dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020), as well as a net CO 2 flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC, which include land use change and fluxes in managed lands(Grassi et al. , 2021). (c) Anthropogenic CH 4 emissions from EDGAR (above), CEDS (above), PRIMAP-hist (above); GAINS -the Greenhouse gas-Air pollution Interactions and Synergies Model(Höglund-Isaksson et al. , 2020), EPA-2019: Greenhouse gas emissions inventory (US-EPA, 2019), FAO -FAOSTAT inventory emissions(FAOSTAT, 2021; Tubiello, 2018;  Tubiello et al. , 2013), (d) anthropogenic N 2 O emissions from GCP -Global Nitrous Oxide Budget(Tian et al. , 2020), CEDS (above), EDGAR (above), PRIMAP-hist (above); GAINS (Winiwarter et al. , 2018) , EPA-2019 (above), and FAO (above). Differences in emissions across different versions of the EDGAR dataset are shown in the Supplement (Fig.
Hansis et al. (2015), b Houghton and Nassikas (2017), c Gasser et al. (2020); d Hurtt et al. (2020); e Chini et al. (2021); f FAO (2015); g based on rangeland-pasture distinction of the HYDE dataset (Klein Goldewijk et al. , 2017) and forest cover map of Hurtt et al. (2020); see Friedlingstein et al. (2020) for details; h Conchedda and Tubiello (2020); i Hooijer et al. (2010).
9, respectively. Financial support. This research has been supported by the Bundesministerium für Bildung und Forschung (grant no. 01LG1910A), the European Commission Horizon 2020 Framework Programme (CONSTRAIN grant no. 820829, VERIFY grant no. 776810, and CoCO2 grant no. 958927), the National Science Foundation (grant no. 1903722), the Australian National Science Program -Climate Systems Hub, and the UK Natural Environment Research Council (grants NE/N016548/1, NE/M014851/1, and NE/I021365/1). This paper was edited by David Carlson and reviewed by Bo Zheng and one anonymous referee.
Literature: a a Hansis et al. (2015), b Houghton and Nassikas (2017), c Gasser et al. (2020); d Hurtt et al. (2020); e Chini et al. (2021); f FAO (2015); g based on rangeland-pasture distinction of the HYDE dataset (Klein Goldewijk et al. , 2017) and forest cover map of Hurtt et al. (2020); see Friedlingstein et al. (2020) for details; h Conchedda and Tubiello (2020); i Hooijer et al. (2010).
9, respectively. Financial support. This research has been supported by the Bundesministerium für Bildung und Forschung (grant no. 01LG1910A), the European Commission Horizon 2020 Framework Programme (CONSTRAIN grant no. 820829, VERIFY grant no. 776810, and CoCO2 grant no. 958927), the National Science Foundation (grant no. 1903722), the Australian National Science Program -Climate Systems Hub, and the UK Natural Environment Research Council (grants NE/N016548/1, NE/M014851/1, and NE/I021365/1). This paper was edited by David Carlson and reviewed by Bo Zheng and one anonymous referee. Review statement.
Data acquisition and Preprocessing EEG signals were recorded using a HD-EEG system (EGI, Electrical Geodesic Inc. , 256 channels), with a sampling frequency of 1000 Hz. 199 electrodes were kept, removing most of the jaw and neck electrodes, as shown in the channel file available in the GitHub repository (see section 2. 8). EEG preprocessing was performed manually using the Brainstorm toolbox  (Tadel et al. , 2011). Preprocessing was performed as follows. First, DC offset removal was applied. Second, a notch filter at 50 Hz and a band-pass filter of 1-100 Hz were applied. Third, signals were visually inspected, and bad channels were removed before being interpolated using Brainstorm's default parameters. Fourth, Independent Component Analysis (ICA) was used to remove eye blinks and muscle artifacts.
i. Forward Model Following the equivalent current dipole model, EEG signals measured from Q channels can be expressed as linear combinations of P time-varying current dipole sources as follows: 7 𝑋 (𝑡) = 𝐺. 𝑆 (𝑡) + 𝑁 (𝑡) (1) Where (× ) represents the forward model, often called the lead field matrix, and  () denotes the additive noise. The lead field matrix is computed from a realistic head model along with the position of electrodes. Here, we used the Boundary Element Method (BEM) head model fitted to the ICBM MRI template  (Kötter et al. , 2001) , downloaded from https://www. mcgill. ca/bic/software/toolsdata-analysis/anatomical-mri/atlases/icbm152lin using the OpenMEEG toolbox  (Gramfort et al. , 2010) , and used the Electrical Geodesic Inc (EGI) configuration for the EEG electrodes.
i. Behavior RT and accuracy are the variables measured during the Simon task. Comparing congruent and incongruent RT of correct responses provides an estimate of the congruence effect that informs about the additional time needed to solve conflict. Incongruent trials are typically associated with increased RT and decreased accuracy. In addition to the analysis of the congruence effect, behavioral data were also analyzed in light of the activation-suppression model  (Ridderinkhof, 2002) , The effect of congruence and group on RT and accuracy were first broadly analysed by using linear and non-linear mixed models, respectively. In both cases, congruence and group were added as fixed factors, while a random slope and intercept was allowed for each subject. RT were log-transformed for increased compliance with the model's assumptions in that case.
Acknowledgements The authors would like to thank all the participants who took part in this study. We also thank  Dr. Adrien Bénard  (MD) and  Dr. Sina Potel  ( MD ) for their help in data acquisition. We also would like to thank  Bretagne Atlantique Ambition (BAA)  as well as the  Rennes Clinical Neuroscience Institute  (INCR: www. incr. fr)who funded this work.
Data and Methods Two main types of data are used in this analysis: iceberg sightings and remotely sensed ocean colour, the latter being a proxy for surface chlorophyll a concentration. There is a record of monthly iceberg numbers crossing the 48 o N parallel extending back to 1900  (Bigg et al. , 2014) , and daily iceberg sighting maps back to 2004 for regions south of 52 o N. In addition, a more complete database of iceberg sightings in the Labrador Sea exists from 1960  (IIP, 2016). This does not have all size and shape fields specified for each sighting, varies in format over time, and certainly earlier data has some quality control issues  (Wilton et al. , 2015). However, the location and timing of all iceberg sightings south of 52 o N is available for several decades. Ocean colour data is available in a fairly regular form only since the SeaWiFS satellite was launched in 1998, but to obtain a full time series since then requires merging of data from more than one.
Ocean Colour The chlorophyll data were retrieved from https://oceancolor. gsfc. nasa. gov/ for the two satellites SeaWiFS and MODIS-Aqua for the periods  1998-2002 and 2003-2015  respectively. The following 4 km resolution, Level 3 processed data were collected and averaged over a 1 o x1 o grid: a) monthly averages; b) 8 day averages that included the 15 th of each month (not always over the same days of the month) to give a mid-month series; c) the 8 day averages that followed those from b); and d) daily averages that were used to calculate 2-7 day averages over the whole year. Note that the mean chlorophyll levels from these two satellites differ, and this difference has some spatial variability  (Djavidnia et al. , 2010) , with the SeaWiFS chlorophyll typically being lower than the MODIS-Aqua values, with a weaker seasonal cycle.
Iceberg data Two types of iceberg data were used in this study. As a measure of the overall flux of icebergs in the Labrador Current monthly estimates of the number of icebergs crossing 48 o N are compiled by the International Ice Patrol (IIP); here we define this as I48N. These data are available from 1900 to 2020 (http://www. navcen. uscg. gov/pdf/iip/International_Ice_Patrols_Iceberg_Counts_1900_to_2011. pdf , supplemented by subsequent annual reports available at https://www. navcen. uscg. gov/?pageName=IIPAnnualReports), although this study only uses the 1998-2015 period for analyses involving I48N, for consistency with the individual sightings database discussed below. A full discussion of the creation and quality of this dataset is given by  Christensen and Luzader (2012)  and  Wilton et al.
To extend the record back to 1998, to coincide with the beginning of the SeaWiFS observation period, we used the IIP iceberg sightings database  (IIP, 2016). The observations in this database, which often only contain position and no size information, are predominantly south of 52 o N so the area over which the iceberg number density was calculated for this study was limited to being south of 52 o N. This region, from 52 o N to the most southerly iceberg measurement and from the eastern coast of North America out to the most easterly iceberg observation at 35 o W, is defined as the main iceberg area, or as region A. However, a technique needed to be designed to reconstruct the monthly map equivalent data from this sightings database over the 1998-2008 period, when iceberg location maps were not available.
The period from 2009-2015 was used to test techniques for reconstructing number density fields in region A in the pre-2009 period, as the daily charts changed format in 2009, with iceberg numbers becoming numerical rather than being denoted by symbols from that year. The iceberg density reconstruction technique selected used the fact that trajectories of individually identified icebergs in the sightings database could be calculated. This enabled the generation of a Markov chain describing the probability that, after successive days, an iceberg would travel from a given grid square to another. Markov chains of varying temporal lengths can be evaluated, which enabled the determination of the shortest period of prior information required to produce a statistically robust reconstruction for a particular day's iceberg density.
The procedure for creating an iceberg density map for the 15 th of each month in this way is described in Appendix A. This was the only day's density calculated each month, for compatibility with the 2009-2015 mid-month data set, described above. I sightings (x,y) is defined as the number of icebergs at the grid point (x,y) as reconstructed from applying the Markov chain. Measures of the number of icebergs found from the mapping database, N maps  (x,y,n) , and the sightings database, N sightings  (x,y,n) , respectively are then defined. Here n is the size of area being compared and varies over squares of size 1 o , 3 o , 5 o or 7 o. Thus (2) and (3) with (x,y) being (longitude, latitude) of each respective square, I maps (x,y) being the number of icebergs at the grid point (x,y) on the IIP maps and I sightings (x,y) deriving from the iceberg sightings database as described above.
, 2016). Two methods, using different mixtures of iceberg and chlorophyll datasets, were employed to try to disentangle any signal from what will be a complex and multiply-forced environment. This task is made even more complicated by the sub-grid scale size of icebergs in the Labrador Sea, relative to ocean colour pixels, compared to the often much larger Southern Ocean icebergs. The two basic methods are described here, with the results of the relevant analyses given in the Results section.
The analysis is therefore extended to consider this lag component in more detail. Using the technique outlined in Sect. 2. 2. 2, a series of partial correlations over 1998-2015 between 5 day series of I T and chlorophyll were carried out over a range of lags from 0 days to 70 days. A sub-set of the correlations showing those squares with a statistical significant partial correlation at the 0. 05 level is shown in Fig. 6. An animation of this full correlation map sequence is available in the Supplementary Material. Note that the region over which correlations are possible varies with the lag combination due to the persistent presence of cloud cover in the northern Atlantic restricting the number of cloud-free pixels, particularly for the larger dataset of shorter lag. This series of lagged partial correlations shows an interesting evolution, with a distinct zero lag positive correlation in the vicinity of the main iceberg stream in the Labrador Current (that is, the main area of Fig.
https://doi. org/10. 5194/os-2021-61 Preprint. Discussion started: 9 August 2021 c Author(s) 2021. CC BY 4. 0 License. instrument, thus requiring calibration. The next section therefore addresses how consistent datasets over our baseline 82 period of 1998-2015 were created from disparate sources. 83 https://doi. org/10. 5194/os-2021-61 Preprint. Discussion started: 9 August 2021 c Author(s) 2021. CC BY 4. 0 License.
Acknowledgements. We would like to thank  ENSTA Paristech  for giving QJ the opportunity to take a research internship at the  University of Sheffield  during the summer preceding the fourth year of his Engineering degree (2017), when the bulk of the research reported here was carried out. We are grateful to the  US Coast Guard  for compiling and archiving IIP iceberg observations and charts, and to  NASA  for archiving the ocean colour datasets used here. Tom Lees  and  Jacob Marsh  assisted with digitization of the 2009-2015 IIP iceberg charts.
Meteorology, land cover, and burned area Relevant meteorological parameters used in this study are the skin temperature (T skin or land surface temperature), total precipitation, and near surface wind speed. The parameters are extracted daily from the European center for medium-range weather forecast's reanalysis (ERA5;  Hersbach et al. , 2020)  and interpolated in time and space to the IASI morning observations. The resolution of ERA5 datasets is at 0. 25 Â 0. 25 grid (native horizontal resolution of ERA5 is approximately 31 km). T skin is defined as the temperature of the surface at radiative equilibrium. It is derived from the surface energy balance within the land model in ERA5. The total precipitation product combines both large-scale and convective precipitations. The wind speed was calculated from the u and v wind components at 10 m of height above the surface of the Earth.
The u component is defined as the horizontal wind speed heading toward the east, and the v component is the horizontal wind speed moving toward the north. MODIS instruments are orbiting the Earth onboard the Aqua and Terra satellites. The Aqua/Terra MODIS Land Cover product (MCD12Q1 version 6) provides yearly maps of land cover with a 500 m spatial resolution for the period that extends from 2001 until 2019  (Sulla-Menashe and Friedl, 2018). The MOD12Q1 product employs 17 categories of land cover classification  (Belward et al. , 1999). In this work, we extract the croplands, shrub, and bare lands from these datasets to examine the effect of war and human displacement. The classification used in this study to represent croplands is defined as lands comprising of at least 60% cultivated area; A shrubland is defined as an area dominated by woody perennials (plants); Bare lands (also called Barren in the MCD12Q1 product) are defined as lands with an at least 60% nonvegetated area  (Sulla-Menashe and Friedl, 2018).
It could be extended to other parts of the world in order to analyze how political changes influence agricultural practices and industrial activities (e. g. , in Afghanistan before and after 2021). Current emission inventories do not consider war-related events; we think that they should be adapted according to the results of this study and other studies that show disruptions in emissions that are not related only to climatology. The longterm homogeneous IASI dataset, whose continuity will be ensured with the IASI-NG mission  (Crevoisier et al. , 2014)  that should fly from 2023 to 2050, offer a unique tool to monitor the impact of societal changes and perturbations on the atmospheric composition.
Data accessibility statement The IASI-NH 3 used in this study are retrieved from the Aeris data infrastructure (https://iasi. aeris-data. fr/nh3r-era5/). MODIS land cover data are available for download in the following link: https://doi. org/10. 5067/MODIS/ MCD12Q1. 006. MODIS fire data are available for download in the following link: https://lpdaac. usgs. gov/ products/mcd64a1v061/. Images from Sentinel 2 L1C are available at https://apps. sentinel-hub. com/sentinelplayground. ERA5 skin temperature, 10 m u and v wind components, and total precipitation from 1979 to present are available for download in the following DOI: 10. 24381/cds. adbb2d47.
Funding The research was funded by the  F. R. S. -FNRS  and the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  (Prodex arrangement HIRS). M. Van Damme is Postdoctoral Researcher ( Chargede Recherche ) and L. Clarisse is Research Associate (Chercheur Qualifie) both supported by the  Belgian F. R. S. -FNRS.
Since many systematic uncertainties are correlated between the  prompt and   (→) templates, they cancel out in the (/) ratio, minimising their impact on the precision of the result. These include uncertainties related to jet reconstruction, flavour tagging and trigger efficiencies. The remaining dominant uncertainties are the uncertainties in the data driven methods, the theoretical modelling uncertainties and the reconstruction uncertainties are described in the following, and further details are given in Methods. An uncertainty associated with the data-driven templates for the |  0 | distribution of  prompt is derived to account for the differences between the  →  calibration region where they were derived and the signal region. Uncertainties in the data-driven normalisation of the  had background due to the size of the same-sign dataset, the choice of MC generators used and the uncertainty in the subtraction of the other processes in the same-sign control region are included in the fit.
Systematic uncertainties Several of the most important systematic uncertainties in the measurement of (/) merit a more detailed discussion, which is provided here. Due to differences in the hadronic environment around the lepton between  and  t final states and the coarse binning in   T and ||, which may not be able to encapsulate the full shape information, small biases can exist in the data-driven  prompt template distributions. The size of such a possible bias is estimated from the full difference between  prompt |  0 | templates from  and  t in simulation. This uncertainty is split into two components corresponding to the tail, | The breakdown of uncertainties contributing to the  had background control region normalisation in the - (-) channels is: 4% (4%) due to the size of the same-sign dataset; 8% (3%) due to the choice of MC generators used; and 1.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  COST ,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and GIF, Israel; Norwegian Financial Mechanism 2014-2021, Norway;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden ;  The Royal Society and Leverhulme Trust ,  United Kingdom.
As expected, none of the GO samples devoid of HEL was able to stimulate IL2 production by A20 wild type cells (data not shown). To further confirm the ability of the complexes to specifically bind to the HEL-specific BCR, flow cytometry experiments were performed. For this purpose, both the genetically modified A20 D1. 3 and the control A20 B cells were exposed to free HEL, GO-A-HEL and GO-N-HEL for 30 min at 4°C to prevent internalization. As expected from the ELISA results, a clear shift of the HEL-specific cell-associated fluorescence was evidenced upon incubation of A20 D1. 3 cells with free HEL (positive control) but also with the two GO-HEL samples (Figure  4B ). The binding of the conjugates was specifically mediated by the HEL-specific BCR as no staining was observed upon incubation with control A20 cells (Figure  4C ).
Flow cytometry For the cellular targeting purpose, 50 µL of GO-HEL, free HEL or free GO (30 nM or 100 nM in PBS 2 % FBS) were mixed with 20 µL of A20 or A20 D1. 3 cells, diluted in the same buffer (0. 5 × 10 6 cells per well), in a 96-well V-bottom plate and incubated for 30 min at 4 °C in order to avoid any internalization of the compounds. Cells were then washed with PBS 2% For the analysis of the cell viability, 4 × 10 5 cells (A20 or A20 D1. 3) were seeded in 96-well flat-bottom plates in 200 µL of GO-HEL, free HEL or free GO solutions in RPMI (from 10 nM to 600 nM) for 24 h. The cells were stained with both FITC-Annexin V (BD Pharmingen 556419) and propidium iodide (0. 2 μg mL -1 , Sigma-Aldrich) in a calcium-containing buffer. The percentage of live, early apoptotic and late apoptotic/necrotic cells was determined by acquiring at least 30000 events using a Gallios flow cytometer and analyzing the data with FlowJo software.
Acknowledgements We are grateful for financial support from the  CNRS  ( Centre National de la Recherche Scientifique ), the  International Center for Frontier Research in Chemistry (icFRC) , and the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ). We wish to thank  Prof. Batista  and  Dr. Montaner  ( London Research Institute, UK ) for providing A20 D1. 3 cells. The authors also thank  Fanny Bonachera  for helping in the preparation of the scheme.
Genetic Data Samples were genotyped at the MRC Centre for Neuropsychiatric Genetics and Genomics in Cardiff (the United Kingdom) using a custom Illumina HumanCoreExome-24 BeadChip genotyping array covering 570038 genetic variants (Illumina Inc. ). Quality Control. Quality control (QC) entailed removing samples based on call rate (<0. 99), genotypephenotype mismatched information, suspected non-European ancestry, heterozygosity, and relatedness. Single-nucleotide polymorphism (SNPs) were excluded if the minor allele frequency was 5%, if more than 2% of genotype data were missing and if the Hardy-Weinberg Equilibrium P-value < 10 -6 ; nonautosomal markers were also removed. The baseline characteristics of participants who were genotyped or were not genotyped are provided in supplementary table 1.
Methodological Strengths and Limitations This is an extensive multi-site study of FEP with comprehensive data on a variety of environmental and genetic factors. The study included all incidence cases from welldefined catchment areas in 17 sites across 6 countries. As our analyses were focused on people with a diagnosis of FEP, the findings reported in the present study are less likely to be biased toward patients who experience multiple hospital admissions. 61, 62 Given that our study was carried out in major urban and rural sites with heterogeneous populations suggest that the generalizability of our findings may extend to other centers with similar population profiles. Finally, because the calculation of PGS is based on well-powered GWASs, we did not require a large sample to test our hypotheses, which was further confirmed by estimated predictive power in each PGS employed in the analyses.
Acknowledgments R. M. M. has received honoraria from  Janssen ,  Sunovian ,  Lundbeck  and  Otsuka. M. B. has been a consultant for, received grant/research support and honoraria from, and been on the speakers/advisory board of  ABBiotics, Adamed, Angelini, Casen Recordati, Janssen-Cilag, Menarini, Rovi and Takeda. Other authors declare that they have no conflict of interest. All other authors declare no conflict of interest. Funding O. A. is funded by the  National Institute for Health Research (NIHR)  ( NIHR Post-Doctoral Fellowship -PDF-2018-11-ST2-020 ). V. R. was funded by a PhD scholarship supported by  Lord Leverhulme's Charitable Trust  and by the  Velvet Foundation. M. D. F. is funded by  Clinician Scientist Medical Research Council fellowship  (project reference  MR/M008436/1 ). D.