dataesr/scientific-paragraphs-categorization huggingface dataset:
The choice of airway management strategy is made by the physician. The database includes patients managed by 94 MICUs representing 90% of French MICUs. The ReAC form meets the requirements of the French Emergency Medical Service organisations and is structured according to the Utstein universal style. 21 Data are collected in the secured ReAC database (www. registrereac. org). The present study was approved by the French Advisory Committee on Information Processing in Health Research (CCTIRS) and the French National Data Protection Commission (CNIL, authorisation no. 910946). As it was approved as a medical assessment registry study, informed consent was waived. 22.
Variables of interest and study outcomes Patient characteristics obtained from the database included sex, age, CPR initiated by bystander witness, arrest location, on-scene time of first response team and MICU, aetiology of OHCA, initial rhythm, automated external defibrillator (AED) use, no-flow duration (time between collapse and initiation of basic life support), low-flow duration (time between initiation of basic life support and return of spontaneous circulation), airway management strategies, drug administration route (intraosseous, peripheral vein, central vein or endotracheal access) and adrenaline administration. Patients were classified in two groups depending on airway management strategy during CPR: those for whom an ETI was performed and those for whom ventilation was performed by a supraglottic procedure (SGA or BVM).
23 The list of variables used for imputation are available in the Data Supplement (Table  S2 ), including characteristics of CPR and outcomes. Because of the retrospective design of this study, we used the inverse probability of treatment weighting (IPTW) to obtain unbiased estimations of the average treatment effect. 24, 25 The goal of this strategy is to simulate random assignment of the treatment. We firstly estimated the propensity score (PS) of treatment (ETI during CPR), which is defined as the probability of being assigned to the treatment group (ETI) given all relevant covariates. The PS was estimated using a generalised boosted logistic regression model that incorporated all relevant variables listed above. The average treatment effect (ATE) was used to generate balanced groups. After PS was generated, weights were applied to the patients, corresponding to 1/PS for patients in the ETI group and [1/(1 À PS)] for patients in the supraglottic group.
The primary endpoint was adjusted according to the IPTW method. Then, the impact of airway management on good neurological outcome and ROSC were assessed using the same strategy. Results are expressed as odds ratios and standardised marginal probabilities with 95% CIs. The threshold of significance was set at P < 0. 05 and all associations were determined using two-sided testing. Statistical analyses were performed using the R environment (version 3. 4. 4) in Rstudio software (version 1. 2. 1335) using the packages mice (version 3. 6. 0), survey (version 3. 35-1) and twang (version 1. 5).
The model with somatic growth rate of recapture fish as response variable also included 'initial fish length' as a fixed effect, while the number of fish (centred to zero mean) used in the excretion trials was added as a fixed effect in the models with population excretion rates. Linear mixed-effects models (LMMs) were fitted using the 'lme4' package (v. 1. 1. 25; [35] ), and GLMMs using the 'glmmTMB' package (v. 1. 0. 2. 1;  [36] ). The significance of all factors was evaluated using 'Anova' from the 'car' package (v. 3. 0. 10; [37] ). Specifically, Wald chi-square ( χ 2 ) or F Type III tests were performed when interactions were significant, while Type II tests were performed when interactions were not significant  [38]. All interactions were maintained in the final models, independently of whether they were significant or not.
Significant interactions were further analysed using pairwise comparisons carried out using 'emmeans' from the 'emmeans' package (v. 1. 5. 2. 1;  [39] ). Given the large number of comparisons involved, the false discovery rate procedure  [40]  was applied to correct for alpha inflation using the 'p. adjust' function (base-package v. 4. 0. 4). Assumption of linearity and homogeneity of variance on residuals from LMMs were checked visually, and log 10 transformations were applied where royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 8: 210842 required (table  1 ). QQ-plots were also used to detect outliers and three data points (one for daily community respiration, one for Cladocera abundance and one for Nematoda abundance) were removed from the analyses, with no qualitative impact on the results (electronic supplementary material, S4).
Diagnostics for models fitted with 'glmmTMB' were performed using the 'DHARMa' package (v. 0. 3. 3. 0; [41] ) and for each variable, the best distribution (e. g. generalized Poisson or negative binomial distributions) was chosen based on likelihood-ratio tests. Plots display raw data and predicted values (ŷ) from the models were computed using the 'predict' function (base-package v. 4. 0. 4). Additional LMMs and GLMMs were used to test the effects of fish introduction (two-level factor: fish absence versus fish presence), and light intensity in fishless mesocosms on ecosystem metrics and prey community composition (electronic supplementary material, S2). Hedge's effect sizes were calculated to compare the magnitude of the effects of each treatment (i. e. size selection, fish density and light intensity) and fish introduction on ecosystem metrics (i.
Acknowledgements. We are grateful to  Clémentine Renneville  and  Arnaud Le Rouzic  for initiating the medaka lines;  David Carmignac  and  Romain Péronnet  for their help maintaining the fish; and the platform PLANAQUA and the  CEREEP Ecotron Ile-De-France  for the access to the experimental facilities. We thank one anonymous reviewer and the Associate Editor for constructive inputs that improved the manuscript.
Funding. This work was supported by  The Research Council of Norway  (project nos. 251307/F20  and  255601/E40 ) and its mobility programme (project nos. 272354  to C. E. and 268218/MO to B. D. P. ). E. E. was supported by  IDEX SUPER  (project Convergences no. J14U257   MADREPOP ) and by  Rennes Métropole  ( AIS 18C0356 ). The experiments realized in the  CEREEP Ecotron Ile-De-France  benefited from the support received by an ' Investissements d'Avenir' programme  from the  Agence Nationale de la Recherche  ( ANR-10-EQPX-13-01 Planaqua  and  ANR-11-INBS-0001   AnaEE France ).
Specificity of TL8-506 for TLR8 was demonstrated in HEK cells expressing human TLR7, which did not respond to TL8-506 stimulation (supplemental Figure  2B ). Similarly, cells expressing TLR8 but lacking TLR7 did not respond to stimulation with CL264 (supplemental Figure  2F ). For transfection experiments, briefly, 500 ng (or indicated concentration) WT or mutant TLR8 plasmid was transiently transfected into reporter cells (Lipofectamine; Thermo Fisher Scientific, Waltham, MA), and 48 hours after transfection, cells were stimulated with the indicated doses of TLR agonist followed by quantification of SEAP in the supernatant as a readout of NF-kB activity. Fold change in SEAP activity was calculated by normalizing data to untransfected wells without TLR8 stimulation. Statistical analysis was performed by 1-way analysis of variance (ANOVA) using GraphPad Prism 8.
Genetic investigation Exome sequencing identified TLR8 gene variants in all patients, either on initial review or by targeted review of the TLR8 gene evaluating for low-level mosaicism (Figure  1D ). Five patients (P1-P5) had mosaic TLR8 variants; 4 patients (P1, P2, P4, and P5) shared the same variant (c. 1295 C. T; p. P432L), while the fifth patient (P3) harbored a different mosaic variant (c. 1482 C. A, p. F494L). The final patient (P6) had a de novo germline hemizygous variant in TLR8 (c. 1715 G. A, p. G572D). None of the variants were present in the gnomAD database,  14  and all were predicted to be deleterious by Combined Annotation Dependent Depletion (CADD) score (supplemental Table  3 ). No other known disease-causing variants relevant to clinical phenotypes were identified. To determine the frequency of mosaicism and its origin, ddPCR was performed using DNA and cDNA from different cell lineages and tissues including whole blood, PBMCs, purified immune subsets, saliva and/or skin fibroblast lines in patients with mosaic variants (Figure  1E-F ).
200 pg/mL). Data are represented as mean 6 standard deviation; a Mann-Whitney rank sum test was used for statistical evaluation. Multiple samples collected. 6 months apart were assayed for P1 and P3. Young healthy male controls (,6 years) are indicated in red and young adult healthy controls in green. L, ligand; ns, not significant; S, soluble. increased NF-kB signaling in response to TLR8 stimulation (Figure  5C ). As with results from neutrophil assays, undifferentiated iPSC-p. P432L and derived CD34 1 HPCs did not have detectable WT TLR8 sequence; however, following differentiation into macrophages, there was again an emergence of a small population (13%) of cells with WT TLR8 DNA that was not detected in the transcript (supplemental Figure  9E ). Macrophages derived from iPSC-p. P432L cells secreted more IL-6 and TNF-a compared with iPSC-derived cells with WT TLR8 following overnight stimulation with a low dose of TLR8 agonist (TL8-506, 25 ng/mL; Figure  5D ).
Deletion of Tlr8 in the mouse leads to increased TLR7 signaling due to the lack of inhibition by TLR8, with dendritic cells producing high amounts of cytokines causing spontaneous autoimmunity, autoantibodies, splenomegaly, and reduced B-cell numbers, similar to features in our patients. 32 By contrast, transgenic expression of human TLR8 also caused increased cytokine production with hyperinflammation, even in mice with few as 20% of blood cells containing the human TLR8 transgene, demonstrating a strong biological effect of TLR8 signaling. 33 Our data suggest that TLR7 signaling is not altered with patient TLR8 gain-of-function variants, based on similar cytokine responses with a TLR7 agonist in patient-derived macrophages. The TLR8 variants here lead to a gain-of-function phenotype with patient-derived cells exhibiting hypersensitivity to ligand stimulation including increased NF-kB activation and cytokine production.
Figure5(continued) macrophage differentiation of WT or p. P432L induced pluripotent stem cell (iPSC) clones from patient (P2) and phosphorylated p65 (NF-kB) in response to stimulation with the indicated doses of the TLR8-ligand TL8-506. Cells are gated on CD45 1 CD14 1 macrophages. (D-E) iPSC-derived macrophages with WT or p. P432L TLR8 from patient (P2) were cultured overnight with the indicated doses of TL8-506 (TLR8 agonist), CL264 (TLR7 agonist), or LPS (TLR4 agonist). (D) Cytokines were measured in the cell culture supernatant and demonstrate that macrophages with variant TLR8 produced significantly more IL-6 and TNF-a with low-dose TLR8 stimulation (TL8-506, 25ng/mL) compared with cells with WT TLR8. There was no difference in the cytokine response to TLR7 stimulation (CL264), and doses of CL264 ,100 ng/mL did not result in cellular activation (data not shown).
Acknowledgments The authors would like to dedicate this article to Joseph and his family. The authors also wish to thank  Joie Davis  and  Susan Price  for their contributions toward clinical evaluation and care of patients. Research support was provided by The  Children's Discovery Institute at Washington University and St. Louis Children's Hospital, Center for Pediatric Immunology and St. Louis Children's Hospital Foundation , The  Jeffrey Modell Diagnostic and Research Center for Primary Immunodeficiencies at St. Louis Children's Hospital , The  Immune Deficiency Foundation , and the  Washington University Rheumatic Diseases Research Resource-Based Center through National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases  grant  P30AR073752  (M.
This information will be supplied to land managers in forms relevant to timely decisions and to specific applications  (Sciarretta et al. 2019). Consumers will increasingly demand certification that farming methods are sustainable by ensuring that land use and management is within the land's capability and that natural capital is maintained if not enhanced. Thus, it will be important to match the soil's inherent production capacity to the most appropriate agricultural systems, so as not to diminish its natural capital. DSMA methods will enhance the mapping and understanding of land suitability, thus assisting us to maintain natural capital of the soil resource. High temporal and spatial resolution remotely sensed data will document the current land use and the methods of land management to provide the evidence base for sustainability.
Spatial and temporal resolution While spatial resolution needs are user and context-dependent, future soil data systems will be specified by the needs of its most demanding users. Capacity development will, therefore, build towards the support of extremely fine-scale outputs, as input data improves in resolution. Future DSMA will apply seamlessly across all scales from national to local, enabling application at the most appropriate scale for any given decision. Potential users with particularly fine resolution data needs include: • Precision agriculture: In cropping, row spacings define a minimum cell size. Remote sensing imagery and EM-sensing devices already supply information at the sub-meter scale, a wide range of other datasets will be similarly detailed and available  (Filippi et al.
We see the provision of temporal data as an important focus for the next stages of DSMA. The emphasis is likely to be on time-steps relevant to land management decisions or to the need to respond to significant events (e. g. a major storm leading to a significant soil erosion event. While regular timestep data are needed for some forecasting applications (especially as new modelling approaches are developed that require high temporal intensity), the greater demand will be for soil information at critical decision times within the season or as decisions are needed. This elevates the importance of system architecture/infrastructure and user experience well beyond spatial resolution. Digital soil input datasets would be stored at the resolution at which they were gathered. Modelling at the end user's requested level of spatial detail would then involve disaggregation or aggregation of the source datasets to a common grid cell size, with quantification of the imprecision resulting from those processes.
Increasingly, these additional data will be collected following appropriate statistical sampling methods. (such as de  Gruijter et al. 2006, and Brus 2019)  while also giving sound alternatives when access restrictions are encountered  (Clifford et al. 2014 ). In the near future, soil sampling will be more strategic and coordinated to gain optimum value from each sample. Past sampling will be readily available and analyzed to plan and coordinate future data collection efforts. For example, Fig. 1  shows the environmental coverage of the Australian site data collation  (Searle 2014). This map indicates the relationship between observational data and the environmental covariate feature space that those samples cover. Areas colored yellow are those that have been sampled more intensively in the covariate feature space associated with those locations.
2013). Along with developments in field deployed robotics, the new generations of sensors will measure a wider suite of soil attributes (towards the full Mendeleyev table and biological indicators including DNA identification). A harmonized, standardized, and well governed soil site information infrastructure will underpin future DSMA. Submission of new soil data to managed data systems would be mandated for activities supported by public funding, or subject to government approval processes, and recommended for other private initiatives. This shared infrastructure will be based on a national standard data exchange model and offer quality-flagged bulk data upload options to cater for soil experts through to citizen science applications. User account-associated data sharing permissions will allow discrimination between public and restricted-use-only data.
2020). They will increasingly provide vis-NIR and thermal imagery  (Bokolonga et al. 2016; Saha et al. 2018 ) and fine granular land surface mapping. The next stage will see platforms for conductivity sensors  (Barrowes et al. 2019) , ground penetrating radar  (Wu et al. 2019 ) and gamma-ray spectrometers  (Šálek et al. 2018 ) flown with the required frequency for a given application. Improvements in payload capacity and a broader range of sensors will facilitate the integration of plant and soil dynamics at field scales at appropriately frequent time-steps. UAS will also be used to map change over time and across landscapes including extreme events such as flooding and erosion extent. Our ability to process and manage the vast amount of data collected by UAS will continue to grow apace. Data will be stored, processed and accessed in cloud-based systems.
Nationally, we foresee a coherent and well managed data access system that not only serves the scientific community, but also facilitates the operational application of DSMA products by landholders. Smartphone applications drawing from this system would facilitate presentation and delivery of DSMA data and include a range of userfriendly tools to showcase the available datasets over an area. Each tool would use geolocation to identify an area of interest for the landholder and show a combination of snapshots and dynamic products. Snapshot layers could include crop-specific land suitability and soils maps, where dynamic tools could include plant available water calculations and forecasts similar to the SoilWaterApp, (http://soilwaterapp. net. au/)  (Freebairn et al. 2018).
Federated data systems Easy access to high quality georeferenced soil property data is a key enabler of DSMA. There are a range of approaches that can be used to achieve this from creating a centralised aggregation of data by a single intermediary  (Batjes et al. 2017)  through to federating data from existing data providers. In the data federation approach, the data custodians provide a view of their data according to a community agreed model. End users access web services from multiple data custodians, that conform to the standard (community) structure and semantics  (Box et al. , 2015)  on the fly as data is required. Data aggregation approaches are generally created for a specific purpose typically with a finite life cycle (eg the National Soil Site Collation, Searle 2014). The advantage of the data federation approach is that it leverages existing datasets already maintained by custodians and requires very little additional resourcing.
A broader community involved in DSMA DSMA stakeholder profiles will range from individuals and citizen scientists to multinational corporations. These groups will be involved in a range of activities including funding, collecting and analyzing data, and interpretation, product generation, policy delivery, and decision making. The breadth of involvement will come from the value of information for an extensive number of purposes and so access to and use of soil data will broaden. We see the stakeholder community consisting of: • The general public using the data as their part in community planning and growth (eg. evidence-based input into opportunities for public consultation such local environment plans and development applications), • Landholders, individually or in groups, refining the myriad of decisions from overall strategy to tactical choice within an enterprise mix, • Local authorities and councils using DSMA to guide planning decisions and implementation more effectively and at scale, • Local natural resource management communities, environmental groups using the data for guiding funding and extension activities, • Government agencies that will increasingly rely on DSMA products for assessing and improving ecosystem services and climate adaptation, including natural disaster prediction, prevention, and mitigation, • Companies and the corporate world using DSMA to guide investment decisions, and • State and Federal Governments applying the data in regional planning and broad policy and strategy developments.
Increased regional scale mapping has substantially improved the data and mapping coverage of Australia but due to various funding priorities and access issues there are still large areas of Australia with sparse site data and broad-scale land systems mapping. These input data gaps will be sensibly filled, being informed by DSMA methods, and appropriate products will produced to fill information gaps. Harmonization of traditional soil polygon maps DSM products may still be challenging for some purposes, but practitioners are developing ways for datasets to coexist and have set standards for comparing accuracies between the differing methods. While there are a range of differences between these products, there is no longer a complete separation between them. Increasingly, DSMA works on new and emerging applications such as peri-urban areas, human health (emerging contaminants, antibiotics, various diseases) and on improved land use planning.
Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors acknowledge the  Terrestrial Ecosystem Research Network (TERN) , an  Australian Government  NCRIS-enabled research infrastructure project, for facilitating and supporting  Australian Digital Soil Mapping Community of Practice. Dominique Arrouays  and  Budiman Minasny  are members of the  Research Consortium GLADSOILMAP  supported by  LE STUDIUM Loire Valley Institute for Advanced Studies (France). CSIRO provided funding for the group writing-workshop to develop this paper ( Stradbroke Island, Queensland, Australia ).
Further information about the diluter design, its operation, and effect on the data can be found in the Supplement Sect. S2. 2. The NAIS is a mobility spectrometer designed to determine the number size distribution of ions in the mobility diameter range of 0. 8-42 nm, as well as total (naturally charged and neutral) aerosol particles in the mobility diameter range of ∼ 2-42 nm. The instrument operates at a flow rate of ∼ 54 L min -1. The length of the NAIS sampling tube was 65 cm, with an inner diameter of 30 mm. The SMPS used in this study was composed of a TSI 3081 long differential mobility analyzer (DMA) and a TSI 3025a CPC. It was operated to measure the aerosol particle size distribution between 15 and 740 nm. The aerosol and sheath flows were checked weekly and were set to 0. 3 and 3 L min -1 , respectively.
NAIS. The NAIS data were inverted with the instrumentspecific algorithm (done by the NAIS SPECTOPS software). The data were later corrected for line losses using the Gorm-ley and Kennedy equation for laminar flow  (Gormley and Kennedy, 1948). It is essential to note that the flow through the sampling inlet of the NAIS actually lies in the transient regime (Re = 2376); however, the penetration efficiency using this inlet was comparable for laminar flow and turbulent flow (calculated using the equation of turbulent inertial deposition from  Brockmann, 2011) ; thus, we used the correction based on laminar flow (Fig. S4 ). SMPS. The data from the SMPS were inverted using TSI's Aerosol Instrument Manager software (AIM, version 9. 0). Afterwards, line loss correction was applied using the Gormley and Kennedy equation.