dataesr/scientific-paragraphs-categorization huggingface dataset:
m. signatures related to earthquakes  (Shen et al. , 2011)  and to be launched in 2016. Thermal IR data from other satellites (e. g. ESA Sentinel missions, EUMETSAT consortium satellites etc. ) will provide an integrated dataset to be included in any interdisciplinary study of lithosphere-atmosphere-ionosphere coupling in the search of the best physical model explaining the available ground and satellite based observations. the GIM maps during interval 00:00-10:00 UT on 3 (storm disturbed) and 9 May (not disturbed) 2008, respectively. The latter is clearly more related to a possible seismic effect to ionosphere (adapted from  Zhao et al. , 2008). Top plot is for IMF Bz positive conditions, while bottom plot is for negative conditions. Black and red curves reproduce the modeled auroral ovals (Feldstein model) for quiet (IQ=0) and moderately disturbed (IQ=3) geomagnetic conditions, respectively (adapted from  Alfonsi et al.
Acknowledgements The idea to prepare this paper was born during the active collaborations among most of the coauthors in the framework of the preparation of a proposing project to Horizon 2020. Q. E. and P. -C. F. C. received an  INGV  Fellowship under the SAGA-4- EPR  project funded by the  Italian Foreign Office after an Italian-Chinese bilateral Governmental Agreement. GB acknowledges additional support from the  European Union Seventh Framework Programme  ( FP7-REGPOT-2012-2013-1 ) under grant agreement no. 316210  ( BEYOND -Building Capacity for a Centre of Excellence for EO-based monitoring of Natural Disasters ). The final phase of the work was made under the project  SAFE (Swarm For Earthquake study ) funded by  ESA.
In complete agreement with the observations made during adaptation of MEFs to cell culture, a substantial reduction (more than five-fold) in global 5hmC levels was observed in proliferating CD4 + T cells after three days of culture (Figure  1g, h ). Loss of 5hmC in a pure CD4 + T-cell population suggested that changes in tissue heterogeneity did not contribute to the global loss of 5hmC in culture. To exclude changes in population substructure as a contributing factor to the observed 5hmC loss during MEF establishment, we used both transcriptome profiling and immunohistochemistry. Using published data sets of gene expression in 90 different mouse tissues and cell lines, we identified MEF-specific genes that are a signature of its cellular identity  [25]  (Gene Expression Omnibus (GEO) ID GSE1133).
Genome-wide redistribution of 5hmC is a general feature of cell culture Finally, we sought to determine the generality of the 5hmC redistribution in cultured MEFs by analysis of previously published whole genome profiles of 5mC and 5hmC in mouse CD4 + T cells undergoing differentiation in culture (GEO: GSE59212)  [40, 41]. In a very recent study, Tsagaratou and colleagues  [41]  used cytosine-5methylenesulfonate (CMS)-DIP to dissect the dynamics of 5hmC in murine naïve CD4 + T cells and T-helper cells derived from those cells in vitro. Unlike hMeDIP, the anti-CMS antibody recognizes CMS generated from 5hmC by sodium bisulfite conversion, and exhibits markedly lower background pull-down and less sequencedependent performance than hMeDIP  [42]. First, the authors noted a dramatic and rapid loss of global 5hmC in culture as reported here (Figure  1g ).
To study genome-wide changes in 5mC in cultured CD4 + T cells we analyzed published methyl-binding domain (MBD)-Seq data of mouse CD4 + naïve T cells and their cultured derivatives (GEO: GSE25689)  [40]. MBD affinity purification interrogates methylated CpG-rich sites across the genome  [40]. Whereas gene bodies showed a modest skew towards 5mC loss in culture, promoters were skewed towards gain of 5mC in culture, particularly those promoters undergoing large (greater than two-fold) gains (Figures  S11e, f  in Additional file 1). Thus, the genome-wide redistribution of 5hmC in cultured CD4 + T cells is highly similar to that observed in MEFs, as is the skew towards gains in promoter methylation (5mC). To further validate these findings we performed h/MeDIP-qPCR at loci gaining and losing 5hmC and/or 5mC in MEFs and CD4 + T cells.
Cell division analysis: CSFE Isolated CD4 + T cells were labeled with 5 μM carboxyfluorescein diacetate succinimidyl ester (CFSE; Sigma) at 37°C for 20 minutes. Cells were washed in RPMI 1640 supplemented with 10% FCS and cultured as stated above. At the indicated time-points, cells were harvested and stained with anti-CD4-eFluor450 (clone RM4-5; eBioscience). Flow cytometric data were acquired using a BD LSRFortessa cell analyzer (Becton Dickinson, Oxford, Oxfordshire, UK and data analyzed using FlowJo software (Treestar version 3. 2. 1, Ashland, Oregon, USA).
Cell cycle analysis: propidium iodide Cells were sampled at the indicated time-points and single-cell suspensions were immediately fixed in 2% paraformaldehyde for 20 minutes at 37°C prior to surface staining with anti-CD4-eFluor450. Cells were then re-suspended in ice-cold 90% methanol and stored at -20°C. Cells were washed extensively in PBS containing 2% FCS (Sigma) and stained with propidium iodide (eBioscience) for 30 minutes at 4°C. Flow cytometric data were acquired using a BD LSRFortessa cell analyzer (Becton Dickinson) and data analyzed using FlowJo software (Treestar version 3. 2. 1).
Subsequently, membranes were probed with either a rabbit (α-5hmC and α-ssDNA membranes) or mouse (α-5mC membranes) IgG antibody conjugated to horseradish peroxidase. Following treatment with enhanced chemiluminescence substrate, membranes were scanned on an ImageQuant LAS 4000 (GE Healthcare, Little Chalfont, Buckinghamshire, UK) imaging station. Spot intensity was quantified using ImageJ image processing and analysis software (NIH).
Analysis of microarray data All analysis of NimbleGen microarray data was performed using custom-written scripts implemented in the statistical programming language R. First, each array was subjected to locally weighted scatterplot smoothing (Loess normalization)). The data were then smoothed using a running median in a sliding window of seven probes (approximately 1,700 bp). A peak of 5hmC was defined as any region of five consecutive probes in which at least four probes had an enrichment value greater than the 90th percentile. This definition of a peak resulted in a false discovery rate of <0. 01. Hierarchical clustering was performed using the 'hclust' function in the R programming language; a distance matrix was computed for each data set using Euclidean distance; subsequently samples were clustered using Ward's minimum variance agglomeration method.
Sequencing was carried out at the Genomics Core, Albert Einstein College of Medicine, New York. Analysis of hMeDIP-Seq data hMeDIP-seq reads were generated on an Illumina Genome Analyser IIx and mapped to the mm9 build of the mouse genome using the bowtie short read aligner  [8]. Where a read mapped to a number of potential locations, only the best hit was retained (in terms of the number of mismatches and their qualities; as specified by the bowtie '-best' parameter). BEDTools  [9]  was used to count mapped reads that overlap genomic windows, calculate coverage, and normalize data to total read count. The MACS program was used for peak calling (default parameters).
Analysis of CMS-Seq data Normalized (reads per million) CMS-Seq data for mouse naïve CD4 + T cells and their in vitro derived T-helper type 1 (Th1) counterparts were downloaded from the GEO (accession GSE59212)  [41]. Peaks of 5hmC (CMS) enrichment were determined using MACS ChIP-Seq peak-calling software with default parameters. Gene-body and promoter enrichment was defined as median coverage across the entire genic region. The BEDtools suite of software was used to map enriched peaks to different genic compartments based on mouse genome version 9 (mm9) refGene coordinates  [62]. Genome track images were modified from the Integrated Genome Viewer software  [63].
Analysis of MBD-Seq data Normalized (reads per million) MBD-Seq data for mouse naïve CD4 + T cells and their in vitro derived T-helper type 1 (Th1) counterparts were downloaded from the GEO (accession GSE25689)  [40]. Peaks of methyl-CpG enrichment were determined using MACS ChIP-Seq peak-calling software with default parameters. Genebody and promoter enrichment was defined as median coverage across the entire genic region. Promoters and genes with less than five reads in both samples were excluded. The BEDtools suite of software was used to map enriched peaks to different genic compartments based on mouse genome version 9 (mm9) refGene coordinates  [62]. Genome track images were modified from the Integrated Genome Viewer software  [63].
The sequences were imported into the 'BioEdit' program  [66]  and aligned to a reference sequence for the region, created by in silico bisulfite treatment, using the 'ClustalW' inbuilt program. Clones with identical sequences were removed due to the possibility of PCR bias. Clones with <95% conversion rate of C to T were excluded. The methylation status of each CpG dinucleotide from each bacterial clone was assessed by the presence or absence of C to T conversion in the PCR product, and visualized using 'QUMA' online program  [2, 67]  The overall percentage CpG methylation was calculated for each region.
Acknowledgments We thank  Professors Adrian Bird  and  Nicholas Hastie  for their comments on our manuscript. JT and RO are funded by  IMI-MARCAR  (under grant agreement number ( 115001 ) ( MARCAR  project)). Work in RM's lab is supported by the  MRC, IMI-MARCAR  and the  BBSRC. This work in RM's lab was also initially funded by the  Breakthrough Breast Cancer charity. Work in MB's lab was supported by  Linköping University  strategic research funding and the  Åke Wibergs  fund (3772738). Work in SP's lab is supported by the  BBSRC.
Data analysis We use the GAMIT/GLOBK software package (http://wwwgpsg. mit. edu) to compute the coordinates and velocities of the surveyed GPS sites using a three-step strategy  (Feigl et al. 1993; Dong et al. 1998). To tie our local network to the ITRF reference frame, we include GPS data from 18 International GNSS Service (IGS) stations, when available (BELL, CAGL, CANT, CREU, EBRE, ESCO, GRAS, LLIV, MADR, MARS, MTPL, POTS, SFER, TLSE, VILL, WSRT, WETT, ZIMM). No IGS sites were available in 1992, but in 1994, nine sites were already available (BELL, CREU, EBRE, ESCO, LLIV, MADR, TLSE, WETT and ZIMM). The two continuous GNSS sites close to our network were also added to the analysis (LACA and FJCP). Following  Reilinger et al. (2006) , we account for temporally correlated noise in each continuous GPS time series by using the first-order Gauss-Markov extrapolation (FOGMEX) algorithm proposed by  Herring (2003)  to determine a random-walk noise term, which we then incorporated into the Kalman filter used to estimate the velocities.
Stress tensors To go further in the analysis of the focal mechanisms, we compute the stress tensors. We proceed by a fault-slip inversion using a Monte Carlo search method  (Etchecopar et al. 1981)  through the FSA software  (Burg et al. 2005; Célérier 2011; Célérier et al. 2012). The inversion searches for the stress tensor that explains the majority of focal mechanisms. The magnitude of the events is not taken into account in the inversion. The focal mechanisms are given by one of the nodal planes. Then, we performed two inversions, the first one for the 'first' nodal plane chosen randomly, the second one with the other nodal plane. Next, we obtain the final stress tensor from a third inversion with the nodal planes, one per focal mechanism, having the smallest misfit in the previous two inversions.
In order to include their GPS stations, we extend the western region to the south. The velocity field from  Asensio et al. (2012) , modified to be in a reference frame similar to our study, and the strain-rate tensors are shown in  Fig. 8 (bottom). The strain-rate tensors estimated from the two GPS data sets are similar and compatible within their respective uncertainties. From this comparison, we conclude that both velocity fields suggest a roughly NS extension. 4 ) for the eastern and western parts delimited by the red contour, and for the all Pyrenees in the inset. In red (extension axis) and blue (compression axis) are the 1000 tensors obtained by varying randomly the GPS velocities in their uncertainties (see text for details). Top: this study; bottom: from  Asensio et al. (2012) , the GPS velocities shown here being computed in the same European reference frame as our study.
, 2005). Among the land use types found in the Ardèche, forest dominates throughout the basin (Corine Land Cover database 3  ). Forest is represented by a mix of coniferous (27 %), broadleaf (13 %) and Mediterranean trees (17 %). Shrubs and bushes are also well represented in the catchment, occupying a significant portion of the area (17 %). We also distinguish significant areas of bare soil in the central and southern part of the Ardèche, as well as a few small urban areas and areas of early and late crops. In the Ardèche Basin, there is a strong influence of the Mediterranean climate with seasonally heavy rainfall events during autumn. Historical data show that these events usually lead to flash floods:  Lang et al. (2002)  mention seven rainfall events locally exceeding 400 mm during the 1961-1996 period.
Observations used in the study In the Ardèche catchment, measurements of the hydrological state variables were mainly started in the 1960s for the purpose of flood forecasting. In our study, we use hourly data of precipitation (P ), reference evapotranspiration (ET 0 ) and discharge (Q) from the period 1 January 2000 until 31 December 2008. These data come from operational networks, and not from research catchments as in previous applications of the SDSA, which renders the study challenging and interesting, as operational networks account for a large fraction of the available discharge data in many regions. The analysis is mostly constrained by the availability of discharge data, which were obtained from the national Banque Hydro website (www. hydro. eaufrance. fr) and Electricité de France (france.
The discharge data were available at varying time intervals, and were aggregated to hourly sums. Two types of precipitation data have been examined and are used throughout the analysis. Local rain gauges at the hourly time step provided by the OHM-CV database  (Boudevillain et al. , 2011)  are used as the primary source of rainfall data for the catch-Table  1. Physiographic characteristics of the four examined Ardèche sub-catchments. Strahler stream order, channel length and drainage density are calculated from the 25 m IGN DTM using TauDEM tools  (Tarboton et al. , 2009)  ment Ardèche at Meyras (#1). For the catchments Borne at Nicolaud Bridge (#2), Thines at Gournier Bridge (#3) and Altier at Goulette (#4) we use the SAFRAN reanalysis of Météo-France, based on 8 by 8 km 2 grids  (Quintana-Seguí et al.
, 2008; Vidal et al. , 2010)  since either the local rain gauge shows lack of data and time gaps, or there is no rain gauging station in the catchment (e. g. Thines at Gournier Bridge (#3). These precipitation data are calculated as catchment averages at hourly time steps. To compute the reference evapotranspiration ET 0 , we also used the climate variables of the SAFRAN reanalysis of Météo-France at an hourly time step. ET 0 is calculated using the Penman-Monteith formula according to FAO recommendations  (Allen et al. , 1998). In order to account for vegetation type, we compute potential evapotranspiration (PET) as reference evapotranspiration ET 0 modulated by a crop coefficient depending on the nature of vegetation for each catchment (Eq. 1). PET = K C • ET 0 (1) We also took into account the seasonal variability of vegetation through the definition of three crop coefficient stages: initial (1 January-1 April), mid-season (15 April-15 October) and late season (1 November-31 December).
Periods between initial and mid-season as well as between midseason and late season are interpolated linearly. The values of crop coefficients for the Ardèche catchments were obtained through the FAO database  (Allen et al. , 1998). For each catchment we determined the cover estimates for each vegetation type (Broad-leaf forest, Mediterranean forest, Coniferous forest, Early crops, Late crops, Shrubs and bushes and Bare soil) and we calculated a weighted average crop coefficient per sub-catchment for each stage (see Table  2 ). Reference evapotranspiration ET 0 and ET 0 modulated by the crop coefficient (K C ET 0 ) over the examined period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008)  are given in Table  3.
Exploration of parameter range using Monte Carlo simulations In order to complement the manual sensitivity analysis presented above, to explore the range of these parameters and to assess whether the parameters of the g(Q) function derived from data analysis are representative, we performed Monte Carlo simulations using the model described by Eq. (  14 ) with randomly sampled values of the three parameters C 1 , C 2 and C 3. The parameters were sampled randomly from the a priori defined parameter range given in Table  10. For each simulation, the NSE and NSE log (on the log of discharge) were calculated to assess the performance of the parameter set. The results are presented using dotty plots for the Ardèche at Meyras (#1) catchment in Fig. 10. Table 10 also indicates the range of "behavioural" values for each parameter as derived from the dotty plots, defined as the range where NSE is higher than 0.
Data quality Our study demonstrates that data quality is particularly important for the application of this method. Concerning discharge data, the method is based on the discharge-sensitivity function g(Q), and discharge measurement errors consequently will lead to biases in the appraisal of the catchment functioning. In catchments with artificial reservoirs/dams the assumption of a unique storage-discharge relation will not hold from the SDSA point of view. Thus this will limit the applicability of the SDSA method (and many other catchment models) in practice. In the present study, we used discharge data from operational networks. We have shown in Sect. 2. 2 that there are known issues with the quality of these data for our purposes. Nevertheless, when data consistency is sufficient (e. g.
Acknowledgements. The study is conducted within the  FloodScale  project, funded by the  French National Research Agency (ANR)  under contract no. ANR 2011 BS56 027 , which contributes to the  HyMeX program. The HyMeX database teams (ESPRI/IPSL and  SEDOO/Observatoire Midi-Pyrénées ) helped in accessing the data. The authors acknowledge  Brice Boudevillain  for providing the OHM-CV rainfall data,  Météo-France  for their rainfall and SAFRAN climate data. EdF-DTG provided discharge data from three of the gauges used in this study. We thank the  Region Rhône-Alpes  for its funding of the PhD grant of the first author. We thank  R. Krier  for providing us the codes used to perform the recession analysis and  E. Leblois  for constructive comments on the paper. Edited by:  M. Mikos.
Methods Tag Deployment and Data Extraction Eighty-nine adult female SES were instrumented at Macquarie Island (54 • 35 ′ S, 158 • 58 ′ E) between 1999 and 2005. Deployments were made before their summer or winter foraging trips (October or January/February respectively). All necessary permits were obtained for the described field studies. Elephant seal research was sanctioned by the University of Tasmania Animal Ethics Committee (permit A6738) and the Australian Antarctic Science Advisory Council Ethics Committee (project 2794). Permits and permission to carry out research on Macquarie Island was obtained from Parks and Wildlife Service Tasmania. The seals were approached by foot and temporarily restrained with a head bag and anesthetized intravenously with a 1:1 mixture of tiletamine and zolazepam (0.
Plankton Density Index Light data recorded by the TDLRs were used as an index of plankton density by measuring the integrated light attenuation within the mixed layer depth. Because the sensitivity of the onboard light sensors become diminished below 300 m (see electronic Supplementary Material, Appendix S1) we only consider the influence of plankton on light attenuation in the top 250 m of the water column to give an index of plankton density (PDI). For each dive, the light level at 250 m (LL 250 ) was subtracted from the mean surface light level (LL 0 ) and divided by depth (z) to provide a PDI (for details see the electronic Supplementary Material, Appendix S3). We only considered PDI values 1 h either side of local midday (1100-1300) to minimize variability in the ambient light field (see discussion in  Teo et al.
Path Analysis and Behavioral Metric Estimates Twice daily at-sea locations (at dawn and dusk) were derived from the logged light levels with the R package tripEstimation (R Development Core Team, 2013; for details see  Thums et al. , 2011)  and were processed using state-space model analysis (SSM,  Jonsen et al. , 2005)  to produce an estimated track for each seal trip at 6 h interpolated intervals (i. e. , 0, 6, 12, 18 h). To match daily PDI values only the interpolated locations at 12 h were considered in any further analyses. Output from SSMs was also used to quantify the first of three behavioral metric, known as behavioral state, by giving a probability of the seal exhibiting search behavior at each location, ranging from 0 (low probability) to 1 (high probability) on a continuous scale (details of this behavioral state metric are available in the electronic Supplementary Material, Appendix S5).
Statistical Analysis We fitted linear mixed-effect models (LMMs) using the R software package nlme (R Development Core Team, function lme;  Pinheiro et al. , 2012)  following the steps described in  Zuur et al. (2009)  to examine the relationship between PDI and each behavioral metric calculated at the mean daily scale: (i) behavioral state (horizontal state space analysis); (ii) dive effort (residual bottom time); and (iii) foraging success (fat content). Variables were transformed, where necessary, prior to analyses to correct for non-Gaussian distributions. Behavioral state suffered from unit constraints because it is the proportional likelihood of exhibiting search behavior, ranging between 0 (transit) and 1 (search). As a consequent, behavioral state values were logit transformed. Model selection was achieved following the steps described in  Zuur et al.
Discussion In the past it has been difficult to investigate the linkages between lower trophic levels and the foraging behavior of deep diving predators because concurrent data were often lacking, particularly in the polar regions. Resource distribution in relation to animal behavior is often estimated from satellite-derived chlorophyll-a (e. g. ,  Guinet et al. , 2001; Bradshaw et al. , 2004; Suryan et al. , 2012) , but depth data are lacking. These data also become increasingly scarce and unreliable in polar regions due to cloud cover  (Sumner et al. , 2003) , and are often spatially mismatched with animal behavior due to error inherent in animal location estimates  (Ekstrom, 2004; Costa et al. , 2010b). We provide an approach for investigating the trophic link between a   deep diving predator and concurrent plankton densities in the 3D marine environment by using light data collected by sensors deployed on southern elephant seals (SESs).
Integrated light attenuation in the top 250 m of the water column were used to infer year-round changes to plankton distribution in the Southern Ocean that followed known seasonal patterns in productivity. This is the first dataset (up to 50 SESs) used to examine how seal movement and feeding behavior respond to plankton and its seasonal variability in the Southern Ocean, providing rare evidence of regions of elevated plankton influencing seal foraging behavior. In addition, we demonstrate how the response of seals to resource structuring can give some insight into seasonal foraging strategies at high latitudes.