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- Shah, S, et al.
(författare)
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Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure
- 2020
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 163-
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Tidskriftsartikel (refereegranskat)abstract
- Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.
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| 3. |
- Hadid, L. Z., et al.
(författare)
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Solar Orbiter's first Venus flyby : Observations from the Radio and Plasma Wave instrument
- 2021
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 656
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Tidskriftsartikel (refereegranskat)abstract
- Context. On December 27, 2020, Solar Orbiter completed its first gravity assist manoeuvre of Venus (VGAM1). While this flyby was performed to provide the spacecraft with sufficient velocity to get closer to the Sun and observe its poles from progressively higher inclinations, the Radio and Plasma Wave (RPW) consortium, along with other operational in situ instruments, had the opportunity to perform high cadence measurements and study the plasma properties in the induced magnetosphere of Venus.Aims. In this paper, we review the main observations of the RPW instrument during VGAM1. They include the identification of a number of magnetospheric plasma wave modes, measurements of the electron number densities computed using the quasi-thermal noise spectroscopy technique and inferred from the probe-to-spacecraft potential, the observation of dust impact signatures, kinetic solitary structures, and localized structures at the bow shock, in addition to the validation of the wave normal analysis on-board from the Low Frequency Receiver.Methods. We used the data products provided by the different subsystems of RPW to study Venus' induced magnetosphere.Results. The results include the observations of various electromagnetic and electrostatic wave modes in the induced magnetosphere of Venus: strong emissions of similar to 100 Hz whistler waves are observed in addition to electrostatic ion acoustic waves, solitary structures and Langmuir waves in the magnetosheath of Venus. Moreover, based on the different levels of the wave amplitudes and the large-scale variations of the electron number densities, we could identify different regions and boundary layers at Venus.Conclusions. The RPW instrument provided unprecedented AC magnetic and electric field measurements in Venus' induced magnetosphere for continuous frequency ranges and with high time resolution. These data allow for the conclusive identification of various plasma waves at higher frequencies than previously observed and a detailed investigation regarding the structure of the induced magnetosphere of Venus. Furthermore, noting that prior studies were mainly focused on the magnetosheath region and could only reach 10-12 Venus radii (R-V) down the tail, the particular orbit geometry of Solar Orbiter's VGAM1, allowed the first investigation of the nature of the plasma waves continuously from the bow shock to the magnetosheath, extending to similar to 70R(V) in the far distant tail region.
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| 4. |
- Watson-Parris, D., et al.
(författare)
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ClimateBench v1.0 : A Benchmark for Data-Driven Climate Projections
- 2022
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Ingår i: Journal of Advances in Modeling Earth Systems. - 1942-2466. ; 14:10
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Tidskriftsartikel (refereegranskat)abstract
- Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench-the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection-Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge.
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