Biology subjects

Dowd, J. B.

Publications and source records attributed to Dowd, J. B..

6 recordsLinked to original sources

bioRxiv2026

Variability and persistence in inflation reshape human population dynamics by amplifying sensitivity and eroding demographic buffering

Inflation affects human fertility and survival; persistent inflation can translate into fluctuations in annual population growth rate. Existing studies have yet to systematically examine inflations impact on long-term growth rates across populations. Here, we examine whether high inflation variance and autocorrelation amplify long-term growth rates sensitivity to inflation, across 32 countries over 1971-2024. Additionally, we examine the effect of increasing inflation variance and autocorrelation on humans capacity for demographic buffering, i.e., reducing the temporal variance in vital rates most sensitive to growth. First, we construct inflation-dependent matrix population models using a hierarchical Bayesian framework. We then calculate the stochastic sensitivity and elasticity of population growth rate to inflation to quantify demographic buffering across a variance-autocorrelation parameter space. We find that the stochastic sensitivities of the growth rate increase with inflations variance and autocorrelation. However, we do not find support for the expected adverse effects of inflation variance and autocorrelation on humans capacity to buffer against inflation. Our framework provides a valuable tool for examining the impact of resource uncertainty on life history strategies in stochastic environments. We discuss how our framework can be extended to study the resource-dependent population dynamics within variable environments and across the tree of life.

ecology↗

bioRxiv2020

Strengthening The Organizing and Reporting of Microbiome Studies (STORMS)

BackgroundHuman microbiome research is a growing field with the potential for improving our understanding and treatment of diseases and other conditions. The field is interdisciplinary, making concise organization and reporting of results across different styles of epidemiology, biology, bioinformatics, translational medicine, and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. MethodsA multidisciplinary group of microbiome epidemiology researchers reviewed elements of available reporting guidelines for observational and genetic studies and adapted these for application to culture-independent human microbiome studies. New reporting elements were developed for laboratory, bioinformatic, and statistical analyses tailored to microbiome studies, and other parts of these checklists were streamlined to keep reporting manageable. ResultsSTORMS is a 17-item checklist for reporting on human microbiome studies, organized into six sections covering typical sections of a scientific publication, presented as a table with space for author-provided details and intended for inclusion in supplementary materials. ConclusionsSTORMS provides guidance for authors and standardization for interdisciplinary microbiome studies, facilitating complete and concise reporting and augments information extraction for downstream applications. AvailabilityThe STORMS checklist is available as a versioned spreadsheet from https://www.stormsmicrobiome.org/.

genomics↗

bioRxiv2018

Tobacco exposure associated with oral microbiota oxygen utilization in the New York City Health and Nutrition Examination Study

PurposeThe effect of tobacco exposure on the oral microbiome has not been established. MethodsWe performed amplicon sequencing of the 16S ribosomal RNA gene V4 variable region to estimate bacterial community characteristics in 259 oral rinse samples, selected based on self-reported smoking and serum cotinine levels, from the 2013-14 New York City Health and Nutrition Examination Study. We identified differentially abundant operational taxonomic units (OTUs) by primary and secondhand tobacco exposure, and employed "microbe set enrichment analysis" to assess shifts in microbial oxygen utilization. ResultsCigarette smoking was associated with depletion of aerobic OTUs (Enrichment Score test statistic ES = -0.75, p = 0.002) with a minority (29%) of aerobic OTUs enriched in current smokers compared to never smokers. Consistent shifts in the microbiota were observed for current cigarette smokers as for non-smokers with secondhand exposure as measured by serum cotinine levels. Differential abundance findings were similar in crude and adjusted analyses. ConclusionResults support a plausible link between tobacco exposure and shifts in the oral microbiome at the population level through three lines of evidence: 1) a shift in microbiota oxygen utilization associated with primary tobacco smoke exposure, 2) consistency of abundance fold-changes associated with current smoking and shifts along the gradient of secondhand smoke exposure among non-smokers, and 3) consistency after adjusting for a priori hypothesized confounders. HighlightsO_LICigarette smoke was associated with microbial anaerobiosis in oral rinse specimens C_LIO_LIThe microbiome shifts associated with smoking and secondhand exposure were correlated C_LIO_LIShifts in oral bacterial oxygen utilization may mediate smoking and health outcomes C_LIO_LIWe propose "microbe set enrichment analysis" for interpreting shifts in the microbiome C_LI

epidemiology↗

bioRxiv2018

HMP16SData: Efficient Access to the Human Microbiome Project through Bioconductor

Phase 1 of the NIH Human Microbiome Project (HMP) investigated 18 body subsites of 239 healthy American adults, to produce the first comprehensive reference for the composition and variation of the \"healthy\" human microbiome. Publicly-available data sets from amplicon sequencing of two 16S rRNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

bioinformatics↗

bioRxiv2017

Sociodemographic patterning in the oral microbiome of a diverse sample of New Yorkers

11.1 PurposeVariations in the oral microbiome are potentially implicated in social inequalities in oral disease, cancers, and metabolic disease. We describe sociodemographic variation of oral microbiomes in a diverse sample.\n\n1.2 MethodsWe performed 16S rRNA sequencing on mouthwash specimens in a subsample (n=282) of the 2013-14 population-based New York City Health and Nutrition Examination Study (NYC-HANES). We examined differential abundance of 216 operational taxonomic units (OTUs), and alpha and beta diversity by age, sex, income, education, nativity, and race/ethnicity. For comparison, we also examined differential abundance by diet, smoking status, and oral health behaviors.\n\n1.3 Results69 OTUs were differentially abundant by any sociodemographic variable (false discovery rate < 0.01), including 27 by race/ethnicity, 21 by family income, 19 by education, three by sex. We also found 49 differentially abundant by smoking status, 23 by diet, 12 by oral health behaviors. Genera differing for multiple sociodemographic characteristics included Lactobacillus, Prevotella, Porphyromonas, Fusobacterium.\n\n1.4 ConclusionsWe identified oral microbiome variation consistent with health inequalities, with more taxa differing by race/ethnicity than diet, and more by SES variables than oral health behaviors. Investigation is warranted into possible mediating effects of the oral microbiome in social disparities in oral, metabolic and cancers.\n\nHighlightsO_LIMost microbiome studies to date have had minimal sociodemographic variability, limiting what is known about associations of social factors and the microbiome.\nC_LIO_LIWe examined the oral microbiome in a population-based sample of New Yorkers with wide sociodemographic variation.\nC_LIO_LINumerous taxa were differentially abundant by race/ethnicity, income, education, marital status, and nativity.\nC_LIO_LIFrequently differentially abundant taxa include Porphyromonas, Fusobacterium, Streptococcus, and Prevotella, which are associated with oral and systemic disease.\nC_LIO_LIMediation of health disparities by microbial factors may represent an important intervention site to reduce health disparities, and should be explored in prospective studies.\nC_LI

epidemiology↗

bioRxiv2017

Accessible, curated metagenomic data through ExperimentHub

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut \"enterotypes\", and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

bioinformatics↗