Preterm birth (PTB) is the leading cause of neonatal morbidity and mortality. Previous studies have suggested that the maternal vaginal microbiota contributes to the pathophysiology of PTB, but conflicting results in recent years have raised doubts. We conducted a study of PTB compared with term birth in two cohorts of pregnant women: one predominantly Caucasian (n = 39) at low risk for PTB, the second predominantly African American and at high-risk (n = 96). We profiled the taxonomic composition of 2,179 vaginal swabs collected prospectively and weekly during gestation using 16S rRNA gene sequencing. Previously proposed associations between PTB and lower Lactobacillus and higher Gardnerella abundances replicated in the low-risk cohort, but not in the high-risk cohort. High-resolution bioinformatics enabled taxonomic assignment to the species and subspecies levels, revealing that Lactobacillus crispatus was associated with low risk of PTB in both cohorts, while Lactobacillus iners was not, and that a subspecies clade of Gardnerella vaginalis explained the genus association with PTB. Patterns of cooccurrence between L. crispatus and Gardnerella were highly exclusive, while Gardnerella and L. iners often coexisted at high frequencies. We argue that the vaginal microbiota is better represented by the quantitative frequencies of these key taxa than by classifying communities into five community state types. Our findings extend and corroborate the association between the vaginal microbiota and PTB, demonstrate the benefits of high-resolution statistical bioinformatics in clinical microbiome studies, and suggest that previous conflicting results may reflect the different risk profile of women of black race.pregnancy | prematurity | vaginal microbiota | Lactobacillus | Gardnerella P reterm birth (PTB; delivery at <37 gestational wk) affects ≈12% of US births and is the leading cause of neonatal death and morbidity worldwide. Multiple lines of evidence support a role for the indigenous microbial communities of the mother (the maternal microbiota) in the pathophysiology of PTB. Microbial invasion of the amniotic cavity is one of the most frequent causes of spontaneous PTB (1), and the most common invading taxa are consistent with maternal origin (2-4). Bacterial vaginosis (BV), a condition involving an altered vaginal microbiota, has been consistently identified as a risk factor for PTB (5, 6). Multiple studies have also found chronic periodontitis, another condition associated with an altered microbiota, to be a risk factor for PTB (7,8).High-throughput sequencing methods have facilitated new lines of investigation into the microbial etiology of PTB (9, 10). Amplification and high-throughput sequencing of the 16S rRNA gene (metabarcoding) simultaneously measures the presence and relative abundance of thousands of bacterial taxa (composition), and resolves differences to the level of genus and sometimes species or subspecies. To date, metabarcoding studies of the relationship between the vaginal microbiota and PTB have y...

The primary purpose of this paper is to review a very few results on some basic elements of large sample theory in a restricted structural framework, as described in detail in the recent book by LeCam and Yang (1990, Asymptotics in Statistics: Some Basic Concepts. New York: Springer), and to illustrate how the asymptotic inference problems associated with a wide variety of time series regression models fit into such a structural framework. The models illustrated include many linear time series models, including cointegrated models and autoregressive models with unit roots that are of wide current interest. The general treatment also includes nonlinear models, including what have become known as ARCH models. The possibility of replacing the density of the error variables of such models by an estimate of it (adaptive estimation) based on the observations is also considered.Under the framework in which the asymptotic problems are treated, only the approximating structure of the likelihood ratios of the observations, together with auxiliary estimates of the parameters, will be required. Such approximating structures are available under quite general assumptions, such as that the Fisher information of the common density of the error variables is finite and nonsingular, and the more specific assumptions, such as Gaussianity, are not required. In addition, the construction and the form of inference procedures will not involve any additional complications in the non-Gaussian situations because the approximating quadratic structure actually will reduce the problems to the situations similar to those involved in the Gaussian cases.

Our work focuses on the stability, resilience, and response to perturbation of the bacterial communities in the human gut. Informative flash flood-like disturbances that eliminate most gastrointestinal biomass can be induced using a clinically-relevant iso-osmotic agent. We designed and executed such a disturbance in human volunteers using a dense longitudinal sampling scheme extending before and after induced diarrhea. This experiment has enabled a careful multidomain analysis of a controlled perturbation of the human gut microbiota with a new level of resolution. These new longitudinal multidomain data were analyzed using recently developed statistical methods that demonstrate improvements over current practices. By imposing sparsity constraints we have enhanced the interpretability of the analyses and by employing a new adaptive generalized principal components analysis, incorporated modulated phylogenetic information and enhanced interpretation through scoring of the portions of the tree most influenced by the perturbation. Our analyses leverage the taxa-sample duality in the data to show how the gut microbiota recovers following this perturbation. Through a holistic approach that integrates phylogenetic, metagenomic and abundance information, we elucidate patterns of taxonomic and functional change that characterize the community recovery process across individuals. We provide complete code and illustrations of new sparse statistical methods for high-dimensional, longitudinal multidomain data that provide greater interpretability than existing methods.

Consider a sequence X k = ∞ j=0 cj ξ k−j , k ≥ 1, where cj , j ≥ 0, is a sequence of constants and ξj, −∞ < j < ∞, is a sequence of independent identically distributed (i.i.d.) random variables (r.v.s) belonging to the domain of attraction of a strictly stable law with index 0 < α ≤ 2. Let S k = k j=1 Xj . Under suitable conditions on the constants cj it is known that for a suitable normalizing constant γn, the partial sum process γ −1 n S [nt] converges in distribution to a linear fractional stable motion (indexed by α and H, 0 < H < 1). A fractional ARIMA process with possibly heavy tailed innovations is a special case of the process X k . In this paper it is established that the process n −1 βnis the local time of the linear fractional stable motion, for a wide class of functions f ( y) that includes the indicator functions of bounded intervals of the real line. Here βn → ∞ such that n −1 βn → 0. The only further condition that is assumed on the distribution of ξ1 is that either it satisfies the Cramér's condition or has a nonzero absolutely continuous component. The results have motivation in large sample inference for certain nonlinear time series models.

Some asymptotic properties of the least-squares estimator of the parameters of an AR model of order p, p ≥ 1, are studied when the roots of the characteristic polynomial of the given AR model are on or near the unit circle. Specifically, the convergence in distribution is established and the corresponding limiting random variables are represented in terms of functionals of suitable Brownian motions.Further, the preceding convergence in distribution is strengthened to that of convergence uniformly over all Borel subsets. It is indicated that the method employed for this purpose has the potential of being applicable in the wider context of obtaining suitable asymptotic expansions of the distributions of leastsquares estimators.