dense sampling of large numbers of characters. However, systematic biases and superimposed substitutions make this task very difficult. Model adequacy can sometimes be achieved at the cost of adding large numbers of free parameters, with each parameter being optimized according to some criterion, resulting in increased computation times and large variances in the model estimates. In this study, we develop a simple approach that estimates the relative evolutionary rate of each homologous character. The method that we describe uses the similarity between characters as a proxy for evolutionary rate. In this article, we work on the premise that if the character-state distribution of a homologous character is similar to many other characters, then this character is likely to be relatively slowly evolving. If the character-state distribution of a homologous character is not similar to many or any of the rest of the characters in a data set, then it is likely to be the result of rapid evolution. We show that in some test cases, at least, the premise can hold and the inferences are robust. Importantly, the method does not use a "starting tree" to make the inference and therefore is tree independent. We demonstrate that this approach can work as well as a maximum likelihood (ML) approach, though the ML method needs to have a known phylogeny, or at least a very good estimate of that phylogeny. We then demonstrate some uses for this method of analysis, including the improvement in phylogeny reconstruction for both deep-level and recent relationships and overcoming systematic biases such as base composition bias. Furthermore, we compare this approach to two well-established methods for reweighting or removing characters. These other methods are tree-based and we show that they can be systematically biased. We feel this method can be useful for phylogeny reconstruction, understanding evolutionary rate variation, and for understanding selection variation on different characters.     

Estimating hazard ratios in cohort data with missing disease information due to death

In clinical and epidemiological studies information on the primary outcome of interest, that is, the disease status, is usually collected at a limited number of follow‐up visits. The disease status can often only be retrieved retrospectively in individuals who are alive at follow‐up, but will be missing for those who died before. Right‐censoring the death cases at the last visit (ad‐hoc analysis) yields biased hazard ratio estimates of a potential risk factor, and the bias can be substantial and occur in either direction. In this work, we investigate three different approaches that use the same likelihood contributions derived from an illness‐death multistate model in order to more adequately estimate the hazard ratio by including the death cases into the analysis: a parametric approach, a penalized likelihood approach, and an imputation‐based approach. We investigate to which extent these approaches allow for an unbiased regression analysis by evaluating their performance in simulation studies and on a real data example. In doing so, we use the full cohort with complete illness‐death data as reference and artificially induce missing information due to death by setting discrete follow‐up visits. Compared to an ad‐hoc analysis, all considered approaches provide less biased or even unbiased results, depending on the situation studied. In the real data example, the parametric approach is seen to be too restrictive, whereas the imputation‐based approach could almost reconstruct the original event history information.     

How do high-level specifications of the brain relate to variational approaches?

High-level specification of how the brain represents and categorizes the causes of its sensory input allows to link "what is to be done" (perceptual task) with "how to do it" (neural network calculation). In this article, we describe how the variational framework, which encountered a large success in modeling computer vision tasks, has some interesting relationships, at a mesoscopic scale, with computational neuroscience. We focus on cortical map computations such that "what is to be done" can be represented as a variational approach, i.e., an optimization problem defined over a continuous functional space. In particular, generalizing some existing results, we show how a general variational approach can be solved by an analog neural network with a given architecture and conversely. Numerical experiments are provided as an illustration of this general framework, which is a promising framework for modeling macro-behaviors in computational neuroscience.     

Bayesian Calibration of a Stochastic Kinetic Computer Model Using Multiple Data Sources

Summary .  In this article, we describe a Bayesian approach to the calibration of a stochastic computer model of chemical kinetics. As with many applications in the biological sciences, the data available to calibrate the model come from different sources. Furthermore, these data appear to provide somewhat conflicting information about the model parameters. We describe a modeling framework that allows us to synthesize this conflicting information and arrive at a consensus inference. In particular, we show how random effects can be incorporated into the model to account for between-individual heterogeneity that may be the source of the apparent conflict.     

Preparation of Carbon Nanosheets at Room Temperature

Amphiphilic molecules equipped with a reactive, carbon-rich "oligoyne" segment consisting of conjugated carbon-carbon triple bonds self-assemble into defined aggregates in aqueous media and at the air-water interface. In the aggregated state, the oligoynes can then be carbonized under mild conditions while preserving the morphology and the embedded chemical functionalization. This novel approach provides direct access to functionalized carbon nanomaterials. In this article, we present a synthetic approach that allows us to prepare hexayne carboxylate amphiphiles as carbon-rich siblings of typical fatty acid esters through a series of repeated bromination and Negishi-type cross-coupling reactions. The obtained compounds are designed to self-assemble into monolayers at the air-water interface, and we show how this can be achieved in a Langmuir trough. Thus, compression of the molecules at the air-water interface triggers the film formation and leads to a densely packed layer of the molecules. The complete carbonization of the films at the air-water interface is then accomplished by cross-linking of the hexayne layer at room temperature, using UV irradiation as a mild external stimulus. The changes in the layer during this process can be monitored with the help of infrared reflection-absorption spectroscopy and Brewster angle microscopy. Moreover, a transfer of the carbonized films onto solid substrates by the Langmuir-Blodgett technique has enabled us to prove that they were carbon nanosheets with lateral dimensions on the order of centimeters.     

Evaluating the importance of within- and between-host selection pressures on the evolution of chronic pathogens

Infectious pathogens compete and are subject to natural selection at multiple levels. For example, viral strains compete for access to host resources within an infected host and, at the same time, compete for access to susceptible hosts within the host population. Here we propose a novel approach to study the interplay between within- and between-host competition. This approach allows for a single host to be infected by and transmit two strains of the same pathogen. We do this by nesting a model for the host-pathogen dynamics within each infected host into an epidemiological model. The nesting of models allows the between-host infectivity and mortality rates suffered by infected hosts to be functions of the disease progression at the within-host level. We present a general method for computing the basic reproduction ratio of a pathogen in such a model. We then illustrate our method using a basic model for the within-host dynamics of viral infections, embedded within the simplest susceptible-infected (SI) epidemiological model. Within this nested framework, we show that the virion production rate at the level of the cell-virus interaction leads, via within-host competition, to the presence or absence of between-host level competitive exclusion. In particular, we find that in the absence of mutation the strain that maximizes between-host fitness can outcompete all other strains. In the presence of mutation we observe a complex invasion landscape showing the possibility of coexistence. Although we emphasize the application to human viral diseases, we expect this methodology to be applicable to be many host-parasite systems.     

Comparison of biometrical approaches for QTL detection in multiple segregating families

Detection of QTL in multiple segregating families possesses many advantages over the classical QTL mapping in biparental populations. It has thus become increasingly popular, and different biometrical approaches are available to analyze such data sets. We empirically compared an approach based on linkage mapping methodology with an association mapping approach. To this end, we used a large population of 788 elite maize lines derived from six biparental families genotyped with 857 SNP markers. In addition, we constructed genetic maps with reduced marker densities to assess the dependency of the performance of both mapping approaches on the marker density. We used cross-validation and resample model averaging and found that while association mapping performed better under high marker densities, this was reversed under low marker densities. In addition to main effect QTL, we also detected epistatic interactions. Our results suggest that both approaches will profit from a further increase in marker density and that a cross-validation should be applied irrespective of the biometrical approach.     

A comprehensive method for genome scans

In applications involving the use of genome scans the problem of correcting for multiple testing figures prominently. A frequently used approach is the Bonferroni adjustment, but this is known to be often severely conservative. As an alternative we use the method of importance sampling to accurately and efficiently obtain required exceedance probabilities. This method is comprehensive in the sense that it has application to exceedance probabilities for other classes of test statistics, such as those for linkage disequilibrium or Hardy-Weinberg equilibrium at multiple loci. We illustrate the importance sampling technique by focusing on affected sib pair tests done at a large number of fully informative markers. We demonstrate how our approach can be used to obtain exceedance probabilities for arbitrary marker spacings, and we compare our approach with that of Feingold et al. [1993], which uses the method of large deviations and does not provide the means for adjusting for unequal marker spacing.     

On the change of support problem for spatio-temporal data

In practice, spatial data are sometimes collected at points (i.e. point-referenced data) and at other times are associated with areal units (i.e. block data). The change of support problem is concerned with inference about the values of a variable at points or blocks different from those at which it has been observed. In the context of block data which can be sensibly viewed as averaging over point data, we propose a unifying approach for prediction from points to points, points to blocks, blocks to points, and blocks to blocks. The approach includes fully Bayesian kriging. We also extend our approach to the the case of spatio-temporal data, wherein a judicious specification of spatio-temporal association enables manageable computation. Exemplification of the static spatial case is provided using a dataset of point-level ozone measurements in the Atlanta, Georgia metropolitan area. The dynamic spatial case is illustrated using a temporally extended version of this dataset, enabling comparison at the common time point.*To whom correspondence should be addressed.     

Donor Conception Disclosure: Directive or Non-Directive Counselling?

It is widely agreed among health professionals that couples using donor insemination should be offered counselling on the topic of donor conception disclosure. However, it is clear from the literature that there has long been a lack of agreement about which counselling approach should be used in this case: a directive or a non-directive approach. In this paper we investigate which approach is ethically justifiable by balancing the two underlying principles of autonomy (non-directive approach) and beneficence (directive approach). To overrule one principle in favour of another, six conditions should be fulfilled. We analyse the arguments in favour of the beneficence principle, and consequently, a directive approach. This analysis shows that two conditions are not met; the principle of autonomy should not be overridden. Therefore, at this moment, a directive counselling approach on donor conception disclosure cannot be ethically justified.     

Application of pattern-mixture models to outcomes that are potentially missing not at random using pseudo maximum likelihood estimation

In this work, we fit pattern-mixture models to data sets with responses that are potentially missing not at random (MNAR, Little and Rubin, 1987). In estimating the regression parameters that are identifiable, we use the pseudo maximum likelihood method based on exponential families. This procedure provides consistent estimators when the mean structure is correctly specified for each pattern, with further information on the variance structure giving an efficient estimator. The proposed method can be used to handle a variety of continuous and discrete outcomes. A test built on this approach is also developed for model simplification in order to improve efficiency. Simulations are carried out to compare the proposed estimation procedure with other methods. In combination with sensitivity analysis, our approach can be used to fit parsimonious semi-parametric pattern-mixture models to outcomes that are potentially MNAR. We apply the proposed method to an epidemiologic cohort study to examine cognition decline among elderly.     

Analysing the effect of candidate genes on complex traits: an application in multiple sclerosis

The conventional approach of candidate gene studies in complex diseases is to look at the effect of one gene at a time. However, as the outcome of chronic diseases is influenced by a large number of alleles, simultaneous analysis is needed. We demonstrate the application of multivariate regression and cluster analysis to a multiple sclerosis (MS) dataset with genotypes for 489 patients at 11 candidate genes selected on their involvement in the immune response. Using multivariate regression, we observed that different sets of genes were associated with different disease characteristics that reflect different aspects of disease. Out of 15 polymorphisms, we identified one that contributed to the severity of disease. In addition, the set of 15 polymorphisms was predictive for yearly increase in lesion volume as seen on T1-weighted MRI (p=0.044). From this set, no individual polymorphisms could be identified after adjustment for multiple hypotheses testing. By means of a cluster analysis, we aimed to identify subgroups of patients with different pathogenic subtypes of MS on the basis of their genetic profile. We constructed genetic profiles from the genotypes at the 11 candidate genes. The approach proved to be feasible. We observed three clusters in the sample of patients. In this study, we observed no significant differences in the usual clinical and MRI outcome measures between the different clusters. However, a number of consistent trends indicated that this clustering might be related to the course of disease. With a larger number of genes regulating the course of disease, we may be able to identify clinically relevant clusters. The analyses are easily implemented and will be applicable to candidate gene studies of complex traits in general.     

Function-valued adaptive dynamics and optimal control theory

In this article we further develop the theory of adaptive dynamics of function-valued traits. Previous work has concentrated on models for which invasion fitness can be written as an integral in which the integrand for each argument value is a function of the strategy value at that argument value only. For this type of models of direct effect, singular strategies can be found using the calculus of variations, with singular strategies needing to satisfy Euler’s equation with environmental feedback. In a broader, more mechanistically oriented class of models, the function-valued strategy affects a process described by differential equations, and fitness can be expressed as an integral in which the integrand for each argument value depends both on the strategy and on process variables at that argument value. In general, the calculus of variations cannot help analyzing this much broader class of models. Here we explain how to find singular strategies in this class of process-mediated models using optimal control theory. In particular, we show that singular strategies need to satisfy Pontryagin’s maximum principle with environmental feedback. We demonstrate the utility of this approach by studying the evolution of strategies determining seasonal flowering schedules.     

Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring

Model-based geostatistical design involves the selection of locations to collect data to minimize an expected loss function over a set of all possible locations. The loss function is specified to reflect the aim of data collection, which, for geostatistical studies, could be to minimize the prediction uncertainty at unobserved locations. In this paper, we propose a new approach to design such studies via a loss function derived through considering the entropy about the model predictions and the parameters of the model. The approach includes a multivariate extension to generalized linear spatial models, and thus can be used to design experiments with more than one response. Unfortunately, evaluating our proposed loss function is computationally expensive so we provide an approximation such that our approach can be adopted to design realistically sized geostatistical studies. This is demonstrated through a simulated study and through designing an air quality monitoring program in Queensland, Australia. The results show that our designs remain highly efficient in achieving each experimental objective individually, providing an ideal compromise between the two objectives. Accordingly, we advocate that our approach could be adopted more generally in model-based geostatistical design.     

Quantitative in situ hybridization using initial velocity measurements

In situ hybridization can be used to quantitate viral RNA at the single cell level by measuring levels of hybridization after saturation hybridization with an excess of cDNA probe has been achieved (1,2). In this paper we describe an alternative approach which consists in measuring the initial hybridization rate using a low concentration of cDNA probe and a short hybridization time. Under these conditions, we obtained a linear relationship between the number of autoradiographic grains and the number of viral genomes per cell in the range of 600 to 60,000 copies per cell of a 7-kb RNA genome. This approach allows an accurate measurement of copy number in a range for which saturation in situ hybridization is very difficult to achieve.     

Evaluating the importance of within- and between-host selection pressures on the evolution of chronic pathogens

Infectious pathogens compete and are subject to natural selection at multiple levels. For example, viral strains compete for access to host resources within an infected host and, at the same time, compete for access to susceptible hosts within the host population. Here we propose a novel approach to study the interplay between within- and between-host competition. This approach allows for a single host to be infected by and transmit two strains of the same pathogen. We do this by nesting a model for the host–pathogen dynamics within each infected host into an epidemiological model. The nesting of models allows the between-host infectivity and mortality rates suffered by infected hosts to be functions of the disease progression at the within-host level. We present a general method for computing the basic reproduction ratio of a pathogen in such a model. We then illustrate our method using a basic model for the within-host dynamics of viral infections, embedded within the simplest susceptible–infected (SI) epidemiological model. Within this nested framework, we show that the virion production rate at the level of the cell–virus interaction leads, via within-host competition, to the presence or absence of between-host level competitive exclusion. In particular, we find that in the absence of mutation the strain that maximizes between-host fitness can outcompete all other strains. In the presence of mutation we observe a complex invasion landscape showing the possibility of coexistence. Although we emphasize the application to human viral diseases, we expect this methodology to be applicable to be many host–parasite systems.     

油菜裂外壁小孢子胚在分子水平上具有胚体/胚柄分化

早期合子胚取材困难, 难以开展相关研究。前人的工作表明, 油菜(Brassica napus)裂外壁小孢子胚胎发生系统能够较好地模拟合子胚的分化模式, 因而可替代早期合子胚胎作为研究材料。但目前尚缺乏该胚胎发生系统中胚胎具有胚体/胚柄分化的分子水平的证据。该文首次证明了油菜WOX家族基因能够用于标记胚体/胚柄的分化过程, 利用胚柄标记基因BnWOX8的表达模式, 从分子水平上证明了带胚柄的裂外壁小孢子胚的确存在胚体/胚柄的分化。研究结果为充分利用油菜裂外壁小孢子胚胎发生系统, 解决早期胚胎取材困难的问题奠定了坚实的基础。同时, 建立了活体激光切割分离特定细胞的技术, 结合用于少量细胞RNA提取的活体特异细胞RNA提取技术, 为鉴定少量特异分化细胞的基因表达模式提供了一个可行且明确的解决方案。