57 research outputs found
Backstreaming ions at a high Mach number interplanetary shock: Solar Orbiter measurements during the nominal mission phase
Solar Orbiter, a mission developed by the European Space Agency, explores in
situ plasma across the inner heliosphere while providing remote-sensing
observations of the Sun. Our study examines particle observations for the 30
October 2021 shock. The particles provide clear evidence of ion reflection up
to several minutes upstream of the shock. Additionally, the magnetic and
electric field observations contain complex electromagnetic structures near the
shock, and we aim to investigate how they are connected to ion dynamics. The
main goal of this study is to advance our understanding of the complex coupling
between particles and the shock structure in high Mach number regimes of
interplanetary shocks. We used observations of magnetic and electric fields,
probe-spacecraft potential, and thermal and energetic particles to characterize
the structure of the shock front and particle dynamics. Furthermore, ion
velocity distribution functions were used to study reflected ions and their
coupling to the shock. To determine shock parameters and study waves, we used
several methods, including cold plasma theory, singular-value decomposition,
minimum variance analysis, and shock Rankine-Hugoniot relations. To support the
analysis and interpretation of the experimental data, test-particle analysis,
and hybrid particle in-cell simulations were used. The ion velocity
distribution functions show clear evidence of particle reflection in the form
of backstreaming ions several minutes upstream. The shock structure has complex
features at the ramp and whistler precursors. The backstreaming ions may be
modulated by the complex shock structure, and the whistler waves are likely
driven by gyrating ions in the foot. Supra-thermal ions up to 20 keV were
observed, but shock-accelerated particles with energies above this were not
MDM2 Integrates Cellular Respiration and Apoptotic Signaling through NDUFS1 and the Mitochondrial Network
Signaling diversity and subsequent complexity in higher eukaryotes is partially explained by one gene encoding a polypeptide with multiple biochemical functions in different cellular contexts. For example, mouse double minute 2 (MDM2) is functionally characterized as both an oncogene and a tumor suppressor, yet this dual classification confounds the cell biology and clinical literatures. Identified via complementary biochemical, organellar, and cellular approaches, we report that MDM2 negatively regulates NADH:ubiquinone oxidoreductase 75 kDa Fe-S protein 1 (NDUFS1), leading to decreased mitochondrial respiration, marked oxidative stress, and commitment to the mitochondrial pathway of apoptosis. MDM2 directly binds and sequesters NDUFS1, preventing its mitochondrial localization and ultimately causing complex I and supercomplex destabilization and inefficiency of oxidative phosphorylation. The MDM2 amino-terminal region is sufficient to bind NDUFS1, alter supercomplex assembly, and induce apoptosis. Finally, this pathway is independent of p53, and several mitochondrial phenotypes are observed in Drosophila and murine models expressing transgenic Mdm2
Cosmological parameters from SDSS and WMAP
We measure cosmological parameters using the three-dimensional power spectrum
P(k) from over 200,000 galaxies in the Sloan Digital Sky Survey (SDSS) in
combination with WMAP and other data. Our results are consistent with a
``vanilla'' flat adiabatic Lambda-CDM model without tilt (n=1), running tilt,
tensor modes or massive neutrinos. Adding SDSS information more than halves the
WMAP-only error bars on some parameters, tightening 1 sigma constraints on the
Hubble parameter from h~0.74+0.18-0.07 to h~0.70+0.04-0.03, on the matter
density from Omega_m~0.25+/-0.10 to Omega_m~0.30+/-0.04 (1 sigma) and on
neutrino masses from <11 eV to <0.6 eV (95%). SDSS helps even more when
dropping prior assumptions about curvature, neutrinos, tensor modes and the
equation of state. Our results are in substantial agreement with the joint
analysis of WMAP and the 2dF Galaxy Redshift Survey, which is an impressive
consistency check with independent redshift survey data and analysis
techniques. In this paper, we place particular emphasis on clarifying the
physical origin of the constraints, i.e., what we do and do not know when using
different data sets and prior assumptions. For instance, dropping the
assumption that space is perfectly flat, the WMAP-only constraint on the
measured age of the Universe tightens from t0~16.3+2.3-1.8 Gyr to
t0~14.1+1.0-0.9 Gyr by adding SDSS and SN Ia data. Including tensors, running
tilt, neutrino mass and equation of state in the list of free parameters, many
constraints are still quite weak, but future cosmological measurements from
SDSS and other sources should allow these to be substantially tightened.Comment: Minor revisions to match accepted PRD version. SDSS data and ppt
figures available at http://www.hep.upenn.edu/~max/sdsspars.htm
Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe
Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Whole-genome sequencing reveals host factors underlying critical COVID-19
Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease
Genetic mechanisms of critical illness in COVID-19.
Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 × 10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice
Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study
Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research
SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues
Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to
genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility
and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component.
Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci
(eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene),
including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform
genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer
SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the
diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types
- …