dataesr/scientific-paragraphs-categorization huggingface dataset:
Demographic and disease characteristics of the population at baseline Nine hundred eighty-six patients were recruited and analysed at baseline  [24]. Of these, 630 patients were followed for 2 years (mean follow-up 23 ± 2. 4 months) with clinical and biological data available. These patients therefore constitute the analysed population for the study's primary objective (BP and Pu control after a 2-year follow-up). The occurrence of CV or renal events was analysed in this population as well as in those patients who had died during the study or had been lost to follow-up by year two but who had experienced complications during the first year, i. e. , 729 patients (cf. numbers in bold in Fig. 1 ). Patients characteristics (n = 729) were not different from those described in the 986 analysed at baseline: mean age was 69 years with 31 % of the patients over 75 years, 74 % were male, mean body mass index (BMI) was 30 kg/m 2 and most patients had a long history of diabetes (mean duration of 17 years).
Biological data Blood samples were drawn on admission. The median (interquartile range, IQR) time from symptom onset to blood sampling was 17  hours. Plasma N-terminal fragment of B-type natriuretic peptide (Nt-proBNP) was measured using homogeneous chemiluminescence immunoassay on a Vista analyzer (SIEMENS). Blood glucose was assessed by the enzymatic method on a Vitros 950 analyzer (Ortho Clinical Diagnostics, Rochester, NY). Plasma creatinine levels were measured on a Vitros 950 analyzer (Ortho Clinical Diagnostics, Rochester, NY). Glomerular Filtration Rate was calculated by CKD-EPI formula  [21]. C-reactive protein (CRP) was determined on a dimension Xpand (Dade Behring, Deerfield, IL) using enzymatic methods. The CRP level was dichotomized at 3 mg/L for more clinical relevance.
A second model (model 2) was built with covariates from model 1+ ADMA. The goodness of the fit was tested by the -2Log Likelihood χ² criteria as an index of model quality. The additional prognostic information of ADMA was tested by comparing the -2log likelihoods of model 2 vs model 1 and by category-free Net Reclassification Improvement (NRI). Statistical analyses were performed using SPSS 12. 0 software (IBM Inc, USA).
The samples were then analyzed in a GC-MS system (Thermo Fischer Scientific Inc. , Waltham, MA), using the same settings as in  Khodayari et al. (2013). For this purpose, one microliter of each sample was injected in the GC-MS system using the split mode (split ratio: 25:1). After that, the selective ion monitoring (SIM) mode (electron energy: À70 eV) was used to search for the 60 primary metabolites that are most often found in arthropod samples and that were included in our spectral database (see Appendix S2 for a complete overview of these 60 metabolites). The SIM mode ensured a precise annotation of the detected peaks. The calibration curves were set using samples consisting of 60 pure reference compounds at concentrations of 1, 2, 5, 10, 20, 50, 100, 200, 500, 750, 1000, 1500, and 2000 lmolÁL À1.
Statistics A total of 43 metabolites were identified. In the first step, we examined whether distinct metabotypes existed among the sampled populations of T. urticae by running a MANOVA. Nine metabolites were first removed from the dataset because they showed a more than 85% correlation with (an)other metabolite(s). Then, all remaining metabolites were log-transformed to obtain a normal distribution, which allowed proceeding to the MANOVA. In the second step, we tested whether the metabolic profile was gradually changing as a function of latitude (from range core to edge) or as a function of one of the six life-history traits that were previously shown to covary with latitude (daily and lifetime fecundity, egg survival, longevity, dispersal propensity, and sex ratio; see  Van Petegem et al.
8 in  Tenenhaus 1998 ). In the third step, univariate analyses were performed to test, metabolite by metabolite, whether the global patterns obtained in the previous step could be confirmed. As we aimed at determining whether the metabolite levels showed latitudinal patterns along the expansion, we did not run an ANOVA on individual metabolites, but rather processed regressions. Using SAS 9. 4 (2013; SAS Institute Inc. , Cary, NC, USA), the linear regressions were run for all influential metabolites (VIP scores >1. 2, except for egg survival, as mentioned above). Because all five collected replicates (four for SKA, see earlier) per population originated from only one field sample, we ran the regressions using population averages. As our study is explorative, we wanted to avoid false negatives (with the chance of making a type II error).
, 2012 (Xia et al. , 2015) )  with those metabolites that showed significant effects in the univariate analyses of step three. These pathway analyses were performed with a Fisher's exact test algorithm, which we ran using the metabolic pathways of Drosophila melanogaster (no closer relative of T. urticae was available in the program, but primary metabolites are anyway highly conserved, especially among nonblood-feeding arthropods). The algorithm calculates the match (number of hits) between the metabolites in a dataset and the totality of metabolites present in a specific pathway. Furthermore, it uses a pathway topology analysis to compute a value for the impact of these metabolites on the pathway. As multiple comparisons are made, corrected Holm P-values are provided.
While our spectral database contained 11 sugars, only one sugar (fructose) accounted for the separation among populations. This suggests that the genes involved in encoding the mite's energetic metabolism (i. e. , glycolysis and citric acid cycle, which typically involve sugars) have not been significantly affected during the range expansion of T. urticae. Instead, the observed differentiation in the mites' metabolome probably involved evolutionary changes in the mites' anabolism, where amino acids play a central role in the metabolic turnover of proteins. In more northern and more dispersive populations, the aminoacyl-tRNA biosynthesis was downregulated. In this pathway, aminoacyl-tRNA is formed by charging ). For example, in Figure  3A , LAU is the population with the highest concentration of proline and ODK is the southernmost population (lowest latitude).
Although all mites were kept in common garden, mites from northern, more dispersive populations were found to contain lower essential amino acid concentrations. In line with the recent finding of  Fronhofer and Altermatt (2015)  that a dispersal-foraging trade-off leads to a reduced exploitation of resources at range margins, our results could indicate that northern, more dispersive mites evolved lower essential amino acid concentrations because they consume less of their food source. We should, however, keep in mind that metabolites were measured only at one point in time and from whole-organism samples. We are therefore missing the temporal fluctuations of the metabolome over a day, and our data therefore represent only a snapshot of the existing balance in terms of metabolite demand among metabolic pathways.
Acknowledgments This project was funded by the  Fund for Scientific Research -Flanders (FWO)  (project  G. 0610. 11 ). D. Bonte  and  R. Stoks  were supported by  BelSpo IAP Project "Speedy. "We furthermore thank the  INEE-CNRS (ENVIROMICS call, project  " ALIENS ") for funding D. Renault.
Funding Information This project was funded by the  Fund for Scientific Research -Flanders (FWO)  (project  G. 0610. 11 ). D. Bonte  and  R. Stoks  were supported by  BelSpo IAP Project "Speedy. "We furthermore thank the  INEE-CNRS (ENVIROMICS call, project  " ALIENS ") for funding D. Renault.
The King penguin is a good climatic bio-indicator as confirmed by its strong demographic response to Quaternary climate change. We accurately reconstructed its past demography applying a novel model-flexible approach (the Stairway plot; Supplementary Information section S02. 3), based on the composite likelihood of the derived-allele frequency spectrum  29  calculated on the full highquality genomic dataset. This analysis was compared to multi-locus Bayesian skyline analyses on a subset of the data (Supplementary Information section S02. 4) and pairwise sequentially Markovian coalescent analyses on six additional whole-genome sequences (Supplementary Information section S02. 5) and validated through simulations (Supplementary Information section S02. 6). The King penguin population experienced two bottlenecks: (a) a recent one during the Last Glacial Maximum (LGM: 19-21 kya), and (b) a more ancient one overlapping with the previous Pleistocene glacial episode (Fig.
S1. 3 Sequence alignment and genotyping. Data processing was performed using the following workflow: (i) Sequence demultiplexing. Read quality assessment was made in FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Samples were de-multiplexed according to in-line barcodes using Stacks v1. 28  2, 3  , low-quality reads were discarded, and sequences trimmed to 95 bp. (ii) Read mapping and filtering. Demultiplexed fastq files were mapped to the published contigs of the Emperor penguin genome 4 using Bowtie2 2. 2. 35, with standard settings, allowing only end-to-end mapping. Resulting SAM files were filtered using Samtools [2/25] 0. 1. 196, PicardTools 1. 113 (picard. sorceforge. net), and custom R and shell scripts (github. com/rcristofari/RAD-Scripts. git) in order to discard unpaired reads and full read pairs where at least one mate has a mapping quality score below 30.
The resulting BAM files were then filtered for PCR and optical duplicates by comparing mapping position and CIGAR string, using Picard MarkDuplicates. This process also allowed to filter out most sequencing errors, since MarkDuplicates only retains the read with the highest average Phred score in each duplicate cluster. (iii) SNP calling and genotyping. A draft SNP-calling was done in Stacks v1. 28 for the general assessment of the dataset, using the "rxStacks" correction algorithm, with a maximum of 5 mismatches allowed between alleles at a single locus (both within and between individuals). For SNP-based analysis, joint SNP and genotype calling was performed using the GATK HaplotypeCaller pipeline  5  , with standard parameters, except for population heterozygosity which was set to 0. 01.
S2. 1 Summary statistics. Summary statistics were calculated in Arlequin  13  and with custom R scripts either from filtered SNP calls, or from short RAD haplotypes using 11,724 loci. Pairwise fixation index (Fst), calculated using Reich's estimator  14  , is close to zero (mean pairwise Fst 0. 0132 ± 0. 00567). Nucleotide diversity π and Tajima's D were computed for full RAD haplotypes. In order to avoid possible biases due to low coverage, we randomly sampled one haplotype for each individual, and performed calculations on this haploid subset. Tajima's D is slightly negative, and homogeneous across locations (Dall: -1. 094 ± 0. 672, DHEA: -0. 329 ± 0. 925, DKER: -0. 518 ± 0. 899, DCRO: -0. 546 ± 0. 890, DMAR: -0. 404 ± 0. 00307, DGEO: -0. 448 ± 0. 925, DFLK: -0. 312 ± 0. 953), and nucleotide diversity is low (πALL: 0.
S2. 2. 2 Principal component analysis. Genotype posterior probabilities calculated in ANGSD (S2. 2. 1) were used to perform a principal component analysis (PCA) in ngsTools  16  , including 147,711 variable sites with a maximum-likelihood derived allele frequency at least equal to 1/2N (with N being the number of included samples). PCA was repeated in the R package adegenet  17  , using a filtered SNP dataset (minimum depth of coverage of 4x, and minimum 80% individuals genotyped at each locus, leaving 4,784 polymorphic loci for analysis). , based on genotype likelihoods calculated in ANGSD with a SAMtools model, and allowing for a maximum of 50% missing data in order to process a site, and keeping only positions inferred as variable with a high likelihood (p-value threshold 1e-6). A total of 151,422 sites passed these filters.
We performed 100 bootstrap replicates, with K values ranging from 1 to 10. Best-fitting K was chosen using Evanno's δK method. An independent clustering was [4/25]   performed in FastStructure  19  using a filtered SNP dataset (minimum depth of coverage of 4x, and minimum 80% individuals genotyped at each locus, leaving 4,784 polymorphic loci for analysis), again with 100 replicates and K ranging from 1 to 10. Both approaches unambiguously supported a K=1 model.
S2. 2. 5 Mitochondrial DNA comparison between Crozet and Macquarie Island colony. Comparison of mitochondrial hypervariable control region (HVR) haplotypes from Crozet (Trucchi et al. 22  , from 139 individuals, Genbank accession number KF530582-KF530621) with published sequences from Macquarie Island (Heupink et al. 23  , 35 individuals, Genbank accession number JQ256379-JQ256413) confirms the idea of a single, worldwide and fully panmictic population. Pairwise Fst is low (Fst=0. 032), and a haplotype network does not support any population separation between the two islands (Fig. S2-B ). The Stairway Plot method is a novel method for demographic inference developed by Liu & Fu  24. This model-flexible method relies on the maximisation of the composite likelihood of the observed derivedallele frequency spectrum, without prior hypothesis on demographic history, as opposed to previous spectrum-based demographic inference methods (e.
The full Multiple Sequentially Markovian Coalescent approach (MSMC  36  ), which has a much improved resolution for recent time periods, relies on the accurate phasing of haplotypes, which unfortunately is not possible in a non-model species, in the absence of a large transmission or population dataset. In order to exploit unphased haplotypes, analysis must be restricted to the pairwise case, as PSMC'. However, since recombination events are treated as a Markovian process along the sequence, it is still possible to increase the likelihood of the reconstruction by concatenating [8/25]   several genomes together, thus increasing the independent sampling of TMRCA. We then selected three high-quality samples for the King penguin, and the Emperor penguin to perform whole-genome resequencing. Libraries were prepared with a standard Illumina(c) TruSeq™ PCR-free protocol, and multiplexed on two lanes of a HiSeq 2500 V4 sequencer at the Norwegian Sequencing Center facility, University of Oslo.
S5. 1 AOGCMs choice and multi-model ensemble approach We used the latest generation of AOGCMs from the IPCC Coupled Model Intercomparison Project Phase 5 (CMIP5)  65  , which represent a significant improvement over CMIP3 in the Southern Ocean  44. We applied a multi-model ensemble approach, a common improvement over single-model projections, as only the trends present in most models are retained in the final ensemble mean  44. We selected 15 AOGCMs based on the range of available outputs and their coverage of the Southern Ocean (Table  S1 ). All model outputs were downloaded from the ESGF nodes (pcmdi9. llnl. gov/). In our study, we used the following variables: Sea Surface Temperature (SST) and Sea-Ice Concentration (SIC). For each variable, we calculated the multi-model ensemble mean and standard deviation using the Climate Data Operators toolset (CDO 2015, available at: http://www.
Before using SST outputs derived from AOGCMs, we assessed the accuracy of the representation of the Southern Ocean by comparing each model SST-output for historical runs to satellite-measured SST from december 1981 to december 2005, using the NOAA Optimal Interpolation v2 SST dataset  69. Cell-by-cell (1°x1°) linear correlation of SST was assessed and R 2 , slope and intercept were plotted in order to assess the spatial distribution of model departure from observed values. As modelled SST was generally found warmer than observed SST in the APF zone over the historical period, we followed the correction applied by Péron et al. 66. In order to maximise the fit between observed and modelled SST for each archipelago, we defined four oceanic sectors: South Atlantic Ocean (45°W to 18°E), South Indian Ocean (18°E to 80°E), Macquarie (135°E to 180°E), and Falkland region (75°W to 45°W), ranging in latitude from 45°S to 55°S, but extended to 60°S in the Falkland region to account for the higher latitude of the APF around Cape Horn.
For each of these sectors, we tested the linear correlation between modelled and observed SST, and we corrected the model value linearly when needed (Table  S2 ). The 5°C SST isotherm was then calculated in GDAL (www. gdal. org), and kilometric distance between each island and the 5°C isotherm was calculated using the OGR Python library. Correctness of our model was assessed through 1) correlating the observed and modelled distances to the 5°C isotherm on the 1981-2005 period and 2) consistency between these distances and published data on King penguin foraging areas. Foraging range predictions for the historical period closely matched both observed historical SST, and [15/25]   observed foraging distances at most locations: ~380 km on Crozet (observed: 300-500 km  66  ), ~320 km on Marion (observed: 300 km three decades ago  70  ), ~20 km in the Kerguelen (observed: 270 km in the APF along the 4 and 5°C isotherms 71,72 -the APF is reached immediately, but foraging trips extend further in the productivity zone), ~310 km on Heard (observed: 370 km a decade ago  45  ), ~300 km in South Georgia (observed: 300-600 km over the whole breeding season  72  ).
S5. 1. 2 Winter sea-ice concentration (SIC) Winter Sea-Ice Concentration (SIC) is known to limit the southward expansion of the King penguin's breeding range, as the species overwinter breeding cycle makes open-water conditions a requisite throughout the year  67. Although SIC may still be subject to biases in its representation compared to SST, it has improved since CMIP3  77, 78, 79. We take the 15% concentration isoline as being representative of the effective sea ice edge  78. We only consider the sea-ice concentration at their maximum, during the months of august and September. Compared to satellite-derived historical measures from the NOAA Optimal Interpolation dataset, ensemble reconstruction gives a winter sea ice that tends to be more dense than observed values (mean density of sea ice above 15% concentration over the 1981-2005 period: reconstructed 85 ± 20 %; observed 61 ± 22 %, t-test p-value < 2.
S5. 2 Uncertainties assessment Although the use of a multi-model ensemble mean approach is considered to outperform the use of a single climate model, it is also essential to assess the uncertainties related to AOGCMs to evaluate the confidence that can be attached to our results. Outputs of the different AOGCMs may diverge across time and space because they are based on diverse parameterization of natural processes, downscaling approaches, spatial resolutions, etc. To assess the uncertainties associated with our projections, we calculated, for each rcp scenario, the projected foraging distance derived from each climate model separately. We followed the protocol developed by Goberville et al. 80  by calculating the density [16/25]   distribution of projected foraging distance for each island (i) for the current period (2006-2015), (ii) for the middle of the century (2041-2050) and (iii) for the end of the century (2091-2100) (Extended Data Figure  3 ).
Acknowledgments This work was conducted within the framework of the  Programme 137 of the Institut Polaire Français Paul-Emile Victor  (IPEV; CLB), with additional support from the  French National Research Agency (ANR)   "PICASO" grant  ( ANR-2010-BLAN-1728-01 ; YLM), from  Marie Curie Intra European Fellowships  ( FP7-PEOPLE-IEF-2008 ,  European Commission ; project no. 235962  to CLB and  FP7-PEOPLE-IEF-2010 ,  European Commission ; project no. 252252  to ET), from the  Centre Scientifique de Monaco  through budget allocated to the  Laboratoire International Associé 647 BioSensib  ( CSM/CNRS-University of Strasbourg; CLB, YLM ),  South African National Antarctic Programme (PP)  and the  IPEV Programme 109  (YC). Logistic and field costs of research were supported by the  IPEV   Programme 137 (CLB) , the  South African Department of Environmental Affairs and National Research Foundation (PP).
This work was performed on the Abel Cluster, owned by the  University of Oslo  and the  Norwegian metacenter for High Performance Computing (NOTUR) , and operated by the  Department for Research Computing at USIT , the  University of Oslo. We are very grateful to  Morten Skage ,  Ave Tooming-Klunderud ,  Marianne Selander-Hansen , and the  Norwegian Sequencing Center  for their very valuable help in the laboratory, as well as  Lex Nederbragt  and  Michael Matschiner  for their assistance with the Abel cluster, and  Matteo Fumagalli  and  Thorfinn Korneliussen  for their precious advice regarding ngsTools and ANGSD. We thank  Giorgio Bertorelle  and  Leonida Fusani  for their comments on the manuscritp. Genomic data used in analyses are available in the  GenBank Sequence Read Archive. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S1 of this paper) for producing and making available their model output.
5 Most of the data collected is qualitative in nature, although interviewees were also asked to assign scores between one and five to various potential enabling factors, depending on the perceived importance of those factors for the success of their work. Complete transcripts were uploaded into Atlas. ti, a software program that facilitates qualitative data management and analyses, and interviewees' responses and comments were categorized by theme and compared to identify commonalities. To view a copy of the interview guide, as well as a more detailed description of the data analysis process and an extended report of the study's findings, please contact the authors.
Acknowledgements This research was funded by the Social Sciences and Humanities Research Council of Canada (SSHRC). We wish to thank Jolie Mayer-Smith, Co-Investigator in TEGS, who contributed feedback in the preliminary design of the study; Chessa Adsit-Morris, Grace McRae-Okine, and Sahar Zandieh, Research Assistants in TEGS who contributed feedback on the instrument of data collection and conducted several interviews with Key Players; Paula Wensley, who carried out the transcription of recorded interviews; and Research Assistant Jamie Schaap, who conducted a preliminary literature review for this study.
Dissolved inorganic carbon We computed the dissolved inorganic carbon of bulk ice from TA bulk and pCO 2 bulk using a 2 step computation. We estimated the salinity of brines according to the ice temperature using the relationship of  Cox and Weeks (1983)  derived from data compiled by  Assur (1960). We then computed TA brines at the salinity of brines from the linear relationship between TA and salinity. We computed the total dissolved inorganic carbon (DIC) of brines (denoted as DIC brines ) from TA brines and pCO 2 bulk using the CO2SYS program for the carbonate system  (Lewis and Wallace, 1998). We used the CO 2 dissociation constants of  Mehrbach (1973)  refitted by  Dickson and Millero (1987)  and the other constants advocated by  Dickson and Goyet (1994). We then converted DIC brines to DIC bulk assuming a linear relationship between DIC and salinity.