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To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association studies (GWAS) (N = 10,768 individuals of European ancestry enrolled in pregnancy and/or birth cohorts) and followed up three lead signals in six replication studies (combined N = 19,089). rs7980687 on chromosome 12q24 (P = 8.1 x 10(-9)) and rs1042725 on chromosome 12q15 (P = 2.8 x 10(-10)) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height(1), their effects on infant head circumference were largely independent of height (P = 3.8 x 10(-7) for rs7980687 and P = 1.3 x 10(-7) for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P = 3.9 x 10(-6)). SNPs correlated to the 17q21 signal have shown genome-wide association with adult intracranial volume(2), Parkinson's disease and other neurodegenerative diseases(3-5), indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.
We report on the gamma-ray activity of the blazar Mrk 501 during the first 480 days of Fermi operation. We find that the average Large Area Telescope (LAT) gamma-ray spectrum of Mrk 501 can be well described by a single power-law function with a photon index of 1.78 +/- 0.03. While we observe relatively mild flux variations with the Fermi-LAT (within less than a factor of two), we detect remarkable spectral variability where the hardest observed spectral index within the LAT energy range is 1.52 +/- 0.14, and the softest one is 2.51 +/- 0.20. These unexpected spectral changes do not correlate with the measured flux variations above 0.3 GeV. In this paper, we also present the first results from the 4.5 month long multifrequency campaign (2009 March 15-August 1) on Mrk 501, which included the Very Long Baseline Array (VLBA), Swift, RXTE, MAGIC, and VERITAS, the F-GAMMA, GASP-WEBT, and other collaborations and instruments which provided excellent temporal and energy coverage of the source throughout the entire campaign. The extensive radio to TeV data set from this campaign provides us with the most detailed spectral energy distribution yet collected for this source during its relatively low activity. The average spectral energy distribution of Mrk 501 is well described by the standard one-zone synchrotron self-Compton (SSC) model. In the framework of this model, we find that the dominant emission region is characterized by a size less than or similar to 0.1 pc (comparable within a factor of few to the size of the partially resolved VLBA core at 15-43 GHz), and that the total jet power (similar or equal to 10(44) erg s(-1)) constitutes only a small fraction (similar to 10(-3)) of the Eddington luminosity. The energy distribution of the freshly accelerated radiating electrons required to fit the time-averaged data has a broken power-law form in the energy range 0.3 GeV-10 TeV, with spectral indices 2.2 and 2.7 below and above the break energy of 20 GeV. We argue that such a form is consistent with a scenario in which the bulk of the energy dissipation within the dominant emission zone of Mrk 501 is due to relativistic, proton-mediated shocks. We find that the ultrarelativistic electrons and mildly relativistic protons within the blazar zone, if comparable in number, are in approximate energy equipartition, with their energy dominating the jet magnetic field energy by about two orders of magnitude.
VERITAS and Fermi-LAT Observations of TeV Gamma-Ray Sources Discovered by HAWC in the 2HWC Catalog
(2018)
The High Altitude Water Cherenkov (HAWC) collaboration recently published their 2HWC catalog, listing 39 very high energy (VHE; >100 GeV) gamma-ray sources based on 507 days of observation. Among these, 19 sources are not associated with previously known teraelectronvolt (TeV) gamma-ray sources. We have studied 14 of these sources without known counterparts with VERITAS and Fermi-LAT. VERITAS detected weak gamma-ray emission in the 1 TeV-30 TeV band in the region of DA 495, a pulsar wind nebula coinciding with 2HWC J1953+294, confirming the discovery of the source by HAWC. We did not find any counterpart for the selected 14 new HAWC sources from our analysis of Fermi-LAT data for energies higher than 10 GeV. During the search, we detected gigaelectronvolt (GeV) gamma-ray emission coincident with a known TeV pulsar wind nebula, SNR G54.1+0.3 (VER J1930+188), and a 2HWC source, 2HWC J1930+188. The fluxes for isolated, steady sources in the 2HWC catalog are generally in good agreement with those measured by imaging atmospheric Cherenkov telescopes. However, the VERITAS fluxes for SNR G54.1+0.3, DA 495, and TeV J2032+4130 are lower than those measured by HAWC, and several new HAWC sources are not detected by VERITAS. This is likely due to a change in spectral shape, source extension, or the influence of diffuse emission in the source region.
We present deep VERITAS observations of the blazar PKS 1424+240, along with contemporaneous Fermi Large Area Telescope, Swift X-ray Telescope, and Swift UV Optical Telescope data between 2009 February 19 and 2013 June 8. This blazar resides at a redshift of z >= 0.6035, displaying a significantly attenuated gamma-ray flux above 100 GeV due to photon absorption via pair-production with the extragalactic background light. We present more than 100 hr of VERITAS observations over three years, a multiwavelength light curve, and the contemporaneous spectral energy distributions. The source shows a higher flux of (2.1 +/- 0.3) x 10(-7) photons m(-2) s(-1) above 120 GeV in 2009 and 2011 as compared to the flux measured in 2013, corresponding to (1.02 +/- 0.08) x 10-7 photons m(-2) s(-1) above 120 GeV. The measured differential very high energy (VHE; E >= 100 GeV) spectral indices are Gamma = 3.8 +/- 0.3, 4.3 +/- 0.6 and 4.5 +/- 0.2 in 2009, 2011, and 2013, respectively. No significant spectral change across the observation epochs is detected. We find no evidence for variability at gamma-ray opacities of greater than tau = 2, where it is postulated that any variability would be small and occur on timescales longer than a year if hadronic cosmic-ray interactions with extragalactic photon fields provide a secondary VHE photon flux. The data cannot rule out such variability due to low statistics.
Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.
Coccolithophores have influenced the global climate for over 200 million years(1). These marine phytoplankton can account for 20 per cent of total carbon fixation in some systems(2). They form blooms that can occupy hundreds of thousands of square kilometres and are distinguished by their elegantly sculpted calcium carbonate exoskeletons (coccoliths), rendering them visible from space(3). Although coccolithophores export carbon in the form of organic matter and calcite to the sea floor, they also release CO2 in the calcification process. Hence, they have a complex influence on the carbon cycle, driving either CO2 production or uptake, sequestration and export to the deep ocean(4). Here we report the first haptophyte reference genome, from the coccolithophore Emiliania huxleyi strain CCMP1516, and sequences from 13 additional isolates. Our analyses reveal a pan genome (core genes plus genes distributed variably between strains) probably supported by an atypical complement of repetitive sequence in the genome. Comparisons across strains demonstrate that E. huxleyi, which has long been considered a single species, harbours extensive genome variability reflected in different metabolic repertoires. Genome variability within this species complex seems to underpin its capacity both to thrive in habitats ranging from the equator to the subarctic and to form large-scale episodic blooms under a wide variety of environmental conditions.
During aging, intracranial volume remains unchanged and represents maximally attained brain size, while various interacting biological phenomena lead to brain volume loss. Consequently, intracranial volume and brain volume in late life reflect different genetic influences. Our genome-wide association study (GWAS) in 8,175 community-dwelling elderly persons did not reveal any associations at genome-wide significance (P < 5 x 10(-8)) for brain volume. In contrast, intracranial volume was significantly associated with two loci: rs4273712 (P = 3.4 x 10(-11)), a known height-associated locus on chromosome 6q22, and rs9915547 (P = 1.5 x 10(-12)), localized to the inversion on chromosome 17q21. We replicated the associations of these loci with intracranial volume in a separate sample of 1,752 elderly persons (P = 1.1 x 10(-3) for 6q22 and 1.2 x 10(-3) for 17q21). Furthermore, we also found suggestive associations of the 17q21 locus with head circumference in 10,768 children (mean age of 14.5 months). Our data identify two loci associated with head size, with the inversion at 17q21 also likely to be involved in attaining maximal brain size.
Although a large fraction of the world's biomass resides in the subsurface, there has been no study of the effects of catastrophic disturbance on the deep biosphere and the rate of its subsequent recovery. We carried out an investigation of the microbiology of a 1.76 km drill core obtained from the similar to 35 million-year-old Chesapeake Bay impact structure, USA, with robust contamination control. Microbial enumerations displayed a logarithmic downward decline, but the different gradient, when compared to previously studied sites, and the scatter of the data are consistent with a rnicrobiota influenced by the geological disturbances caused by the impact. Microbial abundance is low in buried crater-fill, ocean-resurge, and avalanche deposits despite the presence of redox couples for growth. Coupled with the low hydraulic conductivity, the data suggest the microbial community has not yet recovered from the impact similar to 35 million years ago. Microbial enumerations, molecular analysis of microbial enrichment cultures, and geochemical analysis showed recolonization of a deep region of impact-fractured rock that was heated to above the upper temperature limit for life at the time of impact. These results show how, by fracturing subsurface rocks, impacts can extend the depth of the biosphere. This phenomenon would have provided deep refugia for life on the more heavily bombarded early Earth, and it shows that the deeply fractured regions of impact craters are promising targets to study the past and present habitability of Mars.
Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.