114 research outputs found

    Input and Output Inventories in General Equilibrium

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    We build and estimate a two‐sector (goods and services) dynamic stochastic general equilibrium model with two types of inventories: materials (input) inventories facilitate the production of finished goods, while finished goods (output) inventories yield utility services. The model is estimated using Bayesian methods. The estimated model replicates the volatility and cyclicality of inventory investment and inventory‐to‐target ratios. Although inventories are an important element of the model’s propagation mechanism, shocks to inventory efficiency or management are not an important source of business cycles. When the model is estimated over two subperiods (pre ‐ and post‐1984), changes in the volatility of inventory shocks, or in structural parameters associated with inventories play a minor role in reducing the volatility of output

    Exome Sequencing in Suspected Monogenic Dyslipidemias

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    Abstract BACKGROUND: -Exome sequencing is a promising tool for gene mapping in Mendelian disorders. We utilized this technique in an attempt to identify novel genes underlying monogenic dyslipidemias. METHODS AND RESULTS: -We performed exome sequencing on 213 selected family members from 41 kindreds with suspected Mendelian inheritance of extreme levels of low-density lipoprotein (LDL) cholesterol (after candidate gene sequencing excluded known genetic causes for high LDL cholesterol families) or high-density lipoprotein (HDL) cholesterol. We used standard analytic approaches to identify candidate variants and also assigned a polygenic score to each individual in order to account for their burden of common genetic variants known to influence lipid levels. In nine families, we identified likely pathogenic variants in known lipid genes (ABCA1, APOB, APOE, LDLR, LIPA, and PCSK9); however, we were unable to identify obvious genetic etiologies in the remaining 32 families despite follow-up analyses. We identified three factors that limited novel gene discovery: (1) imperfect sequencing coverage across the exome hid potentially causal variants; (2) large numbers of shared rare alleles within families obfuscated causal variant identification; and (3) individuals from 15% of families carried a significant burden of common lipid-related alleles, suggesting complex inheritance can masquerade as monogenic disease. CONCLUSIONS: -We identified the genetic basis of disease in nine of 41 families; however, none of these represented novel gene discoveries. Our results highlight the promise and limitations of exome sequencing as a discovery technique in suspected monogenic dyslipidemias. Considering the confounders identified may inform the design of future exome sequencing studies

    Growth rings show limited evidence for ungulates' potential to suppress shrubs across the Arctic

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    Global warming has pronounced effects on tundra vegetation, and rising mean temperatures increase plant growth potential across the Arctic biome. Herbivores may counteract the warming impacts by reducing plant growth, but the strength of this effect may depend on prevailing regional climatic conditions. To study how ungulates interact with temperature to influence growth of tundra shrubs across the Arctic tundra biome, we assembled dendroecological data from 20 sites, comprising 1153 individual shrubs and 223 63 annual growth rings. Evidence for ungulates suppressing shrub radial growth was only observed at intermediate summer temperatures (6.5 degrees C-9 degrees C), and even at these temperatures the effect was not strong. Multiple factors, including forage preferences and landscape use by the ungulates, and favourable climatic conditions enabling effective compensatory growth of shrubs, may weaken the effects of ungulates on shrubs, possibly explaining the weakness of observed ungulate effects. Earlier local studies have shown that ungulates may counteract the impacts of warming on tundra shrub growth, but we demonstrate that ungulates' potential to suppress shrub radial growth is not always evident, and may be limited to certain climatic conditions

    Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

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    Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70M>70 M⊙M_\odot) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≀0.30 < e \leq 0.3 at 0.330.33 Gpc−3^{-3} yr−1^{-1} at 90\% confidence level.Comment: 24 pages, 5 figure

    Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

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    The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages

    Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

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    The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages.Comment: 27 pages, 3 figure

    Observation of gravitational waves from the coalescence of a 2.5–4.5 M ⊙ compact object and a neutron star

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    We report the observation of a coalescing compact binary with component masses 2.5–4.5 M ⊙ and 1.2–2.0 M ⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO–Virgo–KAGRA detector network on 2023 May 29 by the LIGO Livingston observatory. The primary component of the source has a mass less than 5 M ⊙ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of 55−47+127Gpc−3yr−1 for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star–black hole merger, GW230529_181500-like sources may make up the majority of neutron star–black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star–black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap

    Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

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    Despite the growing number of binary black hole coalescences confidently observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include the effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that have already been identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total source-frame mass M > 70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz emitted gravitational-wave frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place a conservative upper limit for the merger rate density of high-mass binaries with eccentricities 0 < e ≀ 0.3 at 16.9 Gpc−3 yr−1 at the 90% confidence level
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