dataesr/scientific-paragraphs-categorization huggingface dataset:
Precipitation data Since the summer campaign of 2005-2006, precipitation samples have been manually collected yearround on a 80 × 120 cm wooden platform covered by a polystyrene/Teflon plate and standing 1 m above the snow surface. It is situated at a distance of about 800 m from Concordia Station. The layer of accumulated snow was collected at 01:00 UTC (Coordinated Universal Time). This snow layer generally has a thickness varying from 0 to 10 mm. A few isolated cases of 30-50 mm thickness related to blowing snow events were also observed. To prevent the snow from being blown off, the plate was shielded by a rail of 8 cm height. We cannot rule out impacts of winds on our sampling: snow may be blown onto (or off) the platform by the wind, with or without precipitation. The sampling frequency has increased with time: since the end of 2007, sampling has been performed on a daily basis.
Stable isotope data Stable isotope ratios of the precipitation samples were measured by isotope ratio mass spectrometry (IRMS) techniques (Thermo-Fisher Delta Plus Advantage) using the wellestablished CO 2 -H 2 /water equilibration method. When the sample amount was less than 5 mL, the measurements were performed using a cavity ring-down spectroscope (CRDS) from PICARRO (model L1102-i), which only requires 0. 3 ml water. Measurements are reported against the Vienna Standard Mean Ocean Water (V-SMOW) international standard. The IRMS provides an analytical precision of ±0. 05 ‰ for δ 18 O and ±0. 7 ‰ for δD, while the CRDS used here warrants a precision of ±0. 1 ‰ for δ 18 O and ±0. 5 ‰ for δD, with a final precision on the calculated deuterium excess of ±0. 8 and ±0. 9 ‰, respectively.
Model data The high-resolution precipitation measurements are used to test the ability of a global weather forecast model to produce realistic snowfall events driven by the synoptic-scale weather. Simulated snowfall (SF) and evaporation (E) are obtained from the ERA-Interim Reanalysis, which is provided by the European Centre for Medium-Range Weather Forecasts. Time series for Concordia Station are extrapolated from the 24 h forecasted precipitation and evaporation fields, with a regular 0. 75 • × 0. 75 • latitude-longitude grid, at the grid model point nearest to the Concordia site coordinates. Atmospheric models (both global and mesoscale) systematically underestimate East Antarctic plateau precipitation, probably because clear-sky precipitation is not adequately parameterized  (Bromwich et al.
, 2004; Van de Berg et al. , 2006). However,  Genthon et al. (2010)  showed that part of these mismatches may, however, also arise from data inaccuracies. The ECMWF model does not account for blowing snow transport/sublimation. However, since the Concordia site is not influenced by strong winds this is expected to have minor importance here, but cannot be completely neglected. In order to test the suitability of the new Concordia dataset to evaluate isotopic-enhanced atmospheric general circulation models (GCMs), here we use LMDZiso (Laboratoire de Météorologie Dynamique Zoom), the isotopic version  (Risi et al. , 2010)  of the LMDZ4 atmospheric GCM  (Hourdin et al. , 2006) , run at a resolution of 2. 5 • × 3. 75 • , following the AMIP (Atmospheric Model Intercomparison Project) protocol. The simulation is nudged to the large-scale ECMWF operational analyses atmospheric circulation fields.
From these 2 years, the relationship between precipitation amount and T 2 m is of 6. 4 % • C -1 , very close to climate model projection results  (Frieler et al. , 2015). Our dataset also shows a much larger number of snowfall events (111 with respect to 31) in 2009 than in 2010 (Fig. 5 ). In 2008-2009, the precipitation amount shows a minimum in late spring-early summer (NDJF) and a maximum in late autumn-winter (MJJA). However, the seasonality is different in 2010, where most of the precipitation occurred in late summer and early autumn (FMA) as diamond dust. A high inter-annual variability of both amount and type of precipitation is observed for the considered time period. Snowfall provided the majority of precipitation for 2009, whereas hoar frost and diamond dust were predominant in 2008 and 2010, respectively (Fig.
Observed δ 18 O -temperature relationship Least-square regression analysis of the relationship between δ 18 O and temperature from daily or monthly observations (using 2 m and inversion temperature) and simulations (LMDZ) are reported in Table  2. Note that for better comparison with the data from other stations (Table  1 ) we did not remove seasonality from our data. We first focus on the daily data. The δ 18 O/T 2 m slope is 0. 49 ± 0. 02 ‰ • C -1 (R 2 = 0. 63, n = 500), lower than the spatial slope of 0. 8 ‰ • C -1 obtained by  Masson-Delmotte et al. (2008)  for their database of Antarctic surface snow iso-topic composition, which is itself close to the value expected from a Rayleigh distillation. Our results are intermediate between those obtained at other East Antarctic plateau sites (Table  1 ).
Again, the uncertainty associated with the sampling of individual precipitation events, and therefore their weight, does not allow further investigation. When considering inter-annual variations, we obtain a high δ 18 O/T 2 m slope (1. 4 ± 1. 1 ‰ • C -1 ) for values calculated from the whole dataset, as well as negative slopes for precipitation weighted values. However, their significance is very low due to the calculation limited to a 3-year period. A further calculation that can be made in order to get rid of the mean seasonal cycle, which is dominating the statistical analysis, is to consider the mean monthly anomalies of temperature and δ 18 O for the study period. This leads to a higher δ 18 O/T 2 m slope of 0. 96 ‰ • C -1 (R 2 = 0. 68) when compared to the one obtained from the mean monthly values (0.
Our results show an antiphase between deuterium excess and δ 18 O, confirming earlier results from surface snow data (Masson-  Delmotte et al. , 2008)  and the 1-year Dome Fuji precipitation dataset  (Fujita and Abe, 2006). Touzeau et al. (2016)  reinforce the view, already suggested by  Uemura et al. (2012) , that the main control on the precipitation deuterium excess in the coldest parts of inland Antarctica, at the very end of the distillation pathways, is indeed the site temperature. Deviations from such an antiphase between deuterium excess and δ 18 O, as observed in Antarctic coastal areas, are expected to reflect changes in moisture source characteristics. We note that the LMDZiso model does capture this antiphase at Concordia, but underestimates its strength, possibly due to the model warm bias.
the-cryosphere. net/10/2415/2016/ The Cryosphere, 10, 2415-2428, 2016 exact determination of the moisture sources for diamond dust is needed to understand the processes at play. This new Concordia dataset can now be used to evaluate isotopic-enhanced atmospheric general circulation models. The comparison of our precipitation amount data with the ECMWF ERA Interim reanalysis shows good skills of the model. Moreover, the capacity of the LMDZiso model to capture some of the observed δ 18 O variance despite a warm bias and low resolution is encouraging. Our new dataset can be used for the evaluation of the model water cycle, for instance by investigating the sensitivity of model performance to spatial resolution or to different parameterizations. The 3-year monitoring of water stable isotopes of daily precipitation at Concordia Station allowed a δ 18 O/T 2 m slope of 0.
Data availability Two-meter temperature data sampled by DCII US AWS are available at http://amrc. ssec. wisc. edu. Radiosounding data sampled at Concordia Station can be requested at http:// www. climantartide. it. Snow stakes accumulation record can be freely downloaded at http://www-lgge. obs. ujf-grenoble. fr/ServiceObs/index. htm. The data from this study are available in the Supplement. Author contributions. Barbara Stenni, Giuliano Dreossi and Mattia Bonazza are responsible for the precipitation measurements and stable isotope analysis, Mauro Valt and Anselmo Cagnati for the crystal analysis, Claudio Scarchilli, Virginia Ciardini and Paolo Grigioni for the meteorological and ECMWF model data and Camille Risi for the LMDZ model data. Daniele Karlicek collected the samples in the field.
Acknowledgements. The precipitation measurements at Dome C as well as the isotopic analysis have been conducted in the framework of the  Concordia Station glaciology and ESF PolarCLIMATE HOLOCLIP  projects funded in Italy by  PNRA-MIUR. This is a HOLOCIP publication number 28. Calculations, model comparison and analysis have been conducted in the framework of the MALOX and  PRE-REC  projects, funded by  PNRA-MIUR. We appreciate the support of the  University of Wisconsin-Madison Automatic Weather Station Program  with the  Dome C II dataset (NSF  grant numbers  ANT-0944018  and  ANT-12456663 ) and the support of the IPEV/ PNRA  Project " Routine Meteorological Observation at Station Concordia ", www. climantartide. itwith the radiosounding data set. We thank all people who were involved in the precipitation sampling in the field, and  Daniele Frosini  and  Laura Genoni  for the 2008 and 2009 sampling collection, respectively.
Cells were washed twice in 50 ml ice cold RPMI followed by centrifugation at 300g for 5 min. Supernatants were discarded and erythrocytes were lysed. Colonic lamina propria, epidermal and dermal leukocytes were isolated as described previously  65, 66. The resulting cell suspension was subsequently passed through a 40 mm strainer before cell counting. All cells were maintained on ice until further use. Cellular content of the preparations was assessed by cell counting using a Casey TT counter (Roche) in combination with multi-colour flow cytometry. Discrimination of vascular and tissue-derived cells. According to published methods  28  , mice were injected i. v. with 1 mg FITC-conjugated anti-CD45. 2 antibody and 5 U heparin sulphate in 100 ml DPBS 2 min before sacrifice (equivalent data was obtained with PE or PE-Cy7-conjugated antibodies).
For the detection of BrdU, cells were fixed as above and incubated with 3 mg DNaseI (Sigma) for 30-60 min, before being washed in PermWash (eBioscience) and then incubated with anti-BrdU antibody for 30 min at room temperature. Samples were acquired using FACS LSRFortessa using FACSDiva software (BD) and analysed with FlowJo version 9 software (Tree Star). Analysis was performed on single live cells determined using forward scatter height versus area and negativity for live/ dead. For analysis of macrophage proliferation, Ki67 expression was used to determine the frequency of all F4/80 hi cells in cycle, whereas a 2 h BrdU pulse before necropsy combined with Ki67 expression was used to identify cells in S phase, as described previously  24. Morphological assessment of cells. Peritoneal myeloid subsets as in Fig.
1b  were FACS-purified using a FACSAriaII. Sorted cells were spun onto poly-L-lysinecoated glass microscope slides (VWR International), fixed in acetone and stained using the Rapid-Romanowsky staining kit (Raymond A. Lamb, Eastbourne, UK). Analysis of microarray data. Microarray data of different myeloid populations produced by the ImmGen consortium (GEO15907) was downloaded from Gene Expression Omnibus. Details of the sorting strategies used to isolate each population can be found at http://www. immgen. org. Raw data was normalized using the Robust Multi Array algorithm and a common threshold for positive expression was determined. All arrays passed visual interrogation of MA plots. Differential gene expression between populations was identified using the Affy  67  and Limma  68  bioconductor packages in R.
Differentially expressed genes were determined using a minimum of a two-fold gene expression and adjusted P value of o0. 01 (t-test) on Log 2 transformed data. Similarity between populations was determined by hierarchical clustering of expressed genes using the Pearson's correlation coefficient generated on Log 2 transformed data with the CBA and/or Affy package in R. Unsupervised K means clustering produced similar data to hierarchical clustering. Statistics. Where required, data were log transformed to achieve normal distribution and equal variance. One-way ANOVA was used to compare all means within a data set, whereas the Holm-Sidak correction was used when individual ttests were performed on multiple cell populations. Paired Student's t-test was used to determine differences in frequency of Ki67 þ cells within donor and host-derived macrophages obtained from tissue-protected BM chimeric mice, whereas unpaired Student's t-test was used for all other two sample analysis.
Statistics were performed using Prism 6 (GraphPad Software). The statistical test used in each experiment is detailed in the relevant figure legend. This work is licensed under a Creative Commons Attribution 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons. org/licenses/by/4. 0/.
Acknowledgements We thank  R. Lynch  and  A. Hoy  for assistance with experiments;  S. Johnston  and  W. Ramsay  for help with flow cytometry and cell sorting;  M. McLeod  for Rosa26-LSL-YFP mice;  D. A. Hume  for the CSF1-Fc reagent; and  J. E. Allen  for critical reading of this manuscript. This work was supported by the  Medical Research Council  ( MR/L008076/1  to SJJ), MG was supported by a  Marie-Curie Career Integration Grant  and several FWO grants, and CLS was supported by a  Marie-Curie Intra-European Fellowship. AMM received funding from the  Wellcome Trust.
Our lack of knowledge of the ecology and evolution of the brown rat is striking given that brown rats are responsible for an estimated $19 billion of damage annually  [9]. Understanding the evolutionary trajectories of brown rats is also a prerequisite to elucidating the processes that resulted in a successful global invasion, including adaptations to a variety of climates and anthropogenic stressors. We inferred the global routes of brown rat expansion, population differentiation and admixture using a dense, genome-wide nuclear dataset, a first for a commensal rodent  [10]. A previous mitochondrial study identified the centre of origin  [11]  but did not resolve relationships among invasive populations. That work, in combination with fossil distributions  [12] , suggested that brown rats originated in the colder climates of northern China and Mongolia before expanding across central and western Asia, possibly through human settlements associated with Silk Road trade routes.
Material and methods We obtained rat tissue samples from field-trapped specimens, museum or institute collections and wildlife markets (electronic supplementary material, tables S1 and S2). As GPS coordinates for individuals were not always available, the sampling location was recorded as either the city, nearest town or island where rats were collected. Samples were genotyped using ddRAD-Seq  [15] , then missing genotype and relatedness filters were applied (see electronic supplementary material, Methods for details) for a final nuclear dataset containing 32 127 single nucleotide polymorphisms (SNPs) genotyped in 314 individuals, and a mitochondrial dataset with 115 SNPs (electronic supplementary material, table  S3 ) that comprised 104 haplotypes in 144 individuals.
(a) Population genomic analyses To describe population structure, we ran ADMIXTURE v. 1. 23  [16]  at each cluster from 1 to 40; given that a single SNP per RAD-tag was retained, we met the criterion for unlinked data for this analysis. Given the known effects of sampling bias on clustering analyses, we repeated this analysis with a subset of the data where four or five samples from each city were randomly selected (n ¼ 158). The CV error was the lowest for K ¼ 14 clusters, which supported the analysis of our full dataset. We also subdivided the full dataset into the Asian and non-Asian clusters and reran ADMIXTURE at each cluster from 1 to 25. We used the CV error values to identify the best-supported clustering patterns across the range. Using the same datasets (full, Asian and non-Asian), we ran principal components analysis (PCA) in EIGENSOFT v.
5. 0. 2  [17]  and identified significant PCs using Tracy-Widom statistics. We also estimated evolutionary clusters using FINESTRUCTURE v. 2. 0. 7  [18] , which elucidates the finest grained clusters by accounting for linkage disequilibrium and allows detailed admixture inference based upon the pairwise co-ancestry coefficients. We limited this analysis to the 20 autosomes (31 489 SNPs), removing SNPs on unassembled scaffolds in the dataset. Data for each chromosome were phased and imputed using FASTPHASE v. 1. 2  [19]. Initial analyses using the linked model indicated our data were effectively unlinked (c-factor 0. 0104); therefore, we ran the unlinked model. We used default settings except for the following parameters: 25% of the data were used for initial EM estimation; 750 000 iterations of the MCMC were run (375 000 of which were burn-in) with 1000 samples retained, 20 000 tree comparisons and 500 000 steps of the tree maximization were run.
We viewed MCMC trace files to confirm the stability of all parameters. To understand the patterns of population divergence, we ran TREEMIX v. 1. 12  [20]. As the R. rattus data (see electronic supplementary material, Methods) were mapped to the R. norvegicus genome, we extracted SNPs at the same genomic positions for 31 black rats (we removed two samples showing admixture; electronic supplementary material, figure  S1 ) with the SAMTOOLS v. 1. 2  [21]  mpileup function using a position list. We selected the sampling location with the largest sample size from each of the well-supported clusters at K ¼ 13 (figure  1  and electronic supplementary material, figure  S2 ), plus the R. rattus samples for the outgroup (which were not subdivided owing to lack of population structure, electronic supplementary material, figure  S3 ).
For the nuclear dataset, we calculated expected heterozygosity (H E ) and F IS within each of the 13 clusters using ARLEQUIN v. 3. 5. 1. 3 [22] , and pairwise F ST using VCFTOOLS v. 0. 1. 13 and the Weir and Cockerham estimator  [23, 24]. For the mitochondrial dataset, we calculated pairwise F ST between the clusters identified in the nuclear dataset in ARLEQUIN.
Results and discussion (a) Evolutionary clustering (i) Nuclear genome Our analyses of 314 rats using 32 127 SNPs identified multiple hierarchical levels of evolutionary clustering (K). PCA distinguished two clusters along the first PC, an Asian cluster that extended to western North America, and a non-Asian cluster found in Europe, Africa, the Americas and New Zealand (electronic supplementary material, figure  S5 ). Higher dimension PCA axes distinguished subclusters (electronic supplementary material, figure  S6 ), then individual sampling sites; in total, 58 axes of variation were significant using Tracy-Widom statistics (20 and 37 axes were significant for PCAs with only Asian or non-Asian samples, respectively). Using the model-based clustering program ADMIXTURE, the Asian and non-Asian clusters divided into five and eight subclusters, respectively (figures 1 and 2; electronic supplementary material, figures S2, S7, S8).
Within these broad geographical regions, many subclusters were identified by ADMIXTURE that likely resulted from either intense founder effects, isolation resulting in genetic drift, the inclusion of second-and third-order relatives in the dataset, or a combination of these factors. In the global analysis, four clusters were nested within W Euro (the island of Haida Gwaii, Canada; Vancouver, Canada; Kano, Nigeria and Sonoma County in the western USA) and two within N Euro (Bergen, Norway; Malmo, Sweden). We identified additional well-supported subclusters within the non-Asian cluster at K ¼ 12, 15, and 17 that represented individual cities (electronic supplementary material, figure  S12 ). Chile K = 2 K = 6 K = 13 K = 26 Our analysis using FINESTRUCTURE identified 101 clusters (figure  2 ).
(b) Range expansion We thinned our dataset to the sampling site with the largest sample size within each of the 13 clusters supported by ADMIXTURE and analysed the data using TREEMIX. We observed divergence within Asia first, followed by the two independent expansions into western North America. Drift along the backbone of the non-Asian cluster was limited, indicating rapid expansion of rats into Africa, Europe and the Americas (electronic supplementary material, figure  S13 ). Both the population tree topology and PCA (electronic supplementary material, figures S2, S6 and S13) indicated that range expansion occurred in three directions, where one southward and two eastward expansions comprised Asian ancestry, and the westward expansion produced the non-Asian cluster.
We found evidence of heterogeneity among cities as some appear to support one population, whereas others support multiple populations. For example, we detected a single population across multiple neighbourhoods in Manhattan (NYC, USA), whereas four genetic clusters (figure  2 ) were observed in a neighbourhood in Salvador, Brazil, a result that confirmed previous microsatellite-based analyses  [32]. Although denser sampling will be needed to confirm whether these groups represent distinct populations or reflect oversampling of intracity pockets of highly related individuals, intracity clustering likely represents substructure considering the global design of our SNP dataset. Observations of highly variable intracity structure suggest the following three scenarios: first, effective population size rapidly increases after invasion, possibly driven by high urban resource levels and thus genetic drift may have a relatively weak effect on population differentiation.
Second, new immigrants that arrive after initial invasion and establishment of rats in a city may be limited in their capacity to either establish new colonies or join existing colonies  [33] , thereby limiting ongoing gene flow from other areas owing to competitive exclusion  [34]. Gene flow into colonies may also be sexbiased as females were recruited more readily than males in a 2 year behavioural study of brown rats  [33]. We did observe gene flow in our dataset, including an individual matching coastal Alaska into the Bay Area and an individual with high Sonoma Valley ancestry in Thailand (figure  1b ), thus migration owing to contemporary human-assisted movement is possible and ongoing. However, given increasing connectivity owing to trade and continual movement of invasive species  [35] , we expected greater variability in ancestry proportions within cities than observed (electronic supplementary material, figure  S7 ).
Acknowledgements. We thank two anonymous reviewers for comments. We thank  Kaitlin Abrams  and  Ian Hays  for assisting with laboratory work. We thank  Annette Backhans ,  Francois Catzeflis ,  Gauthier Dobigny ,  Carol Esson ,  Tim Giles ,  Gregory Glass ,  Sabra Klein ,  Mare Lo  ˜hmus,  Patrick McClure ,  Frank van de Goot ,  Jordan Reed  and his  Mongrol Hoard ,  Richard Reynolds  and the  Ryders Alley Trencher-fed Society (RATS) ,  Thomas Persson Vinnersten  and colleagues at  Anticimex , and partners of the  Network Rodent-Borne Pathogens  for collecting and providing rat samples. The mammal collections at the  University of Alaska Museum of the North, Angelo State University, Berkeley Museum of Comparative Zoology , the  Burke Museum at University of Washington , the  Museum of Southwestern Biology  and the  Museum of Texas Tech University  also graciously provided tissue samples.