5,562 research outputs found

    On shocks driven by high-mass planets in radiatively inefficient disks. I. Two-dimensional global disk simulations

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    Recent observations of gaps and non-axisymmetric features in the dust distributions of transition disks have been interpreted as evidence of embedded massive protoplanets. However, comparing the predictions of planet-disk interaction models to the observed features has shown far from perfect agreement. This may be due to the strong approximations used for the predictions. For example, spiral arm fitting typically uses results that are based on low-mass planets in an isothermal gas. In this work, we describe two-dimensional, global, hydrodynamical simulations of disks with embedded protoplanets, with and without the assumption of local isothermality, for a range of planet-to-star mass ratios 1-10 M_jup for a 1 M_sun star. We use the Pencil Code in polar coordinates for our models. We find that the inner and outer spiral wakes of massive protoplanets (M>5 M_jup) produce significant shock heating that can trigger buoyant instabilities. These drive sustained turbulence throughout the disk when they occur. The strength of this effect depends strongly on the mass of the planet and the thermal relaxation timescale; for a 10 M_jup planet embedded in a thin, purely adiabatic disk, the spirals, gaps, and vortices typically associated with planet-disk interactions are disrupted. We find that the effect is only weakly dependent on the initial radial temperature profile. The spirals that form in disks heated by the effects we have described may fit the spiral structures observed in transition disks better than the spirals predicted by linear isothermal theory.Comment: 10 pages, 8 figures. ApJ, accepte

    Interpreting contemporary trends in atmospheric methane

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    Atmospheric methane plays a major role in controlling climate, yet contemporary methane trends (1982–2017) have defied explanation with numerous, often conflicting, hypotheses proposed in the literature. Specifically, atmospheric observations of methane from 1982 to 2017 have exhibited periods of both increasing concentrations (from 1982 to 2000 and from 2007 to 2017) and stabilization (from 2000 to 2007). Explanations for the increases and stabilization have invoked changes in tropical wetlands, livestock, fossil fuels, biomass burning, and the methane sink. Contradictions in these hypotheses arise because our current observational network cannot unambiguously link recent methane variations to specific sources. This raises some fundamental questions: (i) What do we know about sources, sinks, and underlying processes driving observed trends in atmospheric methane? (ii) How will global methane respond to changes in anthropogenic emissions? And (iii), What future observations could help resolve changes in the methane budget? To address these questions, we discuss potential drivers of atmospheric methane abundances over the last four decades in light of various observational constraints as well as process-based knowledge. While uncertainties in the methane budget exist, they should not detract from the potential of methane emissions mitigation strategies. We show that net-zero cost emission reductions can lead to a declining atmospheric burden, but can take three decades to stabilize. Moving forward, we make recommendations for observations to better constrain contemporary trends in atmospheric methane and to provide mitigation support

    Developmentally vitamin D-deficient rats show enhanced prepulse inhibition after acute δ9-tetrahydrocannabinol

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    Developmental vitamin D (DVD) deficiency has been proposed as a risk factor for schizophrenia. DVD-deficient rats show selective cognitive deficits and novelty-induced hyperlocomotion and enhanced locomotor responses from acute treatment with psychomimetic drugs, such as amphetamine and MK-801. Here we aimed to examine the effect of a drug from a different class of psychomimetic/psychoactive compounds, Δ-tetrahydrocannabinol (THC), on tasks of relevance to the cognitive and positive symptoms of schizophrenia. The aim of this study was to investigate whether DVD deficiency modulates the behavioural effects of THC on tests of delay-dependent memory, sensorimotor gating and locomotion. Adult control and DVD-deficient rats were injected with THC (0, 0.3, 0.6, 1.25, 2.5 mg/kg) 15 min before a delay match to sample (DMTS) task using variable delays (0-24 s). A separate group of rats was injected with either 2.5 mg/kg THC or vehicle before tests of either prepulse inhibition (PPI) of the acoustic startle response or in the open field. Control and DVD-deficient rats showed a similar dose-dependent impairment in performance on the DMTS. The greatest impairment was observed at 2.5 mg/kg for all delays (0-24 s). DVD-deficient rats showed THC-induced enhancement of PPI, which was not observed in control rats. There was no effect of maternal diet on acoustic startle response or locomotor responses in the open field. This study reports the novel findings that DVD-deficient rats were more sensitive to the acute effects of THC on PPI. It appears that prenatal vitamin D deficiency has long-term effects on sensitivity to the behavioural effects of cannabinoids

    Effects of Chemical Feedbacks on Decadal Methane Emissions Estimates

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    The coupled chemistry of methane, carbon monoxide (CO), and hydroxyl radical (OH) can modulate methane's 9‐year lifetime. This is often ignored in methane flux inversions, and the impacts of neglecting interactive chemistry have not been quantified. Using a coupled‐chemistry box model, we show that neglecting the effect of methane source perturbation on [OH] can lead to a 25% bias in estimating abrupt changes in methane sources after only 10 years. Further, large CO emissions, such as from biomass burning, can increase methane concentrations by extending the methane lifetime through impacts on [OH]. Finally, we quantify the biases of including (or excluding) coupled chemistry in the context of recent methane and CO trends. Decreasing CO concentrations, beginning in the 2000's, have notable impacts on methane flux inversions. Given these nonnegligible errors, decadal methane emissions inversions should incorporate chemical feedbacks for more robust methane trend analyses and source attributions

    A TRPV2–PKA Signaling Module for Transduction of Physical Stimuli in Mast Cells

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    Cutaneous mast cell responses to physical (thermal, mechanical, or osmotic) stimuli underlie the pathology of physical urticarias. In vitro experiments suggest that mast cells respond directly to these stimuli, implying that a signaling mechanism couples functional responses to physical inputs in mast cells. We asked whether transient receptor potential (vanilloid) (TRPV) cation channels were present and functionally coupled to signaling pathways in mast cells, since expression of this channel subfamily confers sensitivity to thermal, osmotic, and pressure inputs. Transcripts for a range of TRPVs were detected in mast cells, and we report the expression, surface localization, and oligomerization of TRPV2 protein subunits in these cells. We describe the functional coupling of TRPV2 protein to calcium fluxes and proinflammatory degranulation events in mast cells. In addition, we describe a novel protein kinase A (PKA)–dependent signaling module, containing PKA and a putative A kinase adapter protein, Acyl CoA binding domain protein (ACBD)3, that interacts with TRPV2 in mast cells. We propose that regulated phosphorylation by PKA may be a common pathway for TRPV modulation

    Efficient inference and identifiability analysis for differential equation models with random parameters

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    Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity through parameters that vary according to probability distributions. As our novel method is based on an approximate likelihood function, it is highly flexible; we demonstrate identifiability analysis using both a frequentist approach based on profile likelihood, and a Bayesian approach based on Markov-chain Monte Carlo. Through three case studies, we demonstrate our method by providing a didactic guide to inference and identifiability analysis of hyperparameters that relate to the statistical moments of model parameters from independent observed data. Our approach has a computational cost comparable to analysis of models that neglect heterogeneity, a significant improvement over many existing alternatives. We demonstrate how analysis of random parameter models can aid better understanding of the sources of heterogeneity from biological data.Comment: Minor changes to text. Additional results in supplementary material. Additional statistics regarding results given in main and supplementary materia
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