Fine-scale variability in coral bleaching and mortality during a marine heatwave

Coral bleaching and mortality can show significant spatial and taxonomic heterogeneity at local scales, highlighting the need to understand the fine-scale drivers and impacts of thermal stress. In this study, we used structure-from-motion photogrammetry to track coral bleaching, mortality, and changes in community composition during the 2019 marine heatwave in Kāneʻohe Bay, Hawaiʻi. We surveyed 30 shallow reef patches every 3 weeks for the duration of the bleaching event (August-December) and one year after, resulting in a total of 210 large-area, high-resolution photomosaics that enabled us to follow the fate of thousands of coral colonies through time. We also measured environmental variables such as temperature, sedimentation, depth, and wave velocity at each of these sites, and extracted estimates of habitat complexity (rugosity R and fractal dimension D) from digital elevation models to better understand their effects on patterns of bleaching and mortality. We found that up to 80% of corals experienced moderate to severe bleaching in this period, with peak bleaching occurring in October when heat stress (Degree Heating Weeks) reached its maximum. Mortality continued to accumulate as bleaching levels dropped, driving large declines in more heat-susceptible species (77% loss of Pocillopora cover) and moderate declines in heat-tolerant species (19% and 23% for Porites compressa and Montipora capitata, respectively). Declines in live coral were accompanied by a rapid increase in algal cover across the survey sites. Spatial differences in bleaching were significantly linked to habitat complexity and coral species composition, with reefs that were dominated by Pocillopora experiencing the most severe bleaching. Mortality was also influenced by species composition, fractal dimension, and site-level differences in thermal stress. Our results show that spatial heterogeneity in the impacts of bleaching are driven by a mix of environmental variation, habitat complexity, and differences in assemblage composition

Complex patterns of human antisera reactivity to novel 2009 H1N1 and historical H1N1 influenza strains

Background: During the 2009 influenza pandemic, individuals over the age of 60 had the lowest incidence of infection with approximately 25% of these people having pre-existing, cross-reactive antibodies to novel 2009 H1N1 influenza isolates. It was proposed that older people had pre-existing antibodies induced by previous 1918-like virus infection(s) that cross-reacted to novel H1N1 strains. Methodology/Principal Findings: Using antisera collected from a cohort of individuals collected before the second wave of novel H1N1 infections, only a minority of individuals with 1918 influenza specific antibodies also demonstrated hemagglutination-inhibition activity against the novel H1N1 influenza. In this study, we examined human antisera collected from individuals that ranged between the ages of 1 month and 90 years to determine the profile of seropositive influenza immunity to viruses representing H1N1 antigenic eras over the past 100 years. Even though HAI titers to novel 2009 H1N1 and the 1918 H1N1 influenza viruses were positively associated, the association was far from perfect, particularly for the older and younger age groups. Conclusions/Significance: Therefore, there may be a complex set of immune responses that are retained in people infected with seasonal H1N1 that can contribute to the reduced rates of H1N1 influenza infection in older populations. © 2012 Carter et al

Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3–9; median total sample = 1,279.5, range = 276–3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (Δr = .002 or .014, depending on analytic approach). The median effect size for the revised protocols (r = .05) was similar to that of the RP:P protocols (r = .04) and the original RP:P replications (r = .11), and smaller than that of the original studies (r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00–.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19–.50).Additional co-authors: Ivan Ropovik, Balazs Aczel, Lena F. Aeschbach, Luca Andrighetto, Jack D. Arnal, Holly Arrow, Peter Babincak, Bence E. Bakos, Gabriel Baník, Ernest Baskin, Radomir Belopavlovic, Michael H. Bernstein, Michał Białek, Nicholas G. Bloxsom, Bojana Bodroža, Diane B. V. Bonfiglio, Leanne Boucher, Florian Brühlmann, Claudia C. Brumbaugh, Erica Casini, Yiling Chen, Carlo Chiorri, William J. Chopik, Oliver Christ, Antonia M. Ciunci, Heather M. Claypool, Sean Coary, Marija V. Cˇolic, W. Matthew Collins, Paul G. Curran, Chris R. Day, Anna Dreber, John E. Edlund, Filipe Falcão, Anna Fedor, Lily Feinberg, Ian R. Ferguson, Máire Ford, Michael C. Frank, Emily Fryberger, Alexander Garinther, Katarzyna Gawryluk, Kayla Ashbaugh, Mauro Giacomantonio, Steffen R. Giessner, Jon E. Grahe, Rosanna E. Guadagno, Ewa Hałasa, Rias A. Hilliard, Joachim Hüffmeier, Sean Hughes, Katarzyna Idzikowska, Michael Inzlicht, Alan Jern, William Jiménez-Leal, Magnus Johannesson, Jennifer A. Joy-Gaba, Mathias Kauff, Danielle J. Kellier, Grecia Kessinger, Mallory C. Kidwell, Amanda M. Kimbrough, Josiah P. J. King, Vanessa S. Kolb, Sabina Kołodziej, Marton Kovacs, Karolina Krasuska, Sue Kraus, Lacy E. Krueger, Katarzyna Kuchno, Caio Ambrosio Lage, Eleanor V. Langford, Carmel A. Levitan, Tiago Jessé Souza de Lima, Hause Lin, Samuel Lins, Jia E. Loy, Dylan Manfredi, Łukasz Markiewicz, Madhavi Menon, Brett Mercier, Mitchell Metzger, Venus Meyet, Jeremy K. Miller, Andres Montealegre, Don A. Moore, Rafał Muda, Gideon Nave, Austin Lee Nichols, Sarah A. Novak, Christian Nunnally, Ana Orlic, Anna Palinkas, Angelo Panno, Kimberly P. Parks, Ivana Pedovic, Emilian Pekala, Matthew R. Penner, Sebastiaan Pessers, Boban Petrovic, Thomas Pfeiffer, Damian Pienkosz, Emanuele Preti, Danka Puric, Tiago Ramos, Jonathan Ravid, Timothy S. Razza, Katrin Rentzsch, Juliette Richetin, Sean C. Rife, Anna Dalla Rosa, Kaylis Hase Rudy, Janos Salamon, Blair Saunders, Przemysław Sawicki, Kathleen Schmidt, Kurt Schuepfer, Thomas Schultze, Stefan Schulz-Hardt, Astrid Schütz, Ani N. Shabazian, Rachel L. Shubella, Adam Siegel, Rúben Silva, Barbara Sioma, Lauren Skorb, Luana Elayne Cunha de Souza, Sara Steegen, L. A. R. Stein, R. Weylin Sternglanz, Darko Stojilovic, Daniel Storage, Gavin Brent Sullivan, Barnabas Szaszi, Peter Szecsi, Orsolya Szöke, Attila Szuts, Manuela Thomae, Natasha D. Tidwell, Carly Tocco, Ann-Kathrin Torka, Francis Tuerlinckx, Wolf Vanpaemel, Leigh Ann Vaughn, Michelangelo Vianello, Domenico Viganola, Maria Vlachou, Ryan J. Walker, Sophia C. Weissgerber, Aaron L. Wichman, Bradford J. Wiggins, Daniel Wolf, Michael J. Wood, David Zealley, Iris Žeželj, Mark Zrubka, and Brian A. Nose

Signatures of Environmental Genetic Adaptation Pinpoint Pathogens as the Main Selective Pressure through Human Evolution

Previous genome-wide scans of positive natural selection in humans have identified a number of non-neutrally evolving genes that play important roles in skin pigmentation, metabolism, or immune function. Recent studies have also shown that a genome-wide pattern of local adaptation can be detected by identifying correlations between patterns of allele frequencies and environmental variables. Despite these observations, the degree to which natural selection is primarily driven by adaptation to local environments, and the role of pathogens or other ecological factors as selective agents, is still under debate. To address this issue, we correlated the spatial allele frequency distribution of a large sample of SNPs from 55 distinct human populations to a set of environmental factors that describe local geographical features such as climate, diet regimes, and pathogen loads. In concordance with previous studies, we detected a significant enrichment of genic SNPs, and particularly non-synonymous SNPs associated with local adaptation. Furthermore, we show that the diversity of the local pathogenic environment is the predominant driver of local adaptation, and that climate, at least as measured here, only plays a relatively minor role. While background demography by far makes the strongest contribution in explaining the genetic variance among populations, we detected about 100 genes which show an unexpectedly strong correlation between allele frequencies and pathogenic environment, after correcting for demography. Conversely, for diet regimes and climatic conditions, no genes show a similar correlation between the environmental factor and allele frequencies. This result is validated using low-coverage sequencing data for multiple populations. Among the loci targeted by pathogen-driven selection, we found an enrichment of genes associated to autoimmune diseases, such as celiac disease, type 1 diabetes, and multiples sclerosis, which lends credence to the hypothesis that some susceptibility alleles for autoimmune diseases may be maintained in human population due to past selective processes

Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations.

Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma

Parallel Adaptive Divergence among Geographically Diverse Human Populations

Few genetic differences between human populations conform to the classic model of positive selection, in which a newly arisen mutation rapidly approaches fixation in one lineage, suggesting that adaptation more commonly occurs via moderate changes in standing variation at many loci. Detecting and characterizing this type of complex selection requires integrating individually ambiguous signatures across genomically and geographically extensive data. Here, we develop a novel approach to test the hypothesis that selection has favored modest divergence at particular loci multiple times in independent human populations. We find an excess of SNPs showing non-neutral parallel divergence, enriched for genic and nonsynonymous polymorphisms in genes encompassing diverse and often disease related functions. Repeated parallel evolution in the same direction suggests common selective pressures in disparate habitats. We test our method with extensive coalescent simulations and show that it is robust to a wide range of demographic events. Our results demonstrate phylogenetically orthogonal patterns of local adaptation caused by subtle shifts at many widespread polymorphisms that likely underlie substantial phenotypic diversity

The community impact of the 2009 influenza pandemic in the WHO European Region: a comparison with historical seasonal data from 28 countries

Contains fulltext : 109779.pdf (publisher's version ) (Open Access)BACKGROUND: The world has recently experienced the first influenza pandemic of the 21st century that lasted 14 months from June 2009 to August 2010. This study aimed to compare the timing, geographic spread and community impact during the winter wave of influenza pandemic A (H1N1) 2009 to historical influenza seasons in countries of the WHO European region. METHODS: We assessed the timing of pandemic by comparing the median peak of influenza activity in countries of the region during the last seven influenza seasons. The peaks of influenza activity were selected by two independent researchers using predefined rules. The geographic spread was assessed by correlating the peak week of influenza activity in included countries against the longitude and latitude of the central point in each country. To assess the community impact of pandemic influenza, we constructed linear regression models to compare the total and age-specific influenza-like-illness (ILI) or acute respiratory infection (ARI) rates reported by the countries in the pandemic season to those observed in the previous six influenza seasons. RESULTS: We found that the influenza activity reached its peak during the pandemic, on average, 10.5 weeks (95% CI 6.4-14.2) earlier than during the previous 6 seasons in the Region, and there was a west to east spread of pandemic A(H1N1) influenza virus in the western part of the Region. A regression analysis showed that the total ILI or ARI rates were not higher than historical rates in 19 of the 28 countries. However, in countries with age-specific data, there were significantly higher consultation rates in the 0-4 and/or 5-14 age groups in 11 of the 20 countries. CONCLUSIONS: Using routine influenza surveillance data, we found that pandemic influenza had several differential features compared to historical seasons in the region. It arrived earlier, caused significantly higher number of outpatient consultations in children in most countries and followed west to east spread that was previously observed during some influenza seasons with dominant A (H3N2) ifluenza viruses. The results of this study help to understand the epidemiology of 2009 influenza pandemic and can be used for pandemic preparedness planning

Low frequency observations of linearly polarized structures in the interstellar medium near the south Galactic pole

This is an author-created, un-copyedited version of an article published in The Astrophysical Journal. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.3847/0004-637X/830/1/38We present deep polarimetric observations at 154 MHz with the Murchison Widefield Array (MWA), covering 625 deg^2 centered on RA=0 h, Dec=-27 deg. The sensitivity available in our deep observations allows an in-band, frequency-dependent analysis of polarized structure for the first time at long wavelengths. Our analysis suggests that the polarized structures are dominated by intrinsic emission but may also have a foreground Faraday screen component. At these wavelengths, the compactness of the MWA baseline distribution provides excellent snapshot sensitivity to large-scale structure. The observations are sensitive to diffuse polarized emission at ~54' resolution with a sensitivity of 5.9 mJy beam^-1 and compact polarized sources at ~2.4' resolution with a sensitivity of 2.3 mJy beam^-1 for a subset (400 deg^2) of this field. The sensitivity allows the effect of ionospheric Faraday rotation to be spatially and temporally measured directly from the diffuse polarized background. Our observations reveal large-scale structures (~1 deg - 8 deg in extent) in linear polarization clearly detectable in ~2 minute snapshots, which would remain undetectable by interferometers with minimum baseline lengths >110 m at 154 MHz. The brightness temperature of these structures is on average 4 K in polarized intensity, peaking at 11 K. Rotation measure synthesis reveals that the structures have Faraday depths ranging from -2 rad m^-2 to 10 rad m^-2 with a large fraction peaking at ~+1 rad m^-2. We estimate a distance of 51+/-20 pc to the polarized emission based on measurements of the in-field pulsar J2330-2005. We detect four extragalactic linearly polarized point sources within the field in our compact source survey. Based on the known polarized source population at 1.4 GHz and non-detections at 154 MHz, we estimate an upper limit on the depolarization ratio of 0.08 from 1.4 GHz to 154 MHz.Peer reviewedFinal Accepted Versio

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here

Natural Selection Affects Multiple Aspects of Genetic Variation at Putatively Neutral Sites across the Human Genome

A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries of genetic variation, like allele frequencies, are also correlated with recombination rate and whether these correlations can be explained solely by negative selection against deleterious mutations or whether positive selection acting on favorable alleles is also required. Here we attempt to address these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations. However, models with strong positive selection on nonsynonymous mutations and little negative selection predict a stronger negative correlation between neutral diversity and nonsynonymous divergence than observed in the actual data, supporting the importance of negative, rather than positive, selection throughout the genome. Further, we show that the widespread presence of weakly deleterious alleles, rather than a small number of strongly positively selected mutations, is responsible for the correlation between neutral genetic diversity and recombination rate. This work suggests that natural selection has affected multiple aspects of linked neutral variation throughout the human genome and that positive selection is not required to explain these observations