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Bottom-up accounting methods of carbon dioxide (CO2) emissions can provide high-resolution emissions estimates at a global scale; however, the necessary in situ observations to verify these emissions are limited in coverage. Space-based observations of CO2 in the Earth’s atmosphere expand this coverage to a near-global scale to inform carbon cycle science and record emission trends. This work applied an observing system simulation experiment (OSSE) to characterize the flux information contained in “Snapshot Area Map” (SAM) CO2 measurements from the Orbiting Carbon Observatory-3 (OCO- 3). Unlike previous space-based carbon-observing systems, OCO-3 SAMs provide spatially dense observations of CO2 over targeted urban areas at unprecedented coverage. A Bayesian inversion using synthetic data was applied to these SAMs to explore their effectiveness in optimizing estimates of fossil fuel CO2 (FFCO2) emissions from the Los Angeles Basin. Results demonstrated that errors in the locations of large point sources diminished the inversion’s ability to reduce errors at the sub-city-level. Furthermore, reductions in atmospheric transport error exacerbated these issues. Only after geolocation errors in large point source locations were removed and atmospheric transport error was reduced did individual SAM observations provide modest corrections to prior flux estimates. The aggregation of multiple SAMs proved to be effective in reducing systematic errors in manufacturing- and transportation-related estimates, demonstrating the need for similar measurements in future space-based missions.

Methane (CH4) emissions from oil and gas activities are large and poorly quantified, with onshore studies showing systematic inventory underestimates. We present aircraft measurements of CH4 emissi...

20 pags., 11 figs. 1 tab. -- Open Access funded by Creative Commons Atribution Licence 4.0, Fluxes of halogenated volatile organic compounds (VOCs) over the Southern Ocean remain poorly understood, and few atmospheric measurements exist to constrain modeled emissions of these compounds. We present observations of CHBr, CHBr, CHI, CHClBr, CHBrCl, and CHBr during the O2=N Ratio and CO Airborne Southern Ocean (ORCAS) study and the second Atmospheric Tomography mission (ATom-2) in January and February of 2016 and 2017. Good model-measurement correlations were obtained between these observations and simulations from the Community Earth System Model (CESM) atmospheric component with chemistry (CAM-Chem) for CHBr, CHBr, CHI, and CHClBr but all showed significant differences in model: measurement ratios. The model: measurement comparison for CHBr was satisfactory and for CHBrCl the low levels present precluded us from making a complete assessment. Thereafter, we demonstrate two novel approaches to estimate halogenated VOC fluxes; the first approach takes advantage of the robust relationships that were found between airborne observations of O and CHBr, CHBr, and CHClBr. We use these linear regressions with O and modeled O distributions to infer a biological flux of halogenated VOCs. The second approach uses the Stochastic Time-Inverted Lagrangian Transport (STILT) particle dispersion model to explore the relationships between observed mixing ratios and the product of the upstream surface influence of sea ice, chl a, absorption due to detritus, and downward shortwave radiation at the surface, which in turn relate to various regional hypothesized sources of halogenated VOCs such as marine phytoplankton, phytoplankton in seaice brines, and decomposing organic matter in surface seawater. These relationships can help evaluate the likelihood of particular halogenated VOC sources and in the case of statistically significant correlations, such as was found for CHI, may be used to derive an estimated flux field. Our results are consistent with a biogenic regional source of CHBr and both nonbiological and biological sources of CHI over these regions., We would like to thank the ORCAS and ATom-2 science teams as well as the NCAR Research Aviation Facility and NASA DC-8 pilots, technicians, and mechanics for their support during the field campaigns. This work was made possible by grants from NSF Polar Programs (1501993, 1501997, 1501292, 1502301, 1543457), NSF Atmospheric Chemistry grants 1535364, 1623745, and 1623748, and NASA funding of the EVS2 Atmospheric Tomography (ATom) project, as well as the support of the NCAR Advanced Study Program (ASP) Postdoctoral Fellowship Program and computing support from Yellowstone, provided by NCAR’s Computational and Information Systems Laboratory. The National Center for Atmospheric Research is sponsored by the National Science Foundation. Financial support.

The Southern Ocean plays an important role in determining atmospheric carbon dioxide (CO2), yet estimates of air-sea CO2 flux for the region diverge widely. In this study, we constrained Southern Ocean air-sea CO2 exchange by relating fluxes to horizontal and vertical CO2 gradients in atmospheric transport models and applying atmospheric observations of these gradients to estimate fluxes. Aircraft-based measurements of the vertical atmospheric CO2 gradient provide robust flux constraints. We found an annual mean flux of –0.53 ± 0.23 petagrams of carbon per year (net uptake) south of 45°S during the period 2009–2018. This is consistent with the mean of atmospheric inversion estimates and surface-ocean partial pressure of CO2 (PCO2)–based products, but our data indicate stronger annual mean uptake than suggested by recent interpretations of profiling float observations.

Offshore oil and natural gas platforms are responsible for about 30% of global oil and natural gas production. Despite the large share of global production there are few studies that have directly measured atmospheric methane emanating from these platforms. This study maps CH4 emissions from shallow water offshore oil and gas platforms with an imaging spectrometer by employing a method to capture the sun glint reflection from the water directly surrounding the target areas. We show how remote sensing with imaging spectrometers and glint targeting can be used to efficiently observe offshore infrastructure, quantify methane emissions, and attribute those emissions to specific infrastructure types. In 2021, the Global Airborne Observatory platform, which is an aircraft equipped with a visible shortwave infrared imaging spectrometer, surveyed over 150 offshore platforms and surrounding infrastructure in US federal and state waters in the Gulf of Mexico representing ∼8% of active shallow water infrastructure there. We find that CH4 emissions from the measured platforms exhibit highly skewed super emitter behavior. We find that these emissions mostly come from tanks and vent booms or stacks. We also find that the persistence and the loss rate from shallow water offshore infrastructure tends to be much higher than for typical onshore production.

Neuroblastoma (NB) is a tumor arising from the sympathetic nervous system during infancy and early childhood. High-risk patients who relapse often fail to respond to further therapy, which results in 5-year survival rate for this patient group below 5%. Therefore, there continues to be an urgent need for innovative treatments. Recently, we found that sulfasalazine (SSZ), an FDA-approved drug for the treatment of rheumatoid arthritis and ulcerative colitis induces anti-proliferative effects in NB tumor cells. SSZ was recently shown to inhibit sepiapterin reductase (SPR), a key enzyme that produces tetrahydrobiopterin (BH4) in the nitric oxide (NO) pathway. Here we tested SSZ against purified SPR in vitro, measured the anti-proliferative effect of SSZ on a panel of MYCN amplified and MYCN non-amplified NB cell lines, and assessed the anti-tumor effect of SSZ in NB tumor-xenografted mice. We found that the expression of both SPR mRNA and SPR protein was significantly higher in cell lines without MYCN amplification. SSZ inhibited SPR enzyme activity in vitro and exhibits anti-proliferative activity in a large number of NB cell lines derived from high-risk tumors. Importantly, oral/intraperitoneal (i.p.) SSZ co-administration resulted in measureable anti-tumor effects in vivo. The FDA-approved drug SSZ, a well-tolerated drug in clinical use, could be repositioned to inhibit tumor growth in NB.

We have compared a suite of recent global CO2 atmospheric inversion results to independent airborne observations and to each other, to assess their dependence on differences in northern extratropical (NET) vertical transport and to identify some of the drivers of model spread. We evaluate posterior CO2 concentration profiles against observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft campaigns over the mid-Pacific in 2009–2011. Although the models differ in inverse approaches, assimilated observations, prior fluxes, and transport models, their broad latitudinal separation of land fluxes has converged significantly since the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3) and the REgional Carbon Cycle Assessment and Processes (RECCAP) projects, with model spread reduced by 80 % since TransCom 3 and 70 % since RECCAP. Most modeled CO2 fields agree reasonably well with the HIPPO observations, specifically for the annual mean vertical gradients in the Northern Hemisphere. Northern Hemisphere vertical mixing no longer appears to be a dominant driver of northern versus tropical (T) annual flux differences. Our newer suite of models still gives northern extratropical land uptake that is modest relative to previous estimates (Gurney et al., 2002; Peylin et al., 2013) and near-neutral tropical land uptake for 2009–2011. Given estimates of emissions from deforestation, this implies a continued uptake in intact tropical forests that is strong relative to historical estimates (Gurney et al., 2002; Peylin et al., 2013). The results from these models for other time periods (2004–2014, 2001–2004, 1992–1996) and re-evaluation of the TransCom 3 Level 2 and RECCAP results confirm that tropical land carbon fluxes including deforestation have been near neutral for several decades. However, models still have large disagreements on ocean–land partitioning. The fossil fuel (FF) and the atmospheric growth rate terms have been thought to be the best-known terms in the global carbon budget, but we show that they currently limit our ability to assess regional-scale terrestrial fluxes and ocean–land partitioning from the model ensemble.

Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide ( FFCO2 ), and emissions at the finer, city scales are more uncertain than those aggregated at the global scale. Carbon-observing satellites may provide independent top-down emission evaluations and compensate for the sparseness of surface CO2 observing networks in urban areas. Although some previous studies have attempted to derive urban CO2 signals from satellite column-averaged CO2 data ( XCO2 ) using simple statistical measures, less work has been carried out to link upwind emission sources to downwind atmospheric columns using atmospheric models. In addition to Eulerian atmospheric models that have been customized for emission estimates over specific cities, the Lagrangian modeling approach – in particular, the Lagrangian particle dispersion model (LPDM) approach – has the potential to efficiently determine the sensitivity of downwind concentration changes to upwind sources. However, when applying LPDMs to interpret satellite XCO2 , several issues have yet to be addressed, including quantifying uncertainties in urban XCO2 signals due to receptor configurations and errors in atmospheric transport and background XCO2 . In this study, we present a modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, “X-STILT”, for extracting urban XCO2 signals from NASA's Orbiting Carbon Observatory 2 (OCO-2) XCO2 data. X-STILT incorporates satellite profiles and provides comprehensive uncertainty estimates of urban XCO2 enhancements on a per sounding basis. Several methods to initialize receptor/particle setups and determine background XCO2 are presented and discussed via sensitivity analyses and comparisons. To illustrate X-STILT's utilities and applications, we examined five OCO-2 overpasses over Riyadh, Saudi Arabia, during a 2-year time period and performed a simple scaling factor-based inverse analysis. As a result, the model is able to reproduce most observed XCO2 enhancements. Error estimates show that the 68 % confidence limit of XCO2 uncertainties due to transport (horizontal wind plus vertical mixing) and emission uncertainties contribute to ∼33 % and ∼20 % of the mean latitudinally integrated urban signals, respectively, over the five overpasses, using meteorological fields from the Global Data Assimilation System (GDAS). In addition, a sizeable mean difference of −0.55 ppm in background derived from a previous study employing simple statistics (regional daily median) leads to a ∼39 % higher mean observed urban signal and a larger posterior scaling factor. Based on our signal estimates and associated error impacts, we foresee X-STILT serving as a tool for interpreting column measurements, estimating urban enhancement signals, and carrying out inverse modeling to improve quantification of urban emissions.

Vasodilation is a cornerstone of inflammation physiology. By regulating vasodilation and tissue entry of T cells, CD4+ T lymphocytes expressing choline acetyltransferase (ChAT), a key enzyme for biosynthesis of the vasorelaxant acetylcholine (ACh), critically link immunity with vascular biology in mice. However, the characterization of primary human ChAT+ T cells remained elusive. Here, we identified human ChAT+ T cells and report that ChAT mRNA was induced by activation. Functional studies demonstrated that T cell-derived ACh increased muscarinic ACh-receptor dependent NO-synthase activity and vasorelaxation. Further, single-cell RNA-sequencing revealed ChAT+CD4+ T cells in blood from patients with severe circulatory failure and a high relative frequency of ChAT+CD4+ T cells correlated with better 30-day survival in this cohort. Our findings provide the first insights into ChAT biology in primary human T cells, linking ChAT+ T cells with vasorelaxation as well as survival in a cohort of critically ill patients.

On March 11th , 2020, the World Health Organization (WHO) characterized the COVID-19 respiratory disease caused by the coronavirus (SARS-CoV-2) as a world wide pandemic which led to a massive slowdown in anthropogenic activity as people attempted to "shelter in place". In response to this slowdown NOAA's Global Monitoring Lab (GML), in collaboration with the National Institute of Standards and Technology (NIST), University of Michigan, University of Maryland, Stony Brook University and NOAA's Chemical Science and Atmospheric Resource Laboratories, launched a campaign to measure CO2, CH4, and CO emissions from five major cities along the northeast corridor of the US (Washington, D.C., Baltimore, MD, Philadelphia, PA, New York, NY, and Boston, MA). The month-long campaign which lasted from April 16 to May 16 of 2020 mirrored a campaign that was completed exactly two years prior in April and May of 2018 and which enabled direct comparison of CO2, CH4, CO emissions from these five cities before and during SARS-CoV-2.In this work, we used a Bayesian multi-resolution tiered inversion framework to quantify the CO2, CH4 and CO emissions from these urban areas. We used the HYSPLIT atmospheric transport and dispersion model to calculate the sensitivity of our aircraft observations to surface fluxes (footprints) using three meteorological drivers (NAM, ERA5 and a custom WRF); using three driver models allowed us to account for uncertainties in the transport. To account for biospheric influences on atmospheric CO2, we used a year-specific VPRM simulation that allowed us to isolate the fossil-fuel contribution and solve for it alone. In addition, we also solved for total CO2 and show that not accounting for biogenic activity in lower latitude urban areas could have led to an overestimation of the observed reduction due to biogenic flux differences between the two years.Results show that systematic reductions in CO2 and CO emissions for the five urban areas occurred in April 2020 with signs of recovery in May 2020, which had larger emissions than April 2020. The observed reductions and evolution are consistent with bottom-up estimations based on mobility metrics, which showed the lowest mobility in April with progressive recovery in May. Fuel use from tax records indicates similar reductions. In addition, we show that changes are not homogeneous in space within the urban metropolitan areas and that CO2 and CO emissions reductions are collocated, showing the largest drops in urban centers and roads. While CO2 and CO estimated reductions and evolution are systematic in all cities, CH4 does not show a clear reduction or consistent pattern among cities during the COVID-19 lock-downs. In fact, all the measured changes for CH4 were lower than the standard errors of the differences, implying that the observed changes in CH4 are not significant. Last, we note that since the same prior emissions, constant in time, were used in all the inversions, the anomalous decrease in posterior emissions and subsequent recovery in CO2 and CO observed during the COVID-19 lock-down period are driven by the atmospheric observations and not by temporal changes in the prior emissions.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheri...

Wetlands are the single largest source of methane to the atmosphere and their emissions are expected to respond to a changing climate. Inaccuracy and uncertainty in inundation extent drives differe...

We use TROPOMI (TROPOspheric Monitoring Instrument) tropospheric nitrogen dioxide (NO2) measurements to identify cropland soil nitrogen oxide (NOx = NO + NO2) emissions at daily to seasonal scales in the U.S. Southern Mississippi River Valley. Evaluating 1.5 years of TROPOMI observations with a box model, we observe seasonality in local NOx enhancements and estimate maximum cropland soil NOx emissions (15–34 ng N m−2 s−1) early in growing season (May–June). We observe soil NOx pulsing in response to daily decreases in volumetric soil moisture (VSM) as measured by the Soil Moisture Active Passive (SMAP) satellite. Daily NO2 enhancements reach up to 0.8 × 1015 molecules cm−2 4–8 days after precipitation when VSM decreases to ~30%, reflecting emissions behavior distinct from previously defined soil NOx pulse events. This demonstrates that TROPOMI NO2 observations, combined with observations of underlying process controls (e.g., soil moisture), can constrain soil NOx processes from space., Key Points Daily TROPOMI data provide new opportunities to observe regional cropland NOx emissions from spaceSoil NOx pulsing is identified throughout the growing season with a NOx maximum observed when soils dry to ~30% volumetric soil moistureCropland NOx emissions peak at the onset of the growing season as determined by TROPOMI NO2 enhancements and a box model framework

When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban versus rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes ("SMUrF"), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance flux data, and additional remote sensing-based products, and developed algorithms to gap fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) are extracted from SMUrF and evaluated against 1) non-gapfilled measurements at 67 eddy-covariance (EC) sites from FLUXNET during 2010–2014 (r > 0.7 for most data-rich biomes), 2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from Aug 2017 to Dec 2018 (r = 0.75), and 3) an urban biospheric model based on fine-grained land cover classification within Los Angeles (r = 0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban-rural contrast regarding both the magnitude and timing of CO2 fluxes. By examining a few summertime satellite tracks over four cities, we found that the urban-rural gradient in column CO2 (XCO2) anomalies due to NEE can sometimes reach ~ 0.5 ppm and be close to XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful for informing the biogenic CO2 fluxes over highly vegetated regions during the growing season.

Improved observational understanding of urban CO2 emissions, a large and dynamic global source of fossil CO2, can provide essential insights for both carbon cycle science and mitigation decision making. Here we compare three distinct global CO2 emissions inventory representations of urban CO2 emissions for five Middle Eastern cities (Riyadh, Mecca, Tabuk, Jeddah, and Baghdad) and use independent satellite observations from the Orbiting Carbon Observatory‐2 (OCO‐2) satellite to evaluate the inventory representations of afternoon emissions. We use the column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model to account for atmospheric transport and link emissions to observations. We compare XCO2 simulations with observations to determine optimum inventory scaling factors. Applying these factors, we find that the average summed emissions for all five cities are 100 MtC year−1 (50–151, 90% CI), which is 2.0 (1.0, 3.0) times the average prior inventory magnitudes. The total adjustment of the emissions of these cities comes out to ~7% (0%, 14%) of total Middle Eastern emissions (~700 MtC year−1). We find our results to be insensitive to the prior spatial distributions in inventories of the cities' emissions, facilitating robust quantitative assessments of urban emission magnitudes without accurate high‐resolution gridded inventories., Key Points We use satellite CO2 observations and three global emissions inventories to assess emissions representations of five Middle Eastern citiesEmissions estimates optimized with satellite CO2 observations are insensitive to prior inventory spatial distributionsEmissions estimates suggest afternoon Middle East urban inventory representations are too low

The NASA Orbiting Carbon Observatory-3 (OCO-3) was launched on May 4, 2019 to the International Space Station and has been taking measurements since August. OCO-3, like its predecessor OCO-2, makes hyperspectral measurements of reflected sunlight in three near-infrared bands. However, one of the unique features of OCO-3 is its ability to scan large contiguous areas on the order of 80 km by 80 km using a pointing mirror assembly. This capability, known as snapshot area mapping (SAM) mode, is being used to look at cities, forests, volcanos, and multiple other areas that are of interest to the carbon dioxide (CO2) and solar-induced chlorophyll fluorescence (SIF) scientific communities. For example, OCO-3 can measure column-mean CO2 (XCO2) over the entire Los Angeles, CA basin during the span of only two minutes. With several hundred SAMs having been collected so far and upwards of 25 possible per day, there is a wealth of data to investigate for scientific features and for any potential instrument biases. Additionally, this type of dense sampling will be a proof-of-concept for multiple future wide-swath CO2 missions. Here, we present several OCO-3 SAM mode measurements and discuss interesting features, XCO2 results, and future mission plans.

BackgroundMultiple organ dysfunction syndrome (MODS) occurs in the setting of a variety of pathologies including infection and trauma. Some of these patients will further decompensate and require extra corporeal membrane oxygenation (ECMO) as a palliating maneuver to allow time for recovery of cardiopulmonary function. The molecular mechanisms driving progression from MODS to cardiopulmonary collapse remain incompletely understood, and no biomarkers have been defined to identify those MODS patients at highest risk for progression to requiring ECMO support. We hypothesize that molecular features derived from whole blood transcriptomic profiling either alone or in combination with traditional clinical and laboratory markers can prospectively identify these high risk MODS patients in the pediatric intensive care unit (PICU).Design/MethodsWhole blood RNA-seq profiling was performed for 23 MODS patients at three time points during their ICU stay (at diagnosis of MODS, 72 hours after, and 8 days later), as well as four healthy controls undergoing routine sedation. Of the 23 MODS patients, six required ECMO support (ECMO patients). The predictive power of conventional demographic and clinical features was quantified for differentiating the MODS and ECMO patients. We then compared the performance of markers derived from transcriptomic profiling including (1) transcriptomically imputed leukocyte subtype distribution, (2) relevant published gene signatures and (3) a novel differential gene expression signature computed from our data set. The predictive power of our novel gene expression signature was then validated using independently published datasets.ResultsNone of the five demographic characteristics and 14 clinical features, including The Pediatric Logistic Organ Dysfunction (PELOD) score, could predict deterioration of MODS to ECMO at baseline. From previously published sepsis signatures, only the signatures positively associated with patients mortality could differentiate ECMO patients from MODS patients, when applied to our transcriptomic dataset (P-value ranges from 0.01 to 0.04). Deconvolution of bulk RNA-Seq samples suggested that lower neutrophil counts were associated with increased risk of progression from MODS to ECMO (P-value = 0.03, OR=2.82 [95% CI 0.63– 12.45]). A total of 28 genes were differentially expressed between ECMO and MODS patients at baseline (log2 fold change ≥ 1 or ≤ -1 with false discovery rate ≤ 0.2). These genes are involved in protein maintenance and epigenetic-related processes. Further univariate analysis of these 28 genes suggested a signature of six DE genes associated with ECMO (OR > 3.0, P-value ≤ 0.05). Notably, this contains a set of histone marker genes, including H1F0, HIST2H3C, HIST1H2AI, HIST1H4, and HIST1H1B, that were highly expressed in ECMO. A risk score derived from expression of these genes differentiated ECMO and MODS patients in our dataset (AUC = 0.91, 95% CI 0.79-0.1.00, P-value = 7e-04) as well as validation dataset (AUC= 0.73,95% CI 0.53-0.93, P-value = 2e-02).ConclusionsThis study identified lower neutrophils and upregulation of specific histone related genes as a putative signature for deterioration of MODS to ECMO. This study demonstrates that transcriptomic features may be superior to traditional clinical methods of ascertaining severity in patients with MODS.Author summaryWhy was this study done?Multiple organ dysfunction syndrome (MODS) is a major cause of mortality and morbidity in critically ill pediatric patients who survive the initial physical insult.A variety of triggers including trauma and infections can lead to MODS in pediatric patients.The clinical condition of some MODS patients improve while others deteriorate, needing resource-intensive life support such as extracorporeal membrane oxygenation (ECMO).Mortality is uncommon in PICUs and the need for advanced life support devices, such as ECMO can serve as proxy for mortality.The decision to initiate ECMO in pediatric patients is often subjective made by a committee of physicians that include surgeons, intensivists and a variety of other subspecialists often in the absence of objective data.Despite decades of research, no diagnostic criteria or biomarker has been identified that comprehensively assesses severity in MODS patients who may need subsequent ECMO support in the hyperacute phase of injury.We systematically assessed clinical and transcriptional features as biomarkers for the prediction of the ECMO patients.What did the researcher do and find?We investigated various clinical and transcriptional features in 27 patients with MODS at multiple time points (4 CT, 17 MODS, 6 ECMO) at baseline (0h).We observed that immune response pathways (monocytes, cytokines, NF-kB, and inflammation) were activated in the initiation of MODS, whereas neutrophil level was decreased during deterioration of MODS to ECMO.A total of 51 DE genes were identified in MODS compared to CT and 28 DE in ECMO compared to MODS at baseline (0h).We identified the enrichment of immune-related and glycogenolysis processes in MODS compared to CT and enrichment of protein maintenance, DNA repair and epigenetic-related processes in ECMO compared to MODS at baseline (0h).Logistic regression was used to identify a signature of 6 genes strongly associated with ECMO decision and this signature could help to diagnose MODS patients requiring ECMO.The transcriptomic signature-based risk scores were further evaluated in an independent cohort.What do these findings mean?The compromised level of neutrophils and activation of gene markers including a few histone genes could be used as putative signature for diagnosing the deterioration of MODS to ECMO.A risk score derived from signature genes could be used to predict the need for ECMO.This score is superior to traditional clinical criteria and severity scores used in the Pediatric ICU.The transcriptional signature derived in this study could potentially be used to identify patients in the hyperacute phase of injury that may need higher levels of support like ECMO enabling the selection of an appropriate treatment plan.

Urban methane emissions from major United States East Coast cities are investigated using an aircraft platform equipped with a suite of high precision spectroscopic instrumentation. Sensing details and results will be presented.

Urban methane emissions from major cities are investigated using an aircraft platform equipped with a suite of high precision spectroscopic instrumentation and the latest space-based observations. Sensing details and results will be presented.

Anthropogenic methane emissions from urban centers are important and addressable, yet remain poorly characterized, and the representativeness of studies from individual cities is unknown. A satellite-based approach provides a pathway to tackle this challenge on a national or global scale. Here we present a space-based method that uses the simultaneous, daily observation of methane and carbon monoxide from the TROPOMI satellite to estimate urban methane emissions. We assess and validate the method and demonstrate that using these simultaneous observations enables robust assessment of methane emissions from urban centers without relying on atmospheric transport models. Initial assessments with this approach in eight United States cities suggest emission inventory underestimates previously discovered in older East Coast cities are more broadly representative, with aggregated emissions totaling 1.47 (0.56, 3.19, 95% confidence interval) Tg CH4/year, compared to the Environmental Protection Agency estimate of 0.52 Tg CH4/year. We show this data driven approach provides a pathway to study urban methane emissions across the globe and track how emissions respond if urban mitigation measures are implemented by investigating three additional megacities outside the United States.

Agriculture and waste are thought to account for half or more of the U.S. anthropogenic methane source. However, current bottom-up inventories contain inherent uncertainties from extrapolating limited in situ measurements to larger scales. Here, we employ new airborne methane measurements over the U.S. Corn Belt and Upper Midwest, among the most intensive agricultural regions in the world, to quantify emissions from an array of key agriculture and waste point sources. Nine of the largest concentrated animal feeding operations in the region and two sugar processing plants were measured, with multiple revisits during summer (August 2017), winter (January 2018), and spring (May-June 2018). We compare the top-down fluxes with state-of-science bottom-up estimates informed by U.S. Environmental Protection Agency methodology and site-level animal population and management practices. Top-down point source emissions are consistent with bottom-up estimates for beef concentrated animal feeding operations but moderately lower for dairies (by 37% on average) and significantly lower for sugar plants (by 80% on average). Swine facility results are more variable. The assumed bottom-up seasonality for manure methane emissions is not apparent in the aircraft measurements, which may be due to on-site management factors that are difficult to capture accurately in national-scale inventories. If not properly accounted for, such seasonal disparities could lead to source misattribution in top-down assessments of methane fluxes.

We have compared a suite of recent global CO2 atmospheric inversion results to independent airborne observations and to each other, to assess their dependence on differences in northern extratropical (NET) vertical transport and to identify some of the drivers of model spread. We evaluate posterior CO2 concentration profiles against observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft campaigns over the mid-Pacific in 2009–2011. Although the models differ in inverse approaches, assimilated observations, prior fluxes, and transport models, their broad latitudinal separation of land fluxes has converged significantly since the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3) and the REgional Carbon Cycle Assessment and Processes (RECCAP) projects, with model spread reduced by 80% since TransCom 3 and 70% since RECCAP. Most modeled CO2 fields agree reasonably well with the HIPPO observations, specifically for the annual mean vertical gradients in the Northern Hemisphere. Northern Hemisphere vertical mixing no longer appears to be a dominant driver of northern versus tropical (T) annual flux differences. Our newer suite of models still gives northern extratropical land uptake that is modest relative to previous estimates (Gurney et al., 2002; Peylin et al., 2013) and near-neutral tropical land uptake for 2009–2011. Given estimates of emissions from deforestation, this implies a continued uptake in intact tropical forests that is strong relative to historical estimates (Gurney et al., 2002; Peylin et al., 2013). The results from these models for other time periods (2004–2014, 2001–2004, 1992–1996) and reevaluation of the TransCom 3 Level 2 and RECCAP results confirm that tropical land carbon fluxes including deforestation have been near neutral for several decades. However, models still have large disagreements on ocean–land partitioning. The fossil fuel (FF) and the atmospheric growth rate terms have been thought to be the best-known terms in the global carbon budget, but we show that they currently limit our ability to assess regional-scale terrestrial fluxes and ocean–land partitioning from the model ensemble.

The Southern Ocean plays a critical role in the global climate system by mediating atmosphere–ocean partitioning of heat and carbon dioxide. However, Earth system models are demonstrably deficient in the Southern Ocean, leading to large uncertainties in future air–sea CO2 flux projections under climate warming and incomplete interpretations of natural variability on interannual to geologic time scales. Here, we describe a recent aircraft observational campaign, the O2/N2 Ratio and CO2 Airborne Southern Ocean (ORCAS) study, which collected measurements over the Southern Ocean during January and February 2016. The primary research objective of the ORCAS campaign was to improve observational constraints on the seasonal exchange of atmospheric carbon dioxide and oxygen with the Southern Ocean. The campaign also included measurements of anthropogenic and marine biogenic reactive gases; high-resolution, hyperspectral ocean color imaging of the ocean surface; and microphysical data relevant for understanding and modeling cloud processes. In each of these components of the ORCAS project, the campaign has significantly expanded the amount of observational data available for this remote region. Ongoing research based on these observations will contribute to advancing our understanding of this climatically important system across a range of topics including carbon cycling, atmospheric chemistry and transport, and cloud physics. This article presents an overview of the scientific and methodological aspects of the ORCAS project and highlights early findings.

Natural gas infrastructure releases methane (CH4), a potent greenhouse gas, into the atmosphere. The estimated emission rate associated with the production and transportation of natural gas is uncertain, hindering our understanding of its greenhouse footprint. This study presents a new application of inverse methodology for estimating regional emission rates from natural gas production and gathering facilities in north-eastern Pennsylvania. An inventory of CH4 emissions was compiled for major sources in Pennsylvania. This inventory served as input emission data for the Weather Research and Forecasting model with chemistry enabled (WRF-Chem), and atmospheric CH4 mole fraction fields were generated at 3 km resolution. Simulated atmospheric CH4 enhancements from WRF-Chem were compared to observations obtained from a 3-week flight campaign in May 2015. Modelled enhancements from sources not associated with upstream natural gas processes were assumed constant and known and therefore removed from the optimization procedure, creating a set of observed enhancements from natural gas only. Simulated emission rates from unconventional production were then adjusted to minimize the mismatch between aircraft observations and model-simulated mole fractions for 10 flights. To evaluate the method, an aircraft mass balance calculation was performed for four flights where conditions permitted its use. Using the model optimization approach, the weighted mean emission rate from unconventional natural gas production and gathering facilities in north-eastern Pennsylvania approach is found to be 0.36 % of total gas production, with a 2σ confidence interval between 0.27 and 0.45 % of production. Similarly, the mean emission estimates using the aircraft mass balance approach are calculated to be 0.40 % of regional natural gas production, with a 2σ confidence interval between 0.08 and 0.72 % of production. These emission rates as a percent of production are lower than rates found in any other basin using a top-down methodology, and may be indicative of some characteristics of the basin that make sources from the north-eastern Marcellus region unique.

Methane (CH4) is a potent greenhouse gas and the primary component of natural gas. The San Juan Basin (SJB) is one of the largest coal-bed methane producing regions in North America and, including gas production from conventional and shale sources, contributed ∼2% of U.S. natural gas production in 2015. In this work, we quantify the CH4 flux from the SJB using continuous atmospheric sampling from aircraft collected during the TOPDOWN2015 field campaign in April 2015. Using five independent days of measurements and the aircraft-based mass balance method, we calculate an average CH4 flux of 0.54 ± 0.20 Tg yr–1 (1σ), in close agreement with the previous space-based estimate made for 2003–2009. These results agree within error with the U.S. EPA gridded inventory for 2012. These flights combined with the previous satellite study suggest CH4 emissions have not changed. While there have been significant declines in natural gas production between measurements, recent increases in oil production in the SJB may exp...

Incomplete combustion during flaring can lead to production of black carbon (BC) and loss of methane and other pollutants to the atmosphere, impacting climate and air quality. However, few studies have measured flare efficiency in a real-world setting. We use airborne data of plume samples from 37 unique flares in the Bakken region of North Dakota in May 2014 to calculate emission factors for BC, methane, ethane, and combustion efficiency for methane and ethane. We find no clear relationship between emission factors and aircraft-level wind speed or between methane and BC emission factors. Observed median combustion efficiencies for methane and ethane are close to expected values for typical flares according to the US EPA (98%). However, we find that the efficiency distribution is skewed, exhibiting log-normal behavior. This suggests incomplete combustion from flares contributes almost 1/5 of the total field emissions of methane and ethane measured in the Bakken shale, more than double the expected value if 98% efficiency was representative. BC emission factors also have a skewed distribution, but we find lower emission values than previous studies. The direct observation for the first time of a heavy-tail emissions distribution from flares suggests the need to consider skewed distributions when assessing flare impacts globally.

Rationale: Adult human cardiomyocytes do not complete cytokinesis despite passing through the S-phase of the cell cycle. As a result, polyploidization and multinucleation occur. To get a deeper understanding of the mechanisms surrounding division of cardiomyocytes, there is a crucial need for a technique to isolate cardiomyocytes that complete cell division/cytokinesis. Objective: Markers of cell cycle progression based on DNA content cannot distinguish between mitotic cardiomyocytes that fail to complete cytokinesis from those cells that undergo true cell division. With the use of molecular beacons (MBs) targeting specific mRNAs, we aimed to identify truly proliferative cardiomyocytes derived from human induced pluripotent stem cells. Methods and Results: Fluorescence-activated cell sorting combined with MBs was performed to sort cardiomyocyte populations enriched for mitotic cells. Expressions of cell cycle specific genes were confirmed by means of reverse transcription-quantitative polymerase chain reaction and single-cell RNA sequencing (scRNA-seq) combined with gene signatures of cell cycle progression. We characterized the sorted groups by proliferation assays and time-lapse microscopy which confirmed the proliferative advantage of MB-positive cell populations relative to MB-negative and G2/M populations. Gene expression analysis revealed that the MB-positive cardiomyocyte subpopulation exhibited patterns consistent with the processes of nuclear division, chromosome segregation, and transition from M to G1 phase. The use of dual-MBs targeting CDC20 and SPG20 mRNAs enabled the enrichment of cytokinetic events ( CDC20 high SPG20 high ). Interestingly, cells that did not complete cytokinesis and remained binucleated were found to be CDC20 low SPG20 high while polyploid cardiomyocytes that replicated DNA but failed to complete karyokinesis were found to be CDC20 low SPG20 low . Conclusions: This study demonstrates a novel alternative to existing DNA content-based approaches for sorting cardiomyocytes with true mitotic potential that can be used to study the unique dynamics of cardiomyocyte nuclei during mitosis. Our technique for sorting live cardiomyocytes undergoing cytokinesis would provide a basis for future studies to uncover mechanisms underlying the development and regeneration of heart tissue.

The cost of drug development from initial concept to FDA approval has been estimated to be about 2.6 billion USD.1 This cost precludes development of targeted therapies for rare diseases such as monogenetic cardiomyopathies. As part of the Library of Integrated Network-based Cellular Signatures (LINCS) program funded by the NIH, the Broad Institute of MIT has publicly released transcriptional profiles quantifying the effects of more than 25,000 perturbagens on the expression of 978 genes in up to 77 cell lines.2 Transcriptomics has been shown to be a powerful tool in repurposing drugs3,4 and this dataset affords us the unique opportunity to systematically identify small molecule mimics or inhibitors of specific genes, thereby identifying novel treatments for genetic disorders. In this report, we take this approach to identify a novel drug therapy for a monogenic form of familial dilated cardiomyopathy with the transcriptional profile of FDA approved drugs. This approach could potentially be replicated for a wide range of monogenic diseases.

Urban areas are increasingly recognized as an important source of methane (CH4), but we have limited seasonally resolved observations of these regions. In this study, we quantify seasonal and annual urban CH4 emissions over the Baltimore, Maryland, and Washington, DC metropolitan regions. We use CH4 atmospheric observations from four tall tower stations and a Lagrangian particle dispersion model to simulate CH4 concentrations at these stations. We directly compare these simulations with observations and use a geostatistical inversion method to determine optimal emissions to match our observations. We use observations spanning four seasons and employ an ensemble approach considering multiple meteorological representations, emission inventories, and upwind CH4 values. Forward simulations in winter, spring, and fall underestimate observed atmospheric CH4 while in summer, simulations overestimate observations because of excess modeled wetland emissions. With ensemble geostatistical inversions, the optimized annual emissions in DC/Baltimore are 39 ± 9 Gg/month (1 δ), 2.0 ± 0.4 times higher than the ensemble mean of bottom-up emission inventories. We find a modest seasonal variability of urban CH4 emissions not captured in current inventories, with optimized summer emissions ∼41% lower than winter, broadly consistent with expectations if emissions are dominated by fugitive natural gas sources that correlate with natural gas usage.

California methane (CH4) emissions are quantified for three years from two tower networks and one aircraft campaign. We used backward trajectory simulations and a mesoscale Bayesian inverse model, ...

We present observations of CHBr3, CH2Br2, CH3I, CHClBr2, and CHBrCl2 from the Trace Gas Organic Analyzer (TOGA) during the O2/N2 Ratio and CO2 Airborne Southern Ocean (ORCAS) study and the 2nd Atmospheric Tomography mission (ATom-2), in January and February of 2016 and 2017. We also use CH3Br from the University of Miami Advanced Whole Air Sampler (AWAS) on ORCAS and from the UC Irvine Whole Air Sampler (WAS) on ATom-2. We compare our observations with simulations from the Community Atmosphere Model with Chemistry (CAM-Chem). We report regional enrichment ratios of CHBr3 and CH2Br2 to O2 of 0.19 ± 0.01, and 0.07 ± 0.004 pmol : mol, poleward of 60° S between 180° W and 55° W, and of 0.32 ± 0.02, 0.07 ± 0.004 pmol : mol over the Patagonian Shelf, between 40° S and 55° S and between 70° W and 55° W where we also report enrichment ratios of CH3I to O2 of 0.38 ± 0.03 pmol : mol and of CH2ClBr2 to O2 of 0.19 ± 0.04 pmol: mol. Using the Stochastic Time-Inverted Lagrangian Transport (STILT) particle dispersion model, we use correlations between halogenated hydrocarbon mixing ratios and the upwind influences of chlorophyll a, sea ice, solar radiation, and dissolved organic material to investigate previously hypothesized sources of halogenated volatile organic compounds (HVOCs) in the southern high latitudes. Our results are consistent with a biogenic regional source of CHBr3, and both non-biological and biological sources of CH3I over these regions, but do not corroborate a regional sea-ice source of HVOCs in January and February. Based on these relationships, we estimate the average two-month (Jan.-Feb.) emissions poleward of 60° S between 180° W and 55° W of CHBr3, CH2Br2, CH3I, and CHClBr2 to be 91 ± 8, 31 ± 17, 35 ± 29, and 11 ± 4 pmol m−2 hr−1, and regional emissions of these gases over the Patagonian Shelf to be 329 ± 23, 69 ± 5, 392 ± 32, 24 ± 4 pmol m−2 hr−1 respectively.

BackgroundDespite being a lifesaving intervention for the most critically ill and circulatory compromised patients, veno-arterial extra-corporeal life support (VA-ECLS) is associated with a mortality rate of nearly 60%. Understanding how the immune response to VA-ECLS either promotes or impedes survival would both enhance risk stratification and uncover new therapeutic strategies for these patients. However, conventional enumeration of peripheral blood mononuclear cells (PBMCs) and their subsets have failed to identify determinants of outcome among these cells.MethodsFlow cytometry and plasma cytokine measurement was combined with single cell RNASeq analysis of PBMCs from patients in circulatory shock being started on VA-ECLS to identify clinical, laboratory, and cellular features associated with 72 hour survival.ResultsNon-surviving patients exhibited higher plasma levels of the tissue aggressive inflammatory cytokines IL-1, IL-6, IL-12 and TNF-α. Distribution of cells between conventional PBMC subtypes was not predictive of survival. Single cell RNASeq analysis of discriminatory markers within each PBMC subtype revealed that the proportion of CD8+ Natural Killer T-cells (NKT) that expressed CD52, a known immune-modulator, was associated with improved survival. This cell population correlated inversely with IL-6 production. CD8+/CD52+ NKT cells were quantified by flow cytometry in a second, validation cohort. Those patients with a high proportion of CD52+ cells among all CD8+ NKT cells had more severe disease relative to the low CD52+ group, but nevertheless were nearly 5 time less likely to die in the first 72 hours of VA-ECLS (p=0.043 by log rank test).ConclusionsCD8+/CD52+ NKT cells are associated with survival in patients undergoing VA-ECLS. Fluidics based scRNASeq can reveal important aspects of pathophysiology in complex disease states such as circulatory collapse and VA-ECLS. Further studies in animal models will be required to determine if stimulation of CD8+/CD52+ NKT cell expansion may be an effective therapeutic strategy in this patient population.

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.

We use atmospheric observations to quantify methane (CH4) emissions from Mexico’s most important onshore and offshore oil and gas production regions which account for 95% of oil production and 78% of gas production. We use aircraft-based top-down measurements at the regional and facility-levels to determine emissions. Satellite data (TROPOMI CH4 data and VIIRS night-time flare data) provide independent estimates of emissions over 2 years. Our airborne estimate of the offshore region’s emissions is 2800 kg CH4 h−1 (95% confidence interval (CI): 1700–3900 kg CH4 h−1), more than an order of magnitude lower than the Mexican national greenhouse gas inventory estimate. In contrast, emissions from the onshore study region are 29 000 kg CH4 h−1 (95% CI: 19 000–39 000 kg CH4 h−1), more than an order of magnitude higher than the inventory. One single facility—a gas processing complex that receives offshore associated gas—emits 5700 kg CH4 h−1 (CI: 3500–7900 kg CH4 h−1), with the majority of those emissions related to inefficient flaring and representing as much as half of Mexico’s residential gas consumption. This facility was responsible for greater emissions than the entirety of the largest offshore production region, suggesting that offshore-produced associated gas is being transported onshore where it is burned and in the process some released to the atmosphere. The satellite-based data suggest even higher emissions for the onshore region than did the temporally constrained aircraft data (>20 times higher than the inventory). If the onshore production region examined is representative of Mexican production generally, then total CH4 emissions from Mexico’s oil and gas production would be similar to, or higher than, the official inventory, despite the large overestimate of offshore emissions. The main driver of inaccuracies in the inventory is the use of generic, non-Mexican specific emission factors. Our work highlights the need for local empirical characterization of emissions if effective emissions mitigation is to be undertaken.