Stable Isotope Dynamics in Cownose Rays (Rhinoptera bonasus) within the Northwestern Gulf of Mexico

The cownose ray (Rhinoptera bonasus) is a durophagous mesopredator that exerts for top-down control on commercial shellfish stocks along the Atlantic coast. Although the trophic ecology of this elasmobranch has been the subject of extensive investigation, there is limited information available on feeding patterns of cownose rays in the northwestern Gulf of Mexico. Stable isotope analysis has been used to study the foraging ecology of various species, but only recently applied to elasmobranchs. Therefore, this study conducted a controlled feeding trial to determine incorporation rates and diet-tissue discrimination factors for δ^(13)C and δ^(15)N in cownose ray epidermal tissue. Additionally, this study investigates δ^(13)C and δ^(15)N variability in cownose rays captured via entanglement nets, from surveys along the Texas coast from 2009 – 2012. This is the first study to report δ^(13)C and δ^(15)N incorporation rates in elasmobranch epidermal tissue; estimated δ^(13)C and δ^(15)N incorporation rates were 0.0018 ± 0.0003 days^(-1) and 0.0059 ± 0.0022 days^(-1), respectively. Isotopic incorporation rates were highly variable amongst individuals but did not vary significantly with ray size (disc width or weight). Isotopic equilibrium was not reached between the epidermal tissue and the dietary treatment levels; therefore, estimated diet-tissue discrimination factors (Δ^(13)C = 4.26‰ and Δ^(15)N = 0.69‰) could not be applied for analyses of wild populations. Relative size of Bayesian ellipses, denoting the isotopic niche of cownose rays, varied seasonally in the lower Laguna Madre, with Summer 2012 significantly smaller than all other sampling periods. Female mean δ^(13)C signatures were significantly enriched compared to those of males, indicating that female rays are foraging over longer periods of time within inshore habitats. Isotopic niche size was comparable across the Texas bay systems in 2012, with only the lower Laguna Madre (Spring) significantly smaller. However, mean δ^(13)C and δ^(15)N in cownose rays varied spatially across bay systems along the Texas coast. This initial exploration into the trophic ecology of cownose rays within the northwestern Gulf of Mexico provides evidence of temporal and spatial variability in isotopic signatures, potentially aiding scientists in the management of this species

Isoprene NO_3 Oxidation Products from the RO_2 + HO_2 Pathway

We describe the products of the reaction of the hydroperoxy radical (HO_2) with the alkylperoxy radical formed following addition of the nitrate radical (NO_3) and O_2 to isoprene. NO_3 adds preferentially to the C_1 position of isoprene (>6 times more favorably than addition to C_4), followed by the addition of O_2 to produce a suite of nitrooxy alkylperoxy radicals (RO_2). At an RO_2 lifetime of ∼30 s, δ-nitrooxy and β-nitrooxy alkylperoxy radicals are present in similar amounts. Gas-phase product yields from the RO_2 + HO_2 pathway are identified as 0.75–0.78 isoprene nitrooxy hydroperoxide (INP), 0.22 methyl vinyl ketone (MVK) + formaldehyde (CH_2O) + hydroxyl radical (OH) + nitrogen dioxide (NO_2), and 0–0.03 methacrolein (MACR) + CH_2O + OH + NO_2. We further examined the photochemistry of INP and identified propanone nitrate (PROPNN) and isoprene nitrooxy hydroxyepoxide (INHE) as the main products. INHE undergoes similar heterogeneous chemistry as isoprene dihydroxy epoxide (IEPOX), likely contributing to atmospheric secondary organic aerosol formation

Why do models overestimate surface ozone in the Southeast United States

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx  ≡  NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°  ×  0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer

Organic nitrate chemistry and its implications for nitrogen budgets in an isoprene- and monoterpene-rich atmosphere: constraints from aircraft (SEAC4RS) and ground-based (SOAS) observations in the Southeast US

Formation of organic nitrates (RONO2) during oxidation of biogenic volatile organic compounds (BVOCs: isoprene, monoterpenes) is a significant loss pathway for atmospheric nitrogen oxide radicals (NOx), but the chemistry of RONO2 formation and degradation remains uncertain. Here we implement a new BVOC oxidation mechanism (including updated isoprene chemistry, new monoterpene chemistry, and particle uptake of RONO2) in the GEOS-Chem global chemical transport model with  ∼  25  x  25 km2 resolution over North America. We evaluate the model using aircraft (SEAC4RS) and ground-based (SOAS) observations of NOx, BVOCs, and RONO2 from the Southeast US in summer 2013. The updated simulation successfully reproduces the concentrations of individual gas- and particle-phase RONO2 species measured during the campaigns. Gas-phase isoprene nitrates account for 25-50 % of observed RONO2 in surface air, and we find that another 10 % is contributed by gas-phase monoterpene nitrates. Observations in the free troposphere show an important contribution from long-lived nitrates derived from anthropogenic VOCs. During both campaigns, at least 10 % of observed boundary layer RONO2 were in the particle phase. We find that aerosol uptake followed by hydrolysis to HNO3 accounts for 60 % of simulated gas-phase RONO2 loss in the boundary layer. Other losses are 20 % by photolysis to recycle NOx and 15 % by dry deposition. RONO2 production accounts for 20 % of the net regional NOx sink in the Southeast US in summer, limited by the spatial segregation between BVOC and NOx emissions. This segregation implies that RONO2 production will remain a minor sink for NOx in the Southeast US in the future even as NOx emissions continue to decline

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch