dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors thank  C. Morey Rubio  for sample collection,  J. Collet  for helpful discussions, and the  LIENSs' Plateforme Analyses Elémentaires and Isotopiques  (in particular,  M. Brault-Favrou ,  C. Churlaud  and  G. Guillou ). The  IUF (Institut Universitaire de France)  is acknowledged for its support to P. B.
(2016). At the time of deployment no robust fluorescence calibration method existed for UV-LIF spectrometers. Since this time the first successful calibration methods for WIBS type instruments have become available  (Robinson et al. , 2017). While the data presented here are uncalibrated, the instrument was routinely sent back to the manufacturer for servicing where no significant changes in the PMT voltages and gains or xenon flash lamp powers were found. At the start of the measurement period the instrument response was checked with fluorescent doped polystyrene latex spheres to verify the instrument was responding sensibly; however, absolute comparison between calibrations is not possible due to variation in fluorescent intensity between batches of particles and the degradation of their doping material with time.
The optimum cluster solution was validated using the Calinski-Harabasz criterion and then in-tegrated time series products were generated for each cluster at 5 min time resolution. The method used here is described in full in  Crawford et al. (2015)  and has been compared with other cluster and machine learning techniques by  Ruske et al. (2017). 3 Results The single-particle data were collected at CASLab during the period 18 November to 6 December 2015. A subset of approximately 17 000 particles were identified as exhibiting fluorescent intensities greater than the fluorescence threshold, which comprised 1. 9 % of the total number of particles recorded by WIBS, based on particle sizes D p ≥ 0. 8 µm. The Calinski-Harabasz criterion returned a four-cluster solution for the Ward linkage. A summary of the resultant cluster centroids is given in Table  1  and their relative contributions to the total aerosol population are presented in Fig.
Air mass trajectory analysis Three-day back trajectory analyses were used for possible source attribution. This used the NOAA HYSPLIT tool  (Stein et al. , 2015) , with 6-hourly averaged reanalysis meteorological data archived at the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR), with a 2. 5 • × 2. 5 • spatial resolution. Figure  7  summarises the fraction of time spent over different land classes for the back trajectories to CASLab for each period for the prior 12-120 h. The land class was specified as being one of three types: continental (C: land-coastal ice); open water (OW: where the sea ice fraction was < 5 %); and sea ice (SI: where the sea ice fraction was > 5 %). The fraction of time spent by the air masses below 500 m altitude is The UK Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME) model  (Jones et al.
Acknowledgements. We would like to thank the  University of Manchester  and  British Antarctic Survey  teams conducting the " Microphysics of Antarctic Clouds " project,  Tom Lachlan-Cope  and  Tom Choularton , supported by  NERC  grant  NE/K0142X/1 , for kindly including the WIBS measurements as part of their experiment. We would also like to thank the  Dstl team  for loan of the WIBS instrument and for access to their laboratory databases to support the UV-LIF aerosol interpretation. We thank  Paul Kaye  for his continued support of the WIBS. We would finally like to thank the  BAS team at Halley VI  for all their support. The authors gratefully acknowledge use of the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model used in this publication.
As the MOCAGE model top is set at 35 km altitude, we complete the atmospheric vertical information between 30 and 50 km with data from a global MOCAGE simulation, run at coarser resolution with model top at 0. 1 hPa  (Emili et al. , 2014)  and with climatological profiles above 50 km. Radiometric random noise for IASI and GOME-2 is added to the raw spectra before their ingestion into the retrieval algorithm. For TIR, the nominal noise standard deviation is taken from the literature as 20 nW/(cm 2 cm -1 sr)  (Eremenko et al. , 2008). Therefore, we added a noise value of 13 nW/(cm 2 cm -1 sr), roughly accounting for the reduction of noise due to apodization. For UV, noise is estimated for each wavelength using Muller matrix radiance response elements  (Nowlan et al. , 2011; Cai et al. , 2012).
Long-term trends in mean age have been used to investigate Published by Copernicus Publications on behalf of the European Geosciences Union. A. Engel et al. :  Mean age of stratospheric air derived from AirCore observations long-term changes in the overall overturning circulation of the stratosphere (Brewer-Dobson circulation, BDC). An increase in the strength of the BDC is expected from model calculation with increasing greenhouse gas concentrations  (Austin and Li, 2006; Butchart et al. , 2006; Butchart, 2014). This is reflected in overall shorter transit times, thus also lower mean age values in the models. The experimental database of mean age observations for the verification of such changes is sparse. It relies mainly on very sporadic balloon-borne observations of CO 2 and SF 6 dating back to 1975  (Engel et al.
These instruments require large and expensive balloons to carry them to altitudes above 25 km. The use of these large balloons involves a large operational team and is very expensive. The uncontrolled parachute descent of such large payloads after the flights further presents a large operational constraint due to safety regulations. These safety regulations make it virtually impossible to fly such large payloads in densely populated areas as central Europe. Due to these operational constraints and in order to create a larger and more representative database, an easy to launch and cheap technique to allow for the measurement of age tracers would be required. AirCore, a new technique for sampling air, which has been suggested by  Karion et al. (2010) , may provide such an opportunity. In brief, this technique relies on collecting air in a previously evacuated, long stainless steel tube.
Data retrieval The Picarro analyser will deliver a time series of mixing ratios as a function of measurement time. The absolute values of the Picarro analyser are transferred to the WMO scale (X2007 scale for CO 2 and X2004a scale for CH 4 ) based on a calibration function derived from absolute values of four gas bottles with a range of CO 2 values between 390 and 416 ppm of CO 2 and 1. 07 to 1. 91 ppm of CH 4 (only three gas bottles). The mixing ratios determined by the Picarro analyser have to be matched to the altitude at which the air was sampled by the AirCore during the flight. The basis of this altitude attribution is the ideal gas law and the molar amount sampled at each altitude during the flight. This matching is achieved in a four-stage process. (i) The amount of remaining fill gas is determined, (ii) the sampling of air based on the ideal gas law is calculated.
The University of Frankfurt AirCore on the other hand is capable of capturing some local structure around 21-22 km in altitude, although the two local minima from the high-resolution AirCore are smeared out to one broader minimum. Above 23 km there seems to be a small altitude mismatch between the University of Frankfurt and the high-resolution AirCore, which is, however, less than 1 km. This altitude discrepancy is explained by the uncertainty in matching the Picarro measurements to the AirCore sampling, which is also treated slightly differently in the LMD and the University of Frankfurt retrieval. In order to compare the values of the different AirCores, we binned the data into 1 km intervals and then calculated averages for each AirCore in these bins. In the troposphere (values between 3 and 13 km in altitude), the standard deviation between these 1 km bins is 1.
Extension of long-term time series As explained above, we want to use AirCore observations to extend our long time series of stratospheric mean age observations  (Engel et al. , 2009). The calculation of a temporal trend in mean age is complicated by the sparsity of the data set in combination with a vertical gradient in mean age. As the AirCore observations, in agreement with other balloon data, only show a very small vertical gradient above an altitude of 23-24 km, corresponding to about 30 hPa, we have adopted the same procedure as used in  Engel et al. (2009) ; i. e. we average all data between 30 and 5 hPa and calculate an average value of mean age for this region of the stratosphere. The last data point in  Engel et al. (2009)  was from the year 2005. There is thus a gap of 10 years between these last measurements and the new AirCore data.
Acknowledgements. The work of the University of Frankfurt on AirCore has been funded through the  ROMIC programme of the German Ministry of Science and Education  (Grant no. 01LG1221 ) and the  EU Infrastructure Project RINGO  (Grant agreement no. 730944 ). We would like to thank the  French Space Agency CNES  for balloon operations in Timmins and the team of  Ruud Dirksen  from the  German Weather Service (DWD) in Lindenberg. The support of the workshops and technicians at the  University of Frankfurt  is gratefully acknowledged. Special thanks go to  Huilin Chen  from the  University of Groningen in the Netherlands  for many valuable discussions on AirCore techniques and an introduction to AirCore measurements during measurements from  Sodankylä. Olivier Membrive  was funded by  EIT/Climate-KIC , a body of the  European Union.
Our data point out ERα36 variant as a key node of the gene networks modulated by alkylphenols, involved in proliferation, migration, and apoptosis escape control. In order to investigate low-dose alkylphenol effect on the fetal and neonatal mammary gland morphogenesis, we set up a transgenerational assay of intrabuccal chronic exposure of wild-type or ERα36 transgenic (Tg) C57BL/6J P0 pregnant mice. Male-inherited exposure effects were assessed in F3 litters. The results strongly suggest that a low-dose alkylphenol exposure could promote a mammary gland abnormal development. Finally, we addressed if ERα36 overexpression in xenografted MCF-10A cells combined to systemic alkylphenol exposure would be sufficient to trigger mammary tumor formation in adult Nude mice.
Microarray Experiment Sample Preparation and Data Normalization Transcriptional profile analyses of MCF-10A cells exposed to 1 nM M4 for 1, 8, or 24 h were performed in triplicates on Affymetrix GeneChip U133 2. 0 by the GenomEast Platform (IGBMC, Strasbourg, France). Biotinylated cRNA targets were prepared using the Ambion "MessageAmp™ Premier RNA Amplification Kit" according to the Instruction Manual P/N 4386269 Revision D (revision date: May 16, 2008), starting from 200 ng of total RNA extracted with RNeasy Mini Kit (Quiagen). Following fragmentation, 10 µg of cRNAs were hybridized for 16 h at 45°C, 60 rpm on Human GeneChip ® HG-U133 plus 2. 0 arrays (Affymetrix). The chips were washed and stained in the GeneChip ® Fluidics Station 450 (Affymetrix) using the FS450_0004 script and scanned with the GeneChip ® Scanner 3000 7G (Affymetrix) at a resolution of 1.
56 µm. Raw data (CEL Intensity files) were extracted from the scanned images using the Affymetrix GeneChip ® Command Console (AGCC) version 4. 0. CEL files were further processed with Affymetrix Expression Console software version 1. 3. 1 to calculate probe set signal intensities, using Robust Multi-array Average (RMA) algorithms with default settings. Overall, 3,362 transcripts are differentially expressed compared to t = 0. Distinct kinetic variation pattern and transcript number in each pattern are described in Figure  S2A  in Supplementary Material. A total of 278 transcripts, 76 transcripts, and 2,743 transcripts were significantly up-or downregulated (absolute variation factor ≥2 in triplicate RNA samples, corrected p-value (p < 0. 05) with Benjamini-Hochberg method) after 1-, 8-, or 24 h 1 nM M4 exposure, respectively.
gene Ontology (gO) and Pathway analyses Gene ontology enrichment analyses were performed using the Panther online tool (Protein ANalysis THrough Evolutionary Relationships) 1   (24). MSigDBv5. 0 (Molecular Signature Database) 2  (25, 26)  was used to achieve the pathway enrichment analyses. The overlap between DEG list and (i) GO gene sets derived from the Biological Process part of GO (Table  S1  in Supplementary Material) or (ii) Kyoto Encyclopedia Genes and Genomes (KEGG) pathways (Table  S2  in Supplementary Material) were computed. cell culture MCF-10A cells were purchased in 2015 from ATCC ® (CRL-10317) and maintained as described by Soule et al. (27). Briefly, cells were plated at a density of 2 × 10 4 cells per well in 6-well plates in 5% HS-supplemented DMEM/F12 (GIBCO) medium for 24 h, then starved for 48 h in 2.
Flow cytometry After a 24 h 1 nM M4 or vehicle exposure, medium and adherent cells were harvested. Cells were washed twice with cold 2 http://www. broadinstitute. org/gsea/msigdb/. PBS, resuspended at 1. 10 6 cells/mL in PBS containing 4 µg/mL propidium iodide (PI) (P3566, Life Technologies), and 0. 4 µg/ µL RNAase A (Sigma-Aldrich) for 20 min at room temperature. PI fluorescence level was determined using the argon laser of a FACScalibur flow cytometer (Becton-Dickinson). For each sample, fluorescence intensity of 10,000 cells was analyzed using CellQuest Pro Software.
Protein expression profiles were revealed with Clarity Western ECL Substrate (Biorad) and banding quantification was performed using the Quantity One Chemidoc XRS software (Biorad). immunofluorescence Immunofluorescence was performed as described previously  (13). The following primary antibodies were used: anti-NFκB (GTX102090, GeneTex), anti-β-catenin (E247, Epitomics #1247s), anti-E-cadherin (GTX100443, GeneTex), and anti-N-cadherin (TA326835, OriGene). Goat anti-rabbit secondary antibody was coupled to AlexaFluor 555 (Invitrogen). Images were obtained with DS-Ri1 Nikon camera and Eclipse80i Nikon microscope and quantifications were performed using NIS-Elements BR 4. 20. 00 software (Nikon).
TUnel assay TUNEL assay was performed using the Apo-BrdU-IHC in-situ DNA fragmentation Assay kit (BioVision, USA) following the manufacturer instructions for the staining of cell preparations fixed on slides adapted for the use of AlexaFluor 555 (Invitrogen) goat anti-mouse secondary antibody. Nuclear DNA was stained with Hoechst (bisBenzimide H33342 Trihydrochloride, Sigma-Aldrich). Quantification of stained cell number was performed with NIS elements BR 4. 20. 00 software (Nikon).
scratch-Wound assay Scratch assays were performed using the Ibidi-culture inserts (Ibidi ® /Biovalley) following the manufacturer instructions. Cultures were then washed to remove detached cells and debris and treated with vehicle or 1 nM M4. Quantification of wound mean diameter were performed at t = 0 and after a 6 h exposure by Phase-contrast image analysis with NIS-elements BR 4. 20. 00 software (Nikon).
statistical analysis Statistical analyses were performed with Matlab vR2014b software (MathWorks) by using Student's t-test for unpaired samples and ANOVA followed by Bonferroni post-hoc test for multigroup comparison, with a significant p-value threshold below 5%. *p < 0. 05; **p < 0. 01; ***p < 0. 001. SDs or SEs were indicated on figures as advocated by Altman and Bland  (28).
Focus on cell cycle Expression of key genes and corresponding proteins involved in cell cycle control and regulation was determined by western blot analysis after a 24 h M4 exposure. Quantification of cell viability by crystal violet staining indicated a 23 ± 0. 5% augmentation after M4 versus vehicle 24 h exposure (p < 0. 05, Figure  1A ). Flow cytometry analyses confirmed that a 24 h M4 exposure triggered cell cycle entry even if the data were not statistically significant (66 ± 1. 3% G0/G1 phase in vehicle exposed cells versus 60 ± 0. 8% G0/G1 phase in M4 exposed cells; N = 3). Moreover, the expression of more than 25 genes involved in all cell cycle steps was modified by alkylphenol exposure (Table  1 ), all of them sustaining enhanced cell cycle entry or progression. Figure  1B  shows that Src phosphorylation, Cyclin D1 and Cyclin E1 expression as well as Retinoblastoma (Rb) protein phosphorylation were significantly induced (p < 0.
ACKNOWLEDGMENTS We are grateful to  L. Cazes , photographer at the  UMR 7207-CR2P  (Paris), for providing 3D models of the types of D. bucklandii and D. belli n. sp. , as well as many of the specimen pictures. We also want to thank  P. Hurst  and  C. Mellish  for providing us the new photographs of the types of D. belli n. sp. deposited in at the  NHM, London  and warmly thank  S. Polkowsky  ( Schwerin, Germany ) for his kind donation of paratype MAB. k 3583.
1 m/z. The second group step was performed using density method and a band width of 10. Fillpeaks step was used with the chrom filling method. Finally, annotation by CAMERA was set using a max ion charge of 2, a general ppm error of 5 and a precision of 4 decimals of m/z values. Areas for all peaks were normalized using that of the internal standard in the same sample, and normalized areas were used as ion relative abundances. The multivariate data analysis of relative ions abundances using Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to test for differences in metabolite profiles among grazed and control samples. Further statistical multiple testing analyses were performed to identify the different ions, based on Student T-test and FDR-adjusted p-values (P). The analysis was carried out on Pareto scaling and log10-transformed data using the software SIMCA (13.
0, Umetrics, Umeå, Sweden). For amino acid, free fatty acid and oxylipin targeted profiling in L. spicata samples (n = 3 except for control-48h n = 2), the same extraction and quantification process was applied as for the untargeted analysis (see above), adding 1. 25 μg of 12-OH-lauric acid and 10 μg of atropine in each sample, as internal standards for negative or positive ionization mode analyses, respectively. Free fatty acid and oxylipin profiles were obtained through negative mode LCMS analyses, and amino acid measurements in positive mode. The Hierarchical Clustering analysis of relative abundances changes was carried out using Pearson Correlation values in Multi Experiment Viewer 4. 9  [19]. Statistical Kruskal-Wallis tests were then performed for each metabolite with grazing treatment and time as factors, using the free software R version 3.
cDNA library sequencing and data analysis Total RNA was extracted from 100 mg of frozen algal materials according to Apt et al. [21] , treated with RNAse-free DNAse I (Stratagene, La Jolla, CA, USA) to eliminate genomic DNA contamination, and quantified using a Nanodrop ND 1000 spectrophotometer (Labtech International Ltd, Lewes, UK). For each species, total RNA was extracted from three independent biological replicates of control or grazed algal samples, collected after 6, 12, 24 and 48 hours for L. digitata (Ld) and after 6, 12 and 24 hours for L. spicata (Ls). For cDNA library sequencing, two pools of RNA, corresponding to Control or Grazed treatments for each species, were prepared by pooling the same amount of RNA samples extracted at each time point from independent biological triplicates, i.
e. 12 RNA samples for Ld and 9 RNA samples from Ls. The four non-normalized shot-gun cDNA libraries, namely Ld-Control, Ld-Grazed, Ls-Control and Ls-Grazed, were constructed using the SMART cDNA library kit from Clontech (CA, USA), differentially-tagged with a specific adaptor and sequenced using 454 sequencing technology (Roche, Branford, CT, USA) at the Max Planck Institute of Berlin-Dahlem. Raw sequencing data were deposited in the SRA database at NCBI (accessions SRR4149283 for Ld-Control, SRR4159455 for Ld-Grazed, SRR4240918 for Ls-Control, SRR4240919 for Ls-Grazed sequences). Raw reads were cleaned by removing adaptor sequences, empty reads and filtering reads with poor quality using SeqClean (https://sourceforge. net/projects/seqclean/ files/). The cleaned 454-generated reads constituted the Expressed Sequence Tags (ESTs).
For each species, all ESTs derived from Control and Grazed libraries were assembled together into non-redundant unigenes using Newbler v2. 0. 01. 14 (http://www. my454. com), by including cDNA sequences of the two species available in public databases, to increase the quality of clustering. The unigenes sequences are available upon request. Each EST, belonging to Control or Grazed libraries, was associated to one unigene sequence (contigs or singletons). In order to compare the relative abundance of EST per unigene between the two libraries, we calculated their relative abundance with the following formula: Relative EST abundance of Unigene U in Library Lib = number of EST mapped to Unigene U in Library Lib Ã 1/(Total number of ESTs in Library Lib Ã Length of Unigene U (bp)). Putative differentially-expressed unigenes were further identified according to their relative EST abundance into the two libraries, using IDEG6 web tool  [22] , based on concordant results from four different statistical tests, namely Pairwise Audic & Claverie, Pairwise Fisher Exact, Pairwise Chi-squared and Stekel & Falciani R tests  [23].
Field samples were immediately flash frozen in liquid nitrogen and kept at -80˚C before RNA extraction. Gene expression was then analyzed by qPCR as described by Cosse et al. [9] , using the primers sequences provided in S1 Table. Gene transcript level was normalized to the geometrical mean of two reference gene transcript levels in the same sample (tubulin and EF1a genes for L. digitata; EF1a and RPL36 genes for L. spicata). The Hierarchical Clustering analyses of gene expression changes was carried out on Pearson Correlation values in Multi Experiment Viewer 4. 9  [19]. A two-way analysis of variance (ANOVA) with grazing treatment and time as categorical predictors was applied to qPCR expression data. All tests were performed using Statistica 7 (Statsoft, Tulsa, OK, USA).
Specialist herbivores induced large metabolome alterations in both Chilean and European kelp species Metabolomic profiling of grazed algae by LC-MS in the negative mode provided 211 ion peaks in L. digitata and 818 in L. spicata, that were considered for multivariate analysis (S2 and S3 Tables). Partial least squares discriminant analysis (PLS-DA) of these datasets for L. digitata explained ~50% of the observed variation. In particular, the axis t  [1]  discriminated two groups of grazed and control individuals along t  [2]  axis (Fig  1A ). For L. spicata datasets, PLS-DA analysis explained 80% of the observed variation. Grazed and controlled individuals were distinctively grouped and separated by t  [2]  axis that explained 54% of the observed variation (Fig  1B ). Although multivariate metabolome analysis succeeded to discriminate between control and grazed kelps, PLS-DA analyses failed to distinguish a general pattern according to time in both species.
Metabolic profiling of grazed L. spicata highlighted distinctive accumulation patterns of Amino Acids and Free Fatty Acids We previously demonstrated the regulation of free fatty acid (FFA), oxylipin and amino acid (AA) metabolisms by biotic and abiotic stress in brown algae  [7, 12, 19]. Accordingly, we profiled these metabolites to evaluate their possible functions in grazing defense. Unfortunately the resolution of L. digitata dataset was not sufficient to proceed to these targeted analyses all along the experimental time-course. We therefore carried out this analysis only in L. spicata which provided a highly resolved ion dataset. Indeed LC-MS analysis of extracts obtained from grazed and control individuals of L. spicata identified nine FFA, three oxylipins and nine AAs. Hierarchical Clustering Analysis of metabolite accumulation in grazed algae compared to controls made a clear distinction between early harvested samples at 6h and 12h opposed to late harvested samples at 24h and 48h (Fig  2A ).