Evidence for turnover of functional noncoding DNA in mammalian genome evolution

The vast majority of the mammalian genome does not code for proteins, and a fundamental question in genomics is: What proportion of the noncoding mammalian genome is functional? Most attempts to address this issue use sequence comparisons between highly diverged mammals such as human and mouse to identify conservation due to negative selection. But such comparisons will underestimate the true proportion of functional noncoding DNA if there is turnover, if patterns of negative selection change over time. Here we test whether the inferred level of negative selection differs between different pairwise species comparisons. Using a multiple alignment of more than a megabase of contiguous sequence from eight mammalian species, we find a strong negative relationship between inferred levels of negative selection and pairwise divergence using 21 pairwise comparisons. This result suggests that there is a high rate of turnover of functional noncoding elements in the mammalian genome, so measures of functional constraint based on human-mouse comparisons may seriously underestimate the true value.     

The Problem of Characters Susceptible to Parallel Evolution in Phylogenetic Reconstructions: Suggestion of a Practical Method and Its Application to Cave Animals

We here propose a procedure to treat characters which are susceptible to parallel evolution (in this case, troglomorphisms) as a replacement for the two procedures used so far: to either completely consider or completely disregard these characters. These procedures may lead to one of two opposite errors, respectively, (1) to consider them as true synapomorphies when they are not or (2) to disregard them as true synapomorphies when they are. We suggest herein to recode the characters by splitting each troglomorphic character into as many as the number of taxa which show the given troglomorphic state. For each split character each taxon will have the derived state, while the others will be coded as missing data. We provide three real examples to test our procedure and conclude that it may give results different from those of the other two procedures. This means that our procedure does not repeat the two above "errors." In addition, we believe that the procedure filters the possible biases, resulting in trees in which the troglomorphisms do have phylogenetic signals.     

Optimal sibship selection for genotyping in quantitative trait locus linkage analysis

In this paper we present a novel method for selecting optimally informative sibships of any size for quantitative trait locus (QTL) linkage analysis. The method allocates a quantitative index of potential informativeness to each sibship on the basis of observed trait scores and an assumed true QTL model. Any sample of phenotypically screened sibships can therefore be easily rank-ordered for selective genotyping. The quantitative index is the sibship's expected contribution to the non-centrality parameter. This expectation represents the weighted sum of chi(2) test statistics that would be obtained given the observed trait values over all possible sibship genotypic configurations; each configuration is weighted by the likelihood of it occurring given the assumed true genetic model. The properties of this procedure are explored in relation to the accuracy of the assumed true genetic model and sibship size. In comparison to previous methods of selecting phenotypically extreme sibships for genotyping, the proposed method is considerably more efficient and is robust with regard to the specification of the genetic model.     

Advances toward tissue engineering for the treatment of stress urinary incontinence

Suburethral pubovaginal sling placement is a common surgical procedure for the treatment of stress urinary incontinence. A wide variety of graft materials is available, each associated with inherent desirable and undesirable characteristics and complications. In this article, we discuss the rationale for and application of small intestinal submucosa (SIS) in lower urinary tract tissue engineering, with emphasis on the use of SIS as a suitable and biologically compatible sling material. In addition, we discuss exciting research regarding the engineering of true functional sphincter reconstruction using this biologic scaffold and pre-seeded muscle cells.     

Not all sperm are equal: functional mitochondria characterize a subpopulation of human sperm with better fertilization potential

Human sperm samples are very heterogeneous and include a low amount of truly functional gametes. Distinct strategies have been developed to characterize and isolate this specific subpopulation. In this study we have used fluorescence microscopy and fluorescence-activated cell sorting to determine if mitochondrial function, as assessed using mitochondrial-sensitive probes, could be employed as a criterion to obtain more functional sperm from a given ejaculate. We first determined that mitochondrial activity correlated with the quality of distinct human samples, from healthy donors to patients with decreased semen quality. Furthermore, using fluorescence-activated cell sorting to separate sperm with active and inactive mitochondria we found that this was also true within samples. Indeed, sperm with active mitochondria defined a more functional subpopulation, which contained more capacitated and acrosome intact cells, sperm with lower chromatin damage, and, crucially, sperm more able to decondense and participate in early development using both chemical induction and injection into mature bovine oocytes. Furthermore, cell sorting using mitochondrial activity produced a more functional sperm subpopulation than classic swim-up, both in terms of improvement in a variety of functional sperm parameters and in statistical significance. In conclusion, whatever the true biological role of sperm mitochondria in fertilization, mitochondrial activity is a clear hallmark of human sperm functionality.     

More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses

Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.     

Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error

Summary : We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B‐splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH‐AARP Diet and Health Study and examine its performance in a simulation study.     

Statistical estimation of gene expression using multiple laser scans of microarrays

We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias. AVAILABILITY: FORTRAN 90 code for implementing the method can be obtained from the authors.     

The current status of REH theory

Summary The recent evaluation by Fitch (1980) of REH theory for macromolecular divergence is a severely erroneous and distorted analysis of our work over the past decade. We reply to those distortions here. At present, there is no factual basis for believing Fitch's assessment that corrections which move evolutionary estimates of total mutations fixed closer to the true distance must do so at the expense of an increased variance sufficient to compromise the value of the improvement. By direct calculation the variance in the estimates of total mutations fixed given by REH theory is comparable to that of other models now in the literature for the case in which genetic events are equiprobable. A general argument is given that suggests that, as we consider more and more carefully the selective, functional, and structural constraints on the evolution of genes and proteins, this variance may be expected to decrease toward a lower bound.     

Characterizing positive and negative selection and their phylogenetic effects

Total evidence and the use of large datasets to overcome uncertainty are the state of the art in systematic analysis. This assumes that the only true phylogenetic signal is ancestry and that functional, structural, and other factors will not add an alternative signal. Using gene families, where individual codon positions were sorted into bins based upon average-pairwise dN/dS ratio, we show that standard, common phylogenetic methods that were designed for stochastic, neutral, site-independent processes, generate less robust phylogenetic signal for bins with strong negative or positive selection. This was true for phylogenetic reconstruction with parsimony, distance, and likelihood methods. Further, we present a case for the potential existence of systematic functional or structural signal that competes with ancestral signal. For the example of positive selection, we simulate the evolution of sequences through three dimensional lattice constructs with folding constraint and changing binding functionality and show that total evidence for these lattice genes presents trees with functional signal, but that the neutral synonymous sites in these genes show the true ancestral signal. In this case, sequence convergence is promoted by functional convergence.     

Unravelling the functional interaction structure of a cellular network from temporal slope information of experimental data

Due to the unavoidable nonbiological variations accompanying many experiments, it is imperative to consider a way of unravelling the functional interaction structure of a cellular network (e.g. signalling cascades or gene networks) by using the qualitative information of time-series experimental data instead of computation through the measured absolute values. In this spirit, we propose a very simple but effective method of identifying the functional interaction structure of a cellular network based on temporal ascending or descending slope information from given time-series measurements. From this method, we can gain insight into the acceptable measurement error ranges in order to estimate the correct functional interaction structure and we can also find guidance for a new experimental design to complement the insufficient information of a given experimental dataset. We developed experimental sign equations, making use of the temporal slope sign information from time-series experimental data, without a specific assumption on parameter perturbations for each network node. Based on these equations, we further describe the available specific information from each part of experimental data in detail and show the functional interaction structure obtained by integrating such information. In this procedure, we use only simple algebra on sign changes without complicated computations on the measured absolute values of the experimental data. The result is, however, verified through rigorous mathematical definitions and proofs. The present method provides us with information about the acceptable measurement error ranges for correct estimation of the functional interaction structure and it further leads to a new experimental design to complement the given experimental data by informing us about additional specific sampling points to be chosen for further required information.     

Exploring uncertainty in the calibration of the molecular clock

Calibration is a critical step in every molecular clock analysis but it has been the least considered. Bayesian approaches to divergence time estimation make it possible to incorporate the uncertainty in the degree to which fossil evidence approximates the true time of divergence. We explored the impact of different approaches in expressing this relationship, using arthropod phylogeny as an example for which we established novel calibrations. We demonstrate that the parameters distinguishing calibration densities have a major impact upon the prior and posterior of the divergence times, and it is critically important that users evaluate the joint prior distribution of divergence times used by their dating programmes. We illustrate a procedure for deriving calibration densities in Bayesian divergence dating through the use of soft maximum constraints.     

Construction of phylogenetic trees for proteins and nucleic acids: Empirical evaluation of alternative matrix methods

Summary The methods of Fitch and Margoliash and of Farris for the construction of phylogenetic trees were compared. A phenetic clustering technique - the UPGMA method — was also considered.The three methods were applied to difference matrices obtained from comparison of macromolecules by immunological, DNA hybridization, electrophoretic, and amino acid sequencing techniques. To evaluate the results, we used the goodness-of-fit criterion. In some instances, the F-M and Farris methods gave a comparably good fit of the output to the input data, though in most cases the F-M procedure gave a much better fit. By the fit criterion, the UPGMA procedure was on the average better than the Farris method but not as good as the F-M procedure.On the basis of the results given in this report and the goodness-of-fit criterion, it is suggested that where input data are likely to include overestimates as well as true estimates and underestimates of the actual distances between taxonomic units, the F-M method is the most reasonable to use for constructing phylogenies from distance matrices. Immunological, DNA hybridization, and electrophoretic data fall into this category. By contrast, where it is known that each input datum is indeed either a true estimate or an underestimate of the actual distance between 2 taxonomic units, the Farris procedure appears, on theoretical grounds, to be the matrix method of choice. Amino acid and nucleotide sequence data are in this category.The following abbreviations are used in this work F-M Fitch-Margoliash - UPGMA unweighted pair-group method using arithmetic averages - SD percent standard deviation     

Estimation of Fixed and Random Effects in the Functional Nonlinear Errors-in-Variable Model

Consider an experiment where a nonlinear continuous functional relationship exists between y and X. Assume that this relationship has been measured at n replicated points of X from each of t treatments or populations. Assume further that the X are fixed unknown vectors and that the location parameter v is either a fixed unknown vector or a vector of random variables. In the first case various linear hypotheses are to be tested about v, such as tests for main effects and interaction; in the second case, the mean and variance of the random variable v are to be estimated. A two-step procedure based on asymptotic theory is presented to test hypotheses or develop estimates for the fixed effects or random effects functional errors-in-variable model. An example of a one-way random effects model is given.     

Brain regions essential for improved lexical access in an aged aphasic patient: a case report

Background  

The relationship between functional recovery after brain injury and concomitant neuroplastic changes is emphasized in recent research. In the present study we aimed to delineate brain regions essential for language performance in aphasia using functional magnetic resonance imaging and acquisition in a temporal sparse sampling procedure, which allows monitoring of overt verbal responses during scanning.     

A global analysis of avian island diversity–area relationships in the Anthropocene

Research on island species–area relationships (ISAR) has expanded to incorporate functional (IFDAR) and phylogenetic (IPDAR) diversity. However, relative to the ISAR, we know little about IFDARs and IPDARs, and lack synthetic global analyses of variation in form of these three categories of island diversity–area relationship (IDAR). Here, we undertake the first comparative evaluation of IDARs at the global scale using 51 avian archipelagic data sets representing true and habitat islands. Using null models, we explore how richness-corrected functional and phylogenetic diversity scale with island area. We also provide the largest global assessment of the impacts of species introductions and extinctions on the IDAR. Results show that increasing richness with area is the primary driver of the (non-richness corrected) IPDAR and IFDAR for many data sets. However, for several archipelagos, richness-corrected functional and phylogenetic diversity changes linearly with island area, suggesting that the dominant community assembly processes shift along the island area gradient. We also find that archipelagos with the steepest ISARs exhibit the biggest differences in slope between IDARs, indicating increased functional and phylogenetic redundancy on larger islands in these archipelagos. In several cases introduced species seem to have ‘re-calibrated’ the IDARs such that they resemble the historic period prior to recent extinctions.