dataesr/scientific-paragraphs-categorization huggingface dataset:
For labelling, cells were incubated with phosphate-free-DMEM containing 10% dialysed FBS and 14 µCi/mL [ 32 P]-orthophosphate (Perkin Elmer) for 2 h. The medium was then replaced by isotope-free medium and cells were harvested directly in TriPure Reagent (Roche) 3 h post-labelling. Total RNA was extracted using TriPure Reagent standard procedure and dissolved in formamide. 1 µg of total RNA was denatured at 75 °C for 10 min and separated in a 1% agarose-formaldehyde-tricine/ triethanolamine gel. The gel was dried for 2 h at 80 °C under a vacuum. Labelled rRNAs were visualised by autoradiography using ImageQuant TL software (GEHealthCare) on a Typhoon PhosphorImager, (GE HealthCare). Isotope signal was normalised to ethidium bromide signal for 28 S and 18 S rRNA bands, and quantified using ImageJ (build bad6864e55 -https://imagej.
4 h after the last treatment, mice were sacrificed and tumours were collected for subsequent analysis. Total RNA was isolated using the TRI REAGENT protocol (SIGMA T9424) followed by a clean-up step (RNeasy Micro, QIAGEN). rRNA were gel-purified as described above. Animal experiments were performed within French guidelines for experimental animal studies, under DSV agreement A34-172-13. The maximal authorized tumour burden was 1500 mm 3 , and was not exceeded during the course of the study. Colorectal human tumour samples. Patient tumours were collected and snap frozen. Total RNA was isolated using the TRI REAGENT protocol (SIGMA T9424) followed by a clean-up step (RNeasy Micro, QIAGEN). rRNAs were gel-purified as described above. Human samples were used under clinical agreement #NCT01577511 under the authority of Nîmes Carrémeau University Hospital.
pFL-IV-Luc+ vector was obtained by removing the LucR coding sequence from the pIRES vector. Translation assays were performed as follow: cells were seeded at 1000 cells/well in 96-well plates. Plasmids were transfected 24 h later with X-tremeGENE HP™ reagent (Roche Diagnostics) at 1:10 ratio with 200 ng of DNA according to manufacturer instruction. 24 h after transfection, cells were treated with 5-FU at 10 µM or 50 µM for 24 h. Luciferase activities were measured using the Dual-Luciferase reagent (Promega) on Spark plate reader (Tecan). Results were obtained from background-subtracted values, and ratios were calculated for each well. Data were normalized to the untreated condition (NT). Cell growth and viability assays. HCT116 were seeded onto 96-well plates at 3,000 cells/well. 48 h after seeding, cells were treated for 48 h with 10 µM 5-FU alone or in combination with 5 µM NVP-AEW541 (Sigma-Aldrich), or with DMSO as a control, and for an additional 72 h with 5 µM NVP-AEW541 alone, and cell viability was evaluated by MTT assay (Cell Titer Aqueous One Solution Cell Proliferation Assay, Promega) according to the manufacturer's protocol.
Cell growth was monitored in real-time using the xCELLigence technology (ACEA Biosciences), based on electric impedance generated by cells attached to the well. Signals were normalised against the time obtained with IGF-1 (5 or 10 ng/mL (Peprotech)) treatment. The growth rate was calculated over 72 h as the slope under the curve. Statistics and reproducibility. Except for the bioinformatic analysis of translatomic data detailed above, statistical analysis was performed using the Prism software (version 7. 0. GraphPad). A two-tailed unpaired student t-test was used for evaluating significance. IF, FISH and TEM experiments were performed at least 3 times, and images are representative of several fields observed for each experiment. Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Sandra Ortiz  ( Cancer Research Center of Lyon ) for helpful discussions. We thank  E. Errazuriz  ( Centre d'Imagerie Quantitative Lyon-Est ) and  C. Vanbelle  ( plateau d'imagerie du CRCL ) for technical help. We thank  Véronique Pantesco  and and  Saïd Manship  for editing the manuscript. G. T. and S. N-S. were recipient of a Ph. D. fellowship from la  Ligue Nationale Contre le Cancer and from "La Fondation pour la Recherche Médicale ". M. G. was supported by  ANR  Grant  "INVADE" PC201507. The project was supported by the  "Ligue Contre le Cancer ,  comités départementaux de la Drôme, du Rhône, du Puy-de-Dôme , et de l'Allier", by  Agence Nationale de la Recherche  (grant  RiboMeth -ANR-13-BSV8-0012 , grant  PLAsCAN  - ANR-17-CONV-0002 ), by the  Institut National du Cancer (INCa , grant  FluoRib -PLBIO18-131 ) and by  Programmes d'Actions Intégrées de Recherche (PAIR Sein, RiboTEM ,  ARC_INCa_LNCC_7625 ) to J-J.
D. This work is supported by the  ERiCAN program of Fondation MSD-Avenir  (Reference DS- 2018-0015 ). J. -J. D. is part of the  DevWeCan Labex Laboratory. We thank the ATGC bioinformatic platform for hosting computational analyses (support from  PIA France Génomique  and from  Institut Français de Bioinformatique  ( ANR-11-INBS-0013 )). E. R. and J. R. were supported by the  GEM   Flagship  project funded from  Labex NUMEV  ( ANR-10-LABX-0020 ). E. R. , N. D. V. , A. V. , J. P. and F. C. are CNRS research fellow. A. D. , J. -J. D. , and V. M. are Inserm research fellows.
Standardized data definitions are necessary for the quantification of quality of care and patient outcomes in observational studies and randomised controlled trials (RCTs). The European Unified Registries for Heart Care Evaluation and Randomised Trials (EuroHeart) project of the European Society of Cardiology (ESC) aims to create pan-European data standards for cardiovascular diseases and interventions, including transcatheter aortic valve implantation (TAVI).
11, 12 Besides, VARC harmonizes outcome definitions, but there remains a need to standardize the methods by which the processes of TAVI care delivery are captured. Such a harmonization across TAVI registries, randomised controlled trials (RCTs), and quality improvement initiatives, improves the efficiency of data collection and help generate new knowledge relevant to TAVI. The American College of Cardiology (ACC) and the American Heart Association (AHA) have developed data standards for a range of cardiovascular diseases, but no ACC/AHA data standards exist for TAVI. 13 he European Unified Registries On Heart Care Evaluation and Randomised Trials (EuroHeart) is an ESC initiative that aims to improve the quality of cardiovascular care through the continuous capture of individual patient data by standardizing the definitions of variables and offering a common IT platform for data collection, analysis, and reporting.
Working group and reference group The Working Group for the development of the 2022 EuroHeart data standards for TAVI included 29 members from 12 countries. The Group comprised members of the EuroHeart Data Science Group and nominees from the EAPCI, EACVI and the Working Group on Cardiovascular Surgery, as well as leaders of existing TAVI registries and a patient representative. Further 19 experts from 18 countries formed the Reference Group (Acknowledgment) and provided feedback on the selection and definitions of the included variables in the EuroHeart TAVI dataset.
European Unified Registries On Heart Care Evaluation and Randomised Trials data standards methodology We followed the EuroHeart methodology for cardiovascular data standard development. 16 In brief, this involved: (1) identification of the cardiovascular domain for the development of data standards; (2) conduction of a systematic review of the literature to synthesise a list of 'candidate' variables; (3) selection and prioritisation of variables by domain experts using a modified Delphi method; (4) Reference Group feedback, and (5) programming the final data variables into the EuroHeart IT platform. 16.
Variable level In EuroHeart, variables are classified as Level 1 variables if they are needed for the assessment of the quality of care (for example, information that would be required for the calculation of quality indicators), risk stratification, case-mix adjustment, or clinical outcomes. The Level 1 variables are defined in the EuroHeart data standard documents and are programmed into the EuroHeart IT platform. Level 2 variables collect information that may prove useful for quality assessment and observational, or randomised research, but which may not be universally available, routinely used, or a pre-requisite for the evaluation of quality. The Level 2 variables are defined in the EuroHeart data standard documents but are not implemented into the EuroHeart IT platform. For the countries which adopt the EuroHeart IT platform, a third level of variables can be programmed into the platform to address unique centre-or country-specific requirements.
Scope The Working Group agreed that the EuroHeart-TAVI registry should capture pre-procedural care processes as well as procedural details, inhospital events and follow up data. As far as possible, the data definitions were aligned with these collected in other EuroHeart registries including heart failure, acute coronary syndrome, PCI and atrial fibrillation  14, 15  allowing re-utilization of existing information and avoiding duplication in different registries. Thereby, the TAVI registry may, especially in the Euro-Heart IT-platform, be integrated with these databases to form a unified system for data collection and quality improvement (Figure  1 ).
Systematic literature review The EuroHeart Data Science Group conducted a systematic review of the literature to identify a list of candidate variables for potential inclusion in the EuroHeart TAVI dataset. The search included two databases (Embase and MEDLINE via OVID®) and captured peer reviewed RCTs and prospective observational studies that provided definitions for at least one variable relevant to TAVI diagnosis, management, or outcomes published between 01 January 2011 and 23 July 2021 (Appendix). Two reviewers (SA and GB) assessed retrieved articles against a pre-specified eligibility criteria and extracted potential variables from included studies. Existing TAVI registries  17  and Clinical Practice Guidelines 3 were also reviewed for potential data variables relevant to TAVI.
In-hospital data This domain captures the findings from echocardiography undertaken after the TAVI procedure and before hospital discharge (Supplementary material online, Table  S1 ). In-hospital events that occur after the TAVI procedure are also recorded here. These events include cardiac interventions during the same admission, in-hospital myocardial infarction, stroke, bleeding or death (Supplementary material online, Table  S1 ). The definitions of these events are aligned with the VARC-3 criteria. 10.
Discussion The EuroHeart data standards for TAVI processes of care and inhospital outcomes comprise 93 mandatory (Level 1) and 113 optional (Level 2) variables across 10 TAVI domains. They set forth an internationally agreed and ESC endorsed standards for the conduct of observational studies and RCTs that are relevant to TAVI, provide a platform for monitoring the effectiveness and safety of new devices and treatment strategies, and align with published EuroHeart data standards for other cardiovascular diseases. 14, 15 The standardisation of the definitions of TAVI-related data variables enables the conduction of international comparative analyses and facilitates the development of a comprehensive registry for various forms of valvular heart disease regardless of its treatment strategy. Despite previous efforts, there remains a need for a harmonised, internationally endorsed and feasible set of data standards for TAVI encompassing both processes of care and key outcomes.
9, 11 For instance the ACC and the Society of Thoracic Surgeons (STS) Transcatheter Valve Therapy (TVT) registry comprises 216 variables, 160 of which are mandatory. 25 To reduce the burden of data collection, a recent collaboration between various stakeholders in the US was established to develop a 'Minimum Core Data Elements' for TAVI. 26 However, this dataset defined 132 mandatory variables and maintained an additional 35 variables as mandatory in the ACC/STS TVT registry increasing the total number to 167. 26  Moreover, the definitions of the variables in the updated ACC/STS TVT registry have not been fully aligned with these in other relevant registries. For example, the definition of cerebrovascular disease varies between the ACC PCI  27  and TVT  28  registries limiting the opportunity to combine data from different resources.
6] [7] [8]  However, the heterogeneity in the data standards between these registries hampers the conduction of pragmatic registry-based RCTs (R-RCT) and the performance of post-marketing surveillance of new TAVI technologies across borders. 9 Furthermore, the lack of harmonisation between national TAVI registries and existing databases for other cardiovascular diseases (e. g. PCI) results in redundancy and unnecessary duplication of healthcare data collection. 29 EuroHeart provides an umbrella under which standardised data definitions are developed and harmonised across various cardiovascular disease domains. 16 he data standards for TAVI presented in this document have been developed in collaboration with the EAPCI the EACVI and the ESC Working Group on Cardiovascular Surgery and with involvement of a patient representative.
10 Additionally, the implementation of the developed variables into a web-based interface may facilitate the uptake of the data collection system. 31 The EuroHeart-TAVI data standards comprise key processes and in-hospital outcomes as Level 1 variables which are embedded into an IT platform, with additional variables that capture follow up data as Level 2. We anticipate that the EuroHeart-TAVI registry will provide the means for a wider valvular heart disease registry that capture data relevant to various treatment strategies. We recognise that countries may be interested in adopting the Eu-roHeart definitions and not implementing the EuroHeart-IT platform. Those countries can, of course, use the data standards presented in this document and modify their existing registries accordingly. Whilst long-term follow up outcomes are developed as Level 2 variables in the EuroHeart-TAVI dataset, harmonized clinical outcomes will be an integral component of the EuroHeart Data Standards for Clinical Outcome module which is currently under development.
Acknowledgments We thank the  ESC Association of Cardiovascular Nursing & Allied Professions (ACNAP) , the  ESC Patient Forum , the  ESC Committee for Young Cardiovascular Professionals  for their endorsement of the EuroHeart Data Standards for TAVI. We also thank the following experts for being part of the Reference Group for the 2022 EuroHeart Data Standards for TAVI: Vilhelmas Bajoras (Lithuania),  Darko Boljevic  (Serbia),  Haim Danenberg  (Israel),  Nicolas Dumonteil (France) ,  Andrejs Erglis (Latvia), Alan Fraser (UK) ,  Ingibjorg J. Gudmundsdottir  (Iceland),  Raban Jeger  (Switzerland),  Gudrun Lamm (Austria), Ivo Petrov (Bulgaria), Elton Pllaha (Malta) ,  Andreas Rück (Sweden) ,  Magnus Settergren (Sweden), Oleg Polonetsky (Belarus) ,  Peter Smits (Netherland) ,  Nikoly Stoyanov (Bulgaria) ,  Konstantinos Toutouzas (Greece) ,  Wojciech Wojakowski (Poland) ,  Jonathan Yap  (Singapore).
Forty-nine colonies were purified and stored in 30% of glycerol/TY medium at À80 C. Bacteria isolated from nodules or by direct plating were tested for presence of the nodD gene. We used the Y5 and Y6 primer set developed by  Zeze et al. (2001)  amplifying an 850 bp nodD fragment from Rlv, Rhizobium leguminosarum sv. trifolii and Ensifer meliloti. Strains showing an amplification (18/29 from nodule trapping and 2/49 from direct plating) were then used for gyrB amplification by using primers and protocols already described  (Martens et al. , 2008). Sequences (Table  S2 ) were trimmed and aligned with DAMBE version 6  (Xia, 2017)  and neighbour-joining phylogeny in comparison with other Rhizobium species (Table  S3 ; sequences retrieved from GenBank https://www. ncbi. nlm. nih. gov/genbank), was inferred with MEGA 5  (Tamura et al.
, 2011)  with pdistance model. Three strains isolated from wheat roots, FWPou15, FWPou32 and FWPou38 were selected for further analysis and their genomes were sequenced and analysed as described in Method S3 and Table  S4. Genomes are available in GenBank under the accession numbers JACBGR000000000, JACBGQ000000000 and JACBGP000000000. Hierarchical clustering and heatmaps were built using the pheatmap R package (clustering 'maximum', method 'wardD. 2'). Phenotypic assays were performed on two of these three strains in comparison with the A34 strain used as control (8401 pRL1JI in the study by  Götz et al. , 1985; Oono & Denison, 2010).
Metabolic pathways and comparative genomics analyses A metabolic reconstruction in SBML format  (Hucka et al. , 2003)  was built for each strain with the Carveme software  (Machado et al. , 2018) , a binary matrix containing the presence/absence information for each metabolic reaction in each strain was created from SBML files using ad hoc Java programmes. A correspondence analysis was performed with the dudi. coa method of the R ade4 package  (Dray & Dufour, 2007). The correspondence analysis result was then plotted using the scatterD3 R package. Tryptophan metabolism was analysed with the kbase platform  (Arkin et al. , 2018)  and by using the RAST annotation pipeline  (Brettin et al. , 2015)  and the Model Seed metabolic reconstruction pipeline  (Henry et al. , 2010). Comparative genomic analysis was performed using snippy (https://github.
Wheat root development assays Three-day old germ-free wheat seedlings (Method S1) were placed on 20 Â 20 cm plates containing Farhäeus agar medium (Figure  S2 , Method S5). Seedlings were grown 4 days prior to inoculation with 1 ml of bacterial suspension (OD 600nm = 0. 08) of each strain (see Results section for the bacterial strain used). Control plants were inoculated with 1 ml of sterilized water. The number of experiments (temporal blocks) and plant replicates are shown in Table  S8. Plants were grown at 20 C with a light/dark period of 16 h/8 h. Roots were removed from the plates 12 dpi, washed with water to remove traces of agar, and imaged with an Epson Expression 10000XL scanner. Total root length was estimated by using WINRhizo software and the number of lateral roots was counted manually on the scanned roots.
, 2021). WSM1689 (R. leguminosarum symbiovar trifolii) and E. meliloti 1021 were used as outgroups. Rlv strains are wheat root endophytes We quantified the endophytic and epiphytic wheat root colonization abilities of two strains, FWPou15 and FWPou38, by measuring colony-forming units (cfu) in wheat roots following root surface-sterilization (S) or without root surface-sterilization (NS). Colonization was assessed 4, 7, 14 and 21 days post inoculation (dpi) with single strain inoculations on two T. aestivum varieties, Energo and Numeric, in gnotobiotic conditions. The two strains were detected in both NS and S conditions (Figure  S7a ), suggesting that they can both colonize roots externally and internally. The data also showed that maximal colonization was reached a few days after inoculation.
ACKNOWLEDGEMENTS The authors thank  Adrien Jallais ,  Thomas Py  and  Camille Ribeyre  for their support during the wheat phenotypic assays,  Dorian Guetta , who initiated the in vitro wheat root colonization studies,  Joëlle Fournier  who built the pHC60 derivatives, and the  CREABio Research Station (Auch, France)/Enguerrand Burel  and  Laurent Escalier  who allowed us to sample wheat and provided data about soil composition of the field plots. The authors also thank  Julie Cullimore  and  Clare Gough  for critical reading of the manuscript. This work was supported by the projects  IDEX UNITI 'RHIZOWHEAT' ,  INRAE Department of Plant Health and Environment (SPE)  ' DIBAM ',  ANR  ' WHEAT-SYM'  ( ANR-16-CE20-0025-01 ),  ANR  ' GRaSP ' ( ANR-16-CE20-0021-03 ). This study was set within the framework of the  Laboratoires d'Excellences (LABEX)  ' TULIP'  ( ANR-10-LABX-41 ) and of the  Ecole Universitaire de Recherche (EUR)  ' TULIP-GS'  ( ANR-18-EURE-0019 ).
Funding information Grant/Award Number :  ANR-18-EURE-0019 ;  Laboratoires d'Excellences (LABEX) , Grant/Award Number:  ANR-10-LABX-41 ;  ANR GRaSP , Grant/Award Number:  ANR-16-CE20-0021-03 ;  ANR WHEATSYM, Grant/Award Number :  ANR-16-CE20-0025-01 ;  INRAE Department of Plant Health and Environment (SPE) ;  IDEX UNITI , Grant/Award Number: RHIZOWHEAT.
DATA AVAILABILITY STATEMENT Amplicon sequences are available in Table  S2  (Supporting Information). Genomes of Rlv are available in GenBank under the accession numbers JACBGR000000000, JACBGQ000000000 and JACBGP000000000. All raw data necessary to confirm our results are available in Supplementary Tables. Scripts and pipelines for data analysis and R figures are available in Supplementary Methods. Rlv bacterial collection is available at the LIPME laboratory (INRAE, Toulouse) under request to the corresponding author.
Analysis was performed on two datasets: A34-FWPou15 and IAUb11-FWPou38, as values for controls (plantlets inoculated with water) were different in the two datasets. Significant results obtained after FDR correction are reported in bold. Abbreviations: Chisq, value of the type II Wald chi squared, Df, degree of freedoms. T A B L E 3 Analysis of deviance on the variation of the total root length and number of lateral roots obtained after inoculation with the WT A34, FWPou15, FWPou38 or IAUb11 Rlv strains Energo and numeric Energo and numeric Total root length N lateral roots Total root length N lateral roots A34 and FWPou15 IAUb11 and FWPou38 Factors Chisq Df p value Chisq Df p value Factors Chisq Df p value Chisq Df p value Strain 27. 89 2 9e-07 24. 12 2 6e-06 Strain 1. 61 2 0. 446 11. 87 2 0.
MCA allowed us to find patterns in two space-time datasets that are highly correlated using a cross-covariance matrix  (Lian et al. , 2020). The goal of this analysis was to identify the most important environmental drivers associated with NEE, GPP, and ER across all the sites over time. MCA is appropriate for this study as it can handle data with gaps and unequal lengths in the datasets. Finally, to evaluate the water balance at different times of the season, we estimated the difference between Potential Evapotranspiration (PET) and the actual ET, and the difference between precipitation (PPT) and ET for each of the sites, years, and months (e. g. June, July, and August). This study did not attempt to describe the long-term temporal changes in the anomalies of snowmelt and carbon fluxes, given the short data record available for some of the sites (i.
Site description A total of 11 eddy covariance flux tower sites across the Arctic were used in this study, where each site had at least six summers of flux data available (SI, Table  S1 ). Ecosystem-scale CO2 fluxes were estimated using the eddy covariance method  (Burba et al. , 2008 , Burba et al. , 2012 , Burba et al. , 2013). Details pertaining to the sites, data processing, and gap-filling are provided in the SI Appendix, Table  S1. All sites are located in continuous permafrost tundra regions. The vegetation in the tower sites, the instruments used to measure fluxes, the average environmental conditions at each site, the datasets used in this study for each site, and the references describing the sites are indicated in SI Appendix, Table  S1. As shown in Fig. S2 , the Bowen ratio reported for this study showed similar values to what previously reported during the growing season months in the Arctic (from 3.