Coronavirus disease 2019 (COVID-19) is a mild to moderate respiratory tract infection, however, a subset of patients progress to severe disease and respiratory failure. The mechanism of protective immunity in mild forms and the pathogenesis of severe COVID-19 associated with increased neutrophil counts and dysregulated immune responses remain unclear. In a dual-center, two-cohort study, we combined single-cell RNA-sequencing and single-cell proteomics of whole-blood and peripheral-blood mononuclear cells to determine changes in immune cell composition and activation in mild versus severe COVID-19 (242 samples from 109 individuals) over time. HLA-DR hi CD11c hi inflammatory monocytes with an interferon-stimulated gene signature were elevated in mild COVID-19. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils, and HLA-DR lo monocytes. Our study provides detailed insights into the systemic immune response to SARS-CoV-2 infection and reveals profound alterations in the myeloid cell compartment associated with severe COVID-19.

Abstract. The global methane (CH 4 ) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH 4 over the past decade. Emissions and concentrations of CH 4 are continuing to increase, making CH 4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH 4 sources that overlap geographically, and from the destruction of CH 4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). . Top-down inversions consistently infer lower emissions in China (∼ 58 Tg CH 4 yr −1 , range 51-72, −14 %) and higher emissions in Africa (86 Tg CH 4 yr −1 , range 73-108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models.The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions.

Atmospheric new particle formation (NPF) is an important global phenomenon that is nevertheless sensitive to ambient conditions. According to both observation and theoretical arguments, NPF usually requires a relatively high sulfuric acid (HSO) concentration to promote the formation of new particles and a low preexisting aerosol loading to minimize the sink of new particles. We investigated NPF in Shanghai and were able to observe both precursor vapors (HSO) and initial clusters at a molecular level in a megacity. High NPF rates were observed to coincide with several familiar markers suggestive of HSO-dimethylamine (DMA)-water (HO) nucleation, including sulfuric acid dimers and HSO-DMA clusters. In a cluster kinetics simulation, the observed concentration of sulfuric acid was high enough to explain the particle growth to ~3 nanometers under the very high condensation sink, whereas the subsequent higher growth rate beyond this size is believed to result from the added contribution of condensing organic species. These findings will help in understanding urban NPF and its air quality and climate effects, as well as in formulating policies to mitigate secondary particle formation in China.

12Diagnostic testing for COVID-19 is central to controlling the global pandemic. Recently, To and 13 colleagues reported that 20 of 23 (87%) patients who had SARS-CoV-2 detected by reverse-14 transcriptase PCR (RT-PCR) in nasopharyngeal swabs (NPS) or sputum also had SARS-CoV-2 15 detectable in saliva (1). The use of saliva has several advantages compared to collection of NPS. In 16 particular, the close contact involved in swab collection poses a risk to healthcare workers, and 17 collection of saliva may reduce this risk. Further, saliva collection does not require specialised 18 consumables, causes less patient discomfort, and may be a useful sample for self-collection (2). 20We further investigated the feasibility and utility of saliva collection from ambulatory patients 21 presenting to a dedicated COVID-19 screening clinic at the Royal Melbourne Hospital (RMH), 22Melbourne, Australia. Between 25 th March and 1 st April 2020, 622 patients were tested for COVID-23 19 through the screening clinic. All patients had NPS, and 522/622 (83.9%) patients also provided 24 saliva. Patients were asked to pool saliva in their mouth for 1-2 minutes prior to collection, and gently 25 spit 1-2 mL of saliva into a 25mL collection pot. Neat saliva specimens were transported to the 26 laboratory where an approximate 1:1 ratio of liquid Amies media was immediately added. We 27 specifically chose to use liquid Amies media in order to: (i) evaluate the use of an alternative transport 28 media in the face of global shortages of viral transport medium (VTM), and (ii) to preserve VTM in 29 our own laboratory. The median time from sample collection to addition of media was 180 minutes 30 (range 55 -537 minutes). NPS and saliva specimens underwent nucleic acid extraction on the Qiagen 31 EZ1 platform (QIAGEN, Hilden, Germany). An extraction volume of 200uL of the sample was used, 32 with RNA eluted in 60uL. Reverse-transcriptase PCR (RT-PCR) testing was performed using a 33 multiplex RT-PCR test for SARS-CoV-2 and other seasonal coronaviruses (Coronavirus Typing (8-34 well) assay, AusDiagnostics, Mascot, Australia). All positive NPS samples for SARS-CoV-2 35 underwent confirmatory testing at a local reference laboratory (the Victorian Infectious Diseases 36 Reference Laboratory) using previously published primers (3). 38Overall, 39/622 (6.3%; 95% confidence interval [CI] 4.6%-8.5%) patients had PCR-positive NPS, and 39 33/39 patients (84.6%; 95% CI 70.0%-93.1%) had SARS-CoV-2 detected in saliva. The median cycle 40 threshold (Ct) value was significantly lower in NPS than saliva ( Figure 1A), suggestive of higher viral 41 loads in NPS, and in both samples, there was a correlation between Ct value and days from symptom 42 onset ( Figure 1B). To assess specificity, a subset of saliva specimens from 50 patients with PCR-43 negative swabs was also tested. Of note, SARS-CoV-2 was detected in 1/50 (2%; 95% CI 0.1%-44 11.5%) of these saliva samples, which may reflect differing quality of NPS collection. 46To date, studies assessing the utilit...

Abstract. The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (~biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (T-D, exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories, and data-driven approaches (B-U, including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003–2012 decade, global methane emissions are estimated by T-D inversions at 558 Tg CH4 yr−1 (range [540–568]). About 60 % of global emissions are anthropogenic (range [50–65 %]). B-U approaches suggest larger global emissions (736 Tg CH4 yr−1 [596–884]) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the T-D budget, it is likely that some of the individual emissions reported by the B-U approaches are overestimated, leading to too large global emissions. Latitudinal data from T-D emissions indicate a predominance of tropical emissions (~64 % of the global budget,

No abstract

Increasing atmospheric methane (CH 4 ) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH 4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH 4 emissions from wetlands, the largest natural global CH 4 source, for 2000-2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000-2012, boreal wetland CH 4 emissions increased by 1.2 Tg yr −1 (−0.2-3.5 Tg yr −1 ), tropical emissions decreased by 0.9 Tg yr −1 (−3.2−1.1 Tg yr −1 ), yet globally, emissions remained unchanged at 184 ± 22 Tg yr −1 . Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH 4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH 4 emissions have not contributed significantly to the period of renewed atmospheric CH 4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH 4 emissions, and a decrease in the atmospheric oxidative sink.

Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land‐atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO2) concentration and climate change. In the past decades, a wide variety of studies have been conducted to quantify global SOC stocks and soil C exchange with the atmosphere through site measurements, inventories, and empirical/process‐based modeling. However, these estimates are highly uncertain, and identifying major driving forces controlling soil C dynamics remains a key research challenge. This study has compiled century‐long (1901–2010) estimates of SOC storage and heterotrophic respiration (Rh) from 10 terrestrial biosphere models (TBMs) in the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project and two observation‐based data sets. The 10 TBM ensemble shows that global SOC estimate ranges from 425 to 2111 Pg C (1 Pg = 1015 g) with a median value of 1158 Pg C in 2010. The models estimate a broad range of Rh from 35 to 69 Pg C yr−1 with a median value of 51 Pg C yr−1 during 2001–2010. The largest uncertainty in SOC stocks exists in the 40–65°N latitude whereas the largest cross‐model divergence in Rh are in the tropics. The modeled SOC change during 1901–2010 ranges from −70 Pg C to 86 Pg C, but in some models the SOC change has a different sign from the change of total C stock, implying very different contribution of vegetation and soil pools in determining the terrestrial C budget among models. The model ensemble‐estimated mean residence time of SOC shows a reduction of 3.4 years over the past century, which accelerate C cycling through the land biosphere. All the models agreed that climate and land use changes decreased SOC stocks, while elevated atmospheric CO2 and nitrogen deposition over intact ecosystems increased SOC stocks—even though the responses varied significantly among models. Model representations of temperature and moisture sensitivity, nutrient limitation, and land use partially explain the divergent estimates of global SOC stocks and soil C fluxes in this study. In addition, a major source of systematic error in model estimations relates to nonmodeled SOC storage in wetlands and peatlands, as well as to old C storage in deep soil layers.