dataesr/scientific-paragraphs-categorization huggingface dataset:
For the ice cap, mean thickness values are not very sensitive to the data availability, whereas the not well constrained reconstruction for THPB and WSB produces low biased estimates. Background: grey-scale hill-shaded topography based on a NPI 50 m DEM (http://publicdatasets. data. npolar. no/kartdata/NP_S0_DTM50. zip). magnitudes remain constant as the AMB is close to zero. Unlike this balanced situation, a pronounced surface subsidence over most of Paierlbreen is not explained by the SMB and results in a positive AMB over the entire catchment area. This imbalance is compensated by extensive downwasting implying a gradual flux increase up to the marine ice front. The imbalance itself might partially reflect the long-term geometric adjustment of Paierlbreen to the surge in 1993-1999. Yet we cannot exclude that the SMB model underestimates the magnitude of surface melting or that a bias is introduced by the DEM differencing (Sect.
Ice thickness and bedrock elevation The first-step thickness map (Fig. 4 ) depends on surface slopes, thickness measurements and the ice-flux solution. The latter reflects both climatological and geometric infor- In absence of direct measurements, negative values over larger areas were not anticipated for Gimlebreen and Idunbreen based on a direct interpolation of thickness data  (Pettersson et al. , 2011). Upper and lower panels reflect the respective amount of considered thickness measurements as in Fig. 4. Ice-free background: grey-scale hill-shaded topography based on a NPI 50 m DEM (http://publicdatasets. data. npolar. no/kartdata/NP_ S0_DTM50. zip). Ice-covered background: grey-scale hill-shaded bedrock topography. mation. For VIC, we find a mean thickness of 228 m (Table  C1 ). This value is significantly higher than the previously reported 185 m, which was inferred from a direct kriging interpolation  (Pettersson et al.
Figure5. Bedrock topography associated to the thickness field in Fig. 4for VIC (a, c), THPB and WSB (b, d). In absence of direct measurements, negative values over larger areas were not anticipated for Gimlebreen and Idunbreen based on a direct interpolation of thickness data (Pettersson et al. , 2011). Upper and lower panels reflect the respective amount of considered thickness measurements as in Fig. 4. Ice-free background: grey-scale hill-shaded topography based on a NPI 50 m DEM (http://publicdatasets. data. npolar. no/kartdata/NP_ S0_DTM50. zip). Ice-covered background: grey-scale hill-shaded bedrock topography.
Acknowledgements. This study received primary funding from the  German Research Foundation (DFG)  under grant number  FU1032/1-1. Results presented in this publication are based on numerical simulations conducted at the high-performance computing centre of the  Regionales Rechenzentrum Erlangen (RRZE)  of the  University of Erlangen-Nuremberg. The reconstruction approach also benefits from co-development work of the Elmer/Ice team at the  CSC-IT Center for Science Ltd. (Finland). The velocity analysis on Svalbard was funded by  DFG  within the priority programme  1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas  under contract number  BR2105/9-1  and received financial support from the  Helmholtz Association of the German Research Centres (HGF) Alliance on Remote Sensing and Earth System Dynamics.
Thickness data collection in Wedel Jarlsberg Land was funded by the  Spanish R & D  projects  C11093001  and  C150954001 ,  NCBiR/PolarCLIMATE-2009/2-2/2010  from the  Polish National Centre  for R&D, by  IPY/269/2006  from the  Polish Ministry of Science and Higher Education, by Polish-Norwegian  funding through the  AWAKE  ( PNRF-22-AI-1/07 ) project, by the  EU   FP7 ice2sea  programme (grant number  226375 ) and by funds of the  Leading National Research Centre  (KNOW) received by the  Centre for Polar Studies of the University of Silesia, Poland. The DEM generation in Wedel Jarlsberg Land received financial support from the  European Research Council  (grant  320816 ) and from  ESA  (project Glaciers  CCI, 4000109873/14/I-NB ). TanDEM-X data were provided under AO XTIGLAC6770. The WRF-SMB field was produced within the  PERMANOR  project funded by the  Norwegian Research Council  ( 255331 ).
g. Odén, 1968). Historical anthropic S emissions have been reconstructed using economic data from energy and industrial statistics or official country reports (e. g. Mylona, 1996; Smith et al. , 2011). The historical deposition of anthropic and natural sulfur may also be recorded in a few natural archives such as plants, ice cores or speleothems. Indeed,  Frisia et al. (2005 ) Frisia et al. (2008)  and  Uchida et al. (2013) ,  Badertscher et al. (2014)  demonstrated in pioneer studies that the sulfur content within speleothems was linked with S deposition from atmospheric fallout and recorded both anthropogenic S emissions and volcanic emissions as previous studies had shown for ice core records (e. g. Naftz et al. , 1991; Laj et al. , 1992). S isotopic measurement ( 34 S/ 32 S ratio normalized to a standard and expressed as δ 34 S) was used to distinguish between different S sources: natural or anthropogenic sources  (McArdle and Liss, 1995)  or different anthropogenic sources (power plants, coal burning, etc.
Acknowledgements This work was supported by the  Mairie de Paris  " Paris 2030" program  (Histoire d'eau project). The samples were collected during a program, supported by  INSU , CNRS (EC2CO program ,  Hydrohisteau  project). ASNEP ( Association Sources du Nord Etude et Protection ) is acknowledged for providing access to the aqueduct and water springs. W Borst  and  V Masson Delmotte  are acknowledged for early discussion about the data. The  CNRS Analytical Research facility SARM (Service d'Analyse des Roches et des Minéraux, Nancy, France)  is acknowledged for S content analysis.
RNAseq data analysis. Adapter and base quality trimming (Phred score < 20) was performed with Trim Galore (trim_galore_v0. 3. 7). The read depth was fixed at 47. 0 million reads/sample (down-sampling normalization, Supplementary Table  S1 ) for cycling transcriptome analysis, according to  19. Relative log expression normalization (RLE)  73  was applied instead of down-sampling for differential expression analysis (edgeR version 3. 8. 6  74  ). Sequenced reads were aligned by STAR (STAR_2. 4. 2a  75  ) to both the genome and mitochondrial genome of Crassostrea gigas 76 , Ensemble 1. 30; and NCBI reference NC_001276) and assigned by FeatureCount (1. 4. 5 version 77 ) to annotations of the genome in public databases. Expression levels of genes were presented as cpm (counts per million). Filtering of transcripts with 0 cpm in the whole set of libraries was applied.
Analysis of transcripts. Sequencing outputs and bioinformatics treatments were carefully analyzed and checked several times to ensure the reliability of data supporting the discussion. Cycling transcripts were detected using ARSER  41  with MetaCycle (version 1. 1. 0 78 on R 79 (32-bit, version 3. 2. 2)). ARSER algorithm was developed and applied to high-throughput time-series analysis and particularly adapted to experimental designs with a timeframe of 4 h over 52 hr  41, 78, 80. Post-hocs were corrected for multiple comparisons to remove false discoveries  42, 43. For all cycling detection, a false discovery rate (FDR) of <0. 05 was considered significant. Note that in 24 hr light/dark entrainment, the external circadian cues may directly generate 24 hr rhythms, in an addition of CCGs group.
Amplitudes of oscillating transcripts were calculated as ((high value -low value)/low value)  57. All these genes with a period length between 20 hr and 28 hr comprise circadian transcripts  45. Differentially expressed transcripts were analyzed with edgeR (edgeR version 3. 8. 6), considering the 13 sample times (day 1 ZT 0. 5 to day 3 ZT 0. 5) of each algal treatment as replicates; the results were corrected for multiple tests using the FDR  81. Gene ontology analysis. Gene descriptions were obtained using the C. gigas reference genome  76  on Biomart, Ensembl Metazoa (http://metazoa. ensembl. org/biomart/martview/). Enrichment analyses were performed using the Fisher test on Blast2GO software (version 2. 8), selecting only specific biological processes.
The sampling frequency for each individual was 0. 2 Hz. Data were processed using LabView 8. 0 software (National Instruments). For the VOD, the mean hourly valve opening duration was expressed as the percentage of time that oysters spent with their valves open: 100% indicates that the valves were open for the entire hour and 0% indicates that the valves were closed for the hour. The 48-hr mean hourly opening durations for 16 oysters per condition was used in the analyses. Statistical analysis for RT-qPCR and behavioral analysis. Results are expressed as the means ± the SE. Treatment differences were determined using analysis of variance (ANOVA) after checking assumptions (normality and homoscedasticity of the error term). When assumptions were not met, the non-parametric Kruskall-Wallis test was performed.
Acknowledgements This work was supported by the financial support of the  Nation Research Agency (ANR) ,  ACCUTOX  project  13-CESA-0019  ( 2013-2017 ) and a ministerial PhD scholarship for L. Payton. We thank  O. Bouchez  and  A. Chaubet  ( Génopole Toulouse/Midi-Pyrénées, INRA Auzeville, France ) for sequencing and discussions. We would thank  I. Gonzalez ,  G. Lefort  ( Genotoul, INRA Auzeville, France ) and  Anthony Bertucci  ( UMR EPOC, Arcachon, France ) for their advices in bioinformatics and biostatistics treatment. Authors also thank  Dr. K. Flynn  for English corrections.
, 2010 for details). This procedure was repeated for at least eight pressure steps with a -0. 5 MPa step increment until the PLC reached at least 90%. Rotor velocity was monitored with a 10 rpm resolution electronic tachymeter and xylem pressure was adjusted to about -0. 02 MPa. Conductance measurements were taken using the Cavisoft software (version 2. 0, BIOGECO, University of Bordeaux). Percentage loss of conductance in the xylem as a function of xylem pressure (MPa) represents the vulnerability curve (VC) of the sample. A standard sigmoid function  (Pammenter and Van der Willigen, 1998; Cochard et al. , 2005)  was fitted to the VC of each sample, using SAS v 9. 1 (SAS Institute 2004) and the following equation: 𝑃𝐿𝐶 = 100 1 + 𝑒 ( ! !" * (!!! !" ) where P 50 (MPa) is the xylem pressure that induces 50% loss of conductance and S (% MPa -1 ) is the slope of the vulnerability curve at the inflexion point.
Statistical analysis We tested differences in xylem pressure inducing 50% loss of conductance (P 50 ), slope of the vulnerability curve at the inflexion point (S) and xylem specific hydraulic conductivity (k s ) among species and genera by conducting a twoway nested ANOVA followed by a post-hoc Tukey test (α = 0. 05). The ANOVAs were performed using the Procedure GLM of SAS  (SAS Institute, 2004). Variance components were estimated using the Procedure VARCOMP with the method of restricted maximum likelihood (REML) of SAS  (SAS Institute, 2004 ). In order to find possible evolutionary associations between increasing cavitation resistance and increasing aridity, we conducted a regression analysis between the means of the hydraulic traits of each species (all species, field data only) against the median of three climate variables along each species distribution range: Mean Annual Temperature (MAT), Mean Annual Precipitation (MAP), Annual Aridity Index (AAI: Mean Annual Precipitation/ Mean Annual Potential Evapotranspiration).
We also regressed the means of the hydraulic traits of each species against the extreme values of the climatic variables, calculated as the median of the 5% of highest MAT (MAT_max), lowest MAP (MAP_min) and more arid AAI (AAI_max) values. Climatic and aridity index values averaged per species were estimated from presence points covering the full distribution range of the species (illustrated on Figure  1  for Mexico) and obtained from http://www. worldclim:bioclim and http://www. cgiar-csi:data/global-aridity-and-pet-database, respectively. For Juniperus and Pinus genera, we explored the relationship between the intraspecific variability in hydraulic traits and the climate of each population by regressing the means per population against the provenance climate variables for those two genera.
Acknowledgements Funding was provided to CSR by the joint research funds of the  Mexican Council of Science and Technology (CONACyT) , and the  State of Michoacán  ( CONACyT-Michoacán , grant  2009-127128 ), a sabbatical year fellowship (at  INRA-Cestas, France)  from  CONACyT  (grant  232838 ) and the  Coordinación de la Investigación Científica of the Universidad Michoacana de San Nicolás de Hidalgo (UMSNH) ,  Mexico. Funding provided to CW was from  CONACyT  and the  Ministry of Education  (SEP; Project  CB-2010-01-158054 ). This study was also supported by the program ' Investments for the Future ' ( ANR-10-EQPX-16 , XYLOFOREST) from the  French National Agency for Research  to SD. NGM was supported by the  Agreenskills fellowship program , which has received funding from the  EU's Seventh Framework Program,  under grant agreement  FP7-26719  (Agreenskills contract).
Measurement sites Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), sponsored by the US Department of Energy and several Brazilian and German agencies, took place at multiple surface sites surrounding Manaus, Brazil, from January 2014 through December 2015  (Martin et al. , 2016). This work focuses on the measurements carried out at a downwind site (T3; 3 Depending on the wind direction, the T3 site experienced conditions ranging from nearly natural to heavily polluted. More detailed characterizations of aerosol-and gas-phase chemical composition were carried out at the T3 site during Intensive Operating Periods (IOPs) from 1 February 2014 to 31 March 2014 and from 15 August 2014 to 15 October 2014. In addition, data from two sites normally upwind of Manaus are also used in this study.
0 • C). At a given supersaturation, data were acquired for a minimum of 30 s and until 1500 particles were counted by the CPC or up to a maximum time of 120 s. Depending on the aerosol number size distribution, the measurement cycled through the seven particle sizes in 1-2 h (see Figs. S1  and S2  for further details of the measurement setup and sampling protocol). The sampling sequence was designed so that the change of CCNC supersaturation was mostly accomplished by stepping flow rates, as the CCNC reaches steady state faster following flow changes than temperature changes. Change in the temperature gradient was kept at a minimum frequency but was necessary given the wide range of supersaturation explored. Given the low particle number concentration (e. g. , ∼ 200 cm -3 under background conditions during the wet season), these approaches were important to achieve adequate counting statistics with good time resolution to capture changes of air mass within 10-20 min  (Liu et al.
Derivation of κ for AMS PMF factors PMF was applied to the AMS mass spectra  (Lanz et al. , 2008; Ulbrich et al. , 2009) , and six organic factors were identified for each of the two IOPs  (de Sá et al. , 2017b). For IOP2 the PMF analysis included data from 24 August to 15 October 2014, excluding a major regional biomass burning event from 16 to 23 August, which was treated separately in the PMF analysis. For IOP1 (wet season), the six factors were isoprene-epoxydiol-derived secondary organic aerosol (IEPOX-SOA), more-oxidized oxygenated organic aerosol (MO-OOA, i. e. , highly oxidized organics), less-oxidized oxygenated organic aerosol (LO-OOA), biomass burning organic aerosol (BBOA) with characteristic peaks at m/z = 60 and 73 and correlated with the concentrations of levoglucosan and vanillin, a factor with high contribution from m/z = 91 (Fac91) and correlated with anthropogenic emissions of aromatics, and hydrocarbon-like organic aerosol (HOA).
Hygroscopicity of PMF factors and the variation of organic hygroscopicity with oxidation level The hygroscopicities associated with the AMS PMF factors were estimated through multivariable linear regression using different subsets of the data as well as the entire dataset for each of the two IOPs (IOP1 in Fig. 9  and IOP2 in Fig. 10 ). The different subsets included measurements during day, night, certain sampling periods, and ranges in particle hygroscopicity dispersion. Comparison of the hygroscopicities derived from the different subsets of data allowed us to examine the robustness of this approach. Uncertainty in the derived κ for individual factors was determined by the number of points available to fit in the time series, with greater data coverage and therefore lower uncertainty during the dry season.
For the wet season (IOP1), the hygroscopicities associated with PMF factors derived using different subsets of the data are largely in agreement with those derived from the entire dataset. There are notable differences between the hygroscopicities of MO-OOA and Fac91 factors derived using data under background conditions only and those derived using the entire dataset. Such difference could be partially due to the limited data under the background conditions during NO 3 rBC Org 00:00 04 12:00 16:00 20:00 08:00 24:00 :00 00:00 04 12:00 16:00 20:00 08:00 24:00 :00 00:00 04 12:00 16:00 20:00 08:00 24:00 :00 Figure  5. Diel variations of aerosol properties and meteorological parameters under background conditions during the dry season (analogous to Fig. 3 ). IOP1 (Fig. 3 ). For the dry season (IOP2), the hygroscopicities of PMF factors derived using measurements under background conditions or data with hygroscopicity dispersion less than 0.
4 are quite different from those derived using other data subsets and the entire dry season dataset. The agreement among the PMF factor hygroscopicities derived using different sub-datasets during the wet season and the disagreements for the dry season are attributed to the applicability of the underlying assumption that the bulk volume fractions of PMF factors (i. e. , derived from MS mode data) represented those at the sizes of CCN measurements. For the wet season, the average f 44 was largely independent of particle size from 130 to 400 nm (Fig. S10 ), which is the size range that dominated the bulk aerosol mass concentration measured by AMS. This is consistent with the assumption that the bulk volume fractions of the PMF factors represent those at the two CCN sizes (142 and 171 nm). For the dry season, the f 44 averaged over local biomass burning air masses and the entire IOP2 exhibited an increase with particle diameter from 100 to 300 nm (Fig.
zon2014/5. While the IEPOX-SOA factors identified using different datasets share many similar features, they are not identical and can consist of different groups of compounds. Such differences may be due to varying degrees of oxidation in different environments between the two field campaigns. For comparison with earlier field studies, the values of κ org and O : C were averaged according to the hours of the day over particle diameters of 142 and 171 nm for data under polluted conditions during IOP1 and all data during IOP2  (Figs. 11 and 12). For the 1 h diel averages, the slope of κ org vs. O : C, derived through a bivariate least squares fit (i. e. , orthogonal distance regression), is steeper than that derived from laboratory studies of SOA hygroscopicity, especially during IOP2. This steep slope during IOP2 is consistent with the results from earlier field studies  (Mei et al.
Acknowledgements. We acknowledge the support from the  Central Office of the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) , the  National Institute of Amazonian Research (INPA) ,  Amazonas State University (UEA) , and the  Max Planck Society (MPG). The  Office of Biological and Environmental Research of the Office of Science of the United States Department of Energy  is acknowledged for funding, specifically the  Atmospheric Radiation Measurement (ARM) Climate Research Facility  and the  Atmospheric System Research (ASR) Program. The work was conducted under scientific license  001030/2012-4  of the  Brazilian National Council for Scientific and Technological Development (CNPq). Nga Lee Ng  acknowledges support from  NSF  grant  1242258  and  US Environmental Protection Agency (EPA) STAR  grant  RD-83540301.
Results Observations and measurements were performed in eight disused quarries and one natural outcrop (Sauvage Castel, site 2 -Fig. 1 ). Sandstones are mainly observed close to the valleys as they represent erosion site where quarries have been implanted. Sites were selected: to represent a large area; for the quality of the outcrop; for the orientation of the working face in the quarries and the orientation of the adjacent valleys, in order to discuss the role of gravitational fracturing (see below). We did not perform measurements in the Fontainebleau area (Fig. 1 ) as data are already available and complementary  (Obert, 1984). Fracturing has been observed in all studied sites. The fractures in the Fontainebleau sandstones can be defined as joints (mode I fractures) in the sense of  Mandl (2005) , as they correspond to fractures without visible shear displacement.
Principal Component Analysis (PCA) PCA was performed in order to investigate the signatures of lipid production in the incubations. Trends in the production rates of individual fatty acids and total brGDGTs (i. e. , combined IPL and CL forms) among the experimental incubations were assessed by determining the relative contributions of individual lipids to total production (c. f. Fig. S4 ). Standardized values (z) of these relative production rate data ( were obtained by subtracting the average ( ) and dividing by the standard deviation (S), i. e. ; these values were then subjected to PCA (c. f. Zuur et al. , 2007). Principal component eigenvalues, loading coefficients, and sample scores were determined using MatLab software (version R2014a).
Acknowledgements  T. B. Meador ,  M. Könneke , and  K-U. Hinrichs  as well as laboratory analyses were supported by the  Deutsche Forschungsgemeinschaft through Gottfried Wilhelm Leibniz Award  awarded to  K-U. Hinrichs  (grant #  HI 616-14 ). Julius Lipp and  Lars Wörmer  facilitated QTOF/MS analyses and  J. Lipp  provided GDGT laboratory standards for lipid quantification. Marcus Elvert  assisted with identification of fatty acid derivatives. Xavier Prieto and  Jenny Wendt  are acknowledged for their support of GC and IRMS analyses. CNRS/INSU  is thanked for the funding of the SNO Tourbières. We thank  D. Naafs  and two anonymous reviewers for constructive comments which helped to improve the manuscript.
5 microns FWHM. The XAS spectra given by the product of the attenuation coefficient µ(E) by the sample path length ρ, µ(E) • ρ, were calculated pixel by pixel, or, energy by energy E, by using the Beer-Lambert law's equation The sample was mounted on a motorized rotational stage and positioned in the X-ray beam focus. The tomography data acquisitions were performed by collecting XAS spectra in two dimensions: X and θ, as illustrated in Fig. 1. XAS spectra were thus collected at lateral positions across the sample, equally spaced by 1 to 2 μm, and at 90 to 180 equally-spaced angular orientations over an angle range of 180°. The sample was analysed slice by slice, where each slice consists in a 2D X by θ scan. Several slices are measured in order to obtain the full 3D sample imaging.