dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS NA, DG and CB would like to acknowledge support from grants  UBACyT 20020100100315 , and  PICT 2011-0454. NA, CM, DG and CB work was supported by the  ECOS-MINCyT cooperation program. NA acknowledges  E. Penou  for his assistance. The authors acknowledge Cluster Active Archive (http://caa. estec. esa. int/caa/home. xml) and CDAWeb (http://cdaweb. gsfc. nasfor the support/distribution of the data used in the present work.
INTRODUCTION People living in Pacific islands are already experiencing changes in their climate, such as higher temperatures, shifts in precipitation regimes, changing frequencies of extreme events and rising sea levels  (Stocker et al. 2013 ). These changes are affecting the local population's well-being, water resources management, security and habitable zone. Although sea level rise is a major concern for Pacific islands  (Becker et al. 2012 ), the focus is on the atmospheric component, (1) for practical data availability reasons -precipitation gauges provide observed datasets long enough to conduct climate change studiesand (2) because the atmospheric component holds another crucial aspect for local populations: future water resources. Situated in the middle of the south Pacific Ocean, Ta hi ti, the largest island of French Polynesia (~30 × 30 km 2 ), is a volcanic island with steep topography in its central part (the highest point reaches 2241 m above the sea level).
2). The SST fields, modelled by the CNRM-CM (historical and scenario runs), are used as boundary conditions for ARPEGE, after the application of a SST bias-correction procedure (see Section 3. 4). Even with a 50 km mesh, the island of Tahiti remains unresolved. A second downscaling is then produced by nesting the RCM ALADIN (12 km mesh) in ARPEGE 50 km. This higher-resolution simulation allows the representation of Tahiti by up to 6 land grid-points. The new 12 km mesh simulation over historical and 21st century periods provides a suitable dataset to perform a study of climate change. It should be noted that the RCM carries its own intrinsic biases in addition to the systematic biases inherited from the global host model. The left part of the diagram displayed in Fig. 1  shows the statistical linkage established over the historical period between modelled and available observed precipitation.
Historical RCM outputs Scenario RCM outputs • the deep convection scheme described by  Bougeault (1985) , in which convection is parameterized by a mass-flux scheme where the vertical ascent in the cloud is compensated by a large-scale subsidence, and • the land surface scheme ISBA. The radiation transfer parameterization is the radiation package of  Morcrette (1990) , and the exchange coefficients at the air-water interface are derived from Louis functions  (Louis 1979 ). These last 2 parameterizations differ from the ARPEGE component in CNRM-CM, for practical reasons. This choice does not have a strong impact on the ARPEGE climate. Then, the global atmospheric ARPEGE 50 km model fields are used as lateral boundary conditions for the RCM ALADIN-Climate. A 6-hourly lateral forcing by vorticity, divergence, temperature, relative humidity and surface pressure logarithm is performed.
The large-scale meteorological information is forced on the inner model solution by a classical relaxation scheme  (Davies 1976). With the application of an additional relaxation zone, the method is also capable of damping the artificial reflection and transmission of small-scale waves on the boundaries of the domain of interest. Model prognostic variables are zonal and meridional wind components, temperature, specific humidity and surface pressure. The physical parameterization package of the ALADIN-Climate model corresponds to that of ARPEGE 50 km global model. Although it has a nonhydrostatic version, ALADIN has been used here as a hydrostatic model.
5 and RCP8. 5 runs, are used in this study. As seen above, the erroneous SST pattern is amplified by ocean-atmosphere feedback. A correction method is thus applied to prescribe boundary conditions that are as close to the observations as possible. An observed SST climatology has been calculated on the 1958-2001 period based on SST datasets used for ERA 40 reanalysis (HadISST and Reynolds). The coupled climatological error E is defined as the difference between CNRM-CM SST historical climatology and observed SST 1958-2001 climatology (computed for each grid cell of the global SST field). The error E has 12 fields, 1 for each month of the year. This mean error is then removed from the model SST. For example, the mean January error is removed from all the January months of the historical run.
An improvement of the SPCZ position is achieved in austral summer (Fig. 3a-c ) thanks to the SST bias correction. The GPCP dataset (Fig. 3a ) allows the comparison of simulated austral summer precipitation between CNRM-CM5 and ARPEGE 50 km. Despite a slight overestimation of precipitation in the tropical Pacific, the SPCZ spatial extension is better simulated in ARPEGE 50 km (Fig. 3c ) than in CNRM-CM5 (Fig. 3b ). The spurious eastward extension of the convergence zone south of the equator is reduced in the eastern Pacific, but its orientation is still too zonal in the central Pacific. Furthermore, no progress has been achieved for austral winter. For this reason, the focus will be put on austral summer, which is the wet season and corresponds to the maximum amplitude of ENSO.
Observational precipitation datasets A newly available high-quality dataset is used to conduct this study (courtesy of Météo France Regional Service in Tahiti). Homogenized monthly precipitation amounts from 9 meteorological stations over Tahiti and Moorea were made available by the meteorological service of French Polynesia. The records, which have undergone the homogenization procedure described by  Caussinus & Mestre (2004) , span the period from 1961 to 2011. The 9 stations are shown in Fig. 2c. The stations are not regularly distributed; the majority of them are located on the western part of the island and mainly on the coastline. An analysis of Tahiti's observed precipitation variability from seasonal to interdecadal time scales is available from  Hopuare et al. (2015).
RCM PRECIPITATION CORRECTION OVER THE HISTORICAL PERIOD To assess future changes of precipitation based on future modelled precipitation, the linkage between observations and model results is made over the historical period. A major comment concerns the historical forcing: the SST used does not refer to observational gridded datasets but is from the CNRM-CM historical SST fields, and consequently, no common chronology is possible between observed and modelled variables (e. g. the El Niño events occur in different years). This linkage, based on a long learning period, is assumed to remain valid over the 21st century; details on the assumptions made are presented in Subsection 4. 2.
4 index values. El Niño months correspond to 'model' Niño3. 4 index > 0. 5; La Niña months correspond to 'model' Niño3. 4 index <-0. 5. This threshold allows us to consider not only strong ENSO events but also weak and moderate events. Modelled interannual variability is in agreement with observations as shown by the statistics of Niño3. 4 in Fig. 5 , derived from the reference SST dataset and the corrected SSTs from CNRM-CM5. (3) The model warm and cold ENSO precipitation composites were computed for the historical run following the methodology employed with the observations. The results are presented in Fig. 6. A consistent behaviour, similar to that of the southern stations, can be seen for a few 'sea' grid cells located to the south-east of the island. Warm (cold) events imply positive (negative) precipitation anomalies.
Global Planet Change 80-81: 85-98 Bennett JC, Grose MR, Corney SP, White CJ and others (2014) Performance of an empirical bias-correction of a high-resolution climate dataset. Int J Climatol 34: 2189-2204 Bougeault P (1985) A simple parameterization of the largescale effects of cumulus convection. Mon Weather Rev 113: 2108-2121 Brown JR, Moise AF, Colman RA (2013) The South Pacific Convergence Zone in CMIP5 simulations of historical and future climate. Clim Dyn 41: 2179-2197 Bubnová R, Hello G, Bénard P, Geleyn JF (1995) Integration of the fully elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of the ARPEGE/ALADIN NWP system. Mon Weather Rev 123: 515-535 Caussinus H, Mestre O (2004) Detection and correction of artificial shifts in climate series.
Acknowledgements. The authors thank the Meteorological Service of French Polynesia (DIRPF -CER Etude) for performing the homogenization procedure on Tahiti's precipitation records and making the data available. LITERATURE CITED Ashfaq M, Bowling LC, Cherkauer K, Pal JS, Diffenbaugh NS (2010) Influence of climate model biases and dailyscale temperature and precipitation events on hydrological impacts assessment: a case study of the United States. J Geophys Res 115: D14116, doi: 10. 1029/ 2009 JD 012965 Baigorria GA, Jones JW, Shin DW, Mishra A, O Brien JJ (2007) Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs. Clim Res 34: 211-222 Becker M, Meyssignac B, Letetrel C, Llovel W, Cazenave A, Delcroix T (2012) Sea level variations at tropical Pacific islands since 1950.
Design The study was a cross-sectional survey investigating two different samples of medical doctors: (1) those specialised in palliative care and affiliated to the French Society for Patient Accompaniment and Palliative Care; (2) medical interns (a 'medical intern' corresponds to a medical doctor in training course; after the sixth year of medical studies, the medical intern practices in the hospital, provides medical treatment or performs surgery under the supervision of a senior doctor) of a French medical university (Marseille, South of France) who are in their 7th and 8th years of the training course. Mailing lists were provided by the French Society for Patient Accompaniment and Palliative Care and the university. The exclusion criterion was refusal to participate. A questionnaire including the following data was sent via e-mail to all eligible individuals: i) sociodemographics (sex, age, marital status, children); ii) information relative to professional status (supplementary specialization, palliative care experience); iii) one item relative to believing in God ('Do you believe in God?' yes/no) and one item concerning the history of caring for someone in a palliative care situation; iv) opinion about euthanasia using a 6-point scale encoded from 1 strongly disagree to 6 strongly agree; the question was designed to assess the general opinion about euthanasia and to know what the subjects thought about the case of Vincent Humbert.
Introduction Primary Sjögren's syndrome (SS) is a systemic autoimmune epithelitis affecting exocrine glands, such as salivary and lacrimal glands  (1). The clinical manifestations of SS include dry mouth (xerostomia), dry eyes (keratoconjunctivitis sicca), systemic features, and patients have a 20-to 40-fold increased risk of developing lymphoma  (2) (3) (4). Histological examination shows focal and peri-epithelial T and B cell infiltration plus macrophages in exocrine glands and parenchymal organs, such as kidney, lung, and liver  (5). SS is characterized by the presence of circulating autoantibodies (Ab) against the sicca syndrome (SS)A/Ro and SSB/La ribonucleoprotein particles  (6). It is estimated that there are over 120 million single nucleotide polymorphisms (SNPs) in the human genome (NCBI dbSNP database, Build 143) and, among them, hundreds are disease risk variants for autoimmune diseases (AID) with the particularity that they are for the vast majority excluded from proteincoding regions (exon) and present within regulatory areas  (7, 8).
SS Genetic Risk Factors Data mining based on peer reviewed literature information (PubMed) and publicly available databases (centralgwas. org) served in the compilation of a list of 43 gene risk factors and their reported variants (n = 72) in SS (Table  S1  in Supplementary Material). The number of SS patients and controls were also reported as well as the OR average (95%), when available. The gene list used in this study was manually updated further to include gene function, SNP number (dbSNP database), and genomic location according to the human genome reference GRCh38. Genetic variants and their observed associations with clinical and functional phenotype were submitted to The National Center for Biotechnology Information (NCBI) ClinVar database  1. The gene list was tested with the FatiGO web interface AmiGO2 2  for functional enrichment.
The first group contains an isolated SNP (rs12549796) that was present in an intronic part of the FAM167A gene. A second group (n = 7) was present in the vicinity of the BLK promoter and exon 1, and a third group (n = 2) was present ~35 kb downstream BLK promoter. Next, as revealed by querying the Ensembl database using H3K27Ac to mark active promoters and enhancers, SNPs were positioned within 9/10 H3K27Ac active motifs in B cells (GM12878), which is in contrast to 2/10 H3K27Ac active motifs in monocytes, and none in epithelial cells. Such associations between genetic and epigenetic factors within regulatory elements in B cells for the FAM167A-BLK locus were further reinforced by using the RegulomeDB tool that summarizes results from the ENCODE and Epigenetic Roadmap programs. As indicated Table  2 , the RegulomeDB tool supports that SS risk factors at FAM167A-BLK locus would predominantly affect B cells (lymphoblastoid and naive B cells) and, to a lesser extent, monocytes, T cells (naïve, TH2, and Treg), mesenchymal stem cells, and fibroblasts.
Acknowledgments Authors are grateful to  Simone Forest  and  Genevieve Michel  for their secretarial assistance. This work is supported by the  Ligue Contre le Cancer , the  Region Bretagne , the  Association Française du Gougerot Sjögren et des Syndrome Secs (AFGS) , the  Association Laurette Fugain  (grant  1674 ), and the  Association Dentaire Française (ADF). The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115565 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in-kind contribution.
Statistical analysis Calculations were performed using XLSTAT. Standard statistical methods were used for the calculation of mean and standard deviations. Student test was used to determine significant differences of measured variables between each running condition. ANOVA with repeated measurements was used to compare the changes in parameters during the tests. Normal Gaussian distribution of the data was verified by the Shapiro-Wilks test. Compound symmetry, or sphericity, was verified by the Mauchley test. Multiple comparisons were made with the Tukey HSD post hoc test when the Greenhouse-Geisser epsilon correction factor was > 0. 50, or with the Bonferroni post hoc test when the epsilon was < 0. 50. The Pearson's coefficient was computed to test linear correlation among variables measured at the end of each repetition.
Inverse problem solving Eq (  25 ) is solved iteratively. The center of each survival zone is fixed by conservation of proportions between the three distances. On chromosome 3, the center of HML positions is taken at (0,0), the center of HMR is fixed at (5,0). If d HML-MAT , d MAT-HMR and d HML-HMR are experimental mean distances, the lengths r 0 and r 1 are given by: 5 d HMLÀHMR ¼ r 0 d HMLÀMAT ¼ r 1 d MATÀHMR ð26Þ The center of the MAT zone is defined as the intersection of two circles. The circle with (0,0) center and r 0 as radius. The second circle has (5,0) as a center and r 1 as radius. Iterations are made using the variables ε 1 1 ; ε 1 2 ; ε 2 1 ; ε 2 2 ; ε 3 1 and ε 3 2 , in the range [0;5]. Our code has been parallelized on one hundred CPU cards using MPI (Message Passing Interface).
Folding of the left arm of chromosome 3 is mating type specific The distribution of angles within the triangle formed by MAT, HML and HMR in MATa and MATα cells was statistically significant (S1 and S2A Figs), again suggesting that certain conformations of Chr3 could be mating type specific. To extract folding features from the distribution of the three loci, we generated 2D projections of geometrical coordinates recorded for all nuclei. In the resulting density maps (Fig 2  and S2 -S7 Figs), data are grouped within 10% color-coded increments of occurrence. For each dataset, nine distinct maps are generated and compared to the equivalent maps of another dataset using PCA (see Methods). Correlation coefficients (c) between duplicate experiments using the same strain were greater than 0. 8 (S2C Fig; four independent experiments 223<n<559).
Acknowledgments We are grateful to  Dr D Chattoraj  for kindly providing plasmids pRFG116 and pRFB122. We thank  Daniel Sage  for sharing the Image J '3 spot distance' plugin, and  David Villa , Photographic service of the  FRBT, Toulouse , for the schematic representation of chromosome 3. We acknowledge the  TRI-Genotoul Imaging platform. We benefitted from stimulating discussions with  Job Dekker  and his group.
Genome sequencing information Genome project history S. algae C6G3 was isolated from intertidal marine sediment on the basis of its ability to use large range of electron acceptors particularly nitrate, nitrite and metal-oxides  [8]. The genome of S. algae C6G3 is the second to be reported from that species, the other one being S. algae ACDC  [15]. The genome project of S. algae C6G3 is deposited in the Genome On Line Database  [16]. A summary of the project and information on compliance with MIGS version 2. 0  [17]  are shown (Table  2 ). Growth conditions and DNA isolation S. algae C6G3 was grown aerobically at 30 °C under stirring condition on artificial sea water  [18]  amended with lactate (3 g/L), yeast extract (1 g/L) and tryptone (5 g/L). DNA was extracted from cells collected in exponential growth phase using the protocol of Marteinsson et al.
e. , not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project  [55]. You will find the table of associated MIGS Record in additional file (Additional file 2) depth of 50-fold. The assembly of S. algae C6G3 genome was generated at MR DNA Laboratory using the SeqMan NGen® software assembler (DNASTAR). The final assembly identified 43 contigs generating a genome size of 4,9 Mb.
Genome annotation Genome annotation was performed on two platforms: on RAST  [21]  and on IMG/ER  [22]  (DOE Joint Genome Institute  [23] ). The tRNAscan-SE tool  [24]  (RAST and IMG/ER) was used to find tRNA genes, whereas ribosomal RNAs were detected using RMAmmer  [25]  (IMG/ER) and tool "search_for_rnas" (developed by Niels Larsen (available by the author), RAST). Open Reading Frames (ORFs) were predicted using GLIM-MER2  [26]  in RAST and using GeneMark (v. 2. 6. r) [27]  and GenePRIMP  [28]  as a part of the DOE-JGI genome annotation pipeline. Gene prediction analyses and functional annotations were performed in RAST with a series of BLAST against FIG hands-curated subsystems  [29]. They were also analyzed with FIGfams collection databases, and through comparative approaches with Integrated Microbial Genome -Expert Review platform (RPS-BLAST, BLAST, BLASTp, Hmmsearch (HMMER)) against non-redundant databases including COGs, Pfam  [30] , TIGR-fam  [31] , KEEG  [32] , IMG.
and S. oneidensis MR-1 T (27. 4 % and 20. 4 %,. 1 Phylogenetic position of Shewanella algae C6G3 relative to the genus Shewanella and other strains of Shewanella algae. This Neighborjoining tree is based on 1243 aligned characters of the 16S rRNA gene. The bootstrap percentages higher than 50 % are indicated at the node after 1000 resampled data sets. Branch length corresponds to sequence differences as indicated on the scale bar (substitutions per position). The proposed Shewanella species have been chosen from the List of Prokaryotic names with Standing in Nomenclature (type strain and sequence accession number) for their ability to use nitrate, nitrite and / or metal oxides. Species whose genome has been sequenced are marked with star (*).
Acknowledgements We are grateful to  Dr. Scot E. Dowd  ( Molecular Research LP MR DNA Laboratory ) for sequencing, assembly and annotation on RAST of our genome. We are grateful to the  U. S. Department of Energy Joint Genome Institute  for annotation on IMG/ER platform of our genome. And, we are also grateful to  Jean-François Rontani  (Researcher at  Mediterranean Institute of Oceanography (M. I. O. ), Marseille, France ) and  Rémi Amiraux  (phD at M. I. O. ) for extraction and analysis of fatty acid. This work has been support by  ANR IZOFLUX  ( ANR-10-BLAN-0612 ) and  PROTIDAL  ( ANR-06-BLAN-0040 ) grants.
100 100 99 46 49 99 93 63 45 98 94 57 99 65 0. 01 Fig and S. oneidensis MR-1 T (27. 4 % and 20. 4 %,. 1 Phylogenetic position of Shewanella algae C6G3 relative to the genus Shewanella and other strains of Shewanella algae. This Neighborjoining tree is based on 1243 aligned characters of the 16S rRNA gene. The bootstrap percentages higher than 50 % are indicated at the node after 1000 resampled data sets. Branch length corresponds to sequence differences as indicated on the scale bar (substitutions per position). The proposed Shewanella species have been chosen from the List of Prokaryotic names with Standing in Nomenclature (type strain and sequence accession number) for their ability to use nitrate, nitrite and / or metal oxides. Species whose genome has been sequenced are marked with star (*).
Materials and Methods Triple-negative cell lines BT20, BT549, HCC38, HCC70, HCC1187, HCC1937, MDA-MB-157, MDA-MB-231 and MDA-MB-468 cells were purchased in May 2006 and May 2008 from the American Type Culture Collection (LGC Standards, Molsheim, France). Cells were characterized by DNA and RNA microarrays  [41, 42]  and authenticated in 2013 by short tandem repeat profiling (data not shown). BT20 cells were cultured in MEM(Eagle) (Sigma, Saint Quentin Fallavier, France) containing 1% Glutamax (Invitrogen), 10% fetal bovine serum (FBS, Invitrogen, Cergy Pontoise, France), 1. 5g/L sodium bicarbonate (Invitrogen), 0. 1mM non-essential amino-acids (Invitrogen) and 1mM sodium pyruvate (Invitrogen). BT549 and MDA-MB-468 cells were maintained in RPMI-1640 containing Glutamax (Invitrogen) and supplemented with 10% FBS.
Microarray analysis in cell lines Cells were seeded in six-well plates, serum starved overnight, and then treated with Wnt3a for the indicated times (6, 12 or 24 hours). Triplicates for each condition were included in the experiment. Total RNA was extracted with the RNeasy Mini Kit from Qiagen (Courtaboeuf, France) following the manufacturer's recommendations. After RNA quality and quantity controls, samples were hybridized onto Gene st 1. 1 Affymetrix chips. Samples were processed as described on the website of the company. The data were analyzed with the brainarray HuGen-e11stv1_Hs_ENTREZG version 14 custom chipset definition file for the HuGene11stv1 affymetrix array  [49]. The data were first log2 transformed and normalized with RMA  [50]. A linear model was fitted with limma  [51]  including three factors that were treated as fixed effects: cell line (either HCC38 or MDA-MB-468), time (6, 12 or 24 hours) and treatment (Wnt3a stimulated or control) and all possible interaction terms.