dataesr/scientific-paragraphs-categorization huggingface dataset:
Western blot and immunolocalization analyses of ERFVII stability Western blots were carried out as previously described  26. Primary antibodies used were anti-HA (Sigma, H3663-200UL; 1:1000 dilution), anti-FLAG (Sigma, F1804-200UG, 1:2000 dilution) and secondary antibody Goat anti-Mouse IgG1, HRP from Thermo Fisher Scientific, PA1 74421, (1:10000 dilution). Full scan uncropped Western blots are provided (Source Data 1). Immunolocalization of RAP2. 3 was carried as described  70. Four-day-old seedlings containing the 35S:RAP2. 3 3XHA transgene were treated for 3 h with 50 μM bortezomib or DMSO (control). Anti-HA primary antibody (Roche, Lewes, UK) and Alexa-Fluor-488-coupled anti rat secondary antibody (1:200 dilution) (Molecular Probes, Carlsbad, CA) were used for detection. Seedlings were counter stained using Propidium Iodide and visualised using confocal microscopy.
The LTQ FT Ultra mass spectrometer was equipped with a standard ThermoFisher nanoESI source through which a 1. 8 kV voltage was applied to the Picotip emitter. The inlet capillary of the mass spectrometer was held at 275 °C with a tube lens value of 145 V. For the identification of peptides/proteins the mass spectrometer was operated in data dependent acquisition (DDA) mode on the five most intense ions per each survey scan. Ions were isolated in the linear ion trap within a window of m/z 8 and subjected to collision induced dissociation. Charge-state rejection of +1 ions was employed. The data were submitted to Search GUI database searches and results visualised in Peptide Shaker  72, 73. Parameters within Search GUI were as follows: MS Convert was used to generate. mgf data files using a 0.
5 minimum peak spacing setting; X! Tandem was used for database searching with the following settings: Enzyme = Trypsin, Missed Cleavages = 2, Fixed Modifications = None, Variable Modifications = Carbamidomethylation of C, Oxidation of M, Precursor Tolerance = 0. 5 Da, Product Tolerance = 0. 5 Da, Fragment Ion Type = b/y, Charge State Range = +1 to +5. A FASTA decoy database file containing 250 sequences (forward and reverse) of common contaminant proteins together with the sequences of RAP2. 3 and RAP2. 12 was used for searches. For detailed examination of N-terminal modification of RAP2. 3 and RAP2. 12, data were analysed manually using Xcalibur software (ThermoFischer). Predicted peptides of interest masses and fragmented ion masses were calculated with: http://db. systemsbiology. net:8080/proteomicsToolkit/ FragIonServlet.
Statistical analyses No statistical methods were used in predetermining sample size and experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. For experimental analyses at least three independent replicates with different original biological material are reported for each experiment and each experiment (excluding immunolocalizations) was repeated at least twice. All graphs were produced using Graphpad software (Version 9), which was also used to calculate standard deviation, t-test and one-way ANOVAs.
Data availability Data will to be shared upon request to Michael Holdsworth (michael. holdsworth@nottingham. ac. uk). The RNA-seq data generated in this study have been deposited in the GEO database under accession code GSE224694 (https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSM7029623). The mass spectrometry proteomics data used in this study are available in the ProteomeXchange Consortium via the PRIDE  74  partner repository with the dataset identifier PXD039288. Source data are provided with this paper.
(2020)  observed an increase in the equivalent width of the lines as a function of the activity index R HK (see Appendix A). This effect is visible for stars with log R HK ≥ -5. 0, increases with increasing activity, and produces an artificial growth of the stellar microturbulence and a decrease in effective temperature and metallicity. As part of the activities carried out by the Coordination Unit 8 (CU8) of the Gaia Data Processing and Analysis Consortium (DPAC), we have implemented a method for deriving a stellar activity index using the Ca ii IRT lines observed by the Gaia Radial Velocity Spectrometer (RVS). The method is part of the Extended Stellar Parametrizer for Cool Stars, (ESP-CS) module of the Astrophysical parameters inference system (Apsis)  (Bailer-Jones et al. 2013; Creevey 2022; Fouesneau 2022; Gaia Collaboration 2022).
In this paper we present this method together with the scientific validation of the results available in Gaia DR3. The scientific validation, in particular, provides criteria for discriminating between the purely chromospheric activity regime and the mass accretion regime in T Tauri stars. In Sect. 2 we describe the method implemented, in Sect. 3 we give an outline of the results obtained together with the scientific validation of the data available in Gaia DR3, and in Sect. 4 we draw our conclusions.
Input data The Apsis ESP-CS module takes the RVS average continuum normalised spectra in the stellar rest-frame as input (see Gaia Collaboration 2022; Creevey 2022, for details on the Apsis input data). In order to set up a template spectrum representative of the inactive photosphere, knowledge of the stellar fundamental parameters (T eff , log g, [M/H]) is required (see Sect. 2. 2). These parameters are taken from the output of either the General Stellar Parametrizer from Spectroscopy GSP-Spec module (Recio-Blanco 2022) or the General Stellar Parametrizer from Photometry GSP-Phot module  (Andrae 2022; Liu et al. 2012; Bailer-Jones 2011) ). During the processing, the default value is to use the parameters from GSP-Spec because the activity index is derived from the same data, but when they are not available, the ones from GSP-Phot are used.
Activity index vs T eff Fig. 3  shows the 2D density plot log R IRT vs T eff with input parameters from GSP-Phot and GSP-Spec separately. In the dataset with input parameters from GSP-Phot (Fig. 3 , upper left panel), the high activity part, say for log R IRT -5. 5, is characterised by two almost horizontal branches, bending towards lower R IRT values at decreasing T eff below 4000 K and disappearing below 3500 K. The clustering of R IRT in these three different regimes is further outlined in the histogram shown in Fig. 4. The highest branch (very high activity, VHA, hereafter) has a lower limit between log R IRT -4. 8 and -5. 0, depending on T eff. The second highest branch (high activity, HA, hereafter) has a lower limit between log R IRT -5. 3 and -5. 5, depending on T eff. The limit between the second highest branch and the bulk of low-activity stars (LA hereafter) is at log R IRT -5.
This is outside the scope of the present work and is left for future work, together with the publication of the R HK values from archive FEROS spectra. Here we just aim at verifying the existence of the correlation between R IRT and R HK and at providing some preliminary approximate relationships between the two parameters. Cross-match with the Gaia DR3 activity index leads to 671 objects in common with our R HK estimate from FEROS spectra (362 with GSP-Spec and 309 with GSP-Phot input parameters) and 220 objects in common with the Boro Saikia et al. (  2018 ) dataset. As discussed above (see Figs. 2  and 4 ), R IRT with GSP-Spec input is characterised by a larger dispersion and is more concentrated on the lower activity range, while R IRT with GSP-Phot input has a lower dispersion and extends to a higher activity range.
The cross-match with the FEROS data reflects this situation; in Fig. 7 , showing the R IRT vs R HK dispersion diagram, data with GSP-Spec input covers mostly the lower left part while data with GSP-Phot input covers mostly the upper right part of the diagram. Data with GSP-Spec input is also affected by a larger dispersion than data with GSP-Phot input or the Boro Saikia et al. (  2018 ) dataset. The R IRT with GSP-Phot input is well correlated with FEROS R HK , with a Pearson linear correlation coefficient r = 0. 73 and log R IRT = 0. 68066 log R HK -2. 53461 (8) The R IRT with GSP-Spec input is poorly correlated with FEROS R HK , having r = 0. 31; this is mainly due to the the fact that the GSP-Spec APs in Gaia DR3 concern mostly stars with a low level of activity and therefore a regime in which the activity index signal-to-noise ratio is lower.
The very high activity branch Cross-match of stars in the VHA-branch with the SIMBAD database shows that these objects are mostly classified as PMS stars, including YSO, T Tau, and Orion-type variables. A few RS CVn's are also present. In PMS stars, mass accretion can cause an excess flux in the Ca ii IRT lines that dominates over the chromospheric flux (see Appendix B). Fig. 8  shows the comparison of R IRT with mass accretion rate (log Ṁ) for 17 objects for which an estimate of the mass accretion rate is available. The log Ṁ estimates in Fig. 8  comes from either the Hα width at 10% of the peak  (Natta et al. 2004; Alcalá et al. 2014 ) measured by the Gaia-ESO survey  (Lanzafame et al. 2015)   2  or derived from the excess continuum emission (see, e. g. Manara et al. 2015). The source identification, membership, and log Ṁ references are reported in Table  2.
We find also good correlation with a preliminary R HK estimate made on ESO-FEROS archive spectra, which will be subject of a future work. The correlation of R IRT with the position in the Gaia CMD (Fig. 6 ) is in line with the current knowledge of the activity evolution of low-mass stars (see, e. g. Linsky 2017). Stars in the VHA branch, which according to a cross-match with the SIM-BAD database includes PMS stars and RS CVn systems, are preferentially located above the single star MS, the higher R IRT the higher the tendency to be located at larger distance above the MS. A group of very active early-K stars is found at ∼ 2-4 mag above the MS. Some very active stars are also located close to the single-and binary-star MS. Stars in the HA branch tend to be on the single star MS, with some star on the binary-MS and a few above the MS.
The latter can only be due to a global low level of magneto-acoustic heating of the chromosphere, with the axisymmetry of the surface magnetic field expected to play essentially no role. The evidence points therefore to a global low level of photospheric and chromospheric activ- ity rather than to a high degree of axisymmetry of the surface magnetic field in these fast rotating stars. Moreover, Distefano et al. (2022) , who presented the rotational modulation data in Gaia DR3, find a low correlation between the G variation due to rotational modulation and the (G BP -G RP ) colour variation in LAFR stars, which, combined with the low rotational modulation amplitude and the small chromospheric activity index, strengthen the evidence of the presence of smaller active regions in this branch compared with stars in the HAR branch with similar rotation period.
Non-local thermodynamic equilibrium (NLTE) effects in the line core should also be taken into ing for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https: //archives. esac. esa. int/gaia. The Gaia mission and data processing have financially been supported by, in alphabetical order by country: - de Maeztu') through grant CEX2019-000918-M, the University of Barcelona's official doctoral programme for the development of an R+D+i project through an Ajuts de Personal Investigador en Formació (APIF) grant, the Spanish Virtual Observatory through project AyA2017-84089, the Galician Regional Government, Xunta de Galicia, through grants ED431B-2021/36, ED481A-2019/155, and ED481A-2021/296, the Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), funded by the Xunta de Galicia and the European Union (European Regional Development Fund -Galicia 2014-2020 Programme), through grant ED431G-2019/01, the Red Española de Supercomputación (RES) computer resources at MareNostrum, the Barcelona Supercomputing Centre -Centro Nacional de Supercomputación (BSC-CNS) through activities AECT-2017-2-0002, AECT-2017-3-0006, AECT-2018-1-0017, AECT-2018-2-0013, AECT-2018-3-0011, AECT-2019-1-0010, AECT-2019-2-0014, AECT-2019-3-0003, AECT-2020-1-0004, and DATA-2020-1-0010, the Departament d'.
the Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory; the French Centre National d'Études Spatiales (CNES), the Agence Nationale de la Recherche (ANR) through grant ANR-10-IDEX-0001-02 for the 'Investissements d'avenir' programme, through grant ANR-15-CE31-0007 for project 'Modelling the Milky Way in the Gaiaera' (MOD4Gaia), through grant ANR-14-CE33-0014-01 for project 'The Milky Way disc formation in the Gaiaera' (ARCHEOGAL), through grant ANR-15-CE31-0012-01 for project 'Unlocking the potential of Cepheids as primary distance calibrators' (UnlockCepheids), through grant ANR-19-CE31-0017 for project 'Secular evolution of galxies' (SEGAL), and through grant ANR-18-CE31-0006 for project 'Galactic Dark Matter' (GaDaMa), the Centre National de la Recherche Scientifique (CNRS) and its SNO Gaiaof the Institut des Sciences de l'Univers (INSU), its Programmes Nationaux: Cosmologie et Galaxies (PNCG), Gravitation Références Astronomie Métrologie (PNGRAM), Planétologie (PNP), Physique et Chimie du Milieu Interstellaire (PCMI), and Physique Stellaire (PNPS), the 'Action Fédératrice Gaia' of the Observatoire de Paris, the Région de Franche-Comté, the Institut National Polytechnique (INP) and the Institut National de Physique nucléaire et de Physique des Particules (IN2P3) co-funded by CNES; the German Aerospace Agency (Deutsches Zentrum für Luft-und Raumfahrt e.
Innovació, Universitats i Empresa de la Generalitat de Catalunya through grant 2014-SGR-1051 for project 'Models de Programació i Entorns d'Execució Parallels' (MPEXPAR), and Ramon y Cajal Fellowship RYC2018-025968-I funded by MICIN/AEI/10. 13039/501100011033 and the European Science Foundation ('Investing in your future'); the Swiss State Secretariat for Education, Research, and Innovation through the Swiss Activités Nationales Complémentaires and the Swiss National Science Foundation through an Eccellenza Professorial Fellowship (award PCEFP2_194638 for R. Anderson); the United Kingdom Particle Physics and Astronomy Research Council (PPARC), the United Kingdom Science and Technology Facilities Council (STFC), and the United Kingdom Space Agency (UKSA) through the following grants to the University of Bristol, the University of Cambridge, the University of Edinburgh, the University of Leicester, the Mullard Space Sciences Laboratory of University College London, and the United Kingdom Rutherford Appleton Laboratory //www.
Acknowledgements This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Fundthrough grant  NWO-M-614. 061. 414 , through a  VICI grant  ( A. Helmi ), and through a  Spinoza prize  ( A. Helmi ), and the  Netherlands Research School for Astronomy (NOVA) ; the  Polish National Science Centre through HAR-MONIA  grant  2018/30/M/ST9/00311  and  DAINA grant   2017/27/L/ST9/03221  and the  Ministry of Science and Higher Education (MNiSW)  through grant  DIR/WK/2018/12 ; the  Portuguese Fundação para a Ciência e a Tecnologia (FCT)  through national funds, grants  SFRH/-BD/128840/2017  and  PTDC/FIS-AST/30389/2017 , and work contract  DL 57/2016/CP1364/CT0006 , the  Fundo Europeu de Desenvolvimento Regional (FEDER)  through grant  POCI-01-0145-FEDER-030389  and its  Programa Operacional Competitividade e Internacionalização  ( COMPETE2020 ) through grants  UIDB/04434/2020  and  UIDP/04434/2020 , and the  Strategic Programme   UIDB/-00099/2020  for the  Centro de Astrofísica e Gravitação (CENTRA) ; the  Slovenian Research Agency  through grant  P1-0188 ; the  Spanish Ministry of Economy (MINECO/FEDER, UE) , the  Spanish Ministry of Science and Innovation (MICIN) , the  Spanish Ministry of Education, Culture, and Sports , and the  Spanish Government  through grants  BES-2016-078499 ,  BES-2017-083126 ,  BES-C-2017-0085 ,  ESP2016-80079-C2-1-R ,  ESP2016-80079-C2-2-R, FPU16/03827 ,  PDC2021-121059-C22 ,  RTI2018-095076-B-C22 , and  TIN2015-65316-P  (' Computación de Altas Prestaciones VII') , the  Juan de la Cierva Incorporación Programme  ( FJCI-2015-2671  and  IJC2019-04862 -I for F.
Plot size was 7. 5 m 2 in Clermont-Ferrand, 8. 25 m 2 in Dijon, 6. 34 m 2 in Rennes, and 8. 78 m 2 in Toulouse. Plots were harvested at full physiological maturity during the summers 2015 and 2016, and grain yield was expressed on a 15% water content basis. These data were used to compute two yield stability indices, i. e. , stability across sites and managements, and stability across years (see below).
Lodging resistance It was derived from the quantification of the percentage of the plot surface which was lodged (%LS). This surface was quantified visually using a 5% step scale. Lodging resistance was expressed as (100-%LS), with 0%-highly susceptible, to 100%-fully resistant. Shoot biomass before harvest Shoot (straw and foliage) biomass was harvested from June 1 to June 6, 2015, at the onset of flowering in sub-plots of 50 × 52. 5 cm centered on three rows by uprooting whole plants and by separating shoots from roots. Shoots were dried at 65°C for 72h and weighed. Data are expressed in g m -2. Recovery efficiency of the N-fertilizer by wheat A 15 N labeling experiment was conducted in the 88 experimental plots on the main field experiment in Versailles to quantify the capacity of the monocultures and variety mixtures to exploit the N-fertilizer applied.
Control of yellow rust and Septoria leaf blotch Yellow rust disease levels were assessed three times during the cropping season for the three upper leaves of single stems to catch the dynamics of the epidemic. The date with largest contrast between plots (heading stage) was retained for further analysis. A semi-quantitative scale (10% steps) was used, based on the percentage of the total leaf area covered by sporulating lesions. The number of main stems scored per plot was proportional to the number of cultivars included in the mixture (i. e. , 8 stems per monoculture plot, but 16, 32, and 64 stems in 2-, 4-, and 8-variety mixture plots, respectively) to generate data with sampling efforts that account for mixture complexity. This led to a total of 2688 stems scored over the cropping season.
Data analysis For each ecosystem service, the spatial coordinates (longitude X and latitude Y) of plots, and their squared value, were used to test for possible spatial gradients or border effects. Spatial gradients were detected for 4 services (grain quality, biocontrol of crop pests, regulation of N 2 O production, and shoot biomass) and corrected by retaining the residuals of a linear regression of these services on the 4 spatial variables X, Y, X 2 , and Y 2. To assess tradeoffs and synergies between ecosystem services, Spearman correlations were computed between all pairs of services, either for the 16 pure stands or 72 mixtures, using the PerformanceAnalytics package CRAN 2. 0. 4  (Peterson et al. 2020). Single or multiple linear models and analyses of variance were used to predict each service according to mixture composition in terms of (i) variety number, (ii) functional group number, and (iii) percentage of each functional group.
Acknowledgements We acknowledge the help and inputs of all the members of the  Wheatamix consortium  (the Wheatamix consortium is composed of:  V. Allard ,  B. Andrieu ,  S. Barot ,  J. Borg ,  A Brunet ,  A. A. M. Cantarel ,  C. Cervek ,  F. Coleno ,  Y De Olivera ,  C. De Vallavielle-Pope ,  D. Descoureaux ,  F. Dubs ,  J. Enjalbert ,  E. Forst ,  N. Galic ,  A. Gauffreteau ,  J. Gervaix ,  I. Goldringer ,  M. Hannachi ,  G. Houivet ,  M. Hugoni ,  M. H. Jeuffroy ,  S. Jouanne ,  C. Kerbiriou ,  P. Labarthe ,  J-C. Lata ,  X. Le Roux ,  C. Lecarpentier ,  M. Leconte ,  S. Lemarié ,  L. Lejars ,  B. Lemain ,  F. Leny ,  P. Lusley ,  C. Montagnier ,  A. Niboyet ,  B. Omon ,  R. Perronne ,  F. Poly ,  S. Piaud ,  T. Pommier ,  E. Porcher ,  S. Saint-Jean ,  S. Salmon ,  D. Tropée ,  T. Vidal ).
Data availability Details on the field trial and methodology employed can be found in the method paper on HAL archive (https:// hal. archi ves-ouver tes. fr/ hal-01843 564). The dataset on agroecosystem services generated and analyzed during the current study is available on the INRAE folder of French data-verse: https:// entre pot. reche rche. data. gouv. fr/ datav erse/ inrae. The datasets on variety traits are available through  Cantarel et al. (2021;   https:// acade mic. oup. com/ jxb/ artic le/ 72/4/ 1166/ 59327 89? login= true). Code availability (software application or custom code) The code of the RLQ model and data used for this study are available on the INRAE folder of French data-verse: https:// entre pot. reche rche. data. gouv. fr/ datav erse/ inrae (https:// doi. org/ 10. 57745/ FDNQ3V).
The quantities of fodder (hay or concentrates) provided to the animals are measured daily, and the composition of the fodder (proportion of legumes, chemical composition) is regularly analyzed. Restitutions by manure/slurry spreading are known because they are systematically weighed and analyzed. Finally, meteorological data are collected (temperature, rainfall, nitrogen composition of rainwater) at the site to calculate N deposits. In addition to the data collected by direct observation, we made several assumptions to estimate missing data and quantify all nitrogen flows. For some products without protein content analysis (e. g. , lentils, peas, and einkorn), we rely on data from the open database Feedipedia for crops  (Sauvant et al. , 2013)  and Table  A. 1 in the appendix of  Stark et al.
Leaching losses are estimated using the method developed by Anglade in organic farming systems  (Anglade, 2015) ; we estimate volatilization losses from the synthesis work on livestock waste and emissions by  Peyraud et al. (2012). Data were collected over the period 2011-2015 for the dairy configuration and over the period 2018-2020 for the diversified configuration. The years 2016 and 2017 were transition years between the two configurations and are not included in this study.
This decrease in the sale of animal products was accompanied by an increase in the sale of crop products (+ 40%) due to the prioritization of land use for direct human food production and the choice of a strictly grass diet for ruminants. Fig. 2  shows that sheep and pig production have slightly altered the internal metabolism of the diversified system compared to the dairy system (though cattle remain the majority in the diversified system). Based on the data from the experimental systems, we established the flow matrices used to calculate the various indicators (one matrix per year and per system). The details of the matrices are available in the INRAE DataSet  (Puech, 2021). Table  2  shows that the total activity (TST) of the diversified system is 27% lower than that of the dairy system.
For each location, local mean sowing, anthesis and maturity dates were supplied with qualitative information on vernalization requirements and photoperiod sensitivity (Supplementary Table  S6 ). Popular local winter or spring wheat cultivars were chosen based on the available field experiment data and the dominant type of that region. Modelers were asked to sow at the supplied sowing dates and to calibrate their cultivar parameters against the observed anthesis and maturity dates by considering the qualitative information on vernalization requirements and photoperiod sensitivity. As all locations receive high rates of precipitations or irrigation, we used the same sowing dates for the baseline and future climates. At each site, we considered 1000 kg DM ha -1 of cereal surface residues with a C to N ratio of 80 and Initial soil mineral N were set at 33 kg N-NO 3 -ha -1 and 5.