A stromal cell niche sustains ILC2-mediated type-2 conditioning in adipose tissue.

Group-2 innate lymphoid cells (ILC2), type-2 cytokines, and eosinophils have all been implicated in sustaining adipose tissue homeostasis. However, the interplay between the stroma and adipose-resident immune cells is less well understood. We identify that white adipose tissue-resident multipotent stromal cells (WAT-MSCs) can act as a reservoir for IL-33, especially after cell stress, but also provide additional signals for sustaining ILC2. Indeed, we demonstrate that WAT-MSCs also support ICAM-1-mediated proliferation and activation of LFA-1-expressing ILC2s. Consequently, ILC2-derived IL-4 and IL-13 feed back to induce eotaxin secretion from WAT-MSCs, supporting eosinophil recruitment. Thus, MSCs provide a niche for multifaceted dialogue with ILC2 to sustain a type-2 immune environment in WAT

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

Antimicrobials: a global alliance for optimizing their rational use in intra-abdominal infections (AGORA)

Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs

Assessing the Impacts of Land-Use Change and Ecological Restoration on CH4 and CO2 Fluxes in the Sacramento-San Joaquin Delta, California: Findings from a Regional Network of Eddy Covariance Towers

The Sacramento–San Joaquin Delta in California was drained for agriculture and human settlement circa 1850, resulting in extreme rates of soil subsidence and CO2 emissions due to peat oxidation. As a result of this prolonged ecosystem carbon imbalance where ecosystem respiration exceeded primary productivity, much of the land surface in the Delta now lies 5 to 8 m below sea level. To help reverse subsidence and convert Delta ecosystems from net carbon sources to carbon sinks, land managers have begun converting drained agricultural lands back to flooded ecosystems including wetlands and irrigated rice paddies. However, this comes at the cost of increased CH4 emissions, a much more potent greenhouse gas than CO2.To evaluate the impacts of drained to flooded land-use change on the biosphere-atmosphere exchange of CO2 and CH4 in the Delta, I conducted a full year of simultaneous eddy covariance measurements at two conventional drained agricultural peatlands (a pasture and a corn field) and three flooded land-use types (a rice paddy and two restored wetlands). This research showed that the drained sites were large CO2 and greenhouse gas (GHG) sources. However, this study also found that converting drained agricultural peat soils to flooded rice paddies or wetlands can help reduce or reverse soil subsidence and reduce GHG emissions, despite the potential for considerably higher CH4 emissions. In particular, wetlands offer the greatest potential for reversing subsidence since both restored wetlands were large net carbon sinks.Since natural and managed ecosystems can exhibit large year-to-year variation in CO2 and CH4 exchange, I analyzed 6.5 years of measurements from the irrigated rice paddy to investigate the factors affecting CH4 fluxes across diel to interannual timescales and quantify interannual variability in CO2 and CH4 budgets. Using wavelet analysis, I found that photosynthesis induced the diel pattern in CH4 flux, but soil temperature influenced its amplitude. At the seasonal scale, linear and neural network models indicated that photosynthesis and water levels were the dominant factors regulating daily average CH4 fluxes. However, across years, much of the variability in annual and growing season CH4 sums was driven by soil temperature. Soil temperature also strongly influenced ecosystem respiration, resulting in large interannual variability in the net carbon budget at the paddy. This study emphasizes the need for long-term, continuous measurements particularly under changing climatic conditions. With a growing interest in including wetlands in carbon markets worldwide due to their ability to accumulate large amounts of carbon, there is a need for models that can accurately and cheaply predict wetland CO2 and CH4 fluxes. In the final chapter of this dissertation, I combined eddy covariance CO2 fluxes measurements, flux footprint analysis, and near-surface (i.e. digital cameras) or satellite remote sensing data to investigate the potential of using the light use efficiency approach to accurately and cost-effectively model photosynthesis in wetland systems. Through this analysis, I showed that digital camera and Landsat imagery can be used to model carbon uptake in wetlands, providing inexpensive means of monitoring carbon cycling in these environments that can be used in carbon markets.By measuring trace gas exchange across multiple sites for multiple years, this dissertation provides new and important insights on the impacts of land use change in the Delta, improves our understanding of factors influencing CO2 and CH4 fluxes from agricultural and restored wetlands across diel to interannual timescales, and presents cost-effective and accurate ways of estimating photosynthesis in restored wetlands by combining flux measurements with near-surface and satellite remote sensing. This work helps bridge understanding between biometeorology, biogeochemistry and climate policy, and provides valuable information to help inform management decisions regarding carbon and water management of the Delta

A stromal cell niche sustains ILC2-mediated type-2 conditioning in adipose tissue.

Group-2 innate lymphoid cells (ILC2), type-2 cytokines, and eosinophils have all been implicated in sustaining adipose tissue homeostasis. However, the interplay between the stroma and adipose-resident immune cells is less well understood. We identify that white adipose tissue-resident multipotent stromal cells (WAT-MSCs) can act as a reservoir for IL-33, especially after cell stress, but also provide additional signals for sustaining ILC2. Indeed, we demonstrate that WAT-MSCs also support ICAM-1-mediated proliferation and activation of LFA-1-expressing ILC2s. Consequently, ILC2-derived IL-4 and IL-13 feed back to induce eotaxin secretion from WAT-MSCs, supporting eosinophil recruitment. Thus, MSCs provide a niche for multifaceted dialogue with ILC2 to sustain a type-2 immune environment in WAT

FLUXNET-CH4 : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20 degrees S to 20 degrees N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet. org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.Peer reviewe

FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.ISSN:1866-3516ISSN:1866-350