dataesr/scientific-paragraphs-categorization huggingface dataset:
25% amino acid identity with Merkel cell Polyomavirus (GenBank accession number YP_009111421). PguyPyV-1 also showed 59. 44% nucleotide identity and 52. 99% amino acid identity with the Alphapolyomavirus Myocastor coypus polyomavirus 1 (GenBank accession number NC_040573) from a rodent species originating from South America. The phylogenetic analysis carried out on 166 amino acid-long sequences of the LTAg identified the two monophyletic clades corresponding to the Alphapolyomavirus (posterior probability = 1) and Gammapolyomavirus (posterior probability = 1) genera, whereas Betapolyomavirus genus was not supported. ObicPyV-1, ZbrePyV-1, and HmegPyV-1 polyomavirus sequences were clustered with polyomavirus sequences derived from Sciuridae (Sciurus corolinensis) and Gliridae (Glis glis and Callosciurus prevostii), a group of sequences that belongs to the Betapolyomavirus genus.
e. , RDRP) fragment of 384 nucleotides was chosen for subsequent phylogenetic analyses. This region included the main known mammal HVs and rodent HVs (RHVs) identified in five of the seven species analyzed (i. e. , P. cuvieri with four contigs, P. guyannensis with one contig, Z. brevicauda with two contigs, H. megacephalus with one contig, H. yunganus with three contigs). We attributed arbitrary names by contracting rodent species names with "HV" for Hepacivirus with an incremental number to differentiate sequences from the same host species. The percentages of identity between HV sequences obtained in this study ranged from 36. 67% to 97. 58% in nucleotides and from 27. 27% to 97. 27% in amino acids, showing their great diversity (Supplemental data, Table  S6 ). Briefly, PcuvHV4 (GenBank accession number MT732113), HmegHV1 (GenBank accession number MT732114), and HyunHV3 (GenBank accession number MT732116) sequences shared the highest percentages of identity (97.
27-97. 58% nucleotide identity and 94. 55-96. 36% amino acid identity) while PguyHV1 (GenBank accession number MT732106), PcuvHV1-3 (GenBank accession number MT732107-MT732109), ZbreHV1-2 (GenBank accession number MT732112, MT732112), and HyunHV1 (GenBank accession number MT732110), even if close, showed a greater divergence, with the percentage of identity ranging from 58. 48% to 96. 06% in nucleotides and from 64. 55% to 97. 27% in amino acids. These two groups of sequences were separated into two distinct clusters distantly related to each other. The HyunHV2 sequence (GenBank accession number MT732115) had the highest percentages of identity (64. 24% in nucleotides and 71. 82% in amino acids) with another Sigmodontinae rodent HV (Oligoryzomys nigripes Hepacivirus, accession number MH370348).
Compared with the other RHVs, the first group of sequences (PcuvHV4, HmegHV1, and HyunHV3) showed 54. 64-54. 45% and 60-64. 55% nucleotide identity and amino acid identity, respectively, with Meriones meridianus RHV (Supplemental data, Table  S6 ). This group is also related to Rhabdomys pumilio RHV-I (GenBank accession number KC411806), showing between 58. 48% and 59. 09% nucleotide identity and between 52. 72% and 56. 36% amino acid identity. The second group showed 58. 79-69. 39% and 56. 36-77. 27% nucleotide identity and amino acid identity, respectively, with Proechimys semispinosus HV (GenBank accession number MG822666). Three groups (A, B, C) of sequences regrouping rodent HVs were identified, highly supported with posterior probabilities of 1 (Figure  6 ). Group A was subdivided into three subgroups.
Acknowledgments: We warmly acknowledge  François Catzeflis  for fruitful discussions on the biology, ecology, and dynamics of rodents. We acknowledge  François Catzeflis  and all field volunteers for their assistance in capturing. We thank  Jean-Marc Frigerio  of the  UMR BIOGECO  for his help in data management and analysis. We thank the C3BI teams at the  Institut Pasteur and UMR BIOGECO  for their technical support and access to the clusters. We acknowledge the support of IDRIS National Computing Center for access to Turing and Ada machines through the  DARI  project  A0040307360. We thank  L. Ma  and  M. Tichit  ( Biomics Platform ) for technical assistance.
31 Figure 5. 315 Figure 5. Phylogenetic analysis of partial sequences of the Large T antigen (LTAg) (alignment of 166 amino acid positions) of Polyomaviridae representatives (Alpha, Beta, and Gammapolyomaviruses with Gammapolyomaviruses as the outgroup). The tree was inferred from amino acid sequences using the Bayesian method with the WAG model. Sequence identifiers include the NCBI accession number and the isolate name. Posterior probabilities of the Bayesian analysis (>70%) are shown next to the nodes. The scale bar indicates amino acid substitutions per site. Sequences identified in this study are shown in bold. 3. 4. 2. Rodent Hepaciviruses Hepaciviruses (HVs) constitute a genus that belongs to the ssRNA+ family of Flaviviridae. Their genome is approximately 10 kb long and encodes a single ORF translated.
2018; Arenou et al. 2018) , have been removed or greatly suppressed (cf. Fabricius et al. 2020 ). In addition the problem that two passbands are needed to describe the Gaia DR2 G-band photometry (Maíz Apellániz & Weiler 2018) has been resolved, with only one passband needed for Gaia EDR3 for each of G, G BP , and G RP. Despite the improvements in the astrometry and photometry, several limitations remain in Gaia EDR3 which require taking care when using the data. In Sect. 6 we summarise the known limitations of the present Gaia data release and point out, where relevant, the causes. In Sect. 2020 ) (catalogue validation) we provide additional guidance on the use of Gaia EDR3 results. The reader is strongly encouraged to read these papers and the online documentation 1  to understand the limitations in detail.
2-parameter solutions (2-p) As for previous releases there are sources for which insufficient data of the required quality is available to justify publishing a full 5-p or 6-p solution. For these sources a 5-or 6-parameter solution is in fact made, but with so-called galactic priors on the parallaxes and proper motions  (Michalik et al. 2015; Lindegren et al. 2018 , the fallback solution). Only the positions and their covariance matrix are published for these sources. In principle all sources at G > 21 have a 2-p solution. However this boundary is not strict because the limit in G was decided on the basis of the Gaia DR2 value for the magnitude or that based on the on-board estimate. Hence there are 5-p and 6-p solutions at G > 21, and sources with 2-p solutions at G ≤ 21 for which in principle a 5-p or 6-p solution was possible.
2020b). Along with the astrometry, new data quality indicators are published as part of Gaia EDR3. The renormalised unit weight error, introduced after Gaia DR2 was published (RUWE; Lindegren 2018) 2  , is now part of the release. New quality indicators, related to the image parameter determination process, provide ipd_gof_harmonic_amplitude This statistic measures the amplitude of the variation of the image parameter determination goodness of fit (reduced χ 2 ) as function of the position angle of the scan direction. A large amplitude indicates that the source is double, in which case the phase (next item) indicates the position angle of the pair, modulo 180 degrees. ipd_gof_harmonic_phase This statistic measures the phase of the variation of the IPD goodness of fit as function of the position angle of the scan direction ipd_frac_multi_peak This field provides information on the observation windows used for the astrometric processing of this source.
As in previous releases all sources were treated as single stars when solving for the astrometric parameters. For a binary the parameters may thus refer to either component, or to the photocentre of the system, and the proper motion represents the mean motion of the component, or photocentre, over the 2. 8 years of data included in the solution. Depending on the orbital motion, this could be significantly different from the proper motion of the same object in Gaia DR2, and significantly different from the proper motion of the centre of mass of the binary. The positions and proper motions are provided with respect to the third realisation of the Gaia celestial reference frame (Gaia-CRF3) which is aligned with the International Celestial Reference Frame (ICRF) to about 0. 01 mas root-mean-square (RMS) at epoch J2016.
Photometric data set The photometric data set contains the broad band photometry in the G, G BP , and G RP bands. The mean value of the G-band fluxes is reported for practically all sources while for about 85 per cent of the sources the mean values of the G BP and G RP fluxes are provided. For a small fraction of the sources any of the three bands may be missing (see Sect. 6. 3. 2). As for Gaia DR2, the photometric data processing consisted of three categories, 'Gold', 'Silver', and 'Bronze', which represent decreasing quality levels of the photometric calibration achieved, where in the case of the Bronze sources no colour information is available. The photometric processing category of each source is indicated in the released catalogue by a numeric field (phot_proc_mode) assuming values 0, 1, and 2 for gold, silver, and bronze sources respectively.
At the bright end the photometric uncertainties are dominated by calibration effects which are estimated to contribute 2. 0, 3. 1, and 1. 8 mmag RMS per CCD observation, respectively for G, G BP , and G RP  (Riello et al. 2020). Two new data quality indicators are included with the photometry which allow filtering of sources according to the probability that their photometry is affected by crowding effects. The fields phot_bp_n_blended_transits and indicate the number of transits for a given source believed to be affected by 'blending' or 'contamination'. The former refers to the presence of another source in the same observation window or very nearby, and the latter refers to the presence of sources in the wider neighbourhood of the target source, which are bright enough to contribute flux to the observation window.
Radial velocity data set The radial velocity spectrograph data processing relies on the preliminary astrometric solution (AGIS-3. 1 in  Lindegren et al. 2020b ) in order to have sufficiently accurate source positions to fix the correct wavelength scale of the RVS spectra. This means that the RVS processing can only start later during a processing cycle. In the planning of the Gaia EDR3 and Gaia DR3 releases it was not possible to accommodate the full sequence of RVS processing, radial velocity estimation, and validation, to allow new radial velocities to be published as part of Gaia EDR3. These will instead appear with Gaia DR3 in 2022. At the time of writing, the Gaia DR3 RVS processing is finished and the results are undergoing internal validation through the use of epoch radial velocities in the non-single star pipeline, and of RVS spectra in the astrophysical characterisation of Gaia sources.
The histograms presented in  Seabroke et al. (2020)  show that in particular in the tails of the distribution (at |v rad | > 600 km s -1 ) radial velocities have been removed. It is also noteworthy that of the ∼ 70 000 potentially spurious radial velocities identified by  Boubert et al. (2019) , 96% were retained as reliable in Gaia EDR3. We note that all radial velocity related fields in the Gaia EDR3 archive are prefixed with 'dr2_', leading to dr2_radial_velocity, dr2_radial_velocity_error, etc. For the detailed characteristics of the radial velocity data set (precision, accuracy, limitations) please refer to the relevant Gaia DR2 papers  (Sartoretti et al. 2018; Katz et al. 2019; Gaia Collaboration et al. 2018). The distribution of the astrometric, photometric, and radial velocity data sets in G is shown in Fig.
Acknowledgements. 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. 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: the  Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory ; the  Austrian Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Hertha Firnberg Programme  through grants  T359 ,  P20046 , and  P23737 ; the  BELgian federal Science Policy Office (BEL-SPO)  through various  PROgramme de Développement d'Expériences scientifiques (PRODEX)  grants and the  Polish Academy of Sciences -Fonds Wetenschappelijk Onderzoek  through grant  VS.
aa202039657 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-2018/42  and  ED481A-2019/155 , support received from the  Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC)  funded by the  Xunta de Galicia , the  Xunta de Galicia  and the  Centros Singulares de Investigación de Galicia  for the period 2016-2019 through CITIC, the  European Union  through the  European Regional Development Fund (ERDF)  /  Fondo Europeo de Desenvolvemento Rexional (FEDER)  for the 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-2016-1-0006 ,  AECT-2016-2-0013 ,  AECT-2016-3-0011 , and  AECT-2017-1-0020 , the  Departament d'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 ; the  Swedish National Space Agency (SNSA/Rymdstyrelsen) ; the  Swiss State Secretariat for Education, Research, and Innovation  through the  Mesures d'Accompagnement , the  Swiss Activités Nationales Complémentaires , and the  Swiss National Science Foundation ; 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 (RAL) :  PP/D006511/1 ,  PP/D006546/1 ,  PP/D006570/1 ,  ST/I000852/1 ,  ST/J005045/1 ,  ST/K00056X/1 ,  ST/K000209/1 ,  ST/K000756/1 ,  ST/L006561/1 ,  ST/N000595/1 ,  ST/N000641/1 ,  ST/N000978/1 ,  ST/N001117/1 ,  ST/S000089/1 ,  ST/S000976/1 ,  ST/S001123/1 ,  ST/S001948/1 ,  ST/S002103/1 , and  ST/V000969/1.
Description of spatial genetic patterns For each subspecies, the genetic distances between individuals and between localities were estimated from codominant genotypic distances (GD) and lineal genetic distances (LinGD), calculated according to  Smouse and Peakall (1999) , and the missing data were interpolated. From the coordinates of each individual and the elevation data, geographical distance matrices (GGD) and distance matrices in altitude (ALTD) were calculated, in kilometers, between individuals and between localities of the same subspecies. The matrices were obtained in GeneAlEx v. 6. 5 by a modi cation of the Haversine formula  (Sinnot 1984). Considering the spatial distribution of the individuals, the matrix of real geographic distances (GGD R ) was calculated as the hypotenuse of a triangle where the legs were the GGD and ALTD matrices.
1) Genetic structure We visualized genetic distances between subspecies, localities and individuals with a Principal Coordinates Analysis (PCoA) from the GD matrix in GeneAlEx v. 6. 5. The groups de ned a priori were the two subspecies, as well as the different localities sampled. The method used was the standardized covariance matrix and the scores corresponding to the principal coordinates 1 and 2 were plotted. To estimate the probability of each individual belonging to a subspecies or locality, based on their genotype, we ran two data sets in Structure v. 2. 3  (Pritchard et al. 2000) : grouped by subspecies and grouped by localities. The runs were performed under the following conditions: 10,000 Markov chain Monte Carlo replicates after an initial burn-in of 10,000, using correlated allelic frequencies and assuming the admixture model.
3) Functional connectivity We estimated genetic differentiation between localities through pairwise comparisons of F ST  (Weir and Cockerham 1984) , G ST  (Nei 1973; Nei and Chesser 1983)  and D JOST  (Jost 2008 ) values calculated in R (R Core Team 2020 https://www. R-project. org/) using the fastDivPart function of the package diveRsity  (Keenan et al. 2013 ). Interpretation of genetic differentiation followed the criteria of  Hartl and Clark (1997).
Landscape data acquisition The scenarios corresponding to the Guamuhaya and Sierra Maestra massifs were cut out from the vector map of the land surface of Cuba. Each of the massifs was analyzed separately for landscape data acquisition. For the description of the landscape, we used the vegetation map  (Estrada et al. 2012 ) with a scale of 1: 100,000 referred to as the 'Vegetation' variable in further analyses. We used 6 other variables: the Digital Elevation Model (DEM), the index of the landform (Topography), the slope of the terrain (Slope), Temperature Seasonality (BIO4), Annual Precipitation (BIO12) and Precipitation Seasonality (BIO15), considering their contribution to the distribution models of the Magnolia subsect. Cubenses obtained by  Simón (2020). The layers of the topographic and climatic variables were downloaded from WorldClim (http://www.
3. 4. 12 using the Microsoft Excel add-in tool PopTools. Three groups of analyses were carried out: a) partial correlation between genetic (LinGD) and environmental distances (EnvD, BIO4D, BIO12D and BIO15D), in which the effect of geographical (GGD R ) distances was controlled, b) partial correlation between geographical (GGD R ) and environmental distances (EnvD, BIO4D, BIO12D and BIO15D), in which the effect of genetic distances (LinGD) was controlled, and c) partial correlation between genetic (LinGD) and geographic (GGD R ) distances, in which the effect of environmental distances (EnvD, BIO4D, BIO12D and BIO15D) was controlled. The tests were run in R using the vegan package  (Oksanen et al. 2019) , with 1,000 permutations and the Spearman's correlation coe cient. 3) To evaluate the structural connectivity of the landscape in each massif, minimum cost models (Leastcost-path or LCP) were used  (Adriaensen et al.
Genetic structure The principal coordinates analysis of the three datasets (i. e. dataset 1: data of the two subspecies combined; and datasets 2 & 3: data of each subspecies separately) is presented in Fig. 2. When including the data of both subspecies, the PCoA showed well-de ned groups corresponding to the two subspecies of Magnolia cubensis (Fig. 2a ). At the infraspeci c level for M. cubensis subsp. acunae, no clustering was observed for individuals from the Topes de Collantes and Lomas de Banao localities (Fig. 2b ). In contrast, in M. cubensis subsp. cubensis, the PCoA contains ve well-de ned groups. The clusters containing the individuals of Pico Caracas, Pico Turquino, La Bayamesa and El Gigante present a slight overlap; while that of Gran Piedra does not overlap with the rest of the localities (Fig.
2c ). Barplots respecting the ΔK criterium to determine the optimal K and Structure Harvester output of the structure analyses of the three datasets are shown in Fig. 3  and Online Resource 3, respectively. The structure analyses including both subspecies result in ΔK = 2, dividing the individuals in two genetic groups cf. the subspecies (Fig. 2a ). There is a smaller, secondary peak at K = 4 whereby one genetic group corresponds to Magnolia cubensis subsp. acunae and the other three to M. cubensis subsp. cubensis. When analyzing the subspecies separately, the M. cubensis subsp. acunae dataset has a ΔK = 2, yet, the mean L(K) does not increase between 1 and 2; hence, the most likely true optimal K value for this dataset is 1. The barplots of K = 2 (Fig. 2b ) show a mixed pattern that includes individuals from both locations in each genetic group.
Acknowledgments This work was nancially supported the  Whitley Fund for Nature, Franklinia Foundation, Planta! -Plantlife Conservation Society , the  Ghent University  and the  National Botanical Garden of Cuba. We would like to thank to the  National Center of Protected Areas of Cuba  for the support in the eld trips for the collection of the samples. We are most grateful for all the people who helping us during eld work:  Norlys Albelo ,  José L. Moscoso ,  Alexei Rodríguez ,  Yenia Molina ,  Adonis Sosa ,  Daryl D. Cruz  y  Reinier Nuñez.
, 2017; Obrist et al. , 2018; Sprovieri et al. , 2016; Global Mercury Assessment, 2018). In South America, only a few studies provide observations to explore the seasonal and multiannual trends of atmospheric Hg. Guédron et al. (2017)  give a short record of TGM measured at Lake Titicaca in the Bolivian-Peruvian Andes, whereas  Diéguez et al. (2019)  provided a multi-annual (but not continuous) record of atmospheric Hg species in Patagonia, Argentina. GEM averages for Manaus in the Amazon rainforest of Brazil were also reported by  Sprovieri et al. (2016). Müller et al. (2012)  measured TGM during 2007 in Nieuw Nickerie, Suriname, in the northern part of South America. Lastly, some data on South Amer-ican upper-tropospheric TGM concentrations are provided by CARIBIC flights (https://www. caribic-atmospheric.
, 2015; Obrist et al. , 2018; Phillips et al. , 2008). The goal of this study is to partly overcome the TGM data gap over South America by providing new high-quality Hg measurements from the Global Atmosphere Watch (GAW) station Chacaltaya (CHC), a distinctive site due to its location in the tropical part of the Andes, at 5240 m a. s. l. (above sea level). Between July 2014 and February 2016, we continuously measured TGM at the CHC station, which allowed us to sample air masses of both Altiplanic and Amazonian origin. Through this unique dataset, we explore the seasonal pattern of TGM in the region and discuss possible sources and sinks for atmospheric mercury in the South American tropics.
Raw dataset, routine, and exceptional maintenance and monitoring files were compiled and processed by software developed at the IGE (Institute of Environmental Geosciences) and specifically designed to quality assure and quality control atmospheric mercury datasets in order to produce clean TGM time series. In this automated process, the raw dataset is compared against potential flags corresponding to more than 40 criteria that specifically refer to all operation phases related to the calculation of mercury concentrations and calibration  (D'Amore et al. , 2015). Each raw observation is individually flagged depending on the result of each corresponding criterion and returns, as a temporary output, a flagged dataset  (valid, warning, and invalid). Inclusion of all field notes, implying corrections and invalidations of data regrouped in the flagged dataset step, as well as a clarification step by the site manager according to their knowledge allows for the production of a complete quality-assured and quality-controlled dataset according to the initial temporal acquisition resolution.