dataesr/scientific-paragraphs-categorization huggingface dataset:
(2018)  data leading to a complete dataset of annual means for the period 1840 to 2016, which is shown in Figure  3. Correlation coefficients with GIR and associated pvalues for all fluxes are available in table  1. Ice runoff (from Greenland and from outside of Greenland) and solid ice fluxes are well correlated with GIR. These high coefficient values give enough confidence to extend these three fluxes in the past using a linear regression on GIR. Concerning the two tundra runoff fluxes, the correlation coefficients with GIR are very small. These values could therefore have been kept constant for the extension period. We chose nevertheless to apply the same method as the one used for the other fluxes and discuss this choice below. Greenland tundra runoff fluxes from  Bamber et al. (2018 Bamber et al.
The observed salinity and temperature data We use different datasets derived from direct observations to evaluate the simulated trends of surface salinity and temperature from our experiments in the last section of this study (section 3). All the datasets have been interpolated on the To isolate the signature of the observed melting of GrIS, we present in Figure  6  the anomalous annual SSS and SST diagnosed from the difference between the Melting and Historical ensemble means, over the period 1920-2014. To obtain the areas of significant difference between the ensemble means, we perform a Student's t-test (detailed in subsection 1. 3), after averaging the members over the time period of the experiment. The same test is used to find regions of significant difference for all the other physical variables considered in this section.
In order to compare our model outputs to this observed trend, we apply the protocol of the study of  Friedman et al. (2017)  to evaluate annual means: December to November SSS are averaged and a 1-2-1 temporal filter is applied. The anomalies are calculated with respect to the 1920-2014 period. The trends have opposite signs in the two observational dataset (Figure  14 ) and are significantly different at the 95% level. The Melting and Historical trends also have different signs and are significantly different at the 90% level. Over the whole period, the Historical ensemble mean trend is significantly higher than the  To evaluate the spread among the trends of both ensembles, we present the slopes of the 95-year linear trends of every member in the histogram of Figure  15. We compare these slopes to the one of the observation trend from  Friedman et al.
However, SST trends of the ensemble means remain quite close as they are only significantly different with a 90% level of confidence. It was difficult to analyze the impact on the long-term trend due to uncertainties related to observations. In our experiment, comparison with several observational datasets reveals a reduced sea surface temperature RMSE in the subpolar gyre region as compared to observations for seven out of ten members of the Melting ensemble. Regarding surface salinity, the error is reduced for only four out of ten members of the Melting ensemble. A larger ensemble may help increasing the level of confidence of the signals since the amount of freshwater added in the Melting experiments is relatively modest. Overall, we found some signs that the inclusion of Greenland Melting is bringing the model ensemble closer to the observation, although the two ensembles remain hardly distinguishable.
obtained from ice penetrating radar measurements. The dataset therefore has four different runoff fluxes and one flux of solid ice discharge. The components will be denoted as follow: -Greenland ice runoff -Outside of Greenland ice runoff -Greenland tundra runoff -Outside of Greenland tundra runoff -Greenland solid ice discharge All of these monthly fluxes are provided in Bamber et al. (2018) on a 5 km resolution grid and were interpolated on the eORCA1. 2 mesh grid (Deshayes et al. , in prep), using a nearest neighbour algorithm. The cumulative resulting annual global fluxes are presented in supplementary Figure S1. The total FWF is about 0. 047 Sv (or 1456 km 3. yr -  ), in average over the period 1958-2016.
Monthly fluxes are calculated by weighting the climatology with the fluxes of a given year. The same method is used for every component except the solid ice discharge for which we chose to apply a climatology constructed with the data from the Altiberg project, over the period 1993-2012 (no available data before 1993). Finally, a spatial redistribution of the monthly values for the period 1840-1957 is carried out. The 2D spatial distribution of the runoff components from the data set of Bamber et al. (2018) , and the one calculated for the solid ice discharge in section 1. 1. 2, are averaged and normalized over the period1958-1980. Monthly fluxes are multiplied to the constant 2D map of coefficients between 0 and 1 obtained (this method is the same as described in section 1. 1. 2). The final reconstructed dataset have monthly values of fluxes over a 2D map from 1840 to 1957.
et al. (2017) observation-based estimate and closer to EN4 data. The SSS trend of the Melting ensemble mean is closer to the estimate from Friedman et al. (2017) than to EN4 data. It seems that the inclusion of the melting may have lead this ensemble towards the observation-based estimate from Friedman et al. (2017). In the study of Reverdin et al. (2018) , the authors argued that the observationbased estimates from Friedman et al. (2017)  in the subpolar gyre might be more reliable than EN4 data for the early twentieth century. It indeed uses a larger observational database, especially before the 1950s, because of the inclusion of some in situ data from research vessels and merchant ships that are not taken into account in EN4. Under the assumption that Friedman et al. (2017)  better represents the long-term trends, due to additional data, we can suggest that the Melting ensemble mean trend is closer to the observational data trend than the Historical ensemble mean one.
Friedman et al. (2017) observation-based estimate and closer to EN4 data. The SSS trend of the Melting ensemble mean is closer to the estimate from Friedman et al. (2017) than to EN4 data. It seems that the inclusion of the melting may have lead this ensemble towards the observation-based estimate from Friedman et al. (2017). In the study of Reverdin et al. (2018) , the authors argued that the observationbased estimates from Friedman et al. (2017)  in the subpolar gyre might be more reliable than EN4 data for the early twentieth century. It indeed uses a larger observational database, especially before the 1950s, because of the inclusion of some in situ data from research vessels and merchant ships that are not taken into account in EN4. Under the assumption that Friedman et al. (2017)  better represents the long-term trends, due to additional data, we can suggest that the Melting ensemble mean trend is closer to the observational data trend than the Historical ensemble mean one.
Individual UVR exposure We obtained satellite data on the ambient vitamin D effective UVR dose at wave lengths between 290 and 315 nm  [22, 23]  at each study center, from the Royal Netherlands Meteorological Society (www. temis. nl)  [24, 25]. These data account for the attenuation of UVR due to clouds, varying Sun-Earth distance, axial tilt, surface albedo, surface elevation as well as presence of aerosols. Additionally, we collected data on personal sun behavior, including time spent outdoors during daylight, vacation destinations, clothing and frequency of sunscreen use through a standardized sun behavior questionnaire (Online data supplement section 2). Furthermore we gathered self-reported data on skin type, as an indicator of sun sensitivity  [26, 27]. Subsequently, we applied the following modeling approach integrating data on personal sun behavior, with data on ambient UVR to estimate the daily exposure [24,  28, 29] : E id = UVR d × EF id × (1 -(1 -h id / H d ) 2 ) where E id is the facial UVR exposure in subject i during day d, UVR d is the ambient UVR level in day d, EF id is the exposure fraction (i.
Discussion In this study we evaluated the association of UVR exposure and concentrations of sex hormones and SHBG in postmenopausal women. This study used data from a representative sample of women living across Europe at different latitudes, cultures and climates. We observed that higher exposure to UVR was associated with lower 17β-estradiol and estrone levels as well as with elevated FSH and LH levels. This was most pronounced for an exposure time of one months as compared to one week or three months. Additional adjustment for physical activity and personal sun behavior (vacation destination, body exposure and sunscreen use) yielded very similar results. We further investigated the direct association of dietary vitamin D intake with hormone concentrations, without being able to establish statistically significant associations, indicating that the role of dietary intake of vitamin D is not a driving force of the observed associations.
Kai Triebner has received a postdoctoral fellowship from the University of Bergen. Payam Dadvand is funded by a Ramón y Cajal fellowship (RYC-2012-10995) awarded by the Spanish Ministry of Economy and Finance. The present analyses are part of a project funded by the Norwegian Research Council (Project No. 228174). The coordination of ECRHS III was supported by the Medical Research Council (Grant No. 92091). Bodies funding the local studies are listed in the online data supplement. The funding sources were not involved in the conduct of the research and/or preparation of the article, in study design, in the collection, analysis and interpretation of data, in the writing of the report or in the decision to submit the article for publication.
Funding Kai Triebner has received a postdoctoral fellowship from the University of Bergen. Payam Dadvand is funded by a Ramón y Cajal fellowship (RYC-2012-10995) awarded by the Spanish Ministry of Economy and Finance. The present analyses are part of a project funded by the Norwegian Research Council (Project No. 228174). The coordination of ECRHS III was supported by the Medical Research Council (Grant No. 92091). Bodies funding the local studies are listed in the online data supplement. The funding sources were not involved in the conduct of the research and/or preparation of the article, in study design, in the collection, analysis and interpretation of data, in the writing of the report or in the decision to submit the article for publication.
Field site and instrumentation The seismic monitoring system was installed to monitor snow avalanches  (Heck et al. , 2018). It consisted of seven vertical geophones (Fig. 1b ) with an eigenfrequency of 4. 5 Hz, and data were recorded using a 24-bit acquisition system with a sampling rate of 500 Hz  (van Herwijnen and Schweizer, 2011). To increase the signal-to-noise ratio, the sensors were buried 30 to 50 cm deep as suggested by  Heck et al. (2019). For this study, we used data from two sensors deployed at a distance of 35 m (yellow dots in Fig. 1c ). Data were collected from 17 December 2018 to 11 June 2019. The instrumentation was deployed at the Jenatschalp field site in the Dischma valley above Davos (eastern Swiss Alps; 46. 73 • N, 9. 91 • E; Fig. 1a ). The field site is a flat meadow at an elevation of 1930 m a.
For example, atmospheric pressure changes may probably influence measured dV /V , but we expect the amplitude of this effect to be negligible (less than 0. 1 % for a variation of a few kilopascals) (Le  Breton et al. , 2021; Hotovec-Ellis et al. , 2014). Input parameters contain elastic (P-wave and S-wave seismic velocities) and inertial (density) properties of the medium, modeling the ground subsurface and the snow layers above. From this 1D model, the corresponding surface wave dispersion curve is then obtained as a result of the forward problem solved by the Geopsy package  (Wathelet et al. , 2004) , using the linear theory of elasticity (Wathelet, 2005) and assuming that surface waves are mostly dominated by Rayleigh waves  (Grêt et al. , 2006). In fact, the energy partitioning dynamics favors Rayleigh waves in the early part of the coda, when considering vertical component sensors and with most of the seismic noise sources being at (or almost at) the surface  (Obermann et al.
Acknowledgements. The authors particularly thank  SLF (Davos)  and  SIG ISTerre (Grenoble)  for their valuable assistance with fieldwork, meteo station maintenance and seismic data retrieval. Financial support. This work was supported by the  ANR  LabCom  GEO3iLAB  project and was partly funded by the  WSL research program  Climate Change Impacts on Alpine Mass Movements -CCAMM (https://ccamm. slf. ch/en/index. html,last access: 21 December 2021). Review statement. This paper was edited by  Adam Booth  and reviewed by two anonymous referees.
We used measurements only within the range of accepted standards, and estimated the uncertainty in the cation analysis from the maximum deviation of the calibration standards from the calibration line. We measured major anions (F -, Cl -, NO - 3 and SO 2- 4 ) on a Dionex ICS-1100 chromatograph using a six-point linear calibration and USGS-206 and USGS-212 standards for quality control. Uncertainty estimates were based on the standard deviation from three repeat measurements. Bicarbonate concentrations (HCO - 3 ) were estimated by charge balance, which assumes that the contribution of bicarbonate from organic sources is negligible (Supplementary Text 4). Uncertainties were propagated from the analytical uncertainties of the measured concentrations. All raw measurements are reported in Supplementary Data 1.
1 ). Uncertainties in erosion rates were estimated from the confidence band of the regression. 10 Be concentrations in samples collected in 2012 tend to be relatively lower and more scattered than during other years  27. The decrease in  10  Be concentrations of the 2012 samples was interpreted to result from extensive landsliding triggered by Typhoon Morakot in 2009  27  , and we excluded these data from the analysis (blue points in Extended Data Fig. 1 ). Our water samples extend farther south than existing cosmogenic nuclide data. Therefore, all erosion rates <0. 06 mm yr -1 were extrapolated from the regression (Supplementary Text 4). Unmixing major contributions to the cation solute budget. We consider carbonates, silicates, cyclic water and hotsprings as major contributors to the cation budget (see endmembers in Extended Data Table  3  and Supplementary Text 3).
Following previous authors  4, 8, 33, 37, 55  , this estimate was based on modelling the relative concentrations of three major soluble cations, Na + , Ca 2+ and Mg 2+ , as a mix of a silicate and a carbonate endmember (Extended Data Table  3 ). In addition, here we include the concentration of Cl -, to allow correction of the cyclic and hotspring contribution (Extended Data Table  3 ). Based on these endmembers, we solved the following equations using the linear least squares solver lsqlin in MATLAB: [ Na + Ca 2+ ] m = α Ca,sil [ Na + Ca 2+ ] sil + α Ca,carb [ Na + Ca 2+ ] carb + α Ca,cy [ Na + Ca 2+ ] cy +α Ca,hs [ Na + Ca 2+ ] hs (4) [ Mg 2+ Ca 2+ ] m = α Ca,sil [ Mg 2+ Ca 2+ ] sil + α Ca,carb [ Mg 2+ Ca 2+ ] carb + α Ca,cy [ Mg 2+ Ca 2+ ] cy +α Ca,hs [ Mg 2+ Ca 2+ ] hs (5) [ Cl - Ca 2+ ] m = α Ca,sil [ Cl - Ca 2+ ] sil + α Ca,carb [ Cl - Ca 2+ ] carb + α Ca,cy [ Cl - Ca 2+ ] cy +α Ca,hs [ Cl - Ca 2+ ] hs (6)   under the conditions that: α Ca,sil + α Ca,carb + α Ca,cy + α Ca,hs = 1 (7)   and 0 ≤ α Ca,sil ≤ 1; 0 ≤ α Ca,carb ≤ 1; 0 ≤ α Ca,cy ≤ 1; 0 ≤ α Ca,hs ≤ 1 ( 8 ) where α Ca,sil , α Ca,carb , α Ca,cy and α Ca,hs are the fractions of calcium sourced from silicate weathering, carbonate weathering, cyclic input and hotspring input, respectively.
Acknowledgements This research was enabled by resources from the  Initiative and Networking Fund of the Helmholtz Association. A. B. was supported by an  EU   Marie Skłodowska-Curie Research Fellowship  (no. 841663 ). J. K. C. R. was supported by an  ETH  Fellowship and an Alexander von Humboldt Fellowship. We thank  C. Zorn ,  B. Zimmermann ,  J. Schlegel ,  D. Frick ,  A. Gottsche  and the  HELGES Lab  for laboratory assistance; and  J. Bouchez  and  F. v. Blanckenburg  for valuable discussions.
The RNA integrity was measured using the RNA 6000 Nano Kit (Agilent). Libraries were prepared using the New England Biolabs (NEB) Ultra II Directional RNA Library Prep Kit for Illumina (NEB #E7760) and NEBNext Poly (A) mRNA Magnetic Isolation Module (NEB #E7490). Libraries were sequenced using the Illumina NextSeq 500 sequencer and read 85 bases from library inserts (Genomics Core Facility, EMBL). The bioinformatic analysis was performed using the Galaxy platform  [19]. FASTQ reads were trimmed and ltered for quality and adapter using Trimmomatic  [20]  and aligned to the human reference genome (hg19) using RNA Star  [21]. FeatureCounts was used to quantify gene expression  [22]  and differential expression analysis was performed with DESeq2  [23]  using RNAseq datasets generated for ETCC-006 and ETCC-010 against an MCF-10A RNAseq dataset (Project: PRJEB2623; Sample Accession: SAMEA1484687; Experiment Accession: ERX016686; European Nucleotide Archive (ENA), EMBL-EBI).
05% crystal violet in 20% ethanol for 30 minutes. Excess crystal violet was removed by washing with sterile, distilled water and plates were dried. Staining was assessed using an Odyssey Imaging System and quanti ed using ImageJ software. Wound healing assay MCF-10A, MCF-7, MDA-MB-231, MCF10DCIS. com, ETCC-006 and ETCC-010 cells were seeded on plates with two-well, culture inserts (#80209, Ibidi GmbH, Gräfel ng, Germany) the day before performing the assay to form a monolayer. Removal of the insert created a wound of 500 μm width. Wound closure was monitored 5 hours, 9 hours and 12 hours after removal of the insert. Images were acquired using the EVOS FL Auto Imaging System (Invitrogen, Biosciences Ltd, Ireland). Wound width was quanti ed using the ImageJ software. Anchorage-independent growth MCF-10A, MCF-7, MDA-MB-231, MCF10DCIS.
By comparing the level of expression of transcripts in ETCC-006 and ETCC-010 to those in MCF-10A cells, we aimed to identify if genes in some cancer-related pathways showed altered expression. Before performing RNAseq, we analyzed the quality of the total RNA extracted from our cell lines using bioanalyzer traces (Agilent) to calculate an RNA integrity (RIN) value that is indicative of the level of degradation of the RNA. RIN values range from 0 (degraded) to 10 (intact). We obtained RIN values of 10 for RNA extracted from ETCC-006 and ETCC-010, each with two replicates. This indicated that the RNA was of good quality and could be used for library preparation. After RNA sequencing, reads were trimmed and ltered for quality with Trimmomatic  [20]  and aligned to the human reference genome (hg19) with RNA STAR  [21].
Gene expression was quanti ed with FeatureCounts  [22]  and differential expression was performed using DESeq2  [23]. We compared the transcriptome of the ETCC lines to that of MCF-10A, using an available dataset, as detailed in the Methods. After the differential expression, we excluded genes with a p-value over 0. 05. Figure  4A  is a Venn diagram of the number of genes with higher expression in ETCC-006 and ETCC-010 in orange and lower expression in ETCC-006 and ETCC-010 in blue. In total, 235 and 246 genes were over-expressed in ETCC-006 and ETCC-010, with 99 genes in common; while 194 and 240 genes were under-expressed in ETCC-006 and ETCC-010, with 91 genes in common (Figure  4A ). All genes identi ed from our RNAseq analysis are listed in Additional File 1. Next, reactome pathway-based analyses were performed using an R/Bioconductor package, ReactomePA  [24] , using the lists of over-expressed genes in the ETCC DCIS cell lines to identify enriched pathways (Figure  4B  and 4D ).
Signalling pro le and cell cycle proteins in DCIS cell lines Since signalling by receptor tyrosine kinases was most enhanced in ETCC-006 cells according to our RNAseq data and ReactomePA analysis (Figure  4 ), and ETCC-006 and ETCC-010 have undergone EMT (Figure  7 ), we next decided to investigate levels of some of proteins in major cell signalling pathways. We measured protein levels of two receptor tyrosine kinases, Epidermal Growth Factor Receptor (EGFR) and Insulin-like Growth Factor 1 Receptor (IGF-1R), both critical in the processes leading to metastasis in breast cancer  [46, 47]. To do this, cell lysates were prepared using cells grown in normal conditions. Total levels of IGF-1R, EGFR and the active phosphorylated-EGFR were examined by immunoblotting (Figure  8A  and 8B). Low protein levels of EGFR were detected in MCF-7, with the other cell lines showing similar levels of EGFR.
Results: Tree-based orthology assignments revealed that orthologs of the cephalopod retinochrome and RALBP are present in mollusks outside of cephalopods. By mining our dataset for vertebrate visual cycle components, we also found orthologs of the retinoid binding protein RLBP1, in polyplacophoran mollusks, cephalopods and a phoronid. In situ hybridization and antibody staining revealed that L. asellus retinochrome is co-expressed in the larval chiton photoreceptor cells (PRCs) with the visual rhodopsin, RALBP and RLBP1. In addition, multiple retinal dehydrogenases are expressed in the PRCs, which might also contribute to the rhodopsin-retinochrome system.
Five transcripts containing the CRAL_ TRIO domain were found in the L. asellus data set. Phylogenetic analyses support the existence of many different evolutionary old groups containing CRAL_TRIO domains, including members from diverse taxa (Additional file 2: Fig. S2 ). Cephalopod RALBP fell into a wellsupported group containing Las-RALBP, sequences from other mollusks and several annelid sequences. The resolution of the tree did not allow drawing conclusions concerning the precise evolutionary relationships between RALBP and homologs in deuterostomes. Interestingly, these analyses also grouped one L. asellus sequence (Las-RLBP1) and two Octopus bimaculoides sequences with known chordate and Hydra RLBP1 with high support. These analyses provide, to our knowledge, the first evidence of RALBP outside cephalopods and RLBP1 in protostomes.
Gene cloning and RNA probe generation The RNA-seq assembly of larval L. asellus described in Vöcking et al. [30, 31]  was used for bidirectional blast (tblastn and blastx) to identify contiq sequences for genes of interest in the transcriptome data set  [66, 67]. Subsequently, whole transcripts or fragments were amplified by means of PCR with specifically designed primers (Additional file 6: Table  S2 ) and cDNA prepared with Super Script III (Invitrogen), ligated into pgemT-easy vector (Promega) and cloned into Top10 chemically competent E. coli (Invitrogen). Sanger sequencing was used to verify the cloned sequences before DIG-and FITC-labeled sense and antisense probes were generated with T7-and SP6-RNA Polymerases (Roche) or with Megascript Kit (Ambion).
Sequence analysis and phylogenetic tree reconstruction Public databases (Genbank, JGI, Uniprot) and the Leptochiton transcriptome were screened for homologs by text search, blast (blastp and tblastn) and HMMER (hmmscan and hmmseaarch) with respective query sequences or domain profiles for subsequent gene tree generation  [66] [67] [68]. Sequences were aligned with MAFFT 7. 221 (EINSI option) and manually curated to remove gaps and regions of low alignment quality  [69]. Best fitting substitution models were selected with Prot-Test 3. 4 according to Akaike information, Bayesian information and Decision theory criteria or with the RAXML based ProteinModelSelection script  [70, 71]. Maximum likelihood analyses were run with RAXML 8. 2. 8 on the CIPRES Science Gateway  [72]  with gamma model of rate heterogeneity and rapid bootstrapping with automatic stop by majority rule criterion autoMRE.