dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We are grateful to  J. L Vanherweghem MD, PhD  for critical reading of the manuscript and helpful comments. We acknowledge  S. Deferme, PhD  ( PharmaXL, Belgium ) for his assistance in writing this manuscript. Parts of this work have been presented at the 44 th Annual Meeting of the  American Society of Nephrology  (November  08-13 , 2011, Philadelphia, USA) and the  Annual Scientific Meeting of the Belgian Society of Nephrology  (April 25, 2013,  Brussels, Belgium).
The algal host for the MPN assays was cultured under optimal growth conditions (nutrient replete, ML) and a dilution series without viruses was used as a negative control. The percentage of infective viruses was determined by dividing the number of infectious viruses (MPN) by the number of viral particles (FCM). The infective burst size was calculated by multiplying the fraction infective with the original burst size. The PAM fluorometry (Water-PAM, Walz, Germany) measurements were placed in the dark for approximately 15 min, after which they were analyzed for minimal (F0) and maximal (Fm) fluorescence. The maximal photosynthetic efficiency Fv/ Fm was calculated from F0 and Fm whereby Fv equals Fm-F0 (see  Maxwell and Johnson, 2000). Statistics were done with the program Sigmaplot TM 12. 0 (Systat software Inc, Chicago Il, USA).
PCR Detection and Mapping of E. coli O-Antigen and H-Antigen Genes Mapping of fliC gene variants to their respective H-types was performed as previously described  (Beutin et al. , 2015a,b). Nucleotide sequence data obtained from thirteen fliC H1 and eight fliC H12 genes were used for designing TaqMan R real-time PCR probes and XS probes (minor groove binder replacement, Biolegio, Nijmegen, The Netherlands) and primers for specific detection of all genetic variants of thirteen fliC H1 and eight fliC H12 genes (this work). Real-time PCR probes and primers used in this work were designed with the software Primer Express V3. 0 (Applied Biosystems) and are described in Table  1.
Nucleotide Sequencing The nucleotide sequence of the PCR products were determined as described  (Beutin et al. , 2015b)  and analyzed with the Accelrys DS Gene software package (Accelrys Inc. , USA). The nucleotide sequences of the respective products for fliC homologs were determined and have been submitted to European Nucleotide Archive (ENA). The GenBank Accession numbers are listed in Table  2.
O:H types known to be associated with E. coli causing extraintestinal infections of humans (O2:H1, O4:H1, O6:H1, O25:H1) were detected among the investigated H1 strains. Interestingly, strains belonging to these serotypes originated not only from humans but also from animals and food. Certain strains belonged to serotypes which have not been previously associated with clinical disease and their role of pathogens for humans and animals is not yet known. g Conserved in all 21 fliC H1 and fliC H12 sequences from Table  2. Nucleotide Analysis of E. coli fliC H1 and fliC H12 Genes The nucleotide sequences of the reference strains  (Orskov and Orskov, 1984)  for E. coli flagellar antigens H1 (strain Su1242, GenBank accession AB028471. 1) and H12 (Bi 316-42, GenBank accession AY249997)  (Wang et al.
, 2003)  have been published previously. The length of coding sequence of each fliC H1 and fliC H12 gene is 1788 nucleotides and both sequences have 97. 5% identity (44 nucleotide exchanges) at the nucleotide level and 98. 98% identity and 99. 16% similarity at the amino acid level (7 amino acids (aa) exchanges). Additional fliC nucleotide sequences from six E. coli H1 and five E. coli H12 strains were obtained in this work (Table  2 ). These sequences were compared with seven fliC H1 sequences and three fliC H12 sequences already available in GenBank (Table  2 ). All 21 H1 or H12 flagellin genes have a 1788 nucleotides length that codes for 595 amino acid residues. A cluster analysis performed with thirteen fliC H1 and eight fliC H12 sequences is shown in Figure  1. Four different genotypes were detected among the thirteen fliC H1 strains.
Uropathogenic E. coli O2:H1, O6:H1, O25:H1, and AIEC O83:H1 strains were identical for their fliC H1 sequences and assigned to a large cluster composed by eight strains. A smaller cluster was formed by five fliC H1 strains; four of these were Stx2e producing O139:H1 causing edema disease in pigs. Six different genotypes were found among the eight fliC H12 strains. Identical fliC H12 sequences were only found between two O9:K9:H12 strains and each one O55:H12 and O153:H12 strain, respectively. a The whole genome sequence of the E. coli strain ABU 83972 (GenBank: CP001671. 1) is available  (Zdziarski et al. , 2010). The O-serogroup of this strain was not reported but its wzx gene (position 2372093-2373353) is >99% similar to wzx of E. coli O25 strains E47a (GenBank GU014554. 1) (Wang et al. , 2010).
Therefore, we classified ABU 83972 here as an O25:H1 strain. b The whole genome sequence of E. coli strain LF82 is available (GenBank: CU651637. 1). The O-serogroup of this strain is not reported but its wzx gene (position 2127428-212804) is identical to the wzx gene of E. coli O83:H31 strain H17a GenBank: KJ778808. 1 (unpublished) and of E. coli O83:H1 strain NRG857c (GenBank: CP001855. 1) (Allen et al. , 2008). Therefore, we suggest that LF82 is an O83:H1 strain. c The fliC sequence deposited under GenBank JF308285 is derived from strain EC614 reported as O157:H1  (Goulter et al. , 2010). By Blast search, it is 100% identical to the fliC sequence of the H1 reference strain Su1242 (GenBank accession AY249997). Therefore, the flagellar type of EC614 was classified as H1. d Multiresistant, extended-spectrum-lactamase (ESBL)-producing E.
coli. Cluster analysis was performed using eight fliC H12 and thirteen fliC H1 genes listed in Table  2. GenBank accession numbers are indicated for orientation. The unweighted-pair group method using average linkages was used as a tree-building mode, and the distances were calculated according to  Tajima and Nei (1984)  using the Accelrys DS Gene software package. FIGURE 2 | Genetic relationships between translation products of thirteen fliC H1 and eight fliC H12 genes in different strains and serotypes of E. coli listed in Table  2. GenBank accession numbers are indicated for orientation. The neighbor joining method with absolute differences (best tree) was used as a tree-building mode  (Nei, 1996)  using the Accelrys DS Gene software package. real-time PCR assays for specific detection of fliC H1 and fliC H12 , respectively (Table  1 ).
All these are located in the variable part of flagellin determining antigen specificity  (Wang et al. , 2003). As these changes are characteristic for the respective flagellar antigen, we suppose them to determine the antigen specificities of H1 and H12. The few other aa changes detected in some H1 and H12 strains might thus not be significant as specific characteristics of H1 or H12 types. However, such aa-changes could explain the finding of serological subtypes of H1 which were detected using factor specific H-antisera  (Ratiner et al. , 1995). Interestingly, the genetic distance between fliC H1 (Su1242, GenBank accession AB028471. 1) and fliC H12 sequences (Bi 316-42, GenBank accession AY249997) (97. 5% similarity) is less than that found between different alleles of fliC H28 (92. 0% similarity)  (Beutin et al.
Data analysis Geolocation light-level data were processed with BASTRAK software (BAS, Cambridge, UK), using well-established methodologies  (Guilford et al. , 2012 ; see Appendix S2). Non-breeding periods were defined as the interval between the last and first colony visits. Night visits were identified as periods of nocturnal dryness (≥ 2 h) in saltwater immersion data. Non-breeding locations between May-July (P1) and July-August (P6) were used to assign birds to different spatial areas. Analysis of moult phenology indicated that most birds in the NE Atlantic had finished P1 growth by August, while P6 re-growth predominantly occurred between July and August (see 'Results'). Non-breeding areas were determined using fixed kernel density estimation (KDE) in the KERNSMOOTH package in R  (Wand, 2013)  (projection: Lambert conformal conic; cell size: 1 km 2 ).
Additional data from 10 tracked birds sampled in a second year were used to test discriminant function efficiency. To investigate temporal variability in avian isotope values, differences in d 15 N and d 13 C values in feathers between 2011 and 2012 were tested using linear mixed-effects models, with a random 'individual' intercept. Likelihood ratio tests (LRTs) were used to compare full models with models containing no fixed effect. While the prey of Balearic shearwaters during the nonbreeding period are largely unknown, existing dietary observations collected during breeding  (Oro & Ruiz, 1997; Arcos & Oro, 2002)  were used to suggest four likely prey fish functional groups (pelagic species and demersal species both < 17 cm and > 17 cm in length; Table  1 ). Sampled fishes were assigned to groups, and isotopic compositions were compared using one-way ANOVA and Tukey's post hoc tests.
Where heteroscedasticity was detected, data were logtransformed or one-way ANOVA tests with Welch's correction were applied. To examine diet during the non-breeding period, feather isotopes were compared to prey data from known nonbreeding areas  (Guilford et al. , 2012). Birds distributed around southern Iberia were excluded from dietary analysis as no matching prey samples were obtained here. Isotopic discrimination factors between feather and prey muscle protein have not been published for adult Procellariiformes. Therefore, to compare feather isotopes to prey data, the mean of published discrimination values for comparable species was applied to predator data (d 15 N: 3. 7&, d 13 C: 1. 9&; Table  S4 ). Standard deviations of AE 1& for d 15 N and AE 0. 5& for d 13 C were added to account for uncertainty in discrimination factors.
To aid interpretation of feather isotope data, we examined fishing activity within core non-breeding grounds of the tracked population. Fisheries effort data for two ICES fishing areas closest to Balearic shearwater non-breeding areas in 2012 were obtained (STECF, Report 14-20; STECF, 2014). Yearly totals of hours fished in ICES fishing rectangles were calculated for four gear groups (demersal seines/ trawlers, pelagic seines/trawlers, longlines and unspecified) and mapped in relation to core non-breeding areas. Only data from 2012 were used due to poor data coverage off Iberia in 2011  (STECF, 2014). All analyses were carried out in R version 3. 0. 2 (R Core Team, 2013) and ARCGIS version 10. 0.
Non-breeding distributions Forty-six geolocators were recovered, yielding 32 non-breeding tracks from 22 individuals for which feather isotope data were collected (n, 2011: males = 8, females = 9, 2012: males = 7, females = 8; birds in both years = 11). Geolocators on three birds failed within 6 weeks of the first colony visit, but as most of the non-breeding season was captured these data were retained. Core non-breeding areas of the tracked population were similar between years (Fig. 3 ). Balearic shearwaters migrated to waters stretching from the Alboran Sea in the western Mediterranean to northern France in the north-east Atlantic, utilizing three main non-breeding areas (Bay of Biscay, western Iberia, southern Iberia; Fig. 3 ). 72% of birds spent the non-breeding season in the Atlantic, while 28% utilized both Atlantic and Mediterranean regions.
ACKNOWLEDGEMENTS Thanks to the  Balearic Islands Government Servei de Protecci o de Esp ecies and Natural Park of Sa Dragonera  for research permits and field accommodation, to  Rui Vieira ,  Marina Cunha ,  Laurence Pennors ,  CESAM -University of Aveiro , and staff of  Station Ifremer de Lorient  for field support and laboratory space, to  Greg Morgan ,  Lavinia Suberg ,  Alice Jones  and  Biel Sevilla  for field assistance, to  Anna Peel  and  Teresa Donohue  for assistance with moult scoring and laboratory work, to STECF for fisheries data, and to photographers who contributed photographs (see Supporting Information). We thank  Paco Bustamante  and  Christine Dupuy  (LIENSs), and European project  REPRODUCE  ( EratNet-Marifish ,  FP7 ), for funding fieldwork and analysis of fishes collected during IFREMER PELGAS cruises (with help of Jacques Mass e and Mathieu Doray).
Isotope analysis of these samples was undertaken by the  'Plateforme isotopique' (LIENSs laboratory) , and supported by funding from  Conseil R egional de Poitou Charentes and Agence Nationale pour la Recherche Scientifique. This study was funded by the  Natural Environment Research Council, UK  (grant no. NE/J500227/ 1 ;  EK192-02/12 ),  Total Foundation and National Oceanography Centre.
Automatic 3D image analysis of liver invasion For each microscopic field (0. 28mm 2 ) the total number of amoebae, their z-position and the zdistance of the cell layers from the upper matrix border were determined using an automated protocol developed with the Icy software (  [16] , http://icy. bioimageanalysis. org) that can be found at: http://icy. bioimageanalysis. org/protocol/3D_tissue_invasion_analysis. The z-position of the centre of each amoeba was measured, and compared to the z-position of the reference layer (depending on the model, the matrix border or the LSEC layer). The invasion rate was reported using two measures: (i) the overall crossing rate, calculated as the ratio of amoebae crossing the reference layer (deeper z-position) over the total number of amoebae per field, and expressed as a percentage; (ii) the individual migration distance, measured as the relative position of each amoeba between the reference layer and the hepatocyte layer, and expressed as a percentage (0% for noncrossing amoebae, and 100% for amoebae reaching the hepatocytes).
To establish the optimal setups (Fig  1  and Table 2 ), we tested crucial cell culture parameters as cell numbers, COL-I matrix density (1. 0, 2. 0 and 3. 0mg/ml COL-I), time of cell adhesion (30min to overnight) and time of (co-)culture (24h to 2 weeks). Cell morphology, growth and spreading were monitored for each culture condition. Scaffolds obtained with COL I solutions of 1. 0 mg/ml provided the best Huh-7 spread, producing a homogeneous distribution in a single hepatocytes layer. In contrast, in matrices of higher COL I concentrations, Huh-7 cells were less homogeneously distributed, with many areas of high cell agglomeration (data not shown). In fact, the use of 1. 0 mg/ml COL I solution for the scaffold matrix turned out to be a key element for the production of 3D liver models with a well distributed single layer of hepatocytes that consequentially can mimic the liver sinusoid organization, a prerequisite for the utilization of the models for our infection studies.
histolytica (Table  1  and Figs  1  and 4 ). The setup consists of two Huh-7 layers in a sandwich organization with a COL-I matrix scaffold (setup 2, double hepatocyte 3D liver model, Table  1 ). To evaluate the presence of cell-cell adhesion between Huh-7 cells in the hepatocytes layers, we examined Ecadherin labelling by two-photon microscopy. Huh-7 cultured in this 3D liver model setup presented positive cell surface labelling for E-cadherin in all fields examined (data not shown). This observation, together with the morphological observations of the cells, are corroborated by our previous results showing that Huh-7 layers in the 3D liver model were not permeable for 1μm diameter beads  [9]  and thereby demonstrating that these layers form a cellular barrier at our 3D liver models culture conditions.
The work was funded by the  National French Research Agency  ( ANR-2010-GENM-0011-01 , GENAMIBE), the  French Parasitology consortium Labex ParaFrap  (grant  ANR-11-LABX0024 ). The authors wanted to acknowledge the generous support of  "DIM Ile de France"  for the Two-photon microscope acquirement and for the fellowship to  D. Petropolis. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Bec use long-term, continuous, and reliable field measurements on and close to glaciers and ice caps are particularly scarce in the southern hemisphere mid-latitudes, conclusions on climatic trends and impacts are particularly uncertain. Here we advanced understanding of the climate drivers of glacial wastage at southern mid-latitudes by analyzing variations in the Cook Ice Cap (CIC, covering 410 km 2 in 2001) multi-decadal mass balance in the Kerguelen Islands in the southern Indian Ocean (49°S, 69°E, Fig. 1a ). This ice cap is mainly composed of land-terminating glaciers and variations in its mass are more directly linked with climate variations than other ice caps with calving termini at the same latitudes. The continuous long term observations made since 1951 at Kerguelen (Port-aux-Français (PAF) station), plus dated geomorphologic evidence since the Little Ice Age (LIA)  3, 13, 14  , and glaciological observations since the 1970s, comprise an exceptionally long glacio-meteorological dataset.
Glacier response to climatic trends since 1950. We then interpreted the chronology of glacier wastage  3, 13  after 1950. To quantify the drivers of glacier wastage following the equilibrium in the 1950s, the glacier model was forced with hypothetical meteorological datasets in which the observed warming and drying were successively removed after the 1950s (    in the year 2009, the model suggests that CIC would currently present a positive mass balance (0. 58 mm w. e. a -1 ) if precipitation and temperature were at their 1950s level (Fig. 2c, d ). Indeed, the shrinkage caused the loss of low elevation areas where ablation is the highest. In the 1960s, ablation was lower than in the 1950s and glacier wastage was entirely explained by dryness (Fig. 2c ). Dryness was even more pronounced in the 2000s and despite the warming trend observed at Kerguelen since the 1960s, modeling suggests that 77% (between 75% and 81% for 1000 simulations with random degree day factors distributed according to a Gaussian law, see Methods) of the negative mass balance in the 2000s is still explained by dryness (see Methods, Table  1 ).
Pixels for which the correlation is significant at less than 90% are not shown. The Kerguelen Islands are located inside the green circle. Maps were generated using Matlab R2011b (www. mathworks. com/products/matlab/). strong similarities with the situation described in New Zealand  30. The SAM 29 positive (negative) phase results in strengthening (weakening) of mid-latitude westerly winds. Precipitation is expected to increase during SAM+ , but only if Kerguelen is within the storm track. SAM+ conditions also involve a poleward shift of the westerly winds  31. The direct consequence is that the regions with high precipitation are now located south of the Kerguelen Islands. Before 1975, Kerguelen was under the direct influence of the storm track, and moisture flux over the Kerguelen was correlated with the SAM index.
Calibration of degree day factors. The degree day factors were calibrated with ablation measurements made using stakes and with a sonic gauge. Modeled ablation was also compared to ablation estimated from surface energy balance computations, performed with data from two automatic weather stations (AWS) installed on the glacier (only available in summer) and close to the glacier, at La Mortadelle (Supplementary Table  2 ). Here, we used data from December 21, 2010 to January 4, 2011, and from December 14, 2011 to December 30, 2011. During these two periods, one of the AWS was located on the glacier, and measured albedo, air temperature and humidity and wind speed, allowing a direct comparison between daily ablation from the PDD model and from the surface energy balance. The model is fully described in ref.
However, ten years is a relatively short reference period. To justify this choice, we examined the observed change in temperature and the inferred change in precipitation in previous decades. Air temperature fluctuations before 1950 were examined using the ERSST. v2 historical ocean surface data because they offer an extended image of possible past changes in ocean surface temperature. The ERSST. v2 data are based on the Comprehensive Ocean-Atmosphere Data Set (COADS) dataset, which consists in quality controlled marine surface observations from ships, moored environmental buoys, drifting buoys, and near-surface measurements from oceanographic profiles  15. The ERSST. v2 dataset is generated using in situ SST data (falling within 2° latitude × 2° longitude boxes) and improved statistical methods that allow stable reconstruction despite sparse data, and provides monthly statistics of basic marine variables for each year.
Because ERSST. v2 relies on only sparse historical data, we examined if ERSST. v2 data in the Kerguelen area were based on a significant number of in situ measurements before the 1970s (Supplementary Fig. 3 ). We checked that this SST dataset is representative of the temperature in the Kerguelen Islands, as suggested by the significant correlation between annual values (r = 0. 76, n = 58, p < 10 -8 ). A significant correlation (r = 0. 97, n = 227, p < 10 -8 ) between monthly ocean surface temperatures from a buoy at PAF, in the Gulf of Morbihan (ROSAME network) and monthly mean 2-m air temperature at PAF between 1993 and 2013, confirms that air and ocean surface temperatures are very closely related in this area. The ERSST. v2 (Fig. 2a ) data confirm that the temperature has been relatively stable since 1935, whereas the cold situation following 1963 appears as short term variability, probably caused by the eruption of the Agung Volcano  29, 32.
Acknowledgements This study was funded by  IPEV-1048 GLACIOCLIM-KESAACO  and  LEFE-INSU KCRuMBLE  programs, and the  Agence Nationale de la Recherche  through contract  ANR-14-CE01-0001-01  (ASUMA). Logistical supply to the Kerguelen Islands was provided by the  French Polar Institute (IPEV). We are particularly grateful to  Y. Frenot  who originally proposed this study. We particularly thank  Météo France  for the meteorological and radio sounding data from PAF. E. B. acknowledges support from the  French Space Agency (CNES)  through the TOSCA and ISIS programs. NCEP 1 and 2 reanalyses data were provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their Web site at http://www. esrl. noaa. gov/psd/. ERA-40 and ERA-Interim reanalyses data were downloaded from the ECMWF data portal at http://data-portal.
ecmwf. int/. We acknowledge the  World Climate Research Program 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 1) for producing and making available their model outputs. Sea surface temperature measurements at PAF were provided by  ROSAME (Réseau d'Observation Subantarctique et Antarctique du niveau de la MEr ) at http://www. legos. obs-mip. fr/observations/rosame. ERSST-v2 data were obtained at http://iridl. ldeo. columbia. edu/SOURCES/. NOAA/NCDC/. ERSST/ version2/. SST/. HadSST2  data were obtained at http://www. metoffice. gov. uk/hadobs/hadsst2/andAVHRR data were obtained at http://podaac. jpl. nasa. gov/dataset/NCDC-L4LRblend-GLOB-AVHRR_OI. CRU TS 3. 21 data were provided by the  British Atmospheric Data Centre , at http://badc.
The full-physics approach tries to account for scatteringinduced errors by explicitly modeling the scattering process, and retrieving scattering properties from the data  (Butz et al. , 2010). The proxy method, first introduced by Frankenberg et al. (2005) , takes the ratio of XCH 4 and XCO 2 retrieved at nearby wavelengths (1562 to 1585 nm for XCO 2 and 1630 to 1670 nm for XCH 4 ) so that path length perturbations due to atmospheric scattering largely cancel out in the ratio (see Eq. 1). X ratio is multiplied with model-derived XCO 2 (XCO model 2 ) to derive XCH 4 (XCH proxy 4 ) (see Eq. 2). Here, XCH ns 4 and XCO ns 2 are retrieved assuming a nonscattering atmosphere. XCO model 2 is calculated using a transport model, normally employing CO 2 surface fluxes that have been optimized using surface measurements.