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Audrey White,1 Elizabeth Buschur,2 Cara Harris,2 Michael L Pennell,3 Adam Soliman,2 Kathleen Wyne,2 Kathleen M Dungan2 1Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA; 2Division of Endocrinology, Diabetes & Metabolism, The Ohio State University, Columbus, OH, 43220, USA; 3The Ohio State University College of Public Health, Division of Biostatistics, Columbus, OH, 43210, USACorrespondence: Kathleen M Dungan, The Ohio State University, Division of Endocrinology, Diabetes and Metabolism, 5th Floor McCampbell Hall, 1581 Dodd Drive, Columbus, OH, 43210-1296, USA, Email kathleen.dungan@osumc.eduObjective: To evaluate the relationship between health literacy, social support, and self-efficacy as predictors of change in A1c and readmission among hospitalized patients with type 2 diabetes (T2D).Methods: This is a secondary analysis of patients with T2D (A1c > 8.5%) enrolled in a randomized trial in which health literacy (Newest Vital Sign), social support (Multidimensional Scale of Perceived Social Support), and empowerment (Diabetes Empowerment Scale-Short Form) was assessed at baseline. Multivariable models evaluated whether these concepts were associated with A1c reduction at 12 weeks (absolute change, % with > 1% reduction, % reaching individualized target) and readmission (14 and 30 days).Results: A1c (N=108) decreased > 1% in 60%, while individualized A1c target was achieved in 31%. After adjustment for baseline A1c and potential confounders, health literacy was associated with significant reduction in A1c (Estimate − 0.21, 95% CI − 0.40, − 0.01, p=0.041) and > 1% decrease in A1c (OR 1.37, 95% CI 1.08, 1.73, p=0.009). However, higher social support was associated with greater adjusted odds of reaching the individualized A1c target (OR 1.63, 95% CI 1.04, 2.55, p=0.32). Both higher empowerment (OR 0.23, 95% CI 0.08, 0.64, p=0.005) and social support (OR 0.57, 95% CI 0.36, 0.91, p=0.018) were associated with fewer readmissions by 14 days, but not 30 days.Conclusion: The study indicates that health literacy and social support may be important predictors of A1c reduction post-discharge among hospitalized patients with T2D. Social support and diabetes self-management skills should be addressed and early follow-up may be critical for avoiding readmissions.Clinical Trial: NCT03455985.Keywords: type 2 diabetes, hospital discharge, basal insulin, A1c, readmission, social support, self-efficacy, literacy

OBJECTIVE: To describe how Ohio Appalachian women are over- or under-estimating risk for cervical cancer by comparing an objective measure of cervical cancer risk to subjective interpretations of risk. Methods: A cervical cancer risk index was computed for 530 female participants recruited from clinics in Ohio Appalachia (2006-2008). Risk factors for cervical cancer, including human papillomavirus infection, number of sexual partners, parity, age at first intercourse, age at first pregnancy, oral contraceptive use and cigarette smoking were used to create a cervical cancer risk index. This index was used to categorize participants into low, average or high risk for cervical cancer. Subjective risk was assessed by asking women to indicate whether they believe their risk of cervical cancer to be below, about average, or higher than other women. Objective risk categories were compared to subjective risk to determine if women were accurately perceiving risk, or over- or under-estimating risk. A multinomial logistic regression model was used to determine correlates of risk perception accuracy. The outcome was a categorical variable consisting of over-estimating risk, underestimating risk and accurate risk perception, with accurate risk perception serving as the reference level in the model. Results: Out of 530 women, 33.6% (n = 178) over-estimated risk and 24.9% (n = 132) under-estimated risk. Women with household incomes of <$25 k were more likely to under-estimate cervical cancer risk, as opposed to being accurate in their risk perception, than women in households earning over $50 k [OR = 2.06, 95% CI (1.04-4.07)] as were women with less than a high-school education [OR = 2.52, 95% CI (1.06-5.96)] compared to women with a college education. Also, women who reported recent alcohol use were more likely to under-estimate risk [OR = 1.94, 95% CI (1.18-3.19)] compared to women with no self-reported recent alcohol use. No variables were found to be associated with risk over-estimation. Conclusions: Many women in Ohio Appalachia do not accurately perceive their cervical cancer risk. Low income and education are associated with under-estimating risk. Future studies should determine if risk misperceptions are associated with cervical cancer screening and other preventive behaviors. [ABSTRACT FROM AUTHOR]

Explaining macroevolutionary divergence in light of population genetics requires understanding the extent to which the patterns of mutational input contribute to long-term trends. In the context of quantitative traits, mutational input is typically described by the mutational variance-covariance matrix, or the M -matrix, which summarizes phenotypic variances and covariances introduced by new mutations per generation. However, as a summary statistic, the M -matrix does not fully capture all the relevant information from the underlying mutational architecture, and there exist infinitely many possible underlying mutational architectures that give rise to the same M -matrix. Using individual-based simulations, we demonstrate mutational architectures that produce the same M -matrix can lead to different levels of constraint on evolution and result in difference in within-population genetic variance, between-population divergence, and rate of adaptation. In particular, the rate of adaptation and that of neutral evolution are both reduced when a greater proportion of loci are pleiotropic. Our results reveal that aspects of mutational input not reflected by the M -matrix can have a profound impact on long-term evolution, and suggest it is important to take them into account in order to connect patterns of long-term phenotypic evolution to underlying microevolutionary mechanisms.

The adaptive immune system generates a diverse array of B-cell receptors through the processes of V(D)J recombination and somatic hypermutation. B-cell receptors that bind to an antigen will undergo clonal expansion, creating a Darwinian evolutionary dynamic within individuals. A key step in studying these dynamics is to identify sequences derived from the same ancestral V(D)J recombination event (i.e. a clonal family). There are a number of widely used methods for accomplishing this task but a major limitation of all of them is that they rely, at least in part, on the ability to map sequences to a germline reference set. This requirement is particularly problematic in non-model systems where we often know little about the germline allelic diversity in the study population. Recognizing that delimiting B-cell clonal families is analogous to delimiting species from single locus data, we propose a novel strategy of reconstructing the phylogenetic tree of all B-cell sequences in a sample and using a popular species delimitation method, multi-rate Poisson Tree Processes (mPTP), to delimit clonal families. Using extensive simulations, we show that not only does this phylogenetically explicit approach perform well for the purpose of delimiting clonal families when no reference allele set is available, it performs similarly to state-of-the-art techniques developed specifically for B-cell data even when we have a complete reference allele set. Additionally, our analysis of an empirical dataset shows that mPTP performs similarly to leading methods in the field. These findings demonstrate the utility of using off-the-shelf phylogenetic techniques for analyzing B-cell clonal dynamics in non-model systems, and suggests that phylogenetic inference techniques may be potentially combined with mapping based approaches for even more robust inferences, even in model systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Voss, Kaur, Banerjee, Breden and Pennell.)

Background: The purpose of the Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) Program, a Cancer Moonshot℠ Initiative, is to support research to build the evidence base on multilevel interventions that increase rates of colorectal cancer screening, follow-up, and referral to care to address disparities in colorectal cancer screening. The National Cancer Institute funded eight Research Projects to implement multilevel interventions to improve colorectal cancer screening among communities who traditionally have been medically underserved. To analyze the impact of ACCSIS across Research Projects, the consortium developed a set of common data elements. The purpose of this paper is to describe the process of developing the common data elements to facilitate analysis of ACCSIS data as well as support and inform implementation science research studies., Methods: The ACCSIS Data, Design, and Analysis Work Group was tasked with designing common data elements through a review of existing data collection instruments, examination of data elements proposed by the ACCSIS Research Projects, and deliberations on the data required to compare across the Research Projects. ACCSIS Consortium members drafted, revised, and finalized a common data elements document consisting of variables to collect and surveys to administer to evaluate ACCSIS implementation activities and outcomes in a standardized manner across the Research Projects., Results: The ACCSIS Consortium decided to collect the following categories of common data elements: characteristics across multiple levels of the multicomponent interventions, implementation climate, and determinants; interventions and strategies; implementation outcome constructs and definitions; colorectal cancer screening episodes (screening, diagnostic testing follow-up, cancer detection, and cancer treatment); and cost measures. To assess implementation climate, the consortium prioritized constructs from five domains of the Consolidated Framework for Implementation Research., Conclusions: The ACCSIS common data elements offer a set of harmonization data for future implementation efforts. The consortium is conducting a systematic assessment using both quantitative and qualitative approaches to assess data quality and approaches to improve and sustain data collection. Lessons learned from these ongoing activities will offer additional insights to tailor the ACCSIS common data elements and support efforts to increase colorectal cancer screening for populations experiencing disparities., (© 2024. The Author(s).)

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Schraiber et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

We present haplotype-resolved reference genomes and comparative analyses of six ape species, namely: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan, and siamang. We achieve chromosome-level contiguity with unparalleled sequence accuracy (<1 error in 500,000 base pairs), completely sequencing 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, providing more in-depth evolutionary insights. Comparative analyses, including human, allow us to investigate the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference. This includes newly minted gene families within lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes, and subterminal heterochromatin. This resource should serve as a definitive baseline for all future evolutionary studies of humans and our closest living ape relatives., Competing Interests: COMPETING INTERESTS E.E.E. is a scientific advisory board (SAB) member of Variant Bio, Inc. C.T.W. is a co-founder/CSO of Clareo Biosciences, Inc. W.L. is a co-founder/CIO of Clareo Biosciences, Inc. The other authors declare no competing interests.

Long-read sequencing technologies have revolutionized genome assembly producing near-complete chromosome assemblies for numerous organisms, which are invaluable to research in many fields. However, regions with complex repetitive structure continue to represent a challenge for genome assembly algorithms, particularly in areas with high heterozygosity. Robust and comprehensive solutions for the assessment of assembly accuracy and completeness in these regions do not exist. In this study we focus on the assembly of biomedically important antibody-encoding immunoglobulin (IG) loci, which are characterized by complex duplications and repeat structures. High-quality full-length assemblies for these loci are critical for resolving haplotype-level annotations of IG genes, without which, functional and evolutionary studies of antibody immunity across vertebrates are not tractable. To address these challenges, we developed a pipeline, "CloseRead", that generates multiple assembly verification metrics for analysis and visualization. These metrics expand upon those of existing quality assessment tools and specifically target complex and highly heterozygous regions. Using CloseRead, we systematically assessed the accuracy and completeness of IG loci in publicly available assemblies of 74 vertebrate species, identifying problematic regions. We also demonstrated that inspecting assembly graphs for problematic regions can both identify the root cause of assembly errors and illuminate solutions for improving erroneous assemblies. For a subset of species, we were able to correct assembly errors through targeted reassembly. Together, our analysis demonstrated the utility of assembly assessment in improving the completeness and accuracy of IG loci across species., Competing Interests: Competing interests The authors report no competing interests.

Telomere-to-telomere (T2T) assemblies reveal new insights into the structure and function of the previously 'invisible' parts of the genome and allow comparative analyses of complete genomes across entire clades. We present here an open collaborative effort, termed the 'Ruminant T2T Consortium' (RT2T), that aims to generate complete diploid assemblies for numerous species of the Artiodactyla suborder Ruminantia to examine chromosomal evolution in the context of natural selection and domestication of species used as livestock., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Many remarkable innovations have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by the same underlying gene families, while other times many different gene families are involved. Conversely, a gene family may be repeatedly recruited for a single trait or many different traits. To understand the general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic traits and gene count in 14,710 gene families from 993 species of Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we discovered that gene family expansion and contraction was significantly linked to trait gain and loss in 45/56 (80%) of traits. While 601/746 (81%) of significant gene families were associated with only one trait, we also identified several 'keystone' gene families that were significantly associated with up to 13/56 (23%) of all traits. These results indicate that metabolic innovations in yeasts are governed by a narrow set of major genetic elements and mechanisms., Competing Interests: Competing Interest Statement A.R. is a scientific consultant for LifeMine Therapeutics, Inc. The other authors declare no other competing interests.

RNA and protein expressed from the same gene can have diverse isoforms due to various post-transcriptional and post-translational modifications. For the vast majority of alternative isoforms, It is unknown whether they are adaptive or simply biological noise. As we cannot experimentally probe the function of each isoform, we can ask whether the distribution of isoforms across genes and across species is consistent with expectations from different evolutionary processes. However, there is currently no theoretical framework that can generate such predictions. To address this, we developed a mathematical model where isoform abundances are determined collectively by cis -acting loci, trans -acting factors, gene expression levels, and isoform decay rates to predict isoform abundance distributions across species and genes in the face of mutation, genetic drift, and selection. We found that factors beyond selection, such as effective population size and the number of cis -acting loci, significantly influence evolutionary outcomes. Notably, suboptimal phenotypes are more likely to evolve when the population is small and/or when the number of cis -loci is large. We also explored scenarios where modification processes have both beneficial and detrimental effects, revealing a non-monotonic relationship between effective population size and optimization, demonstrating how opposing selection pressures on cis - and trans -acting loci can constrain the optimization of gene product diversity. As a demonstration of the power of our theory, we compared the expected distribution of A-to-I RNA editing levels in coleoids and found this to be largely consistent with non-adaptive explanations.

The adaptive immune response relies on a diverse repertoire of B-cell receptors, each of which is characterized by a distinct sequence resulting from VDJ-recombination. Upon binding to an antigen, B-cells undergo clonal expansion and in a process unique to B-cells the overall binding affinity of the repertoire is further enhanced by somatic hypermutations in the receptor sequence. For B-cell repertoires it is therefore particularly important to analyze the dynamics of clonal expansion and patterns of somatic hypermutations and thus it is necessary to group the sequences into distinct clones to determine the number and identity of expanding clonal families responding to an antigen. Multiple methods are currently used to identify clones from sequences, employing distinct approaches to the problem. Until now there has not been an extensive comparison of how well these methods perform under the same conditions. Furthermore, since this is fundamentally a phylogenetics problem, we speculated that the mPTP method, which delimits species based on an analysis of changes in the underlying process of diversification, might perform as well as or better than existing methods. Here we conducted extensive simulations of B-cell repertoires under a diverse set of conditions and studied errors in clonal assignment and in downstream ancestral state reconstruction. We demonstrated that SCOPer-H consistently yielded superior results across parameters. However, this approach relies on a good reference assembly for the germline immunoglobulin genes which is lacking for many species. Using mPTP had lower error rates than tailor-made immunogenetic methods and should therefore be considered by researchers studying antibody evolution in non-model organisms without a reference genome.

The oxygen gradient across the intestine influences intestinal physiology and the microbial environment of the microbiome. The microbiome releases metabolites that communicate with enterochromaffin cells, neuronal cells, and resident immune cells to facilitate the bidirectional communication across the gut-brain axis. Measuring communication between various cell types within the intestine could provide essential information about key regulators of gut and brain health; however, the microbial environment of the intestine is heavily dependent on the physiological oxygen gradient that exists across the intestinal wall. Likewise, there exist a need for methods which enable real-time monitoring of intestinal signaling ex vivo yet this remains challenging due to the inability to adequately culture intestinal tissue ex vivo while also exposing the appropriate locations of the intestine for probe insertion and monitoring. Here, we designed and fabricated a 3D printed microfluidic device to maintain the oxygen gradient across precision cut murine intestinal slices with the capability to couple to external neurochemical recording techniques. The gradient is maintained from outlets below while allowing access to the slice from above for detection with fast scan cyclic voltammetry (FSCV) and carbon-fiber microelectrodes. A series of 11 outlet ports were designed to lay underneath the slice which were connected to channels to deliver oxygenated vs. deoxygenated media. Outlet ports were designed in an oval shape where deoxygenated media was delivered to the center of the slice and oxygenated media is delivered to the outer portion of the slice to mimic the location of oxygen across the intestine. An oxygen sensitive fluorescent dye, tris(2,2'-bipyridyl)dichlororuthenium(II), was used to characterize the tunability of the gradient. Viability of the tissue was confirmed by both fluorescence microscopy and FSCV. Additionally, we measured simultaneous serotonin and melatonin signaling with FSCV in the intestine for the first time. Overall, this chip provides a significant advance in our ability to culture intestinal slices ex vivo with the added benefit of direct access for measurements and imaging.

The Saccharomycotina yeasts ("yeasts" hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild. Here, we trained machine learning models on 12,816 terrestrial occurrence records and 96 environmental variables to infer global distribution maps at ~1 km 2 resolution for 186 yeast species (~15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversity. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high-diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many long-standing macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and a positive relationship between latitude and range size (Rapoport's rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios., Competing Interests: Competing interests statement:A.R. is a scientific consultant of LifeMine Therapeutics, Inc. The authors declare no other competing interests.

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Evolutionary dynamics operating across deep time leave footprints in the shapes of phylogenetic trees. For the last several decades, researchers have used increasingly large and robust phylogenies to study the evolutionary history of individual clades and to investigate the causes of the glaring disparities in diversity among groups. Whereas typically not the focal point of individual clade-level studies, many researchers have remarked on recurrent patterns that have been observed across many different groups and at many different time scales. Whereas previous studies have documented various such regularities in topology and branch length distributions, they have typically focused on a single pattern and used a disparate collection (oftentimes, of quite variable reliability) of trees to assess it. Here we take advantage of modern megaphylogenies and unify previous disparate observations about the shapes embedded in the Tree of Life to create a catalog of the "major features of macroevolution." By characterizing such a large swath of subtrees in a consistent way, we hope to provide a set of phenomena that process-based macroevolutionary models of diversification ought to seek to explain., (© The Author(s) 2023. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Explaining broad molecular, phenotypic and species biodiversity patterns necessitates a unifying framework spanning multiple evolutionary scales. Here we argue that although substantial effort has been made to reconcile microevolution and macroevolution, much work remains to identify the links between biological processes at play. We highlight four major questions of evolutionary biology whose solutions require conceptual bridges between micro and macroevolution. We review potential avenues for future research to establish how mechanisms at one scale (drift, mutation, migration, selection) translate to processes at the other scale (speciation, extinction, biogeographic dispersal) and vice versa. We propose ways in which current comparative methods to infer molecular evolution, phenotypic evolution and species diversification could be improved to specifically address these questions. We conclude that researchers are in a better position than ever before to build a synthesis to understand how microevolutionary dynamics unfold over millions of years., (© 2023. Springer Nature Limited.)

Mutualisms are associations in which interacting species provide services or resources to each other. It has been suggested that being party to a mutualism can spur the diversification of the interacting species due to several potential hypothesized mechanisms. There is empirical evidence to both support and refute this prediction. However, this evidence comes from a variety of different methodologies, some of which have been found to be unreliable when the phylogenetic model is misspecified, and different data types and it is therefore difficult to weigh together. Here, we synthesize phylogenetic comparative datasets and analyze the data in a consistent manner using both sister-clade comparisons and hidden-trait state-dependent speciation and extinction models. The results are mixed-for the majority of the datasets we find no evidence for an effect on diversification rates in either direction, with several showing significant positive associations and a few showing significant negative associations. In contrast to the generally mixed findings between datasets, we find that qualitative results to be consistent when analyzing taxonomically overlapping datasets using different methods, suggesting that the detected variation in diversification is due to the nature of the mutualism and not due to differences in methodology., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Society for the Study of Evolution (SSE). All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so., Competing Interests: Declaration of interests V.G. declares advisory board positions in aiNET GmbH, Enpicom B.V, Specifica Inc, Adaptyv Biosystems, EVQLV, Omniscope, Diagonal Therapeutics, and Absci. V.G. is a consultant for Roche/Genentech, immunai, and Proteinea., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Although the use of electronic order sets has become standard practice for inpatient diabetes management, there is limited decision support at discharge., Objective: In this study, we assessed whether an electronic discharge order set (DOS) plus nurse follow-up calls improve discharge orders and postdischarge outcomes among hospitalized patients with type 2 diabetes mellitus., Methods: This was a randomized, open-label, single center study that compared an electronic DOS and nurse phone calls to enhanced standard care (ESC) in hospitalized insulin-requiring patients with type 2 diabetes mellitus. The primary outcome was change in glycated hemoglobin (HbA 1c ) level at 24 weeks after discharge. The secondary outcomes included the completeness and accuracy of discharge prescriptions related to diabetes., Results: This study was stopped early because of feasibility concerns related to the long-term follow-up. However, 158 participants were enrolled (DOS: n=82; ESC: n=76), of whom 155 had discharge data. The DOS group had a greater frequency of prescriptions for bolus insulin (78% vs 44%; P=.01), needles or syringes (95% vs 63%; P=.03), and glucometers (86% vs 36%; P<.001). The clarity of the orders was similar. HbA 1c data were available for 54 participants in each arm at 12 weeks and for 44 and 45 participants in the DOS and ESC arms, respectively, at 24 weeks. The unadjusted difference in change in HbA 1c level (DOS - ESC) was -0.6% (SD 0.4%; P=.18) at 12 weeks and -1.1% (SD 0.4%; P=.01) at 24 weeks. The adjusted difference in change in HbA 1c level was -0.5% (SD 0.4%; P=.20) at 12 weeks and -0.7% (SD 0.4%; P=.09) at 24 weeks. The achievement of the individualized HbA 1c target was greater in the DOS group at 12 weeks but not at 24 weeks., Conclusions: An intervention that included a DOS plus a postdischarge nurse phone call resulted in more complete discharge prescriptions. The assessment of postdischarge outcomes was limited, owing to the loss of the long-term follow-up, but it suggested a possible benefit in glucose control., Trial Registration: ClinicalTrials.gov NCT03455985; https://clinicaltrials.gov/ct2/show/NCT03455985., (©Audrey White, David Bradley, Elizabeth Buschur, Cara Harris, Jacob LaFleur, Michael Pennell, Adam Soliman, Kathleen Wyne, Kathleen Dungan. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 26.07.2022.)

Numerous phylogenetic studies reported the existence of a pervasive scaling relationship between the ages of extant eukaryotic clades and their estimated diversification rates. The causes of this age-rate-scaling (ARS), whether biological and/or artifactual, remain unresolved. Here we fit diversification models to thousands of eukaryotic time-calibrated phylogenies to explore multiple potential causes of the ARS including parameter non-identifiability, model inadequacy, biases in taxonomic practice, and an important and ubiquitous form of sampling bias-preferentially analyzing larger extant clades. We distinguish between two mechanism by which such sampling biases can cause an ARS: First, by favoring clades that happen to be unusually large merely by chance (i.e., due to the stochastic nature of the cladogenic process), thus leading to rate overestimation, and second, by favoring clades that have truly higher diversification rates. We find that, of the proposed explanations, only sampling biases are likely to contribute to the observed ARS. We develop methods for fully correcting for sampling bias mechanism 1, and find that despite these corrections a substantial ARS remains. We then confirm using simulations that preferring trees with truly higher rates (mechanism 2) likely explains this residual ARS. Since we do not have a completely unbiased sample of clades, including extinct ones, for phylogenetic analyses, it is difficult to demonstrate unambiguously that sampling biases are the sole cause of the ARS. Sampling biases are, however, a parsimonious and plausible explanation for this widely observed macroevolutionary pattern, and this has implications for how we interpret the distribution of diversification rate estimates in extant clades., (© 2022 The Authors. Evolution © 2022 The Society for the Study of Evolution.)

The processes that allow some lineages to diversify rapidly at a global scale remain poorly understood. Although earlier studies emphasized the importance of dispersal, global expansions expose populations to novel environments and may also require adaptation and diversification across new niches. In this study, we investigated the contributions of these processes to the global radiation of crows and ravens (genus Corvus). Combining a new phylogeny with comprehensive phenotypic and climatic data, we show that Corvus experienced a massive expansion of the climatic niche that was coupled with a substantial increase in the rates of species and phenotypic diversification. The initiation of these processes coincided with the evolution of traits that promoted dispersal and niche expansion. Our findings suggest that rapid global radiations may be better understood as processes in which high dispersal abilities synergise with traits that, like cognition, facilitate persistence in new environments., (© 2022. The Author(s).)

Background: Early in vitro studies suggested that flavophospholipol has plasmid-curing effects and could inhibit conjugation by disrupting pilus formation between bacteria., Objectives: This 36-day controlled-challenge study aimed to evaluate the anti-conjugative and plasmid-curing effect of flavophospholipol in vivo on plasmid-mediated antimicrobial resistance (AMR) in MDR transconjugant Salmonella Enteritidis in chickens., Methods: A total of 270-day-old chicks were randomly assigned to four control and four treatment groups with two doses of in-feed flavophospholipol (10 ppm and 64 ppm) and in the presence and absence of ampicillin in drinking water. Chicks were orally challenged with Salmonella Enteritidis with known plasmid-encoded AMR factors. Cloacal swabs were collected on Day 7, 14 and 23. On Day 35, all chickens were euthanized, and caecal tissue and content were collected. Antimicrobial susceptibility testing was done with a panel of 12 antimicrobials and interpreted according to CLSI breakpoints., Results: Flavophospholipol given in-feed at 64 ppm had an anti-conjugative effect. There was a significant reduction of acquisition of resistance to ampicillin, streptomycin and tetracycline by the recipient strains of Salmonella Enteritidis in treatment groups given flavophospholipol in-feed at 64 ppm ( P <  0.05). This was not seen with flavophospholipol given in-feed at 10 ppm., Conclusions: The results demonstrate that flavophospholipol given in-feed at 64 ppm had an anti-conjugative effect. The results also suggest that AMR is reduced through other mechanisms of action, which are yet to be determined. There is insufficient evidence that flavophospholipol at 64 ppm in feed alone or with sub-therapeutic levels of antibiotics had a plasmid-curing effect., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.)

Objectives: Health warning messages could be an effective means of communicating the health risks associated with waterpipe (WP) smoking. The objective of this study was to select a message that conveyed the risks associated with WP smoking., Methods: A mixed-methods approach was used to explore the effectiveness of the Food and Drug Administration's (FDA's) mandated message, and others, among young adults using focus groups and surveys. Two focus group studies and one convenience survey were conducted to examine the FDA's mandated message and 11 other WP warning messages. The final study, conducted with a random sample of first-year university students, examined the effectiveness and reactance of the chosen message using previously validated items (scored on a 1-5 scale)., Results: The FDA's mandated message did not resonate well with focus group participants. In the random sample of students, the top message ( WARNING: Hookah smoke contains poisons that cause lung and oral cancers ) had high effectiveness (M = 4.49) and a low reactance (M = 2.12)., Conclusions: Our studies suggest that other messages are more effective for communicating the risks associated with WP smoking than the FDA's message for WP tobacco., Competing Interests: Conflict of Interest Disclosure Statement No conflicts to report.

Autologous stem cell transplant (ASCT) is the standard of care for patients with multiple myeloma (MM). The clinical significance of peripheral blood T lymphocyte (PBTL) immunologic changes associated with ASCT is poorly understood. Here we evaluated T cell transcriptional messenger RNA profiles and immunophenotypes to correlate immunologic senescence, exhaustion, and anergy with clinical endpoints in a cohort of patients with MM undergoing ASCT. ASCT induced global transcriptional T cell changes and altered molecular levels of markers of T cell subtypes, T cell activation, and exhaustion. These included reduced CD4/CD8 ratio, skewing toward the Th1 subset, reduced expression of costimulatory receptors CD27 and CD28, heightened T cell activation, and increased expression of immune modulatory molecules LAG3 and PD1. Multicolor flow cytometry experiments confirmed altered circulating CD4 and CD8 subsets and skewing toward differentiated effector cells. Moreover, ASCT promoted an exhausted immunophenotype in CD3 + CD4 + subsets and a senescent immunophenotype in CD3 + CD8 + subsets. Subset-specific altered expression was also seen for surface molecules with immunomodulatory function. ASCT affected soluble levels of molecules with immunomodulatory function by increasing plasma HVEM and TIM3. High molecular LAG3 level was associated with inferior event-free survival post-ASCT (hazard ratio = 5.44; confidence interval, 1.92 to 15.46; P = .001; adjusted P [controlling for false discovery rate] = .038). Using a comprehensive evaluation of PBTLs on a molecular and phenotypic level, we have identified that ASCT induces global T cell alterations with CD4 and CD8 subset-specific changes. Moreover, LAG3 emerged as an early biomarker of adverse events post-ASCT. These findings will support the development of treatment strategies targeting immune defects in MM to augment or restore T cell responses., (Copyright © 2019 American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved.)

Tissues are increasingly being analyzed at the single cell level in order to characterize cellular diversity and identify rare cell types. Single cell analysis efforts are greatly limited, however, by the need to first break down tissues into single cell suspensions. Current dissociation methods are inefficient, leaving a significant portion of the tissue as aggregates that are filtered away or left to confound results. Here, we present a simple and inexpensive microfluidic device that simultaneously filters large tissue fragments and dissociates smaller aggregates into single cells, thereby improving single cell yield and purity. The device incorporates two nylon mesh membranes with well-defined, micron-sized pores that operate on aggregates of different size scales. We also designed the device so that the first filtration could be performed under tangential flow to minimize clogging. Using cancer cell lines, we demonstrated that aggregates were effectively dissociated using high flow rates and pore sizes that were smaller than a single cell. However, pore sizes that were less than half the cell size caused significant damage. We then improved results by passing the sample through two filter devices in series, with single cell yield and purity predominantly determined by the pore size of the second membrane. Next, we optimized performance using minced and digested murine kidney tissue samples, and determined that the combination of 50 and 15 μm membranes was optimal. Finally, we integrated these two membranes into a single filter device and performed validation experiments using minced and digested murine kidney, liver, and mammary tumor tissue samples. The dual membrane microfluidic filter device increased single cell numbers by at least 3-fold for each tissue type, and in some cases by more than 10-fold. These results were obtained in minutes without affecting cell viability, and additional filtering would not be required prior to downstream applications. In future work, we will create complete tissue analysis platforms by integrating the dual membrane microfluidic filter device with additional upstream tissue processing technologies, as well as downstream operations such as cell sorting and detection.

Background: The Ki67 Index has been extensively studied as a prognostic biomarker in breast cancer. However, its clinical adoption is largely hampered by the lack of a standardized method to assess Ki67 that limits inter-laboratory reproducibility. It is important to standardize the computation of the Ki67 Index before it can be effectively used in clincial practice., Method: In this study, we develop a systematic approach towards standardization of the Ki67 Index. We first create the ground truth consisting of tumor positive and tumor negative nuclei by registering adjacent breast tissue sections stained with Ki67 and H&E. The registration is followed by segmentation of positive and negative nuclei within tumor regions from Ki67 images. The true Ki67 Index is then approximated with a linear model of the area of positive to the total area of tumor nuclei., Results: When tested on 75 images of Ki67 stained breast cancer biopsies, the proposed method resulted in an average root mean square error of 3.34. In comparison, an expert pathologist resulted in an average root mean square error of 9.98 and an existing automated approach produced an average root mean square error of 5.64., Conclusions: We show that it is possible to approximate the true Ki67 Index accurately without detecting individual nuclei and also statically demonstrate the weaknesses of commonly adopted approaches that use both tumor and non-tumor regions together while compensating for the latter with higher order approximations.

Purpose: Vasomotor symptoms (VMS) are a common side effect of breast cancer treatment, yet modifiable factors that may predict VMS among breast cancer survivors are unknown., Methods: We estimated multivariable-adjusted odds ratios and 95% confidence intervals (aOR, 95% CI) for predictors of VMS among 3595 breast cancer survivors enrolled in the Life and Longevity after Cancer (LILAC) study, an ancillary study of the Women's Health Initiative (WHI)., Results: VMS post-diagnosis were reported by 790 (22.0%) participants. Risk of VMS after diagnosis was positively associated with prior chemotherapy (aOR 1.80, 95% CI 1.21-2.68) and adjuvant hormone therapy (aOR 2.73, 95% CI 2.08-3.58), postmenopausal hormone therapy use (aOR 1.67, 95% CI 1.30-2.13), prior VMS (aOR 2.20, 95% CI 1.73-2.80), bilateral oophorectomy (aOR 1.77, 95% CI 1.37-2.27), and baseline antidepressant use (aOR 1.49, 1.06-2.09). VMS post-diagnosis were less likely among younger women (aOR 0.94, 95% CI 0.93-0.96), women younger at menopause (aOR 0.98, 95% CI 0.97-1.00), women with more time since diagnosis (aOR 0.92, 95% CI 0.90-0.94), and diabetics (aOR 0.45, 95% CI 0.21-0.95). Metabolic syndrome was not associated with post-diagnosis VMS (aOR 0.76, 95% CI 0.45-1.28)., Conclusions: VMS following breast cancer diagnosis was related to a number of modifiable factors, but was unrelated to metabolic syndrome., Implications for Cancer Survivors: Identification of factors that predispose women to VMS following a breast cancer diagnosis may allow clinicians to recognize and address VMS in the subset of women who are most likely to experience such symptoms.

Automatic and accurate detection of positive and negative nuclei from images of immunostained tissue biopsies is critical to the success of digital pathology. The evaluation of most nuclei detection algorithms relies on manually generated ground truth prepared by pathologists, which is unfortunately time-consuming and suffers from inter-pathologist variability. In this work, we developed a digital immunohistochemistry (IHC) phantom that can be used for evaluating computer algorithms for enumeration of IHC positive cells. Our phantom development consists of two main steps, 1) extraction of the individual as well as nuclei clumps of both positive and negative nuclei from real WSI images, and 2) systematic placement of the extracted nuclei clumps on an image canvas. The resulting images are visually similar to the original tissue images. We created a set of 42 images with different concentrations of positive and negative nuclei. These images were evaluated by four board certified pathologists in the task of estimating the ratio of positive to total number of nuclei. The resulting concordance correlation coefficients (CCC) between the pathologist and the true ratio range from 0.86 to 0.95 (point estimates). The same ratio was also computed by an automated computer algorithm, which yielded a CCC value of 0.99. Reading the phantom data with known ground truth, the human readers show substantial variability and lower average performance than the computer algorithm in terms of CCC. This shows the limitation of using a human reader panel to establish a reference standard for the evaluation of computer algorithms, thereby highlighting the usefulness of the phantom developed in this work. Using our phantom images, we further developed a function that can approximate the true ratio from the area of the positive and negative nuclei, hence avoiding the need to detect individual nuclei. The predicted ratios of 10 held-out images using the function (trained on 32 images) are within ±2.68% of the true ratio. Moreover, we also report the evaluation of a computerized image analysis method on the synthetic tissue dataset.

Maximizing the speed and efficiency at which single cells can be liberated from tissues would dramatically advance cell-based diagnostics and therapies. Conventional methods involve numerous manual processing steps and long enzymatic digestion times, yet are still inefficient. In previous work, we developed a microfluidic device with a network of branching channels to improve the dissociation of cell aggregates into single cells. However, this device was not tested on tissue specimens, and further development was limited by high cost and low feature resolution. In this work, we utilized a single layer, laser micro-machined polyimide film as a rapid prototyping tool to optimize the design of our microfluidic channels to maximize dissociation efficiency. This resulted in a new design with smaller dimensions and a shark fin geometry, which increased recovery of single cells from cancer cell aggregates. We then tested device performance on mouse kidney tissue, and found that optimal results were obtained using two microfluidic devices in series, the larger original design followed by the new shark fin design as a final polishing step. We envision our microfluidic dissociation devices being used in research and clinical settings to generate single cells from various tissue specimens for diagnostic and therapeutic applications.

The advance of high resolution digital scans of pathology slides allowed development of computer based image analysis algorithms that may help pathologists in IHC stains quantification. While very promising, these methods require further refinement before they are implemented in routine clinical setting. Particularly critical is to evaluate algorithm performance in a setting similar to current clinical practice. In this article, we present a pilot study that evaluates the use of a computerized cell quantification method in the clinical estimation of CD3 positive (CD3+) T cells in follicular lymphoma (FL). Our goal is to demonstrate the degree to which computerized quantification is comparable to the practice of estimation by a panel of expert pathologists. The computerized quantification method uses entropy based histogram thresholding to separate brown (CD3+) and blue (CD3-) regions after a color space transformation. A panel of four board-certified hematopathologists evaluated a database of 20 FL images using two different reading methods: visual estimation and manual marking of each CD3+ cell in the images. These image data and the readings provided a reference standard and the range of variability among readers. Sensitivity and specificity measures of the computer's segmentation of CD3+ and CD- T cell are recorded. For all four pathologists, mean sensitivity and specificity measures are 90.97 and 88.38%, respectively. The computerized quantification method agrees more with the manual cell marking as compared to the visual estimations. Statistical comparison between the computerized quantification method and the pathologist readings demonstrated good agreement with correlation coefficient values of 0.81 and 0.96 in terms of Lin's concordance correlation and Spearman's correlation coefficient, respectively. These values are higher than most of those calculated among the pathologists. In the future, the computerized quantification method may be used to investigate the relationship between the overall architectural pattern (i.e., interfollicular vs. follicular) and outcome measures (e.g., overall survival, and time to treatment). © 2017 International Society for Advancement of Cytometry., (© 2017 International Society for Advancement of Cytometry.)

Immunohistochemical detection of FOXP3 antigen is a usable marker for detection of regulatory T lymphocytes (TR) in formalin fixed and paraffin embedded sections of different types of tumor tissue. TR plays a major role in homeostasis of normal immune systems where they prevent auto reactivity of the immune system towards the host. This beneficial effect of TR is frequently "hijacked" by malignant cells where tumor-infiltrating regulatory T cells are recruited by the malignant nuclei to inhibit the beneficial immune response of the host against the tumor cells. In the majority of human solid tumors, an increased number of tumor-infiltrating FOXP3 positive TR is associated with worse outcome. However, in follicular lymphoma (FL) the impact of the number and distribution of TR on the outcome still remains controversial. In this study, we present a novel method to detect and enumerate nuclei from FOXP3 stained images of FL biopsies. The proposed method defines a new adaptive thresholding procedure, namely the optimal adaptive thresholding (OAT) method, which aims to minimize under-segmented and over-segmented nuclei for coarse segmentation. Next, we integrate a parameter free elliptical arc and line segment detector (ELSD) as additional information to refine segmentation results and to split most of the merged nuclei. Finally, we utilize a state-of-the-art super-pixel method, Simple Linear Iterative Clustering (SLIC) to split the rest of the merged nuclei. Our dataset consists of 13 region-of-interest images containing 769 negative and 88 positive nuclei. Three expert pathologists evaluated the method and reported sensitivity values in detecting negative and positive nuclei ranging from 83-100% and 90-95%, and precision values of 98-100% and 99-100%, respectively. The proposed solution can be used to investigate the impact of FOXP3 positive nuclei on the outcome and prognosis in FL.

Background: Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias., Methods: In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured., Results: FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates "acceptable" diagnostic performance., Conclusions: The results of this study show that FLAGS can be useful in increasing the pathologists' accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists' grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.

Tumors tissues house a diverse array of cell types, requiring powerful cell-based analysis methods to characterize cellular heterogeneity and identify rare cells. Tumor tissue is dissociated into single cells by treatment with proteolytic enzymes, followed by mechanical disruption using vortexing or pipetting. These procedures can be incomplete and require significant time, and the latter mechanical treatments are poorly defined and controlled. Here, we present a novel microfluidic device to improve mechanical dissociation of digested tissue and cell aggregates into single cells. The device design includes a network of branching channels that range in size from millimeters down to hundreds of microns. The channels also contain flow constrictions that generate well-defined regions of high shear force, which we refer to as "hydrodynamic micro-scalpels", to progressively disaggregate tissue fragments and clusters into single cells. We show using in vitro cancer cell models that the microfluidic device significantly enhances cell recovery in comparison to mechanical disruption by pipetting and vortexing after digestion with trypsin or incubation with EDTA. Notably, the device enabled superior results to be obtained after shorter proteolytic digestion times, resulting in fully viable cells in less than ten minutes. The device could also be operated under enzyme-free conditions that could better maintain expression of certain surface markers. The microfluidic format is advantageous because it enables application of well-defined mechanical forces and rapid processing times. Furthermore, it may be possible to directly integrate downstream processing and detection operations to create integrated cell-based analysis platforms. The enhanced capabilities enabled by our novel device may help promote applications of single cell detection and purification techniques to tumor tissue specimens, advancing the current understanding of cancer biology and enabling molecular diagnostics in clinical settings.

To date, no study has evaluated the short- and long-term effects air pollution exposure on emphysematous subjects who have undergone lung volume reduction surgery (LVRS). Data from the National Emphysema Treatment Trial study (1998-2003) included 1,218 subjects, aged 39 to 84. Daily values of ambient fine particulate matter (aerodynamic diameter < 2.5 μm; PM2.5) and ozone were obtained. Mixed-effects models tested the association between short- and long-term pollutant concentrations and changes in pulmonary function. Cumulative air pollution exposure was strongly associated with worsened respiratory function and symptoms. Mean PM2.5 was associated with poorer lung function. Lagged exposures were poorly associated with respiratory health outcomes. There were detrimental respiratory and pulmonary effects observed in response to even low levels of ambient air pollutants among study participants. These results are indicative that exposures even below those of air quality standards may still pose significant risks to severe chronic obstructive pulmonary disease (COPD) subjects.