dataesr/scientific-paragraphs-categorization huggingface dataset:
For magnitudes fainter than 13. 6, Gaia GSP-Spec has about 61 000 stars (reaching G=16. 1 mag), GALAH has about 130 000 stars (20% of the survey, reaching G=18 mag) and APOGEE about 314 000 stars (43% of the survey, reaching G 20 mag). Concerning the nucleosynthesis diagnostics 4  through individual abundance estimates, Gaia DR3 GSP-Spec explores five different nucleosynthetic channels with 13 chemical elements, while GALAH covers seven nucleosynthetic channels with 21 elements, and APOGEE six channels with 24 elements. The goal of this paper is to demonstrate the scientific quality of Gaia's chemical cartography through a chemo-dynamical analysis of disc and halo populations. To this purpose, Sect. 2 presents the data that are used, including (i) DR3 atmospheric parameters and chemical abundances (Sect.
Sub-A&A proofs: manuscript no. 43511corr sequently, Sect. 7 is dedicated to chemo-dynamical relations in solar cylinder populations using individual element abundances, and in Section 8 we use the open clusters population to study chemo-kinematical correlations and the temporal evolution of disc radial chemical gradients. Finally, Sect. 9 summarises the results of our Galactic chemo-dynamical analysis using the Gaia RVS GSP-Spec database. In particular, we discuss the observed chemical markers of Milky Way structure (Sect. 9. 1), disc kinematic disturbances (Sect. 9. 2), and satellite accretion (Sect. 9. 3). This is completed with the examination of the detected chemodynamical trends of the last billion years (Sect. 9. 4) and, finally, with a discussion of the Sun's chemo-dynamical properties in the context of its Galactic environment (Sect.
Stellar atmospheric parameters and chemical data This work makes use of the stellar physical parameters and chemical abundances derived from the Gaia RVS spectra by the GSP-Spec module and available through the astrophysi-cal_parameters table of Gaia DR3. It is worth mentioning that the present work does not use the global metallicity [M/H] derived from BPRP spectra by the General Stellar Parametrizer from Photometry (GSP-Phot) and published in the GaiaSource and AstrophysicalParameters tables (mh_gspphot field). Although GSP-Phot metallicities are suitable for different scientific purposes, their application to large-scale Galactic chemodynamical studies requires a calibration that at the time of writing this paper was not available. GSP-Spec estimates the main atmospheric parameters (effective temperature T eff , stellar surface gravity log(g)  5  , global metallicity [M/H] 6  , and the global abundance of α-elements 7  with respect to iron [α/Fe]), together with the individual abundances of 12 different chemical elements from RVS spectra of single stars.
Astrometric data and distances High-precision astrometric parameters (α, δ, µ α * , µ δ , ) from Gaia EDR3 (Gaia Collaboration,  Brown, A. G. A. et al. 2021)  complement the above-described radial velocities, allowing three-dimensional study of stellar velocity. Thanks to the bright magnitude limit of the spectroscopic sample, the median parallax uncertainty is better than 20 µas for most of our targets and the median uncertainty increases up to a maximum of ∼ 40 µas for the faintest stars with magnitude G 14. Based on these Gaia EDR3 astrometric data, Bailer-Jones et al. (2021) geometric distances, r Geo , are adopted in this study. To test the implications on our analysis of the Galaxy prior (a 3D model of the stellar distribution and extinction) adopted by  Bailer-Jones et al. (2021) , we defined an astrometric control sample (ACS, Appendix A).
Galactocentric positions, velocities, and orbits For the computation of the stellar positions (Galactocentric Cartesian X, Y, and Z positions, and cylindrical radius R) and the Galactocentric cylindrical velocities (V R , V φ , and V Z in a right-handed frame with positive V φ for most of the disc stars), we adopted the Sun's Galactocentric position (R, Z) = (8. 249, 0. 0208) kpc  (Gravity Collaboration et al. 2021; Bennett & Bovy 2019)  and Galactocentric cylindrical velocities  -9. 5, 250. 7, 8. 56 ) km s -1  (Reid & Brunthaler 2020; Gravity Collaboration et al. 2021) , as well as Gaia's α, δ, line-of-sight velocity, and Bailer-Jones et al. (2021) geometric distances for each target. The orbital parameters (actions J R , J φ , J Z , apocenter and pericenter distance R a , R p , maximum distance from the plane reached during the star's orbit Z max ) are computed using the Stäckel fudge method  Binney (2012)  implemented in the Galpy code  (Bovy 2015) , combined with a rescaled version of the McMillan (2017) axisymmetric Galactic potential.
Global chemical properties of the Milky Way The ∼5. 5 million star database of spectroscopic stellar parameters and chemical abundances (see Sect 2. 1), as part of the Gaia all-sky data collection, provides significant radial and vertical coverage of our Galaxy, but also probes a considerable range in azimuth. In this section, we first analyse the global spatial distribution of the stars in the Milky Way and their chemical Article number, page 5 of 49 Fig. 2. Same as Fig. 1  but colour coded with the median of the stellar metallicity [M/H] in each pixel. In the Galactic plane, the higher metallicity values of the thin disc are visible. In the central Galactic regions, a more metal-poor mix of bulge and thick disc populations is present. Far from the disc plane, highmetallicity thin disc stars at low distances from the Sun and more distant metal-poor halo stars are present.
Exploration of the selection function The large spatial coverage of the Gaia chemical database requires a global presentation of the underlying parameter distribution across the Galaxy. In addition, the large variety of stellar types included in the catalogue demands that we understand the underlying spatial distribution across the Kiel diagram. Without entering into a detailed characterisation of the selection function, Article number, page 7 of 49 in this section we explore its impact so as to help DR3 users and to support the chemo-dynamical analysis of this paper.
Vertical velocity oscillations We focus on the Z versus V Z distribution, where vertical wavelike motions have been detected in the form of a snail-like pat-tern especially present when stars are colour-coded according to their azimuthal velocities  (Antoja et al. 2018 ). Here, we analyse the Gaia DR3 GSP-Spec database selecting 3 229 518 stars from the Medium Quality sample (with the above-described additional conditions on the astrometric parameters), requiring that the stars be within |Z| < 1 kpc, |V Z | < 60 km s -1 , and Galactocentric distance range 5. 25 < R < 11. 25 kpc. This region is approximately centred in the smaller volumes of the solar neighbourhood studied previously by  Antoja et al. (2018)  and other authors such as  Bland-Hawthorn et al. (2019). Figure  20  shows the projection of phase space in vertical position and velocity, Z versus V Z.
2019; Feuillet et al. 2020 Feuillet et al. , 2021)). In conclusion, Fig. 28  confirms that the Gaia chemical database possesses the required quality to provide chemical diagnostics of accretion through the abundance ratio of α-elements to iron. Article number, page 23 of 49 A&A proofs: manuscript no. 43511corr Fig. 26. Zoomed-in version of the area enclosed by grey rectangles in Fig. 25  imposing the selection criteria described in  Koppelman et al. (2019). The colour code is saturated in the high-density regions to emphasise the details of the selected areas. No solar neighbourhood area is selected in this figure because it would contain no stars due to the applied selection criteria.
Chemical markers of Milky Way structure The large radial and vertical coverage of the database, together with its substantial azimuthal coverage, allow us to explore the detail of the structure of the Milky Way within an unprecedentedly large volume. Sections 3, 4, and 5 explore the Galaxy's spatial symmetry through chemo-kinematical considerations. While radial and azimuthal substructures are detected (cf. Sect. 9. 2), the Galaxy shows an important degree of vertical symmetry (agreement within 0. 05 dex in [M/H], see for instance Figs. 13  and 14 ) in the region of radial distances of between 5 and 10 kpc from the centre, and within 2 kpc of the plane. Outside this region, selection functions and statistical fluctuations perturb the diagnostics. This vertical symmetry imposes an important constraint on simulations, in the context of the rich accretion history of the Milky Way.
A detailed spatial analysis of chemical fluctuations is nevertheless out of the scope of this performance verification paper. 9. 3. Chemical markers of satellite accretion. The large spatial coverage and large number statistics of the Gaia GSP-Spec database allow a chemo-dynamical study of halo substructure (cf. Sect. 6). From this analysis, which is not intended to be exhaustive, three main conclusions emerge. First of all, halo substructure in the orbital space (cf. Figs. 26 and 31) is clearly observed, extending previous observations (cf. Koppelman et al. 2019)  using Gaia DR2 and ground-based data. Second, the new Gaia chemical data are of sufficient quality to provide chemical diagnostics of accretion through the abundance ratio of α-elements to iron (cf. Fig. 28 ). This opens new horizons for in-depth analysis of accretion debris and stellar streams.
In addition, the present study clearly detects stars with chemical patterns typical of the disc, but on halo orbits. The number of these puffed-up disc stars increases as we move towards the disc region in the energy versus L Z plane. Finally, the chemodynamical properties of the Milky Way system of GCs can also be explored. 9. 4. Chemo-dynamics of the most recent billion years. The Gaia chemical database includes 687 open clusters (c. f Sect. 8) and about 30 000 young stars tracing the spiral arms (cf. Sect. 3). These encode the evolution of the last billion years, anchoring the long Galactic history to the present. On one hand, age estimates of Galactic open clusters allow us to assess the temporal evolution of the metallicity radial gradient of the cluster. Flatter gradients are progressively observed in the younger open cluster populations (cf.
Conclusions This article explores the scientific quality of the Gaia chemical database produced by the GSP-Spec module from RVS spectra and published as part of the third Gaia data release (DR3). Combined with Gaia EDR3 astrometric data and DR3 radial velocities, the all-sky Gaia chemical cartography allows a powerful and precise chemo-dynamical view of the Milky Way with unprecedented spatial coverage. In this framework, chemical abundances carry pivotal information on the origins of the stars. With the exception of some elements that suffer from internal mixing in certain stellar evolutionary phases, the chemical composition of a star shows a preserved pattern of element abundances encoding the conditions of the interstellar medium at the time and place of its forma-tion. Therefore, chemical markers are crucial for validating kinematical and dynamical diagnostics, providing constraints on the properties of matter and their temporal evolution in theoretical modelling and analysis.
In addition, understanding the history of the Milky Way requires the exploration of a complex physico-chemical space that only huge data sets such as the Gaia catalogue can appropriately probe. Large and precise databases not only improve the confidence level of the derived general trends, but also allow researchers to reveal the crucial but rare non-standard populations in distribution tails. Adequately resolving the natural scatter in chemo-dynamical properties of Galactic stellar populations is essential for uncovering the imprint of physical processes of evolution. This scatter is indeed the consequence of star and gas dynamics, of discontinuous star formation histories, and of galaxy accretion, among other mechanisms. The results of the present analysis illustrate how this chemodynamical distribution tail allows us (i) to unveil the pronouncedly flared structure of the disc, (ii) to robustly assess the Galaxy vertical symmetry, (iii) to chemically trace the largescale motions and seismology of the disc, (iv) to uncover galaxy accretion debris and heated disc stars on halo orbits, and (v) to anchor the Galactic history to its present through the young but scarce and ephemeral massive stars and open clusters.
Gaia DR3 chemo-dynamical diagnostics open new horizons before the era of ground-based wide-field high-resolution spectroscopic surveys like WEAVE  (Dalton et al. 2020)  or 4MOST (de Jong et al. 2019) which will provide detailed abundances for millions of Milky Way stars. Moreover, the Gaia satellite allows all-sky, continuous data collection over timescales measurable in years, which is impossible to achieve from the ground. This results in an extraordinary homogeneity of data, and allows Gaia to include detailed bias modelling in its analysis and data-treatment procedures, which is at the core of its precision and observational success. A beautiful illustration of this are the Gaia DR3 heavy element abundances, which at first appeared to be out of reach for the RVS instrumental characteristics.
Acknowledgements. This work presents results from the  European Space Agency (ESA)  space mission Gaia (https://www. cosmos. esa. int/gaia). Gaia data are processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia MultiLateral Agreement (MLA). The Gaia archive website is https://archives. esac. esa. int/gaia. Acknowledgments from the financial institutions are given in Appendix H. Part of the calculations used for this article have been performed with the high-performance computing facility SIGAMM, hosted by the  Observatoire de la Côte d'Azur. 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 Gaia era' (MOD4Gaia) , through grant  ANR-14-CE33-0014-01  for project 'The  Milky Way disc formation in the Gaia era' (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 galaxies' (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 Gaia of 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.
a Time course analysis of Rif1 expression throughout development; whole embryo protein extracts were analyzed by western blotting against indicated proteins, tubulin was used as loading control; MBT (midblastula transition), UF (unfertilized egg). b Quantification of Rif1 abundance in three biological replicates and two technical replicates of western blot series of embryonic whole cell extract, plotted as mean OD normalized to Tubulin, scaled using min and max: xscaled = (xxmin)/(xmax-xmin), Mean with SED, n = 5 (data points). c Time course analysis during the S phase; sperm nuclei were incubated in Xenopus egg extracts and chromatin was isolated for immunoblotting at indicated time points before and during DNA replication. d Rif1 nuclear localization during S phase; sperm nuclei were incubated in egg extracts, rhodamine-dUTP was added at the beginning of the incubation and stopped during the early S phase (30 min), left panel, or for 2 min at the end of S phase (60-62 min), right panel, reactions were stopped and nuclei were isolated and processed for immunofluorescence.
45 µm Immobilon®-P membranes, subsequently incubated with the indicated primary antibodies followed by the appropriate horseradish peroxidase-labeled antibodies (1/10000, Sigma-Aldrich or GE Healthcare, see Supplementary Methods 1). Immunodetection was performed using Super Signal West Pico or Femto Chemiluminescence Kit (Pierce) on a ChemiDoc Touch Imaging System (BioRad). Quantification was done using BioRad ImageLab software. Molecular combing of DNA and detection by fluorescent antibodies. Biotinlabeled sperm DNA was extracted and combed as described  60  after Mock or Rif1 depletion. Biotin was detected with AlexaFluor594 conjugated streptavidin followed by anti-avidin biotinylated antibodies. This was repeated twice, then followed by mouse ssDNA antibody, AlexaFluor488 rabbit anti-mouse, and AlexaFluor488 goat anti-rabbit for enhancement  61.
Images of the combed DNA molecules were acquired and measured as described  60. The fields of view were chosen at random. 4000-6000 DNA fibers (around 400 Mb) were analyzed for each  experiment (Supplementary Table  1 ). Measurements on each molecule were made using Fiji software  62  and compiled using macros in Microsoft Excel. Replication eyes were defined as the incorporation tracks of biotin-dUTP. Replication eyes were considered to be the products of two replication forks, and incorporation tracks at the extremities of DNA fibers were considered to be the products of one replication fork. Tracts of biotin-labeled DNA needed to be at least 1 kb to be considered significant and scored as eyes. When the label was discontinuous, the tract of unlabeled DNA needed to be at least 1 kb to be considered a real gap.
5 kb/min 6 (see also Supplementary methods 2 for summary definitions of replication parameters). Numerical simulations. A Monte Carlo method was used to simulate the DNA replication program in Matlab (versionR2020b); the results were compared to the DNA combing data for Mock-and Rif1-depleted samples by using a genetic optimization algorithm  41, 42. Briefly, in the simulation, the genome is constituted as a vector of 10 6 blocks of value 0 for the unreplicated blocks and 1 for the replicated blocks. Each block represents 1 kb. Potential origins are randomly distributed along the genome with an average density of one origin per 2. 3 kb  63. Initiation is regulated by the encounter between a not activated origin and a limiting factor, whose initial number N 0 linearly increases with a rate J during S phase progression.
Nuclei were spun onto coverslips through 1 ml of 20% sucrose cushion in PBS. Coverslips were blocked in 1xPBS, 0. 2% Triton X100 containing 20% goat serum for 3 h, washed and incubated overnight in the same buffer in the presence of anti-Rif1 serum (1/600)  9. After washing, coverslips were incubated for 2 h at 37 °C in goat anti-rabbit Alexa Fluor 488 (A488) secondary antibody (1/200) in the presence of Hoechst 33258. After washing, mounted coverslips were imaged for Hoechst, rhodamine, and A488 using an Olympus BX63 fluorescence microscope. The Red and Green fluorescence intensity profiles were generated using Fiji software. Quantitative analysis of replication foci was adapted from  64. Statistics and reproducibility. Statistical analysis (GraphPad Prism software, version 8. 3. 0 and Matlab, version R2020b) were performed using statistical tests with p values and sample size as mentioned in the figures, legends and in Supplementary Data.
Acknowledgements We thank  J. Walter  for the gift of XCdc7 antibody. Anti-Xenopus Orc2 antibody, developed by JL. Maller was obtained from the  European Xenopus Resource Centre , curated with funding from the  Wellcome Trust/BBSRC  and maintained by the  University of Portsmouth, School of Biological Sciences. We are grateful to  A. Donval  for her help with frog fertilizations. We thank  B. Miroux  and  J. -L. Ferrat  for critical reading of the paper. This research was supported by grants to OB from  ARC  ( Association pour la Recherche sur le Cancer) ,  Association Retina France ,  Fondation Valentin Haüy , and  UNADEV (Union Nationale des Aveugles et Déficients Visuels)  in partnership with  ITMO NNP (Institut Thématique Multi-Organisme Neurosciences , sciences cognitives, neurologie, psychiatrie)/AVIESAN (alliance nationale pour les sciences de la vie et de la santé).
HTS data analyses. Sequencing reads were quality-trimmed using CLC Genomic workbench software version 21. 0. 3 (Qiagen) or Geneious Prime (Biomatters Ltd, Auckland, New Zealand). Following de novo assembly of contigs, a BlastX analysis was performed against the GenBank non-redundant (nr) protein database restricted to viruses, to identify viral contigs. Sequence datasets were also analyzed by mapping trimmed reads on a collection of reference viral genomes (min length fraction=0. 9; min similarity fraction=0. 7). The initially identified luteoviral contigs were then scaffolded (if needed) and extended by multiple rounds of mapping using residual reads in CLC Genomics Workbench to generate nearly complete genomic sequences. For isolates of known viruses, no further effort was made to fill small internal gaps or the genome terminal ends, but for newly discovered viruses, the genomic sequences were completed as described below.
Obtained RACE products sequenced (Eurofins Genomics, Ebersberg, Germany) using the virus-specific primers. All specific RACE primers used were designed from the sequence of the identified viral contigs and are listed in Supplementary Table  S1. Data mining. To uncover potential new luteoviruses in publicly available RNA-Seq data, we performed an analysis on SRA using Serratus, an open-source cloud computing infrastructure  (Edgar et al. 2022 ) that seeks the closest matched SRA sequences to an input virus using a 102 amino acid (aa) viral RNA-dependent RNA polymerase sequence (RdRp palmprint). The sequence of the contig thus identified from a Prunus humilis SRA from China (SRR12442710) has been deposited in GenBank under the BK061315 accession number. Phylogenetic, recombination and genetic population analyses.
Multiple alignments of nucleotide (nt) or amino acid (aa) sequences were performed using the ClustalW Phytopathology program  (Thompson et al. 1994 ) implemented in Mega 7  (Kumar et al. 2016). Phylogenetic trees were constructed using the neighbor-joining technique with strict nt or aa distances and randomized bootstrapping to evaluate branching validity. Mean diversities, and genetic distances (p-distances calculated on nt or aa identity) were calculated using Mega 7. The RDP4 program  (Martin et al. 2015)  was used to search for potential recombination events in the luteovirus genomic sequences obtained in this study.
Identification of four novel NSPaV infection was also identified in a P. brigantina sample (S9), but the low viral concentration precluded the assembly of large contigs. The infection status of all samples was in all cases validated using virus-specific RT-PCR assays and sequencing of the amplicons. The near-complete genomic sequences of MaLV and NSPaV isolates have been deposited in GenBank under the following accession numbers: ON408233 (NSPaV, P. cerasus), ON408235 (MaLV, P. incisa) and ON408237 (MaLV, P. armeniaca), (Supplementary Table  S2 ). The genomes of ChLVA, MaLV, PaLV2, PhaLV, and PmaLV encode six to eight ORFs and have an organization similar to those of other members of the genus Luteovirus (Table  2  and Fig. 1A ). The main variability observed concerns the short P6 and P7 ORFs, which are missing in some viruses or isolates: ORF6 is absent in one isolate of MaLV (S3 from P.