The relationship between weight change and daytime sleepiness: the Sleep Heart Health Study

ObjectiveThrough a causal framework, we aim to assess the association between weight change and daytime sleepiness, and the role of obstructive sleep apnoea (OSA) in this relationship.MethodsFrom the Sleep Heart Health Study, we selected individuals who were: (1) 40–64 years old, with (2) body mass index (BMI) ≥18.5 kg/m², (3) no history of stroke, treatment for OSA, and tracheostomy at baseline. We used multiple linear regression to assess the relationship between five-year weight change and daytime sleepiness (assessed through Epworth Sleepiness Scale (ESS)) at five years, adjusting for daytime sleepiness, demographics, diabetes, subjective sleep duration, sleep disturbance, smoking status, weight, and use of antidepressants and benzodiazepines at baseline, in those with complete data (N = 1468). We further assessed the potential mediating role of OSA in this relationship.ResultsAt baseline, the study participants were on average 55 years old, 46% males, with mean BMI 28 kg/m²; and 25% had ESS>10. ESS at five years worsened by 0.36 units (95% confidence interval (CI) 0.12–0.61, p = 0.004) with every 10-kg weight gain. When stratified by sex, this relationship was only found in women (0.55, 95% CI 0.25–0.86, p < 0.001; p-interaction = 0.02). Approximately one-fifth of the relationship between weight change and daytime sleepiness was mediated by severity of OSA at five years.ConclusionWeight gain has a detrimental effect on daytime sleepiness, mostly through pathways other than OSA. This study provides further evidence and understanding of the relationship between obesity and excessive daytime sleepiness.     

Rank‐based principal stratum sensitivity analyses

We describe rank‐based approaches to assess principal stratification treatment effects in studies where the outcome of interest is only well‐defined in a subgroup selected after randomization. Our methods are sensitivity analyses, in that estimands are identified by fixing a parameter and then we investigate the sensitivity of results by varying this parameter over a range of plausible values. We present three rank‐based test statistics and compare their performance through simulations, and provide recommendations. We also study three different bootstrap approaches for determining levels of significance. Finally, we apply our methods to two studies: an HIV vaccine trial and a prostate cancer prevention trial. Copyright © 2013 John Wiley & Sons, Ltd.     

Bayesian species delimitation using multilocus sequence data

In the absence of recent admixture between species, bipartitions of individuals in gene trees that are shared across loci can potentially be used to infer the presence of two or more species. This approach to species delimitation via molecular sequence data has been constrained by the fact that genealogies for individual loci are often poorly resolved and that ancestral lineage sorting, hybridization, and other population genetic processes can lead to discordant gene trees. Here we use a Bayesian modeling approach to generate the posterior probabilities of species assignments taking account of uncertainties due to unknown gene trees and the ancestral coalescent process. For tractability, we rely on a user-specified guide tree to avoid integrating over all possible species delimitations. The statistical performance of the method is examined using simulations, and the method is illustrated by analyzing sequence data from rotifers, fence lizards, and human populations.     

Myology of the forelimb of Majungasaurus crenatissimus (Theropoda,Abelisauridae) and the morphological consequences of extreme limb reduction

Forelimb reduction occurred independently in multiple lineages of theropod dinosaurs. Although tyrannosaurs are renowned for their tiny, two‐fingered forelimbs, the degree of their reduction in length is surpassed by abelisaurids, which possess an unusual morphology distinct from that of other theropods. The forelimbs of abelisaurids are short but robust and exhibit numerous crests, tubercles, and scars that allow for inferences of muscle attachment sites. Phylogenetically based reconstructions of the musculature were used in combination with close examination of the osteology in the Malagasy abelisaurid Majungasaurus to create detailed muscle maps of the forelimbs, and patterns of the muscular and bony morphology were compared with those of extant tetrapods with reduced or vestigial limbs. The lever arms of muscles crossing the glenohumeral joint are shortened relative to the basal condition, reducing the torque of these muscles but increasing the excursion of the humerus. Fusion of the antebrachial muscles into a set of flexors and extensors is common in other tetrapods and occurred to some extent in Majungasaurus. However, the presence of tubercles on the antebrachial and manual elements of abelisaurids indicates that many of the individual distal muscles acting on the wrist and digits were retained. Majungasaurus shows some signs of the advanced stages of forelimb reduction preceding limb loss, while also exhibiting features suggesting that the forelimb was not completely functionless. The conformation of abelisaurid forelimb musculature was unique among theropods and further emphasizes the unusual morphology of the forelimbs in this clade.     

Nonparametric coalescent inference of mutation spectrum history and demography

As populations boom and bust, the accumulation of genetic diversity is modulated, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum (relative mutation rates in different local nucleotide contexts). Here, we develop theoretical tools in the framework of Kingman’s coalescent to accommodate mutation spectrum dynamics. We present mutation spectrum history inference (mushi), a method to perform nonparametric inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and calibrate the timing of a previously reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be placed in a well-studied theoretical setting and rigorously inferred from genomic variation data, like other features of evolutionary history.

Over the past decade, population geneticists have developed many sophisticated methods for inferring population demography and have consistently found that simple isolated populations of constant size are far from the norm (reviewed in refs. 1–3). Population expansions and founder events, as well as migration between species and geographic regions, have been inferred from virtually all high-resolution genetic datasets. We now recognize that inferring these nonequilibrium demographies is often essential for understanding the histories of adaptation and global migration. Population genetics has uncovered many features of human history that were once virtually unknowable by other means, revealing a complex series of migrations, population replacements, and admixture networks among human groups and extinct hominoids.Although demographic inference methods can model complex population histories, the germline mutation process that creates variation has long received a comparatively simple treatment. A single parameter, μ, is used to represent the mutation rate per generation at all loci, in all individuals, and at all times. In humans, μ is estimated from parent–child trio sequencing studies, and modest variation in μ can have major effects on the interpretation of inferred parameters, such as times of admixture and population divergence. In other organisms, for which trio sequence data are usually unavailable, μ is estimated from sequence divergence between species with a fossil-calibrated divergence time, and these estimates come with still higher uncertainty.A growing body of evidence indicates that simple, constant mutation rate models may not adequately describe how variation accumulates on either inter- or intraspecific timescales (4–7). Germline mutation rates appear to have evolved during the speciation of great apes and the divergence of modern human populations (reviewed in ref. 8). Much of this evolution might be caused by nearly neutral drift (9), but a contributing factor could be selection on traits, like life history and chromatin structure, that indirectly affect mutation accumulation. Because mutation is intimately tied to the basic housekeeping process of cell division, gamete production, and embryonic development, the accumulation of mutations is likely to be complexly coupled to other biological processes (10–12).It is difficult to disentangle past changes in mutation rate from past changes in effective population size, which modulate levels of polymorphism even when the mutation rate stays constant. However, evolution of the mutation process can be indirectly detected by measuring its effects on the mutation spectrum: the relative mutation rates among different local nucleotide contexts. Hwang and Green (13) modeled the triplet context dependence of the substitution process in a mammalian phylogeny, finding varying contributions from replication errors, cytosine deamination, and biased gene conversion and showing that the relative rates of these processes varied between different mammalian lineages. Many cancers also exhibit somatic hypermutability of certain triplet motifs due to different DNA damage agents and failure points in the DNA repair process (14, 15). Harris (6) and Harris and Pritchard (7) examined the variation of triplet spectra between closely related populations, counting single-nucleotide variants in each triplet mutation type as a proxy for mutational input. They found that human triplet spectra distinctly cluster by continental ancestry group and that historical pulses in mutation activity influence the distribution of allele frequencies in certain mutation types. The divergence of mutation spectra among human continental groups has been replicated in independently generated datasets (7, 16), and similar patterns have been observed in other species, including great apes (17), mice (18), and yeast (19). Some of the mutation spectrum divergence between mice and yeast lineages has been mapped to mutator alleles (19, 20).Emerging from the literature is a picture of a mutation process evolving within and between populations, anchored to genomic features and accented by spectra of local nucleotide context. If probabilistic models of population genetic processes are to keep pace with these empirical findings, mutation deserves a richer treatment in state-of-the-art inference tools. In this paper, we build on classical theoretical tools to introduce fast nonparametric inference of population-level mutation spectrum history (MuSH)—the relative mutation rate in different local nucleotide contexts across time—alongside inference of demographic history. Whereas previous work has uncovered mutation spectrum evolution using summary statistics of standing variation, we shift perspective to focus on inference of the MuSH, which we model on the same footing as demography.Demographic inference requires us to invert the map that takes population history to the patterns of genetic diversity observable today. This task is often simplified by first compressing these genetic diversity data into a summary statistic such as the sample frequency spectrum (SFS), the distribution of derived allele frequencies among sampled haplotypes. The SFS is a well-studied population genetic summary statistic that is sensitive to demographic history. Inverting the map from demographic history to SFS is a notoriously ill-posed problem, in that many different population histories can have identical expected SFS (21–25). One way to deal with the ill posedness of demographic inference is to specify a parametric model of population size change, usually piecewise linear or piecewise exponential. An alternative, which generalizes to other inverse problems, is to allow a more general space of solutions but to regularize by penalizing histories that contain biologically unrealistic features (e.g., high-frequency population size oscillations). Both approaches shrink the set of feasible solutions to the inverse problem so that it becomes well posed and can be thought of as leveraging prior knowledge. In particular, regularization constrains the population size from changing on arbitrarily small timescales since significant population size change usually takes at least a few generations.In this paper, we extend a coalescent framework for demographic inference to accommodate inference of the MuSH from an SFS that is resolved into different local k-mer nucleotide contexts. This is a richer summary statistic that we call the k-SFS where, for example, k=3 means triplet context. We show using coalescent theory that the k-SFS is related to the MuSH by a linear transformation while depending nonlinearly on the demographic history. We infer both demographic history and MuSH by optimizing a cost that balances a data-fitting term using the forward map from coalescent theory, along with regularization terms that favor solutions with low complexity. Our open-source software mushi (mutation spectrum history inference) is available in ref. 26 as a Python package with extensive documentation. Using default settings and modest hardware, mushi takes only a few seconds to infer histories from population-scale sample frequency data.The recovered MuSH is a rich object that illuminates dimensions of population history and addresses biological questions about the evolution of the mutation process. After validating with data simulated under known histories, we use mushi to independently infer histories for each of the 26 populations (from 5 superpopulations defined by continental ancestry) from the 1000 Genomes Project (1KG) Consortium (27) using recent high-coverage sequencing data (28). We demonstrate that mushi is a powerful tool for demographic inference that has several advantages over existing demographic inference methods and then go on to describe the illuminated features of human MuSH.We recover demographic features that are robust to regularization parameter choices, including the out-of-Africa event and the more recent bottleneck in the ancestors of modern Finns, and we find that effective population sizes converge ancestrally within each superpopulation, despite being inferred independently. Decomposing human MuSH into mutation signatures varying through time in each population, we see global divergence in the mutation process that impacts many mutation types and reflects population and superpopulation relatedness. Finally, we revisit the timing of a previously reported ancient pulse of elevated TCC → TTC mutation rate, active primarily in the ancestors of Europeans and absent in East Asians (6, 7, 29, 30). We find that the extent of the pulse into the ancient past is sensitive to the choice of demographic history model but that all demographic models that fit the k-SFS yield a pulse timing that is significantly older than previously thought, seemingly arising near the divergence time of East Asians and Europeans.With mushi, we can quickly reconstruct demographic history and MuSH without strong model specification requirements. This adds an approach to the toolbox for researchers interested only in demographic inference. For researchers studying the mutation spectrum, demographic history is necessary for time calibration of events in mutation history, so we expect that jointly modeling demography and MuSH will be important for studying mutational spectrum evolution in population genetics.     

4种药物多输入多输出滴注用模糊逻辑系统的决策制定

目的 研究多年来老年心脏病手术及手术后监护时，4种药物多输入多输出滴注用模糊逻辑系统的决策制定的原理及方法。方法 从模糊逻辑原理起到4种药物对老年心脏病6种症状的病理状态及治疗策略加以研究。结果 求得典型的模糊逻辑用语言结构，表层结构及电脑用的深度结构。结论 在模糊逻辑理论的概念基础上，可算得模糊化输出，代入病人症状的主要生理参数MAP，MPAD和CO检测值。应用模糊匹配及准则计算的结果就是4种药物多输入多输出滴注时的决策制定成功。以一种狗实验的结论证明此种模糊决策制定模块确能成功应用于临床。     

Mental health assessment: Inference,explanation, and coherence

Mental health professionals such as psychiatrists and psychotherapists assess their patients by identifying disorders that explain their symptoms. This assessment requires an inference to the best explanation that compares different disorders with respect to how well they explain the available evidence. Such comparisons are captured by the theory of explanatory coherence that states 7 principles for evaluating competing hypotheses in the light of evidence. The computational model ECHO shows how explanatory coherence can be efficiently computed. We show the applicability of explanatory coherence to mental health assessment by modelling a case of psychiatric interviewing and a case of psychotherapeutic evaluation. We argue that this approach is more plausible than Bayesian inference and hermeneutic interpretation.     

Integrative modeling of multi‐platform genomic data under the framework of mediation analysis

Given the availability of genomic data, there have been emerging interests in integrating multi‐platform data. Here, we propose to model genetics (single nucleotide polymorphism (SNP)), epigenetics (DNA methylation), and gene expression data as a biological process to delineate phenotypic traits under the framework of causal mediation modeling. We propose a regression model for the joint effect of SNPs, methylation, gene expression, and their nonlinear interactions on the outcome and develop a variance component score test for any arbitrary set of regression coefficients. The test statistic under the null follows a mixture of chi‐square distributions, which can be approximated using a characteristic function inversion method or a perturbation procedure. We construct tests for candidate models determined by different combinations of SNPs, DNA methylation, gene expression, and interactions and further propose an omnibus test to accommodate different models. We then study three path‐specific effects: the direct effect of SNPs on the outcome, the effect mediated through expression, and the effect through methylation. We characterize correspondences between the three path‐specific effects and coefficients in the regression model, which are influenced by causal relations among SNPs, DNA methylation, and gene expression. We illustrate the utility of our method in two genomic studies and numerical simulation studies. Copyright © 2014 John Wiley & Sons, Ltd.     

A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment‐induced intermediate confounding

Recently, several authors have shown that natural direct and indirect effects (NDEs and NIEs) can be identified under the sequential ignorability assumptions, as long as there is no mediator–outcome confounder that is affected by the treatment. However, if such a confounder exists, NDEs and NIEs will generally not be identified without making additional identifying assumptions. In this article, we propose novel identification assumptions and estimators for evaluating NDEs and NIEs under the usual sequential ignorability assumptions, using the principal stratification framework. It is assumed that the treatment and the mediator are dichotomous. We must impose strong assumptions for identification. However, even if these assumptions were violated, the bias of our estimator would be small under typical conditions, which can be easily evaluated from the observed data. This conjecture is confirmed for binary outcomes by deriving the bounds of the bias terms. In addition, the advantage of our estimator is illustrated through a simulation study. We also propose a method of sensitivity analysis that examines what happens when our assumptions are violated. We apply the proposed method to data from the National Center for Health Statistics. Copyright © 2014 John Wiley & Sons, Ltd.