dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The authors acknowledge support from the  French National Research Agency (ANR)  funded project  PEPPER  ( ANR-20-CE31-0002 ). The results are based on public data released under Programme IDs 60. A9257 ( D-E ),  ID 104. 20V6 , and  ID 106. 21L4. This work was supported by the  Action Spécifique Haute Résolution Angulaire (ASHRA) of CNRS/INSU  co-funded by  CNES , as well as from the  Programme National de Physique Stellaire (PNPS)  from  CNRS. It is also supported by the  French government  through the  UCA-JEDI Investments  in the Future project managed by the  National research Agency (ANR ) with the reference number  ANR-15-IDEX-01  and the  Hungarian government  through  NKFIH  grant  K132406. The authors have made extensive use of the  Jean-Marie Mariotti Center  (JMMC) tools.
, 2015 Sharma et al. , , 2019;; Lerner et al. , 2018; Pecori et al. , 2022)  families, respectively. Reverse transcriptase incorporates guanosines (G) and thymidines (T) into cDNA at positions where inosines and uridines are present in the RNA, leading to base changes not present in the genomic DNA. For this reason, editing sites can be detected by directly comparing RNAseq to DNA-seq data or a reference genome  (Levanon et al. , 2004; Picardi and Pesole, 2013; Wang et al. , 2016; John et al. , 2017; Piechotta et al. , 2017). Several bioinformatics pipelines have been developed for the analysis of next-generation sequencing (NGS) data to detect RNA editing sites  (Ramaswami and Li, 2016; Eisenberg and Levanon, 2018; Diroma et al. , 2019) , leading to a constant increase of entries in their catalogs and the generation of new databases  (Kiran and Baranov, 2010; Ramaswami and Li, 2014; Picardi et al.
, 2017; Mansi et al. , 2021). Despite these improvements, RNA editing detection in NGS datasets remains challenging due to the many sources of DNA-RNA sequence mismatches, leading to the necessity of routine validation by reverse transcription-polymerase chain reaction (RT-PCR). RT-PCR is a two-step method in which the RNA is first retrotranscribed into cDNA, and then cDNA is amplified at specific locations via PCR. This method has some variations; for example, cDNA can be produced from oligo-dT, random hexamers, or specific primers for a particular transcript. In this latter case, and when a Hot Start DNA Polymerase is used, the reverse transcription and PCR amplification of a specific target take place one after the other in the same tube, in a so-called one-step RT-PCR reaction. This method allows a fast and easy RT-PCR setup, optimal for RNA editing detection validation.
Materials and methods 2. 1 Cell lines, treatments, and transfections RCK8 cells (DSMZ, Cat# ACC-561, RRID: CVCL_1883) and U2932 (DSMZ, Cat# ACC-633, RRID: CVCL_1896) were cultured at 37 °C, 5% CO 2 , in RPMI-1640 medium (Sigma-Aldrich, Cat# R8758), supplemented with 15% fetal bovine serum (PAN Biotech, Cat# P40-37100) and 1% of Penicillin/ Streptomycin (Sigma-Aldrich, Cat# P4333). A549 cells (DSMZ, Cat# ACC-107, RRID: CVCL_0023) were cultured at 37 °C, 5% CO 2 in high-glucose DMEM (Sigma-Aldrich, Cat# D6429) supplemented with 10% fetal bovine serum (PAN Biotech, Cat# P40-37100) and 1% penicillin/streptomycin (Sigma-Aldrich, Cat# P4333). HEK293T cells (obtained from DKFZ, ATCC, Cat# CRL-3216, RRID: CVCL_0063) were cultured at 37 °C, 5% CO 2 in high-glucose DMEM (Sigma-Aldrich, Cat# D6429) supplemented with 5% FBS (PAN Biotech, Cat# P40-37100) and 1% penicillin/streptomycin (Sigma-Aldrich, Cat# P4333).
Plasmids pcDNA3-hLincRNAp21-MS2 contains the 3898 nt-long LIsoE2 isoform of the human lincRNA-p21 (GenBank: KU881768. 1) tagged with 24 copies of MS2 RNA hairpins, as previously described  (Chillón and Pyle, 2016). LentiCRISPRv2 was a gift from Feng Zhang (Addgene, plasmid #52961; https://addgene. org/52961; RRID: Addgene_ 52961)  (Sanjana et al. , 2014). DNA oligos #12-13 were cloned into this plasmid following the instructions of "lenti-CRISPRv2 and lentiGuide oligo cloning protocol" (Addgene plasmid #52961) to generate lenti-CRISPR-ADAR1 exon 4 [from  Pestal et al. (2015) ; Supplementary Figure  S7A ]. As a control, lenti-CRISPR-NT (Lenti-NT) was cloned accordingly using oligos #14-15 based on control 800 from the GeCKO v2 library  (Sanjana et al. , 2014). pCMVDR8. 91 (coding for HIV gag-pol) and pMD2.
Genome-wide A-to-I sense-antisense RNA editing analysis Ribo-depleted RNA-seq experiments from seven human tissues (Supplementary Material S1) were selected from the "RNA Atlas" project  (Lorenzi et al. , 2021)  and downloaded from GEO under the accession GSE138734. Known annotations for antisense and protein-coding genes were obtained from Gencode (v38), downloaded in gtf format, and converted into bed format. Antisense and proteincoding annotations were intersected by means of the "intersect" function embedded in the Bedtools package  (Quinlan, 2014) , discarding overlapping intervals less than 300 bp. The resulting genomic coordinates of overlapping sense-antisense genes were used as input in a modified version of REDItools  (Picardi and Pesole, 2013) , able to split reads according to their orientation.
Only editing candidates supported by more than five reads and organized in non-redundant clusters (represented by A-to-G or T-to-C mismatches according to gene strandness) were retained. All the editing sites considered in this analysis are described in Supplementary Material S1. Circular heatmaps were generated using the R package circlize  (Gu et al. , 2014)  and the cytoband representation of the human genome assembly hg38. Heatmaps color scale represents an RPKM-like normalization of editing events. The entire pipeline and scripts are available at https://github. com/BioinfoUNIBA/antisenseEditing.
Observation of a persistent U-to-C base change in DDX58 RNA-seq data analysis represents a powerful method to detect new RNA editing sites. Unfortunately, these technologies are not error-free; thus, validation of these newly discovered RNA editing sites is still necessary. This validation is performed via PCR amplification of a specific region containing the editing sites to be validated from either DNA or cDNA (the latter represents the RNA). In a recent work from our lab, we identified RNA editing sites comparing RNA-and DNA-seq data in a cohort of Diffuse large B cell lymphoma (DLBCL) patients  (Pecori et al. , 2021). We used a one-step RT-PCR reaction to validate some of those sites due to its higher sensitivity in RNA editing detection for low edited or expressed transcripts  (Wacker and Godard, 2005; Kluesner et al.
A-to-I antisense RNA editing in NGS After observing antisense A-to-I RNA editing in both an mRNA and a lincRNA, we asked what the impact of this process at the transcriptome level in different tissues is. To elucidate this point, we investigated antisense A-to-I RNA editing genomewide by using seven ribo-depleted strand-oriented RNA-seq experiments from various human tissues of the "RNA Atlas" project  (Lorenzi et al. , 2021). We created a catalog of senseantisense gene overlaps based on Gencode annotations to provide an unbiased overview of antisense editing. Known antisense transcripts were initially selected from Gencode, then overlapping regions of at least 300 bp with sense transcripts were collected to a total number of 1677 suitable overlaps. For each one of these, corresponding to a well-defined genomic interval, we called RNA editing using pre-aligned RNAseq reads and a modified version of the REDItools software  (Picardi and Pesole, 2013)  able to split reads according to their orientation.
Acknowledgments We thank the  Flow Cytometry unit of the Imaging and Cytometry Core Facility ,  German Cancer Research Center (DKFZ) , for providing excellent sorting service. We thank  Qiang Pan Hammarström  ( Karolinska Institutet, Sweden ) and  Ralf Bartenschlager  ( University Hospital of Heidelberg ) for the generous gift of  RCK8 ,  U2932  cells, and  A549 , respectively. We also thank  Bruno Fosso  ( University of Bari, Italy ) for realizing figures related to sense and antisense editing in NGS data.
Funding This work was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 649019 ), the  DFG German Research Foundation  ( TRR319-RMaP  and  SPP1784 ) awarded to FP and the  HI-TRON Kick-Start Seed Funding Program 2021  awarded to RP. Work in the Marcia lab has partly been funded by the  Agence Nationale de la Recherche  ( ANR-15-CE11-0003-01 ), by  ITMO Cancer  ( 18CN047-00 ), and by the  Fondation ARC pour la recherche sur le cancer  ( PJA-20191209284 ).
Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: The RNA datasets analyzed in this study can be downloaded from Gene Expression Omnibus at NCBI under the accessions GSM4118041, GSM4118068, GSM4118074, GSM4118077, GSM4118080, GSM4118083, GSM4118086, respectively for samples RNAAtlas285, RNAAtlas294, RNAAtlas296, RNAAtlas297, RNAAtlas298, RNAAtlas299 and RNAAtlas300. Computer code used for the A-to-I senseantisense RNA editing analysis can be found at https://github. com/BioinfoUNIBA/antisenseEditing.
Once designed, it can be carved into a plate of desired thickness at low cost, for example using hot-wire cutting of Styrofoam (Burte et al. 2022) or, the solution we recommend here, laser cutting of PMMA plates. High-resolution images of the entire maze are taken every minute for several hours, and the provided image analysis pipeline detects the location of each individual at each time point. Subsequent coordinate transformations and analyzes (see Fig. 2 ; and Methods) yield automated and accurate estimates of diffusion coefficients and dispersal kernels  32, 33. An original skeleton fragmentation algorithm allows to compute for each individual the linear distance from the center of the tunnel, for arbitrary shapes of the tunnel (Fig. 2  and 3 ). Figure  2 : Diagram of the functioning of the automatic pipeline as well as the main steps of data processing using R software.
In black, the steps related to the initialization of the file processing with all the experimental images. In light blue, all the steps corresponding to the creation and processing of the spiral skeleton. In pink, the steps concerning the distance to the edge of the individuals. In dark blue, the processing steps with the R software. The dotted line corresponds to a file. The grey boxes with dotted lines correspond to the very first file before any analysis ("Image Sequence") and the final clean file ready to be analyzed with calculations ("File ready to be analyzed"). The entire sequence of analyses can be automated thanks to an ImageJ/Fiji macro and an ImageJ/Fiji plugin, available at https://forgemia. inra. fr/ludovic. mailleret/double_maze_arena. Filtering of the outputs can be conducted with an R script available at the same address.
e. 360. To reduce the needs in memory, images are loaded by packs of 60 images, in a sliding window manner (adjustable parameter). Here is the main sequence of actions carried out in ImageJ by our pipeline: -File>Import>Image Sequence: Importation of the image sequence to be analyzed. -Image>Type>8-bit: conversion of the images in 8-bit, i. e. , in shades of grey. The software assigns to each pixel a value from 0 (black) to 255 (white). -Image>Stacks>Z project: tracking and analysis of the particles of an image over time to define a still background image, on which the particles move. The background image is subtracted from the actual image to distinguish the moving particles. -Process>Image calculator: particles tracking on each image, based on the background calculated previously. During this process, the background is removed to leave only mobile particles (the "Subtract" option must be chosen and "32-bit must be used.
Acknowledgments We warmly thank all the members of the  "SoFAB" (the FabLab of Sophia Antipolis)  who helped us to develop the double spiral maze by sharing with us their knowledge and their know-how with the laser cutting machine. Fundings are from  INRAE  ( SPE Department ) and  Université Côte d'Azur  ( IDEX UCA-JEDI) which supported Mélina COINTE 's PhD.
Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e. g. , anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH 4 emissions, over the updated 2015-2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.
However, this can be logistically and computationally difficult when dealing with complex processbased models. Despite the important insights gained from complementary BU and TD emission estimates, it should be noted that comparisons with the official reported is not always straightforward. BU estimates often share common methodology and input data, and, through harmonization, structural differences between BU estimates and NGHGIs can be bridged. However, the use of common input data, albeit to varying extents, restricts the independence between the datasets and, from a verification perspective, may limit the conclusions drawn from the comparisons. On the other hand, TD estimates are constrained by independent atmospheric observations and can serve as an additional, almost fully independent quality control for NGHGIs.
Non-Annex I parties will be required to follow the 2006 IPCC guidelines and provide regular (biennial) national GHG inventory reports to the UN-FCCC, alongside developed countries, which will continue to submit their inventories on an annual basis. Some developing countries will face challenges to construct and subsequently update their NGHGIs and meet the more-stringent reporting requirements. The work presented in this paper covers dozens of distinct datasets and models, in addition to the individual country submissions to the UNFCCC of the EU member states and the UK. As Annex I parties, the NGHGIs of the EU member states and the UK are consistent with the general guidance laid out in  IPCC (2006)  yet still differ in specific approaches, models and parameters, in addition to differences underlying activity datasets.
A comprehensive investigation of detailed differences between all datasets is beyond the scope of this paper, though systematic analyses have been previously made for specific sectors (e. g. , agriculture;  Petrescu et al. , 2020)  and by the Global Carbon Project CH 4 and N 2 O syntheses  (Saunois et al. , 2020; Tian et al. , 2020). The focus of this paper is on updates of the information from  Petrescu et al. (2021) , discussing whenever needed the changes in terms of emissions and trends. The data from  Petrescu et al. (2021)  are labeled as v2019, while the latest results are labeled as v2021. Except for one on N 2 O, the global inversions did not provide an update for v2021, and, therefore, the earlier results are incorporated into this synthesis. As  Petrescu et al. (2021)  is the most comprehensive comparison of the NGHGI and research datasets (including both TD and BU approaches) for the EU27 + UK to date, the focus of the current paper is on improvement of estimates in the most recent version in comparison with the previous one, including changes in the uncertainty estimates and identification of the knowledge gaps and added value for policy making.
Such exercises of yearly updates are needed to improve the different respective approaches and furthermore can inform the development of formal verification systems. Official NGHGI emissions are compared with research datasets, including necessary harmonization of the latter on total emissions to ensure consistency. Differences and inconsistencies between emission estimates were analyzed, and recommendations were made towards future evaluation of NGHGI data. It is important to remember that uncertainties provided by the NGHGIs are intended to be used in prioritization and decision-making  (vol. 1, chap. 3, IPCC, 2006)  and not to enable comparisons between countries or other datasets. In addition, individual spatially disaggregated research emission datasets often lack quantification of uncertainty.
The referenced data used for the figures' replicability purposes are available for download at https://doi. org/10. 5281/zenodo. 7553800 (Petrescu et al. , 2023). Upon request, the codes necessary to plot the figures in the same style and layout can be provided. The focus is on EU27 + UK emissions. In the VERIFY project, an additional web tool was developed which allows for the selection and display of all plots shown in this paper (as well as the companion paper on CO 2 ), not only for the EU member states and UK but for a total of 79 countries and groups of countries in Europe (Table  A1 , Appendix A). The data, located on the VERIFY project website (http://webportals. ipsl. jussieu. fr/VERIFY/FactSheets/, last   Figs. 10, 11, 14 and B2 (b)  for N 2 O) Waste (NGHGI, GAINS, EDGARv6. 0) a For consistency with the NGHGI, here we refer to the five reporting sectors as defined by the UNFCCC and the Paris Agreement decision (18/CMP.
(2017)  found that eutrophic, central European lakes show CH 4 emission rates which are about 1 order of magnitude higher than those of oligotrophic boreal lakes, and this study's model results are consistent with it. The N 2 O anthropogenic emissions from inventory datasets belong predominantly to agriculture and are associated with two main categories: (1) direct emissions from the agricultural sector where synthetic fertilizers and manure were applied, as well as from manure management, and (2) indirect emissions on non-agricultural land and water receiving anthropogenic N through atmospheric N deposition, leaching and runoff (also from agricultural land). Additional anthropogenic emissions result from industrial processes, in particular, adipic and nitric acid production, which are declining owing to the implementation of emission abatement technologies.
CH 4 and N 2 O emission data from inversions Atmospheric inversions optimize prior estimates of emissions and sinks through modeling frameworks that utilize atmospheric observations as a constraint on fluxes. Emis-A. M. R. Petrescu et al. :  The consolidated European synthesis of CH 4 and N 2 O emissions for the EU27 + UK sion estimates from inversions depend on the dataset of atmospheric measurements and the choice of the atmospheric model, as well as on other inputs (e. g. , prior emissions and their uncertainties). Inversion results were taken from original publications without evaluation of their performance through specific metrics; e. g. , fit to independent cross-validation atmospheric measurements  (Bergamaschi et al. , 2013 (Bergamaschi et al. , , 2018;; Patra et al. , 2016). Some of the inversions allow for explicit attribution to different sectors, while others optimize all fluxes in each grid cell and then attribute emissions to sectors using prior grid cell fractions (see details in  Saunois et al.
, 2020, for global inversions). For CH 4 , the same set of 9 regional inversions and 22 global inversions as listed in Table  3  and presented in  Petrescu et al. (2021)  was used. While many different inversions exist, it should be stressed that the variants are not completely independent of one another. Table  B4  in Appendix B in  Petrescu et al. (2021)  illustrates this by documenting to what extent the transport models, priors and atmospheric measurement data vary between the inversion datasets. The subset of InGOS inversions  (Bergamaschi et al. , 2018b)  belongs to a project where all models used the same atmospheric data over Europe covering the period of 2006-2012. The global inversions from  Saunois et al. (2020)  were not updated for this work and cover a period until 2017. The regional inversions generally use both higherresolution prior data and higher-resolution transport models, and, e.
The total NGHGI estimates include emissions from all sectors (excluding LULUCF) and are plotted and compared to fluxes from global datasets, BU models and inversions. There is a good agreement noted in absolute total values between inventories, as well as between regional and global inversion ensembles, but uncertainties (min/max ranges) are large. This match can be explained by interdependencies in input data (activity data and emission factors, AD and EFs) for the BU estimates  (Petrescu et al. , 2020)  and similar prior information used by inversions  (Petrescu et al. , 2021). In Fig. 1 , hatched transparent bars represent the 2011-2015 mean while color-filled bars represent the new updated 2015-2019 mean values. For GAINS and some inversions that do not have annual estimates for all 5 years, only the average of available years is calculated (e.
(2020)  to be consistent with UNFCCC NGHGI (2018) data, EDGARv6. 0 reports higher estimates than EDGARv5. 0 (∼ 8 % higher) and falls outside the 9. 6 % UNFCCC uncertainty range. Over the 1990-2019 period, the trends in emissions agree well between the two BU datasets and NGHGI, showing linear trend reductions of 40 % for EDGARv6. 0 and 36 % for GAINS and NGHGIs. The average yearly reduction trend was 2 % yr -1 for all three data sources. Sectoral time series of anthropogenic CH 4 emissions (excluding LULUCF) and their means are shown in Fig. 3be. For the energy sector (Fig. 3b ), both EDGARv6. 0 and GAINS agree in trends with the NGHGI thanks to updated methodology that derives emission factors and accounts for country-specific information about associated petroleum gas generation and recovery, venting and flaring  (Höglund-Isaksson, 2017).
The differences between inversion results in the current study and  Petrescu et al. (2021)  can be summarized   2021 ), current study. Anthropogenic emissions from these inversions are obtained by removing natural emissions and biomass burning from total TD CH 4 emissions shown in Fig. 4a  and b. UNFCCC NGHGI (2021) reported uncertainties computed with the error propagation method (95 % confidence interval) were calculated for each year of the time series and represent the gap-filled harmonized member-state-reported uncertainty for all sectors (including LULUCF). The time series mean was computed for the common period of 2005-2018 between datasets (excluding InGOS). as follows: for the version used in this study, FLExKF-CAMSv19r_EMPA, the background mole fraction was taken from a global CAMSv19r assimilation run with assimilation of surface observations of CH 4 only (no satellite data) where the domain was cut out following the two-step approach of  Rödenbeck et al.
, 2020)  and is post-processed with prior ratio estimates for wetland CH 4 emissions (Appendix B2, Table  B4 ). For the common period between datasets (2010-2016), the two ensembles of regional and global models give a total CH 4 emission mean (Fig. 6a ) of 23 Tg CH 4 yr -1 (GOSAT) and 24 Tg CH 4 yr -1 (SURF) for the EU27 + UK compared to 19 ± 2. 3 Tg CH 4 yr -1 of NGHGI (Fig. 6a ). The mean of the natural wetland emissions from the global inversions is 1. 3 Tg CH 4 yr -1 and partly explains the positive difference between total emissions from inversions and NGHGI anthropogenic emissions. To quantify the European TD anthropogenic CH 4 component, the GCP inversion wetland emissions and those from geological, inland water sources and biomass burning emissions (reported by the global inversions) were subtracted from the total CH 4 emissions (Fig.