Mapping and validating predictions of soil bacterial biodiversity using European and national scale datasets

Recent research has highlighted strong correlations between soil edaphic parameters and bacterial biodiversity. Here we seek to explore these relationships across the European Union member states with respect to mapping bacterial biodiversity at the continental scale. As part of the EU FP7 EcoFINDERs project, bacterial communities from 76 soil samples taken across Europe were assessed from eleven countries encompassing Arctic to Southern Mediterranean climes, representing a diverse range of soil types and land uses (grassland, forest and arable land). We found predictable relationships between community biodiversity (ordination site scores) and land use factors as well as soil properties such as pH. Based on the modelled relationship between soil pH and bacterial biodiversity found for the surveyed soils, we were able to predict biodiversity in ∼1000 soils for which soil pH data had been collected as part of national scale monitoring. We then performed interpolative mapping utilising existing EU wide soil pH data to present the first map of bacterial biodiversity across the EU member states. The predictive accuracy of the map was assessed again using the national scale data, but this time contrasting the EU wide spatial predictions with point data on bacterial communities. Generally the maps were useful at predicting broad extremes of biodiversity reflective of low or high pH soils, though predictive accuracy was limited for Britain particularly for organic/acidic soil communities. Spatial accuracy could however be increased by utilising published maps of soil pH calculated using geostatistical approaches at both global and national scales. These findings will contribute to wider efforts to predict and understand the spatial distribution of soil biodiversity at global scales. Further work should focus on enhancing the predictive power of such maps, by harmonising global datasets on soil conditioning parameters, soil properties and biodiversity; and the continued efforts to advance the geostatistical modelling of specific components of soil biodiversity at local to global scales

Impacts of zero tillage on soil enzyme activities, microbial characteristics and organic matter functional chemistry in temperate soils

Zero tillage management of agricultural soils has potential for enhancing soil carbon (C) storage and reducing greenhouse gas emissions. However, the mechanisms which control carbon (C) sequestration in soil in response to zero tillage are not well understood. The aim of this study was to investigate the links between zero tillage practices and the functioning of the soil microbial community with regards to C cycling, testing the hypothesis that zero tillage enhances biological functioning in soil with positive implications for C sequestration. Specifically, we determined microbial respiration rates, enzyme activities, carbon source utilization and the functional chemistry of the soil organic matter in temperate well drained soils that had been zero tilled for seven years against annually tilled soils. Zero tilled soils contained 9% more soil C, 30% higher microbial biomass C than tilled soil and an increased presence of aromatic functional groups indicating greater preservation of recalcitrant C. Greater CO2 emission and higher respirational quotients were observed from tilled soils compared to zero tilled soils while microbial biomass was 30% greater in zero tilled soils indicating a more efficient functioning of the microbial community under zero tillage practice. Furthermore, microbial enzyme activities of dehydrogenase, cellulase, xylanase, β-glucosidase, phenol oxidase and peroxidase were higher in zero tilled soils. Considering zero tillage enhanced both microbial functioning and C storage in soil, we suggest that it offers significant promise to improve soil health and support mitigation measures against climate change

Soil networks become more connected and take up more carbon as nature restoration progresses

Soil organisms have an important role in aboveground community dynamics and ecosystem functioning in terrestrial ecosystems. However, most studies have considered soil biota as a black box or focussed on specific groups, whereas little is known about entire soil networks. Here we show that during the course of nature restoration on abandoned arable land a compositional shift in soil biota, preceded by tightening of the belowground networks, corresponds with enhanced efficiency of carbon uptake. In mid- and long-term abandoned field soil, carbon uptake by fungi increases without an increase in fungal biomass or shift in bacterial-to-fungal ratio. The implication of our findings is that during nature restoration the efficiency of nutrient cycling and carbon uptake can increase by a shift in fungal composition and/or fungal activity. Therefore, we propose that relationships between soil food web structure and carbon cycling in soils need to be reconsidered

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression

Results From the Global Rheumatology Alliance Registry

Funding Information: We acknowledge financial support from the ACR and EULAR. The ACR and EULAR were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Publisher Copyright: © 2022 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.Objective: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. Methods: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. Results: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. Conclusion: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.publishersversionepub_ahead_of_prin

Inborn errors of OAS-RNase L in SARS-CoV-2-related multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe condition that follows benign COVID-19. We report autosomal recessive deficiencies of OAS1, OAS2, or RNASEL in five unrelated children with MIS-C. The cytosolic double-stranded RNA (dsRNA)-sensing OAS1 and OAS2 generate 2'-5'-linked oligoadenylates (2-5A) that activate the single-stranded RNA-degrading ribonuclease L (RNase L). Monocytic cell lines and primary myeloid cells with OAS1, OAS2, or RNase L deficiencies produce excessive amounts of inflammatory cytokines upon dsRNA or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stimulation. Exogenous 2-5A suppresses cytokine production in OAS1-deficient but not RNase L-deficient cells. Cytokine production in RNase L-deficient cells is impaired by MDA5 or RIG-I deficiency and abolished by mitochondrial antiviral-signaling protein (MAVS) deficiency. Recessive OAS-RNase L deficiencies in these patients unleash the production of SARS-CoV-2-triggered, MAVS-mediated inflammatory cytokines by mononuclear phagocytes, thereby underlying MIS-C

Support design methods applied to excavations of the CIGEO waste repository : advices in numerical modelling

In the framework of the design of the French nuclear waste repository called Cigéo, a two years collaborative work including French nuclear waste producers (EDF, CEA and AREVA) and the French National Radioactive Waste Management Agency (Andra) has been conducted in order to improve methods of modelling support for design of drift. Among a lot of identified issues, the main idea was to well take into account the real excavation steps and all elements of the support depending on the excavation/support method (tunnelling machine, road header, delayed in place casted support,…) in the modelling approach and identify the effect on the long term loading of the lining assuming creep of the rock mass. Calculations have been performed with the finite difference code FLAC using two different elasto-viscoplastic models (Kleine 2007, Souley et al 2011). The sensibility to various numerical parameters has been analysed as: mesh of support elements, interfaces between elements,…Supports taking into account compressible material have also been considered in the modelling with models for the Highly Deformable Elements. The paper sums up the main results of this fruitful collaboration and exhibits conclusions which could feed and help the project designer of Cigéo