dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, we used ELAI inferences as reference to perform power and false discovery rate (FDR) analyses of the RNDmin method. This analysis was restricted to introgression frequencies in the range that could be detected by ELAI (maximum 65%). RND windows only partially overlapping ELAI segments were not considered. On this basis, we estimated the FDR and power of the detection of introgression by RND as a function of the RNDmin threshold. A threshold predicting a FDR of 10% was used (Additional file 2: Figure  S2 ). Regions of the genome with outlier high frequencies of introgression were defined based on the simulated demographic scenario using SPLATCHE2. For each parameter set, we recorded the minimum frequency of introgression at which at least 95% of the simulation replicates suggest a lower proportion of introgressed markers than was inferred in the empirical dataset (for all three RND window lengths).
GO enrichment analyses We tested for functional enrichment of genes with high introgression frequencies (combining the evidence from the three RND window lengths) using the g:Profiler R package  [131, 132]. Categories with less than five genes were excluded and the Benjamini-Hochberg correction for multiple testing was applied. Only genes within or overlapping RND windows with more than 50 informative sites in any of the three RND window length analyses were considered for the background list of genes. We used both the rabbit GO term annotation and the more complete mouse one. For the latter, only one-to-one rabbit to mouse orthologous genes were considered. GO terms were summarized using REVIGO  [133].
Analyses of nuclear genes with mitochondrial functions We generated a list of nuclear genes with mitochondrial functions (mitonuc genes) by combining two public databases: InterMitoBase  [49]  and MitoCarta2. 0  [50]. These databases provide lists of human annotated genes encoding proteins that are present in the mitochondria. We identified rabbit orthologous genes using the Ensembl Biomart query tool  [134]. Of the 708 human annotated nuclear genes in InterMitoBase, 615 were found annotated in the rabbit, while 1030 genes from the 1147 nuclear genes from Mitocarta2. 0 were annotated in the rabbit genome. The union of the two databases resulted in 1210 mitonuc rabbit annotated genes. We further added one OXPHOS gene (NDUFA4L2) that was missing from both databases. From the sets of mitonuc genes, we verified those showing a geographic introgression pattern mimicking that of mtDNA: i) absence of introgression in southern individuals (no mtDNA introgression is found in the south)  [21, 30] ; ii) at least two introgressed haplotypes in the five northernmost samples.
Gene variation statistics and functional impact of amino acid differences We produced species pairwise alignments (between L. timidus and L. granatensis and L. timidus and L. americanus) from the phased genomes for all rabbit annotated genes  (19, 280). For each gene, we obtained the exon coordinates of the largest transcript from the Ensembl Biomart query tool. We excluded from the alignments sites with more than two alleles. Alignments including SNPs with allele frequencies markedly deviating from Hardy-Weinberg proportions in either L. timidus or L. granatensis (exact test p value < 0. 01; using Plink 1. 9) were discarded, as it may result from the inclusion of paralogs. Sequences with more than 50% missing data were removed from the alignments. Furthermore, haplotypes in L. granatensis inferred to be of L.
timidus origin were excluded from the L. granatensis alignment. Sites with less than four haplotypes with information in either L. timidus or L. granatensis or with no information in L. americanus were masked with Ns. Finally, alignments with less than 100 codons or with premature stop codons were removed. We estimated dN and dS (Jukes-Cantor; rates of non-synonymous and synonymous substitutions, respectively) using the Bioperl DNAStatistics module (available in http://search. cpan. org/ dist/BioPerl/Bio/Align/DNAStatistics. pm) and dN/dS was calculated as the average of dN/dS pairwise estimates. In order to examine the potential functional impact of amino acid differences, we used the SIFT Aligned Sequences tool implemented in SIFT v1. 03  [48]  (available at http://sift. jcvi. org). This method assumes that amino acid changes occurring in a given lineage at positions otherwise conserved at a deeper phylogenetic scale likely affect protein function.
Acknowledgments We thank  Liliana Farelo  for preparing the DNA samples. Library preparation and sequencing were performed at the  Genome Analysis Centre (TGAC, Norwich, presently Earlham Institute ). Data analysis was performed on the  Montpellier Bioinformatics Biodiversity computational platform (MBB) , with support from  LabEx CeMEB  ( Laboratoire d'Excellence, Centre Méditerranéen Environnement et Biodiversité, Montpellier, France ). We thank  Khalid Belkhir ,  Rémy Dernat , and  Julien Veyssier  for precious assistance on this platform. Additional instrumentation, laboratory, and computational support was provided by the  CIBIO NEW-GEN  sequencing platform, supported by  European Union 's  Seventh Framework Programme  for research, technological development, and demonstration under grant agreement number  286431.
Funding This study was funded by research project  HYBRIDADAPT , co-supported by  Portuguese National Funds  through the  Fundação para a Ciência e a Tecnologia, FCT  ( FCT-ANR/BIA-EVF/0250/2012 ) and  French Agence Nationale de la Recherche ,  ANR  ( ANR-12-ISV7-0002-01 ). Support was also obtained from POPH-QREN funds from the  European Social Fund and Portuguese MCTES/FCT  ( SFRH/BD/87126/2012  PhD grant to F. A. S. , and  IF/00033/2014/CP1256/CT0005  Investigador  FCT grant  to J. M. F. ), and project  NORTE-01-0145-FEDER-000007  supported by the  Norte Portugal Regional Operational Programme  ( NORTE2020 ), under the  PORTUGAL 2020 Partnership Agreement , through the  European Regional Development Fund (ERDF). This research also received support in the frame of the  Laboratoire International Associé (LIA) "Biodiversity and Evolution " supported by  InEE  ( Institut Ecologie et Environnement ,  CNRS, France)  and  FCT  ( Fundação para a Ciência e a Tecnologia, Portugal ).
Availability of data and materials The dataset supporting the conclusions of this article is available in the Sequencing Read Archive (https://www. ncbi. nlm. nih. gov/sra) repository as study PRJNA399194 (accession numbers SRX3108378-SRX3108391)  [140]. Additional whole genome sequencing data for the L. americanus individual were retrieved from NCBI SRA  [141]. The rabbit genome reference assembly used to build the hare pseudo-reference assembly was downloaded from Ensembl  [142].
Observational data set from the AMBRE Project This work is based on ESO archived spectra data from the AMBRE project. We recall that this project is dedicated to the parametrization of the HARPS, FEROS, UVES, and GIRAFFE spectral archives (de Laverny et al. 2013), providing robust automatic determinations of the radial velocity (V rad ), effective temperature (T eff ), surface gravity (log(g)), metallicity ([M/H]), and global α enrichment with respect to iron ([α/Fe]) together with their associated errors. The present study focuses on a subsample of the first three spectral sets that have already been parametrized: HARPS  (De Pascale et al. 2014) , FEROS  (Worley et al. 2012) , and UVES  (Worley et al. 2016 ). These analysed subsamples consist of spectra with a good AMBRE quality flag (lower or equal to 1; see e.
(2006): 4 085. 57, 4 191. 08, 4 316. 08, 4 483. 32 , and 4 498. 28 Å. -Weak contribution of the hyperfine splitting is predicted for dysprosium so we do not take it into account in our spectral synthesis. We adopted the atomic data from  Wickliffe et al. (2000)  for the two lines considered: 4 073. 12, 4 449. 70. We note that all lines are not observed, detected, and unblended in each star. We then adopted a line-list for the atomic blends from the Vienna Atomic Line Database (VALD3,  Kupka et al. 1999 Kupka et al. , 2000) )  over the domains covered by the adopted lines for the abundance analysis. Additionally, the line-lists of twelve molecular species were also taken into account: CN  (Sneden et al. 2014) , TiO  (Plez, priv. comm. ) , C2  (Brooke et al. 2013; Ram et al. 2014 ), CH (Masseron et al.
8 dex, probably caused by a contamination by halo stars. We note that the thick disc clearly presents a smaller Ba abundance with respect to the thin disc, in the same metallicity range, and that both discs show the same dispersion (σ [Ba/Fe] = 0. 15 dex). In addition, our thick disc data do not match the [Ba/Fe] model of  Bisterzo et al. (2017) , predicting an increase in [Ba/Fe] as a function of [M/H]. Delgado Mena et al. (  2017 ) also observed a flat trend in their data, Fig. 3. For the Ba abundance, line-to-line scatter as a function of the error due to the atmospheric parameters for the FEROS sample. The thin disc (blue dots, 917 stars), thick disc (red dots, 112 stars), and metal-rich α-rich (green dots, 43 stars) samples are shown, with their corresponding normalized distributions.
Conclusion In this study, our goal was understanding the evolution of Milky Way disc r-process abundances. We built a homogeneous catalogue of chemical abundances of Ba (pure selement) and Eu, Gd, and Dy (pure r-elements). In the literature, such a catalogue with high statistics is still lacking. As a result, the chemical evolution of pure r-process elements is still a matter of debate. We took advantage of the HARPS, FEROS, and UVES ESO archives, coupled with the atmospheric parameters of the AMBRE project  (de Laverny et al. 2012). We performed an automatic derivation of individual chemical abundances and errors of Ba (5 057 stars), Eu (6 268 stars), Gd (5 431 stars), and Dy (5 479 stars) thanks to the pipeline GAU-GUIN  (Guiglion et al. 2016). It is the first time that such a homogeneous data set has been provided, especially for Gd and Dy, and that covers such a wide metallicity range (-1.
Supraglacial lake area To document the supraglacial lake expansion, we analysed 25 Level 3B tiles collected by the PlanetScope Dove satellite constellation between 27 March and 26 October 2017 (Table  S1  in the Supplement). These 4-band data have a ground sampling distance of 3. 7 m but are resampled to 3 m during orthorectification, and digital numbers (DNs) contain scaled at-sensor radiance values for the blue (B: 455-515 nm), green (G: 500-590 nm), red (R: 590-670 nm), and near-infrared (NIR: 780-860 nm) spectral ranges. We also used several RapidEye level 3B tiles for pond coverages and geomorphic interpretations. These are 5-band data (B: 440-510 nm; G: 520-590 nm; R: 630-685 nm; red edge: 690-730 nm; NIR: 760-850 nm) with a ground sampling distance of 6. 5 m, resampled to 5 m during orthorectification  (Planet Team, 2017).
Lake volume estimation Using the pre-lake March 2017 Pléiades DEM, we identified 142 closed surface depressions and determined deptharea-volume relationships for each by progressively filling them with an increment of 0. 1 m depth (following e. g. Watson et al. , 2017). We then calculated stored water volumes in the supraglacial lake area for each PlanetScope scene by www. the-cryosphere. net/12/3891/2018/ The Cryosphere, 12, 3891-3905, 2018 estimating the volume of each individual pond in the area of the supraglacial lake, then summed these to estimate the total ponded volume in the study area (Fig. 3 ). On 16 July the lake was partially obscured by clouds, so for this scene we instead estimated the water level and volume from a partial shoreline data set (Figs. S1 and S2 in the Supplement).
This is a crucial observational gap, as the hydrological storage and discharge of debris-covered glaciers have significant consequences for glacial hazards, surface ablation, glacier dynamics, proglacial sediment dynamics, and water supply with direct impacts on downstream populations. Data availability. All derivative data used in this study (e. g. lake coverages, dH zones) are available upon request. Please contact Evan Miles for this purpose (evan. miles@wsl. ch). PlanetScope and RapidEye data are freely available in reasonable quantities for research and education; see https://www. planet. com/markets/ education-and-research/ (Planet Team, 2017). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-12-3891-2018-supplement.
Acknowledgements. The authors gratefully acknowledge Planet for provision of PlanetScope and RapidEye imagery. This research was supported by the  "EverDrill" Natural Environment Research Council Grant awarded  to the  University of Leeds  ( NE/P00265X ) and  Aberystwyth University  ( NE/P002021 ). C. Scott Watson  acknowledges support from the  NASA   High Mountain Asia  grant  NNX16AQ62G. Etienne Berthier  acknowledges support from the  French Space Agency (CNES)  through the  TOSCA programme. The discharge data were collected as part of two projects funded by the  French National Research Agency (ANR) ,  France  (references  ANR-09-CEP-0005-04/PAPRIKA  and  ANR-13-SENV-0005-04/05-PRESHINE ). KEM is funded by an  AberDoc PhD Studentship. Evan S. Miles ,  Katie E. Miles , and  Duncan J. Quincey  acknowledge  Himalayan Research Expeditions  and  Mahesh Magar  in particular for field logistical support.
e. position relative to nearby stars) is never used. Many anomalous data are also rejected by the same module. The post-processing appends the calibrated photometry to the data of each observation (determined by an independent pipeline, see Sect. 4) and groups all the observations of a same target. Eventually, a "Validation" task rejects anomalous data. The origin of the anomalies are multiple: for instance, data can be corrupted for technical reasons, or a mismatch with a nearby star on the sky plane can enter the input list. Identifying truly anomalous data from peculiarities of potential scientific interest is a delicate task. Most of this article is devoted to the results obtained on the general investigation of the overall data properties, and draws attention to the approaches needed to exploit the accuracy of Gaia and prepare a detailed scientific exploitation.
-Minimum number of positions in a transit. The final check was to assess how many positions were left in a transit. For Gaia DR2, we set the limit to two because we relied on an a priori list of transits to be processed (see Sec. 2. 1). SSOs have also gone through a further quality check and filtering according to internal processing requirements established to take into account some expected peculiarities of SSO signals. Three control levels were implemented: -Standard window checking. Only centroids/fluxes from windows with standard characteristics were accepted and transmitted to the following step of the processing pipeline. -Checking of the quality codes in the input data, resulting from the signal centroiding. Only data that successfully passed the centroid determination were accepted.
This happens in particular for asteroids whose apparent motion with respect to stars is non-negligible over the transit duration. A main-belt asteroid with a typical motion of 5 mas/s drifts with respect to the computed window by several pixels during the ≈ 40s of the transit in the Gaia FOV. As provided by the photometric processing, a total of 234,123 transits of SSOs have an associated, fully calibrated magnitude (81% of the total). Fig. 8  shows the distribution of the relative error per transit σ G of the whole dataset before filtering. Article number, page 7 of 24 We found out that the sharp bi-modality in the distribution correlates positively with transits of fast moving objects. For this reason, we decided to discard all transits that fell in the secondary peak of large estimated errors σ G >10% as they almost certainly correspond to fluxes with a large random error and might be affected by some (unknown) bias.
3% RMS in flux. This strongly suggests that Gaia photometry is probably better than 2% RMS, within the limitations imposed by the shape model accuracy and the assumptions on the scattering model. Moreover, Gaia data seem to offer an opportunity to improve the currently accepted shape solution for Lutetia, which is based partly upon ground-based data. The results obtained for (2867) Šteins, for which a highresolution shape model is also available, strongly support the conclusion that the photometry is indeed very accurate. For (2867) Šteins two pole solutions exist, essentially differing only by the value of the origin of the rotational phase. By directly using the shape model to reproduce Gaia data, resampled at 5 degree resolution, with a Lommel-Seeliger scattering corresponding to E-type asteroid phase functions, the RMS value of the O-C is 1.
Validation of the astrometry The processing of the solar system data described above has eventually produced a list with 14 124 objects (all numbered SSOs), 290 704 transits, and 2 005 683 CCD observations. The sky distribution is shown in Fig. 16  in a density plot in equatorial coordinates. As expected, most SSOs are found in a limited range of ecliptic latitudes. The distribution in longitude is not uniform because over a relatively short duration of 22 months, the Gaia scanning returned to the same regions of the sky, only in a limited number of areas. Assessing the quality of the astrometry is challenging, and it needs an ad hoc treatment. Various filters have been applied during the activity of the astrometric reduction processing. The filtering process ensures the rejection of a maximum number of bad detections, while keeping the number of good positions that are rejected as small as possible (for more details, see the Gaia DR2 documentation).
We also added the contribution of 16 massive main-belt asteroids (see A). We used a relativistic force model including the contribution of the Sun, the planets, and the Moon, namely the Einstein-Infeld-Hoffman approximation  (Moyer 2003)  or  (Will 1993). As a result of the orbit determination process, we obtained for every object a corrected orbit fitted on Gaia data only together with the post-fit residuals. The core of the least-squares procedure is to minimise the target function  (Milani & Gronchi 2010) , Q = 1 m ξ T Wξ, (1) where m is the number of observations, ξ are the residuals (observed positions minus computed positions), and W is the weight matrix. The solution is given by the normal equations,  where x is the vector of the parameters to be solved for. The differential corrections produce the adjustments ∆x to be applied to the orbit: C = B T W B; D = -B T Wξ B = δξ δx , (2) ∆x = C -1 D.
The average is an analogue of accuracy, as it is expected to provide the overall discrepancy of the SSO position with respect to the fitted orbital solution around the transit epoch. The result of the transit-level average of residuals is shown in Fig. 27. The large residuals associated with the AC direction are due to the AF lack of resolution, with the exception of bright (G<13) targets. The plot in AL is much more impressive, as it catches the full accuracy of Gaia DR2 data for SSOs, with an average well below 1 mas for all sources G<19. 5. The best performance appears to be around G∼17. The scatter of the residuals during a transit (Fig. 28 ) can be considered as an indication of the transit-level astrometric precision. We compute this precision as a standard deviation. Of course, given the small number of data points (at most nine per transit), this is a rather poor statistical estimator of the true standard deviation of the population.
We found no clear evidence of a difference between centreof-mass and photocentre. Although the phase angle of Gaia observations is rather high, the typical photocentre shift can reach at most a few percent of the SSO diameter, but its direction can have any orientation with respect to the AL position angle. When we select asteroids with similar astrometric accuracy (i. e. similar G within a one-magnitude interval, for instance), no clear trend of the average residuals with respect to phase angle is found. The effect can be considered of second order with respect to the other uncertainty sources illustrated above. The orbit fitting discloses interesting properties of Gaia asteroid observations. Figure  32  shows the results of the current situation (22 months of Gaia) obtained as output of the orbit determination process and what we may expect at the end of the nominal  The data represent the uncertainty in the semi-major axis (a good and easy to determine indicator of the orbit quality) as a function of the semi-major axis a (in au).
Analysis of the orbits The simulations show an improvement of almost a factor 10 in the orbit determination after 5 years of mission, except for Jupiter trojans, which indeed have a period of 11 years. Thus after 10 years of mission, the improvement is clearly visible not Article number, page 17 of 24 A&A proofs: manuscript no. DR2-SSO only for main-belt asteroids (a factor 20), but also for the trojans. On the other hand, the Gaia DR2 short time-span of 22 months (compared to tens/hundreds years for AstDyS, and 5 or 10 years for Gaia itself) represents a limitation on the expected quality of the orbits. However, some asteroids observed by Gaia already reach a quality equivalent to ground-based data (and in 350 cases, it is even better). Figure  33  shows the uncertainty on the semi-major axis obtained as a result of the orbit determination process using only Gaia observations, with respect to the current uncertainty from the orbits available on the AstDyS website as function of the perihelion distance (q).
NOD2-dependent ISG response in the gut of Nlrp12 -/-mice. We next examined at high-resolution the colon and caecum of Nlrp12-deficient mice for any spontaneous signs of inflammation as a consequence of an impaired tolerance to MDP derived from the gut microbiota. To this end, whole-genome expression analysis was performed on RNA extracted from the caecum of wildtype and mutant mice. A set of 106 genes were differentially regulated as a result of NLRP12 deficiency, among which 35 genes were significantly up-regulated by at least one log2-fold change in Nlrp12-deficient mice compared to controls (Supplementary Data 1). Of these, gene-ontology analysis identified a significant overrepresentation of ISG (Fig. 5a ), including those encoding interferon-induced protein 44 (IFI44), interferon-induced protein with tetratricopeptide repeats 2 (IFIT2), Apolipoprotein L9 (APOL9a/b) and 2'-5'-Oligoadenylate synthetase 2 (OAS2).
17c ). This was associated with spenomegaly (Supplementary Fig. 17d ) and tissue pathology as evidenced respectively by about 47 percent increase in spleen weight (Supplementary Fig. 12d ) and by the enhanced histological score (Supplementary Fig. 17e ) and a 30 percent increase in the crypt length (Supplementary Fig. 17f-g ). To further examine the basis of how NOD2-mediated host defense may protect Nlrp12-deficient mice against C. rodentium, RNA was extracted from the caecum of infected mice at day 0 and day 7 post-infection and a genome-wide analysis of the acute transcriptional response to the pathogen was performed (Supplementary Data 1). As expected, C. rodentium infection was found to robustly induce expression of many genes involved in Th1-and Th17-mediated host defense  37. We identified a set of 551 transcripts whose expression was modulated at least one log 2 -fold change in response to the infection independently of NLRP12 expression (overlapping zone in Venn diagram; Supplementary Fig.