Multi-stage arc magma evolution recorded by apatite in volcanic rocks

Gold open access fee paid by NHMProtracted magma storage in the deep crust is a key stage in the formation of evolved, hydrous arc magmas that can result in explosive volcanism and the formation of economically valuable magmatic-hydrothermal ore deposits. High magmatic water content in the deep crust results in extensive amphibole ± garnet fractionation and the suppression of plagioclase crystallization as recorded by elevated Sr/Y ratios and high Eu (high Eu/Eu*) in the melt. Here, we use a novel approach to track the petrogenesis of arc magmas using apatite trace element chemistry in volcanic formations from the Cenozoic arc of central Chile. These rocks formed in a magmatic cycle that culminated in high-Sr/Y magmatism and porphyry ore deposit formation in the Miocene. We use Sr/Y, Eu/Eu*, and Mg in apatite to track discrete stages of arc magma evolution. We apply fractional crystallization modeling to show that early-crystallizing apatite can inherit a high-Sr/Y and high-Eu/Eu* melt chemistry signature that is predetermined by amphibole-dominated fractional crystallization in the lower crust. Our modeling shows that crystallization of the in situ host-rock mineral assemblage in the shallow crust causes competition for trace elements in the melt that leads to apatite compositions diverging from bulk-magma chemistry. Understanding this decoupling behavior is important for the use of apatite as an indicator of metallogenic fertility in arcs and for interpretation of provenance in detrital studies.© 2020 The Authors Gold Open Access: This paper is published under the terms of the CC-BY license (https://creativecommons.org/licenses/by/4.0/). The attached file is the published pdf

Porphyry indicator minerals and their mineral chemistry as vectoring and fertility tools

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Carbon stable isotopes as a palaeoclimate proxy in vascular plant dominated peatlands

Carbon stable isotope (δ¹³C) records from vascular plant dominated peatlands have been used as a palaeoclimate proxy, but a better empirical understanding of fractionation processes in these ecosystems is required. Here, we test the potential of δ¹³C analysis of ombrotrophic restiad peatlands in New Zealand, dominated by the wire rush (Empodisma spp.), to provide a methodology for developing palaeoclimatic records. We took surface plant samples alongside measurements of water table depth and (micro)climate over spatial (six sites spanning > 10 latitude) and temporal (monthly measurements over 1 year) gradients and analysed the relationships between cellulose δ¹³C values and environmental parameters. We found strong, significant negative correlations between δ¹³C and temperature, photosynthetically active radiation and growing degree days above 0 C. No significant relationships were observed between δ¹³C and precipitation, relative humidity, soil moisture or water table depth, suggesting no growing season water limitation and a decoupling of the expected link between δ¹³C in vascular plants and hydrological variables. δ¹³C of Empodisma spp. roots may therefore provide a valuable temperature proxy in a climatically sensitive region, but further physiological and sub-fossil calibration studies are required to fully understand the observed signal

Thinking outside the curve, part I: modeling birthweight distribution

<p>Abstract</p> <p>Background</p> <p>Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the first of a two-part series that introduces such a framework.</p> <p>Methods</p> <p>We propose describing a birthweight distribution via a normal mixture model in which the number of components is determined from the data using a model selection criterion rather than fixed <it>a priori</it>.</p> <p>Results</p> <p>We address a number of methodological issues, including how the number of components selected depends on the sample size, how the choice of model selection criterion influences the results, and how estimates of mixture model parameters based on multiple samples from the same population can be combined to produce confidence intervals. As an illustration, we find that a 4-component normal mixture model reasonably describes the birthweight distribution for a population of white singleton infants born to heavily smoking mothers. We also compare this 4-component normal mixture model to two competitors from the existing literature: a contaminated normal model and a 2-component normal mixture model. In a second illustration, we discover that a 6-component normal mixture model may be more appropriate than a 4-component normal mixture model for a general population of black singletons.</p> <p>Conclusions</p> <p>The framework developed in this paper avoids assuming the existence of an interval of birthweights over which there are no compromised pregnancies and does not constrain birthweights within compromised pregnancies to be normally distributed. Thus, the present framework can reveal heterogeneity in birthweight that is undetectable via a contaminated normal model or a 2-component normal mixture model.</p

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenario

Next-generation mitogenomics: A comparison of approaches applied to caecilian amphibian phylogeny

Mitochondrial genome (mitogenome) sequences are being generated with increasing speed due to the advances of next-generation sequencing (NGS) technology and associated analytical tools. However, detailed comparisons to explore the utility of alternative NGS approaches applied to the same taxa have not been undertaken. We compared a 'traditional' Sanger sequencing method with two NGS approaches (shotgun sequencing and non-indexed, multiplex amplicon sequencing) on four different sequencing platforms (Illumina's HiSeq and MiSeq, Roche's 454 GS FLX, and Life Technologies' Ion Torrent) to produce seven (near-) complete mitogenomes from six species that form a small radiation of caecilian amphibians from the Seychelles. The fastest, most accurate method of obtaining mitogenome sequences that we tested was direct sequencing of genomic DNA (shotgun sequencing) using the MiSeq platform. Bayesian inference and maximum likelihood analyses using seven different partitioning strategies were unable to resolve compellingly all phylogenetic relationships among the Seychelles caecilian species, indicating the need for additional data in this case

A Genome-Wide Homozygosity Association Study Identifies Runs of Homozygosity Associated with Rheumatoid Arthritis in the Human Major Histocompatibility Complex

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with a polygenic mode of inheritance. This study examined the hypothesis that runs of homozygosity (ROHs) play a recessive-acting role in the underlying RA genetic mechanism and identified RA-associated ROHs. Ours is the first genome-wide homozygosity association study for RA and characterized the ROH patterns associated with RA in the genomes of 2,000 RA patients and 3,000 normal controls of the Wellcome Trust Case Control Consortium. Genome scans consistently pinpointed two regions within the human major histocompatibility complex region containing RA-associated ROHs. The first region is from 32,451,664 bp to 32,846,093 bp (−log10(p)>22.6591). RA-susceptibility genes, such as HLA-DRB1, are contained in this region. The second region ranges from 32,933,485 bp to 33,585,118 bp (−log10(p)>8.3644) and contains other HLA-DPA1 and HLA-DPB1 genes. These two regions are physically close but are located in different blocks of linkage disequilibrium, and ∼40% of the RA patients' genomes carry these ROHs in the two regions. By analyzing homozygote intensities, an ROH that is anchored by the single nucleotide polymorphism rs2027852 and flanked by HLA-DRB6 and HLA-DRB1 was found associated with increased risk for RA. The presence of this risky ROH provides a 62% accuracy to predict RA disease status. An independent genomic dataset from 868 RA patients and 1,194 control subjects of the North American Rheumatoid Arthritis Consortium successfully validated the results obtained using the Wellcome Trust Case Control Consortium data. In conclusion, this genome-wide homozygosity association study provides an alternative to allelic association mapping for the identification of recessive variants responsible for RA. The identified RA-associated ROHs uncover recessive components and missing heritability associated with RA and other autoimmune diseases

The effects of integrated care: a systematic review of UK and international evidence

BACKGROUND: Healthcare systems around the world have been responding to the demand for better integrated models of service delivery. However, there is a need for further clarity regarding the effects of these new models of integration, and exploration regarding whether models introduced in other care systems may achieve similar outcomes in a UK national health service context. METHODS: The study aimed to carry out a systematic review of the effects of integration or co-ordination between healthcare services, or between health and social care on service delivery outcomes including effectiveness, efficiency and quality of care. Electronic databases including MEDLINE; Embase; PsycINFO; CINAHL; Science and Social Science Citation Indices; and the Cochrane Library were searched for relevant literature published between 2006 to March 2017. Online sources were searched for UK grey literature, and citation searching, and manual reference list screening were also carried out. Quantitative primary studies and systematic reviews, reporting actual or perceived effects on service delivery following the introduction of models of integration or co-ordination, in healthcare or health and social care settings in developed countries were eligible for inclusion. Strength of evidence for each outcome reported was analysed and synthesised using a four point comparative rating system of stronger, weaker, inconsistent or limited evidence. RESULTS: One hundred sixty seven studies were eligible for inclusion. Analysis indicated evidence of perceived improved quality of care, evidence of increased patient satisfaction, and evidence of improved access to care. Evidence was rated as either inconsistent or limited regarding all other outcomes reported, including system-wide impacts on primary care, secondary care, and health care costs. There were limited differences between outcomes reported by UK and international studies, and overall the literature had a limited consideration of effects on service users. CONCLUSIONS: Models of integrated care may enhance patient satisfaction, increase perceived quality of care, and enable access to services, although the evidence for other outcomes including service costs remains unclear. Indications of improved access may have important implications for services struggling to cope with increasing demand. TRIAL REGISTRATION: Prospero registration number: 42016037725

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease