dataesr/scientific-paragraphs-categorization huggingface dataset:
The genomic analysis of the newly isolated phages led to interesting observations. First of all, we isolated and sequenced four different phages that all share a high genomic identity with X. axonopodis phage CP2, among which only FC03-Usme was able to infect Xf strains. This suggest that the CP2-like phages, whose first isolate came from Yokohama (Japan), are very abundant and are distributed all over the world, probably with the Xanthomonaceae strains they infect. A similar conclusion applies to the phage FC12-Bacata, whose genome sequence is homologous to that of the phage Salvo isolated in Texas (USA). Then come phage genomes with very low identity to any genome present in the databases, such as FC23-Cota and FC41-Suba, which can be considered new phage species since their genomes only match a few conserved genes in the X.
Acknowledgments: This work has benefited from the expertise of  Artemis Kosta  and  Hugo Le Guenno  at the  IMM Platform for Microscopy ,  Yann Denis  from the  IMM transcriptomic Platform  and  Quentin Beaurepère  from the  INRA-IRHS. Strains used in this study have been provided by the  CIRM-CFBP: International Center for Microbial Resources-French Collection for Plant-associated Bacteria ,  IRHS UMR 1345 INRA-ACO-UA , 42 rue Georges Morel, 49070  Beaucouzé Cedex France  (https://www6. inrae. fr/cirm_eng/CFBP-Plant-Associated-Bacteria, accessed on 10 March 2021). We thank all members of our respective research groups for stimulating discussions regarding this work. The authors also wish to thank  Jérôme Gouzy  ( CATI BBRIC ) for the genome assembly pipeline and  Khalid El Karkouri (LCB)  for help in genome submission.
Results. With the improved astrometry and photometry of EDR3, we find that: i) the dynamics of the Galactic disc are very complex with oscillations in the median rotation and vertical velocities as a function of radius, vertical asymmetries, and new correlations, including a bimodality with disc stars with large angular momentum moving vertically upwards from below the plane, and disc stars with slightly lower angular momentum moving preferentially downwards; ii) we resolve the kinematic substructure (diagonal ridges) in the outer parts of the disc for the first time; iii) the red sequence that has been associated with the proto-Galactic disc that was present at the time of the merger with Gaia-Enceladus-Sausage is currently radially concentrated up to around 14 kpc, while the blue sequence that has been associated with debris of the satellite extends beyond that; iv) there are density structures in the outer disc, both above and below the plane, most probably related to Monoceros, the Anticentre Stream, and TriAnd, for which the Gaia data allow an exhaustive selection of candidate member stars and dynamical study; and v) the open clusters Berkeley 29 and Saurer 1, despite being located at large distances from the Galactic centre, are on nearly circular disc-like orbits.
Introduction As for previous releases, the Early Third Data Release (EDR3, Gaia Collaboration et al. 2020a) of the Gaia mission (Gaia  Collaboration et al. 2016 ) of the European Space Agency is accompanied with performance verification articles showing the quality of the data, the improvements with respect to previous releases, and the scientific potential for multiple research areas in astrophysics (see also Gaia  Collaboration et al. 2020c,d,b). In the present study we focus on a specific area in the sky that allows us to explore different elements of the Milky Way's (MW) structure and history: the Galactic anticentre. This region of the Galaxy has the advantage that from astrometric measurements alone (proper motions and parallaxes), one can calculate the vertical and azimuthal (rotation) motion of the stars with a negligible contribution of the line-of-sight velocity.
2018; Cheng et al. 2020). While some of these could be related to the structures such as the bar, the poorly-known spiral arms and the warp, more recently, the Gaia vertical phase spiral  (Antoja et al. 2018; Bland-Hawthorn et al. 2019 ) suggests a phase mixing event after the perturbation of Sagittarius (Binney & Schönrich 2018;  Laporte et al. 2019b). All of these have proven to be difficult to understand, and also to disentangle or relate. Here we look at the rotation and vertical velocities of the outer disc, showing the power that Gaia data can have in our understanding of this complexity and the role that recent and past, internal and external, perturbations have had in the MW. Second, we go from the current disc of the Galaxy to more ancient components. Two distinct populations were clearly apparent in the Hertzsprung-Russell (HR) diagram of stars with large tangential velocities near the Sun (i.
g. Frinchaboy et al. 2004 ). Attempts to characterise the orbits of these two objects have returned widely discrepant results  (Carraro et al. 2007; Vande Putte et al. 2010) , mainly due to their poorlyconstrained proper motions since at such large distances, small proper motion errors translate into large uncertainties in physical velocities. An additional issue has been the uncertain membership status of individual stars. Here, thanks to Gaia EDR3, we perform a robust analysis of the membership of these clusters and derive their orbits with high confidence. To investigate all these aspects, the main Gaia data products that we use here are the astrometric measurements. For EDR3 these show a substantial decrease of uncertainties resulting from the use of 34 months of data (12 more than for DR2).
Apart from a higher completeness at the faint end, there is a significantly larger number of stars at a given parallax precision. The combination of all these improvements essentially means that we can now explore distant regions of the Galaxy in the direction of the anticentre, even reaching around 16 kpc from the Galactic centre and beyond (see Sect. 2 for details), and thus, the very outskirts of the disc, for a sample with positions and velocities of excellent quality. Moreover, important improvements in the pipelines of the Gaia photometry have resulted in photometric bands with significantly less systematic error, from which, combined with the improved parallaxes, cleaner HR diagrams can be built and used to select different populations and components. The paper illustrates how, once more, the new Gaia data are set to revolutionise our knowledge of the Galaxy and its past.
Additionally, we describe practical aspects of the data and examples of its use that might be of interest for the community, such as queries in the Gaia Archive, quality cuts, derivation of distances (Bayesian inference, considerations on the parallax zero point), etc. We also complement our analysis with the use of simulated data from the Gaia Object Generator (GOG,  Luri et al. 2014 ) & the Gaia Universe Model Snapshot (GUMS,  Robin et al. 2012 ), now available directly in the Gaia Archive, to evaluate the effects of selection, errors and extinction. We start our paper by explaining the different datasets used and demonstrating the different improvements (but also limitations) of the EDR3 data in the anticentre direction (Sect. 2). We continue by explaining how the distances and phase space coordinates are derived  (Sect.
Main datasets In this work we explore the Galactic anticentre region using different data selections obtained from Gaia EDR3 (Gaia  Collaboration et al. 2020a ) that can be accessed through the Gaia Archive (https://gea. esac. esa. int/archive/). More details on the data and validation are given also in  Lindegren et al. (2020b) ,  Riello et al. (2020)  and  Fabricius et al. (2020). The main datasets are shown in Fig. 1  and listed below. The number of stars for these samples and a comparison with DR2 are in Table  1. (1) AC20: A square on the sky centred at ( , b) = (180, 0) deg of 20 deg on a side (blue square in Fig. 1 ). This sample is used to explore Galactic disc kinematics in Sect. 4. It contains 14 120 029 stars but most of the time we use only the selection with /σ > 3 (see Sect. 3.
Complementary datasets For validation and other purposes, we also used the following complementary data: (i) 6Dsample: a full sky sample with stars that have DR2 lineof-sight velocity in EDR3 (Seabroke 2020), thus with full 6D phase space information. After filtering, this sample contains 6 156 684 stars and is used mainly in Sect. 4. (ii) DR2: the same selections as above (AC20, ACV) but for DR2. These are used for comparison with EDR3. Gaia Collaboration et al. : Gaia Early Data Release 3: The Galactic anticentre (iii) GOG & GUMS: the same selections as above but for GOG  (Luri et al. 2014 ; GEDR3 documentation Chapter 2) which is a mock Gaia catalogue based on the Besançon model  (Robin et al. 2003) , and for GUMS  (Robin et al. 2012)  which contains the intrinsic properties of the mock sources before applying the Gaia instrument modelling.
Here we used the GOG version 20. 0. 3. with uncertainties that have been scaled to 34 months of data (but see Fig. E. 1). These samples are used for the evaluation of completeness and extinction effects and they do not contain any kinematic substructure or asymmetries. Furthermore, GOG and GUMS were used in Appendix C for testing how robust each of the distance estimation methods is. These simulated data were retrieved through the Gaia Archive querying from the corresponding tables (gaiaedr3. gaia_source_simulation and gaiaedr3. gaia_universe_model). (iv) 2MASS: We used the official crossmatch of EDR3 with the 2MASS point source catalogue  (Skrutskie et al. 2006 ), provided in gaiaedr3. tmass_psc_xsc_best_neighbour. For the AC20 sample, this yields about 55% anticentre objects with 2MASS photometry.
EDR3 data quality and completeness In this section we examine the quality of the EDR3 data and compare it to DR2. The most relevant improvements in EDR3 for our study include a larger number of sources at the faintest magnitudes and a significant decrease of the astrometric uncertainties and thus a significantly larger number of stars with a certain parallax precision. Below we show these aspects in more detail focusing mostly on the AC20 region as an example. 1. General description. Figure  2  shows the AC20 region in Galactic coordinates coloured according to different quantities in bins of 0. 1 deg. In panel a we show the number of stars per bin while the rest of the panels show median quantities. As expected, the counts anti-correlate with the patterns seen in the extinction map from  Green et al.
Completeness. The evaluation of the completeness of the Gaia data is a difficult task given that there is no deeper survey with a comparable resolution. Distinct methodologies to assess the data completeness can be found in  Boubert & Everall (2020) ,  Lindegren et al. (2020b)  and  Fabricius et al. (2020). Here we examine it in a simpler way. First we note that the AC20 sample (without any cuts) has about one million more stars in EDR3 compared to DR2 (see Table  1 ). Figure  3  shows histograms of the G magnitude for stars in the AC20 sample in DR2 and EDR3 (blue solid and dashed histograms) showing a great increase in the number of sources at the faint magnitudes with respect to DR2. This was expected given that the detection on board prioritises bright magnitudes and the effect of more months of observations produces new detections mostly at the faintest bins.
This improvement in the astrometric quality allows us to have now a much larger sample of stars with very good relative parallax errors, and reach farther distances from the Sun. We also note that the uncertainty in µ δ is smaller than for µ α *. This is due to a geometrical scaling factor on the uncertainties resulting from the scanning law which in the direction of the anticentre favours µ δ. As in previous releases, the astrometric Gaia data suffer from some systematics. The median astrometric quantities and their uncertainties show checkered patterns that somehow correlate with the scanning law, as illustrated for the median parallax (Fig. 2f ) and median uncertainty in µ δ (Fig. 2g ). The later shows additionally some of the large scale bands mentioned above. The amplitude of these systematics has, however, been reduced in EDR3 (see  Lindegren et al.
We work primarily with distance estimates from a Bayesian approach (d PM ), similar to that applied by  Schönrich & Aumer (2017) , with a prior that is derived iteratively to be consistent with the data. These distances use a prior P(d) ∝ d 2 P ρ (r(d))S (d), where r(d) is the position at distance d along a given lineof-sight, so P ρ (r(d)) is proportional to the density of a model Galaxy. The term S (d) is the selection function -i. e. the probability that a randomly chosen star at a distance d enters the catalogue. The distance estimate, d is then found as the expectation value of d given this prior and the measured parallax (and its uncertainty). As explained in the previous section, default distances are computed considering a fixed parallax zero point of -17 µas. The distances and the code can be found at https:// zenodo.
org/record/4415706 and https://zenodo. org/ record/4415669, respectively. More details can be found in Appendix C. To check that our results are robust, we compared to results when we estimate the distance as simply the inverse of the parallax, and also with a different Bayesian approach based on that from Bailer-Jones et al. (2018). We tested each of these approaches on GOG data, and further details are given in Appendix C. From these tests, we conclude that using a parallax quality cut of /σ > 3 is a good compromise between the performance of the estimate and the number of stars of our samples. However, we emphasise that all the estimators tested here return somehow imperfect distances, which in the Bayesian case depend also on how close the assumed prior on the Galaxy distribution is to the Galaxy model used in GOG (i.
Acknowledgements. We thank the referee  Dr. James Binney  for his comments. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being 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 mission website is https://www.
This aeolian dust has been transported by air masses associated with the major climate subsystems of the region, chiefly the westerlies  15  , with additional contributions from northerly transport or local katabatic winds  13, 19. Despite the potential of its widespread aeolian deposits  20  to record palaeoclimatic information  [21] [22] [23] [24] [25]  , palaeoclimate datasets generated for ACA, thus are either coarse in timescale  15  or focus on recent, short-term intervals insufficient to inform us about long-term feedbacks  14, 26. Continuous archives of terrestrial response to atmospheric drivers in ACA over Plio-Pleistocene timescales so far remain largely unexplored. The Charyn Canyon sedimentary succession (43°16′ N, 78°59′ E) addresses this imbalance by providing a quasi-continuous record of late Cenozoic change for central Eurasia (Fig.
Dried again at 70°C, samples were gently disaggregated in an agate mortar. Sample weight was measured after each step. Samples were then dispersed in 0. 05% (NaPO 3 ) 6 solution and passed through a Microtrac MRB Bluewave analyzer, coupled with a Sample Dispersion Controller, at the CORE Laboratory of Instituto Oceanografico (USP, São Paulo) (Supplementary Data 1). Low field-magnetic susceptibility was measured in situ using a Bartington Instruments MS3 magnetic susceptibility meter with MS2K sensor (Supplementary Data 2), and discrete samples have been replicated in the IAG laboratory at the Universidade de São Paulo (USP, Brazil) with an AGICO Kappabridge MFK-FA. Palaeomagnetism. Fine-grained and cohesive sediments for palaeomagnetic analyses were sampled in the field using a non-magnetic cordless drill yielding 2.
5 cm diameter cores. The sampling positions were all placed within threedimensional space using the total station and palaeomagnetic samples were oriented in the field with a magnetic compass (Supplementary Data 3). Palaeomagnetic analyses were performed at the Alpine Laboratory of Palaeomagnetism (ALP, Peveragno, Italy) on a total of 121 cores collected at 50 cm intervals along the sequence. The structure of the natural remanent magnetization (NRM) was investigated for both thermal and alternating fields (AF) progressive demagnetization in 10-15 steps, using a 2G-Enterprises 600 Degausser and an ASC TD48 furnace, respectively. The NRM was measured with a 2G-Enterprises 755 cryogenic magnetometer located in a magnetically shielded room (ambient field < 500 nT). Intensity generally ranged from 5 to 15 mA/m, with a few samples yielding up to 30 mA/m.
Reliable ChRM directions were isolated in 102 samples (84%) and show antipodal distributions (Supplementary Fig. 3b ) that deviate from being coaxial by only 3. 5°, giving a positive Class B reversal test (95% confidence angle = 8. 3°6  6  ). The ChRM is interpreted to be the primary magnetization of the Charyn Canyon sediments, acquired at or soon after deposition (DRM). The virtual geomagnetic polar (VGP) latitudes (i. e. the latitude of the magnetic pole which accounts for the direction of the ChRM at the site) were used to build the magnetostratigraphy of Charyn Canyon (Supplementary Data 3). 238 U/ 206 Pb dating. The absolute constraint of the palaeomagnetic age model was undertaken using 238 U-206 Pb dating of pedogenic carbonates and a bovid tooth. Four large pedogenic carbonate nodules from key layers within the magnetostratigraphy and a bovid tooth stratigraphically correlated to -6.
25 m and corresponds to a time slice at ca. 2. 5 Ma, were prepared for measurement on polished thick sections. Isotope measurements were made using laser ablation-sector fieldinductively coupled plasma-mass spectrometry (LA-SF-ICPMS)  67, 68  at the Frankfurt Isotope & Element Research Center (FIERCE) at the Goethe Universität, Germany (Supplementary Fig. 4 ). Uranium, thorium, and lead isotopes were analyzed using a ThermoScientific Neptune+ MC-ICP-MS coupled to a RESO-Lution S-155 (Resonetics) 193 nm ArF Excimer laser (CompexPro 102, Coherent), equipped with a two-volume ablation cell (Laurin Technic, Australia). Results are summarized in Supplementary Data 4 for the samples and for the reference materials and Supplementary Data 5 for the data report. Samples were ablated in a helium atmosphere (0.
3 l/min) and mixed in the ablation funnel with 1. 0 l/min argon and 0. 08 l/min nitrogen. Static ablation used a spot size of 104 µm in diameter for carbonate samples and 50 µm for phosphate samples, a crater depth of ~15 µm and a fluence of ca. 2 J/cm 2 at 8 Hz for carbonates samples and 2 J/cm 2 at 6 Hz for phosphate samples. The detection limits for 206 Pb and 238 U were ~0. 1 and ~0. 03 ppb, respectively. Prior to analysis, each spot was pre-ablated (1 µm) to remove surface contamination. For each spot, the analysis comprised 20 s of background acquisition followed by 18 s of sample ablation. Soda-lime glass SRM-NIST614 and -NIST612 were used as reference glasses together with four carbonate and two apatite standards, to bracket sample analysis (Supplementary Data 4). Raw data were corrected offline using an in-house MS Excel © spreadsheet program  67, 68.
We undertook a linear interpolation between nine successive age control points using the software AnalySeries  73  (Fig. 1b  and Supplementary Fig 4). The lower section correlates with the Gilbert chron. We identified two age control points for this part of the sequence, the upper part of Nunivak (C3n. 2n 74 , 4. 3 Ma). This correlation is supported by independent age control provided by a 238 U-206 Pb laser ablation age of 5. 0 ± 1. 2 Ma. The middle section correlated with the Gauss and Matuyama chrons. We determined three age control points to build the age model: the upper part of the chron C2An. 3n  74  (3. 3 Ma), and the normal polarity interval of C2An. 1n  74  (3. 0 and 2. 6 Ma, Gauss-Matuyama transition). Independent age control for this part of the section was provided by a U/Pb laser ablation age of 2.
Stable isotope analysis of pedogenic carbonates. Stable isotope analysis was undertaken on 201 pedogenic carbonates, collected with a sampling resolution ranging between 20 and 80 cm (Supplementary Data 6, Supplementary Fig. 5 ). Pedogenic nodule samples were cut transversally with a circular saw and three replicates per sample were analyzed. Samples were collected using a handheld dentist drill with a diamond-coated drill bit, which was carefully cleaned between samples. Between 0. 3 and 0. 5 mg of carbonate powder was digested with phosphoric acid at 70°C in an exetainer vial and flushed with helium gas. The liberated CO 2 was analyzed using a Thermo Finnigan GasBench II preparation device interfaced with a Thermo Delta V advantage mass spectrometer at the Max Plank Institute of Chemistry in Mainz.
1 °C in a common acid bath. The resulting CO 2 was first purified and then analyzed. CO 2 gases equilibrated at 1000 °C and 25 °C, respectively, were analyzed along with the samples to test the linearity of the mass spectrometer and to determine the empirical transfer function (ETF). Each sample gas was analyzed in 13 acquisitions consisting of ten cycles with an ion integration time of 20 s each. Background with no gas in the ion source was not determined. The negative background below m/z 47 was continuously monitored on the m/z 47. 5 half-cup, and subtracted from the corresponding m/z 47 raw-intensities of sample and reference gas measurements using a scaling factor of -0. 988. In δ 47 vs Δ 47 space, background-corrected at 1000°C and 25°C gases displayed-within errors-no further residual slopes anymore (Supplementary Data 7).
Acknowledgements We thank all assisting with fieldwork, including  A. Kossenko ,  A. Umarkhojiyev , and  D. Karlov. Thanks  for the laboratory assistance with stable ( S. Brömme ) and clumped isotopes ( D. Bajnai ,  K. Methner ,  N. Löffer ,  E. Krsnik , and  S. Hofmann ), carbonate thin sections (A. Sorowka), and grain-size analysis ( G. Battaglini Job ). R. I. F. Trindade welcomed G. S. to use his palaeomagnetic facility at  IAG-USP. We are very grateful to the Director of the  Charyn National Natural Park , who gave us the permission to sample the site. Samples were exported under a permit « Phytosanitary certificate » (in Russian: Фитосанитарный сертификат) issued by the  Committee of State Inspection, Ministry of Agriculture, Republic of Kazakhstan. This work was funded by a  Max Planck Research Group  awarded to  K.
Biogeochemical redox zone classification We classified the sediment horizons as either oxic, nitrogenous, or ferruginous zones (see Fig. 1 , Supplementary Table  1 , and Supplementary Fig. 1 ) according to Canfield and Thamdrup  [15]  based on in situ oxygen profiles from the same locations  [26] , and the penetration depth of nitrate measured in parallel on board (Supplementary Table  1 ). Both the oxygen and nitrate profiles can be found in the PAN-GAEA repository under the project title "HADES-ERC" (www. pangaea. de). For further description on the procedures see the Supplementary Material and Methods section.