dataesr/scientific-paragraphs-categorization huggingface dataset:
EDGAR uses a bottom-up approach that calculates gridded (0. 1° × 0. 1°) monthly CO 2 emissions for activity sectors based on: statistics that track national levels of each activity; proxy data representing the spatial and temporal distribution of each activity; the mix of technologies used to perform each activity; the fuel mix used by each technology, and; emissions factors for the technology and fuel combinations, which are also corrected for the emission control technologies in place. Fig. 1  A conceptual depiction of the emissions scaling protocol used to produce GCP-GridFED as described in section 2. 2. Descriptions of the input datasets are provided in section 2. 1. This figure does not depict the procedure used to calculate emissions uncertainty or O 2 combustion; however, the figure indicates the stages at which these additional outputs are produced within the CO 2 emissions scaling protocol (marked as '*' and '#' , respectively).
Uncertainties in CO 2 concentration are calculated for the EDGAR dataset in the year 2010 and scaled using the same factors as the central estimates for CO 2 emission. O 2 combustion estimates are calculated using oxidative ratios applied to the CO 2 emissions from fuel combustion, prior to the final step of the protocol. Full details of the procedure used to produce gridded CO 2 uncertainties and O 2 combustion estimates can be found in sections 2. 3 and 2. 4, respectively. A detailed description of EDGAR's gridding procedure is available elsewhere (refs. 41, 48 ) however we summarise below the key features of its design: • 28 EDGAR activity sectors are based on the 48 sectors defined by IPCC guidelines  46, 47. • Activity in each sector is tracked from 1970-2015 using statistics that represent demand and supply of goods and energy, including: fuel-specific energy balances, fuel production, commodity production and cement clinker production and agriculture-related activities.
• Emission factors are taken from the guidelines issued by the IPCC  46, 47  and are assigned to each country in the following order of preference: national, regional, country group (Annex I/non-Annex I). • National emissions of CO 2 from each sector are distributed across months using sector-specific or, preferentially, technology-specific monthly shares. • Emissions are distributed in space using spatial proxy data (that vary stepwise over time 1990-2010), such as population density, point source locations and transport routes. Some of the uncertainties associated with using proxy data to disaggregate emission in time and space are considered in later sections (see 'CO 2 emissions uncertainty'). EDGar sectors included in GCP-GridFED. Of the 28 EDGAR sectors, the 18 relating to fossil fuel combustion, non-combustion use of fossil fuel and cement production were used in GCP-GridFED.
These 18 sectors were selected to correspond as closely as possible with the activities included in the GCB-NAE emission estimates. The 18 activity sectors incorporated from EDGAR into GCP-GridFED are shown in Table  1. Where possible, emissions from each EDGAR sector were further separated into specific fuels using fuel-specific data from an intermediate processing step of the EDGAR gridding protocol  41. Where this was not possible, it was necessary to make the assumptions that follow about the fuels that contribute to emissions in each   41  ). Assumptions made as to the fuels contributing to unstratified EDGAR sectors are as follows (see section 2. 1. 2 for further detail): (a) all emissions from off-road, rail and pipeline transport relate to the combustion of oil. (b) all emissions from the production of steel, iron and non-ferrous metals relate to the combustion of coal.
National CO 2 emissions data were extracted from the EDGAR datasets for the purpose of national annual emissions scaling. National masks were based on the 'countries 2016' dataset of the Geographic Information System of the European Commission (EU-GISCO)  49. The appropriate positioning of power plants is key to distributing total emissions accurately because the power sector accounts for ~45% of global emissions  50. Changes in the available datasets of power plant geolocations are common, and hence we note the importance of recording which datasets are used in each release of gridded   0) plant coordinates, correcting inverted (lon, lat) coordinates and adding some additional points for Russia. National power sector emissions for each fuel type are distributed across plants in proportion to their reported capacities.
For larger countries (e. g. USA) with a non-uniform distribution of coal power plants, the fuel-specific distribution of emissions is considered a significant improvement over foregoing approaches. Emissions from each power plant reflect the fuel mix of the plant and the respective carbon intensity of emissions from that fuel mix. However, details of the technologies used by each plant, including carbon capture and storage, are not available. Alternative mappings of point sources can be based on night light detections by satellite  52  or population data  53  but these are least aligned with EDGAR's 'bottom up' approach  41. Heating and cooling degree day (HCDD) Correction. The monthly distribution (seasonality) of global CO 2 emissions is principally determined by seasonality of climate in the Northern Hemisphere, and thus a peak in emissions occurs in the boreal winter months and a trough occurs in the boreal summer months.
Although this seasonality is predictable, inter-annual variability in weather influences the distribution of emission across the months. Because the monthly emissions distribution in the EDGAR dataset is derived only from 2010 data, we applied a correction to the EDGAR data to account for the impacts of inter-annual variability on emissions. Specifically, we used a heating and cooling degree day (HCDD) correction to implement inter-annual variability in the monthly distribution of CO 2 emissions from selected EDGAR sectors (power industry, 1A1a; buildings, 1A4; manufacturing, 1A2; and road transport, 1A3b; see Table  1 ). The HCDD correction approach was implemented as follows. First, monthly (m) HCDDs were calculated based on gridded (0. 5° × 0. 5°) daily mean temperature (T) data for the years 1959-2018 from the Climatic Research Unit time-series version 4.
The global gridded (i, j) monthly (m) CO 2 emissions were summed across the EDGAR activity sectors (s) in each source class used in this study (S; see Table  1 ). The monthly distribution of annual emissions was adjusted in advance using Eq. 7. ∑ = EDGAR E DGAR (8) S m i j s m i j , , , , , , 2. Extract gridded emissions data from EDGAR for each country. A subset of gridded monthly CO 2 emissions from each GCP-GridFED source class (see Table  1 ) was extracted for each country (c) using country masks (True/False) from the EU-GISCO dataset  49. No subset was extracted for the bunker fuels source  class; the entire grid layer was scaled globally. Hence, all grid cells were included in an 'international' mask (True in all cells) and treated thereafter in the same way as each country. , , [ ], [ ]  , , [  ],  [ ] 3.
∑∑ α = ⋅ GridFED E DGAR ( 12 ) S m i c j c S c S m i c j c , , [ ], [ ] , , , [ ], [ ] 6. Collate national data to a global output. Scaled monthly CO 2 emissions grids from all nations were merged into a single grid for each GCP-GridFED source class. We do not attempt to adjust the EDGARv4. 3. 2 grids (year 2010) for a range of historical changes to the spatial distribution of emissions, for instance due to the expansion of road networks or flight routes, the commissioning/decommissioning of facilities or large-scale population migration. The resolution of these issues will be prioritised in future developments to the GCP-GridFED protocol. We note that developments introduced in EDGARv5. 0 (ref. 42 ) include refined spatial proxy records and national temporal profiles covering the period 1970-2012, which will support further developments to the GCP-GridFED protocol.
Dedicated datasets of fuel-specific monthly CO 2 emissions are also emerging for some countries, including India 57 and the USA  58  , and could be used preferentially in the GCP-GridFED protocol. Additional sources such as the diffusive coal mine oxidation CO 2 , as derived for the dataset CHE-EDGARv4. 3. 2_FT2015  41, 59, 60 will also be considered. We do not consider emissions of non-CO 2 carbon emissions that later influence atmospheric CO 2 (in particular, CO and CH 4 ). Here all fossil carbon is assumed to be emitted as fossil CO 2 , whereas a fraction is in reality emitted as CO and later represents a diffuse fossil CO2 source after oxidation to CO 2 (~1. 8 Pg CO 2 -equivalent year -1 ) 61. In GridFEDv2019. 1, the diffuse nature of this CO 2 source is not considered, and the source is instead placed at the surface at the time and location of oxidation.
Meanwhile, fossil CH 4 fugitive emissions represent an additional diffuse source of CO 2 emissions (0. 4 Pg CO 2 -equivalent year -1 ) that is not considered here  62. These diffuse CO 2 sources will also be considered in future developments to the GCP-GridFED protocol. CO 2 emissions uncertainty. We provide gridded uncertainties to complement all gridded layers of the GCP-GridFED dataset, however we note here the incomplete nature of our uncertainty assessment. The gridded uncertainties are based on the total fossil CO 2 emissions uncertainty assessment from the GCB  3  , combined with variation in relative uncertainties across emission sectors from the recent TNO assessment (Table  2 )  34  or uncertainties in national total CO 2 emissions, we adopt the values presented in the uncertainty assessment of the GCB; 5% for the 42 Annex I countries that report annually to the UNFCCC  44  and 10% for other countries  3,63 (1σ).
Annex I countries are assigned lower uncertainty because for these countries more detailed energy and activity statistics are available, and they are periodically reviewed externally  3. We used data presented in the TNO uncertainty assessment to evaluate the ratio of the uncertainties for each sector (U_TNO s ) to the uncertainty in total emissions (U_TNO Tot ). We then scaled the ratios to the uncertainties in total emissions that are adopted for Annex I and other countries from GCB-NAE. Table  6. Regional summary statistics relating to seasonality of total CO 2 emissions from GridFEDv2019. 1. =        × × U U U UNFCCC Annex I U TNO U TNO other 5%, _ _ 10%, (13) Table  2  shows sectoral TNO uncertainty estimates and the resulting uncertainties adopted in GCP-GridFED for Annex I and other countries, for each sector.
The gridded uncertainty estimates presented here do not include the uncertainties associated with the spatial or temporal (monthly) disaggregation of national emissions, nor do we present a formal assessment of those    disaggregation uncertainties. 5] [66] [67]  ). Spatial disaggregation uncertainties occur due to incomplete proxy data coverage (e. g. unmapped or mislocated point sources), poorly constrained nonlinearities (e. g. differences in the emissions intensity between equally dense rural and urban populations), shortcomings in continuous proxy values (e. g. poorly constrained population density) or inappropriate spatial representativeness (e. g. the spatial representativeness of roadmaps for traffic volume). By construction, these uncertainties are larger for years distant from our reference year 2010 and at monthly resolution.
Dedicated analyses of regional emissions at high temporal resolution are yielding new data with which to quantify temporal disaggregation uncertainties  57, 68  and to assess the robustness of the temporal profiles employed here and elsewhere  42. A full quantitative assessment of these issues, to support the development of comprehensive grid-level uncertainties associated with GCP-GridFED, will be the subject of future work. Overall, our approach to uncertainty quantification is broadly representative of the sectoral contributions to total emissions in each grid cell, which changes throughout the time series. Inversion models may utilise these uncertainty grids but with the freedom to build more complex covariance structures to suit their requirements. O 2 Uptake. The relationship between CO 2 and O 2 fluxes during oxidation reactions can be expressed as an oxidative ratio (OR = flux of O 2 from the atmosphere/flux of CO 2 to the atmosphere, unitless)  36, 69.
technical Validation We provide Figs. 2 3 4 5 6 7 8 9 10 11 12 , the summary statistics in Tables 4-7 and Online-Only Table  1  to outline the key features of GCP-GridFEDv2019. 1 and assist with its technical validation. GCP-GridFED is designed to distribute national annual emissions from GCB-NAE over a spatio-temporal grid based on EDGARv4. 3. 2. We validated the outputs from GCP-GridFED by comparing the global annual emissions from the output grids (the sum of emissions across the global grid) with the input data supplied to the gridding protocol from GCB-NAE. Throughout the time series of emissions and across all source classes, the global annual emissions totals from GCP-GridFED were always within 0. 0077% of the GCB-NAE input data throughout the annual time series (Figs. 2  and 3 ). The discrepancies were caused by unscalable (zero or NoData) values in sectors of the EDGAR dataset at the national level in 13 countries (EDGAR data summed within the national masks as per Eq.
10). These 13 countries make a small contribution to total global emissions (0. 047% in 2018). Online-Only Table  1  provides national-level comparisons of the emissions estimates from GCP-GridFED and GCB-NAE. For the 13 countries where maximum absolute discrepancies exceeded 1% of GCB-NAE emissions, we provide a brief description of the cause of the discrepancy. The GCP-GridFED outputs are robust to within 0. 0001% of GCB-NAE values in 195 countries, plus bunker fuels, comprising 99. 9% of global emissions in 2018. Hence, we conclude that the GCP-GridFEDv2019. 1 is consistent with GCB-NAE emissions estimates for the years 1959-2018. We also observed a close match between the seasonality seen in the year 2010 in the GCP-GridFED dataset and that seen in the same year of the EDGAR input data, both at the global scale and in large austral and boreal extratropical nations (Fig.
acknowledgements The authors thank  Pierre Friedlingstein ,  Christian Rödenbeck ,  Wouter Peters  and  Ingrid Luijkx  for their helpful feedback on the manuscript. We further thank  Wouter Peters  for his part in securing the funding of the  H2020  ' CHE ' project. We thank  Penelope Pickers ,  Julia Steinbach  and  Nicolas Mayot  for their insights regarding the available data on oxidative ratios. This work was principally funded by  European Commission Horizon 2020  ( H2020 ) ' CHE'  project (grant no. 776186 ). M. W. J. , F. C. , P. C. and G. J. -M. were funded by the  H2020  ' CHE'  project (grant no. 776186 ). G. P. P. , R. M. A, P. C. , F. C. , G. J. -M. and M. W. J. were funded by the  H2020  ' VERIFY ' project (no. 776810 ). A. J. D. -G. , R. M. A. and G. P. P. were funded by the  H2020 '4 C' project  (no.
Data Records All GCP-GridFEDv2019. 1 output grids can be accessed via the Zenodo data repository  70  (https://doi. org/10. 5281/ zenodo. 3958283). The data records include 60 files in Network Common Data Form (NetCDF) format with the naming convention GCP_Global_{YYYY}. nc, where YYYY is the year represented by the contents. Each NetCDF file includes 3 dimensions: time (month of the year expressed as days since the first day of YYYY, n = 12); latitude (Degrees North of the equator [cell centres], n = 1800); longitude (Degrees East of the Prime Meridian [cell centres], n = 3600). Each NetCDF file includes three groups representing CO 2 emissions, CO 2 emissions uncertainty, and O 2 uptake (CO2, CO2_uncertainty and O2, respectively). Each group contains five variables representing.
Code availability The code used to perform all steps described here and shown in Fig. 1  can be accessed via the Zenodo dataset repository entry for GCP-GridFEDv2020. 1 (https://doi. org/10. 5281/zenodo. 4277267)  74. GCP-GridFEDv2020. 1 uses the same code and methodology as GCP-GridFEDv2019. 1 but includes updated estimates of national annual emissions through to 2019 from the GCP, as discussed in the Usage Notes and also detailed at ref. 74.
41. The location, fuel type and seasonality of power plant emissions derives from the CARMAv3. 0 dataset51. The 2010 gridded emissions dataset used here as the scaling basis includes over 60,000 plants mapped globally in CARMAv3. 0 in the year 2007. Standard QA/QC screening was applied to the CARMAv3. 0 dataset, including gap-filling of missing (0,.
Methods This multicenter prospective study was approved by the Ethical Committee Ile de France VIII (July 10, 2012) and registered on the international platform www. clinicaltrials. gov (NCT017 32133). Written informed consent was obtained from all parturients participating in the trial with a specific justification related to the "protected population" (pregnancy), in accordance with French law. It was performed in one university hospital and in one private nonprofit hospital between January 2013 and December 2015.
Recorded data The following demographic characteristics of the pregnant women and their newborns were collected: age, weight, height, gestational weeks, indication for cesarean delivery, neonatal umbilical vein pH, and Apgar score at 1, 5 and 10 min. The tolerance of both devices was reported by the parturients based on a visual analogue scale (VAS; 0: extremely uncomfortable to 10: perfect). Any adverse event was collected, immediately analyzed, and declared to the health authorities if necessary. Paired measurements were obtained using the Nexfin TM (CNBP) and the BPosc every minute from the end of spinal injection. To obtain a single CNBP value in parallel to the BPosc value, the data for CNBP were filtered. An average value surrounding the "minute of interest" was calculated over an interval of 20 + 20 consecutive seconds that corresponded to the median duration of the oscillometric measurement (Fig.
Data filtering and analysis were conducted using the Python Pandas library (version 0. 19. 2). All statistical analyses were conducted using R version 3. 2. 4 (R Core Team, 2016; R Foundation for Statistical Computing, Vienna, Austria). The significance level was set to. 05. The data that support the findings of this study are openly available in the Dryad repository at https://datadryad. org/search [https://doi. org/10. 5061/dryad. 280gb5mpj].
Results A total of 174 parturients were included nonconsecutively in the study from January 2013 to December 2015. Data for 14 parturients were not available. Finally, analysis was performed using data from 160 parturients (Fig. 2 ). The main characteristics of these parturients, their surgeries, vasoactive drug requirements and neonatal outcomes are reported in Table  1. In 50% of the cases, the indication for cesarean delivery was previous cesarean delivery, while abnormalities in fetal presentation (e. g. , breech position) were the second indication in 30% of cases. The mother's convenience represented 8% of the indications, and the last 12% were miscellaneous indications (abnormalities of placenta implantation, poor conditions for a natural birth such as pelvic trauma, previous instrumental extraction,.
Discussion We investigated the performance of a continuous CNBP for detecting arterial hypotension during spinal anesthesia for cesarean delivery and compared the results with those obtained using routine intermittent measurement of BP by oscillometry. Notably, this study showed that 1. the intermittent traditional method (oscillometry) has data gaps unlike the continuous method (Nexfin TM ), 2. there was a longer delay in detecting hypotension with oscillometry, and 3. the agreement limits are too wide to suggest replacing oscillometry with the Nexfin TM. Ilies ¸et al  [12]  previously compared the continuous noninvasive arterial pressure (CNAP) monitor with discontinuous automated oscillometric measurements obtained every three minutes during cesarean delivery under spinal anesthesia. The authors showed that SBP was detected at lower limits and that hypotensive periods were identified more often by continuous measurement than by standard monitoring every three minutes.
Limitations of the study The limitations in the current study should be mentioned. The first point concerns the number of patients included. The standard approach is to calculate this number based on an expected difference between methods, its variance, and the alpha and beta risks. We choose to follow the advice from Linnett,  [17]  which recommends studying more than 150 patients to yield a power of 90% and a bilateral risk of 5% when comparing two methods. We studied data from 160 parturients, which translated to 2640 pairs of data. Second, some of our results could have been modified by the inflation and deflation of the brachial cuff every minute, which induces reactive hyperemia, and by the systemic changes in vasomotor tone produced by vasopressor drug boluses. [27, 28] The third limitation concerns the definition of arterial hypotension which is variously described in the literature and we chose a well-accepted one as a 20% decrease of the baseline.
Cell surface proteins were identified with Cell Surface Proteins Atlas (CSPA) and Gene Ontology enrichment. Among all the surface proteins identified in the different cell lines we have confirmed the expression of 66 of these in patient's glioblastoma using spatial proteomic guided by MALDI-mass spectrometry. Moreover, 87 surface proteins overexpressed or exclusive in GBM cell lines have been identified. Among these, we found 11 specific potential targets for GBM including 5 mutated proteins such as RELL1, CYBA, EGFR, and MHC I proteins. Matching with drugs and clinical trials databases revealed that 7 proteins were druggable and under evaluation, 3 proteins have no known drug interaction yet and none of them are the mutated form of the identified proteins. Taken together, we discovered potential targets for immune therapy strategies in GBM.
Thus, these 11 proteins could be potential therapeutic targets for CAR-based immunotherapy or mRNA vaccine strategies. new surfaceome proteomic dataset provided experimentally to CSPA database and from mutated peptides from XMan v. 2 database  (15). In this context, we compared astrocytoma GBM cell lines to normal astrocytes cell line. We could identify 11 specific potential targets for GBM including 5 mutated proteins (PLAUR, ITGB3, and MHC I proteins). The specificity of these proteins to GBM has been validated using the human protein atlas and a cohort of 50 GBM patients.
Protein ID and Data Analysis Proteins were identified by comparing all MS/MS data with the proteome database of the complete reviewed proteome of Homo sapiens (Uniprot, release November 2020; 20,370 entries), using the MaxQuant software version 1. 6. 10. 43  (16, 17). Trypsin specificity was used for the digestion mode with two missed cleavages. N-terminal acetylation and methionine oxidation were selected as the variable modifications. For MS spectra, an initial mass tolerance of 6 ppm was selected, and the MS/MS tolerance was set to 20 ppm for HCD data  (18). For identification, the FDR at the peptide spectrum matches (PSMs) and protein level was set to 0. 01, and a minimum of 2 peptides per protein in which 1 was unique. Relative, label-free quantification of proteins was performed using the MaxLFQ algorithm integrated into MaxQuant with the default parameters.