Biology subjects

Larremore, D. B.

Publications and source records attributed to Larremore, D. B..

6 recordsLinked to original sources

bioRxiv2023

Gendered hiring and attrition on the path to parity for academic faculty

Despite long-running efforts to increase gender diversity among tenured and tenure track faculty in the U.S., women remain underrepresented in most academic fields, sometimes dramatically so. Here we quantify the relative importance of faculty hiring and faculty attrition for both past and future faculty gender diversity using comprehensive data on the training and employment of 268,769 tenured and tenure-track faculty rostered at 12,112 U.S. PhD-granting departments, spanning 111 academic fields between 2011-2020. Over this time, we find that hiring had a far greater impact on womens representation among faculty than attrition in the majority (90.1%) of academic fields, even as academia loses a higher share of women faculty relative to men at every career stage. Finally, we model the impact of five specific policy interventions on womens representation, and project that eliminating attrition differences between women and men only leads to a marginal increase in womens overall representation--in most fields, successful interventions will need to make substantial and sustained changes to hiring in order to reach gender parity.

scientific communication and education↗

bioRxiv2021

Bayesian estimation of population size and overlap from random subsamples

Counting the number of species, items, or genes that are shared between two sets is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two sets becomes an estimation problem. Furthermore, to calculate normalized measures of {beta}-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the sets being compared. Previous efforts to address these problems have assumed knowledge of total population sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address populations of unknown size and show that this produces systematically better estimates--both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species count data to refine estimates of population size in a Bayesian joint model of populations and overlap.

ecology↗

bioRxiv2020

Jointly modeling prevalence, sensitivity and specificity for optimal sample allocation

The design and interpretation of prevalence studies rely on point estimates of the performance characteristics of the diagnostic test used. When the test characteristics are not well defined and a limited number of tests are available, such as during an outbreak of a novel pathogen, tests can be used either for the field study itself or for additional validation to reduce uncertainty in the test characteristics. Because field data and validation data are based on finite samples, inferences drawn from these data carry uncertainty. In the absence of a framework to balance those uncertainties during study design, it is unclear how best to distribute tests to improve study estimates. Here, we address this gap by introducing a joint Bayesian model to simultaneously analyze lab validation and field survey data. In many scenarios, prevalence estimates can be most improved by apportioning additional effort towards validation rather than to the field. We show that a joint model provides superior estimation of prevalence, as well as sensitivity and specificity, compared with typical analyses that model lab and field data separately.

immunology↗

bioRxiv2020

Longitudinal analysis of naturally acquired antibodies to PfEMP1 CIDR domain variants and their association with malaria protection

Malaria pathogenicity is determined, in part, by the adherence of Plasmodium falciparum infected erythrocytes to the microvasculature mediated via specific interactions between PfEMP1 variant domains to host endothelial receptors. Naturally acquired antibodies against specific PfEMP1 variants can play an important role in clinical protection against malaria. We evaluated IgG responses against a repertoire of PfEMP1 CIDR domain variants to determine the rate and order of variant-specific antibody acquisition and their association with protection against febrile malaria in a prospective cohort study conducted in an area of intense, seasonal malaria transmission. Using longitudinal data, we found that IgG to the pathogenic domain variants CIDR1.7 and CIDR1.8 were acquired the earliest. Furthermore, IgG to CIDR{gamma}3 was associated with reduced prospective risk of febrile malaria and recurrent malaria episodes. Future studies will need to validate these findings in other transmission settings and determine the functional activity of these naturally acquired CIDR variant-specific antibodies.

immunology↗

bioRxiv2020

Plasmodium falciparum population genetic complexity influences transcriptional profile and immune recognition of highly related genotypic clusters

As transmission intensity has declined in Senegal, so has the genetic complexity of circulating Plasmodium falciparum parasites, resulting in specific genotypes emerging and persisting over years. We address whether changes in parasite genetic signatures can alter the immune repertoire to variant surface antigens, and whether such responses can influence the expansion or contraction of specific parasite genotypes in the population. We characterize parasites within genotypic clusters, defined as identical by a 24-SNP molecular barcode and a haplotype identifier for other highly polymorphic loci; we measure expression of variant surface antigens (VSA) such as PfEMP-1 by transcript expression typing and expressed var DBL1 sequencing in ex vivo and short-term adapted RNA samples; and we measure IgG responses against VSAs from short-term adapted parasites. We find that parasites within genotypic clusters are genetically identical at other highly polymorphic loci. These parasites express similar Ups var classes and largely the same dominant var DBL1 sequences ex vivo. These parasites are recognized similarly by anti-VSA antibodies after short-term adaptation to culture; however, antibody responses do not correlate with genotype frequencies over time. Both genotype-specific and multiple genotype-reactive surface IgG responses are observed in this population. Parasites with identical genomes are extremely similar in their expression and host antibody recognition of VSAs. Monitoring changes in population-level parasite genomics and transmission dynamics is critical, as fluctuations will influence the breadth of resulting host immune responses to circulating parasite genotypes. These findings suggest shared immune recognition of genetically similar parasites, which has implications for both our understanding of immunity and vaccine development strategies in malaria elimination settings.

microbiology↗

bioRxiv2018

Estimating the overlap between two malaria parasites’ var repertoires

Measuring the overlap between the var gene repertoires of two P. falciparum parasites is, in principle, easy. Each parasite genome contains a repertoire of approximately 60 var genes, so upon fully sequencing both parasites genomes, the number of shared var sequences can be directly counted. In practice, however, only a fraction of each parasites var repertoire is likely to be sampled due to the difficulties of whole-genome sequencing for var genes and the stochastic sample provided by PCR techniques. Although a method exists for quantifying repertoire overlap under these subsampled conditions, its bias is well documented and the uncertainty of its estimates cannot be quantified. Here we derive and validate a method to rigorously estimate the repertoire overlap between two parasites from the overlap of their subsampled repertoires. By solving a Bayesian inference problem, this method takes into account the rates of subsampling and produces unbiased and Bayes-optimal estimates of overlap. In addition, it provides a natural framework for computing the uncertainty of its estimates, and can be used in laboratory planning by quantifying the tradeoff between sequencing effort and uncertainty.

genomics↗