dataesr/scientific-paragraphs-categorization huggingface dataset:
Every protein identifier was thus converted to a corresponding gene ID in the TAGI r12 database. MapMan v3. 5. 1 was loaded with T. aestivum TAGI r12 database downloaded from the MapManStore (http://mapman. gabipd. org/web/guest/mapmanstore) website. Our listing IDs were loaded into MapMan after the above-mentioned conversion along with fold change (FC) values for each entry. All MapMan diagrams were analyzed using a color scheme and a scale level that allowed an easy visualization of up-(blue) or down-(red) regulated genes or proteins. Detection of carbonylated proteins. The proteins were extracted as above described  32  and 20 μg of proteins were derivatized with 10 mM 2,4-dinitrophenylhydrazine (DNPH) in 2 M HCl. 2 M HCl was added to the negative control. The mixture was incubated for 30 min at and then neutralized by adding 1 volume 2 M Tris, 30% glycerol.
After a 12% SDS-PAGE, proteins were transferred to polyvinylpyrrolidone (PVP) membrane (Serva Electrophoresis GmbH, Heidelberg, Germany) and the oxidatively modified proteins were detected using anti-DNPH antibodies (antidinitrophenyl-group antibodies; Sigma, USA) and visualized by SuperSignal detection kit (, Pierce Biotechnology, Rockford, IL, USA). Duplicate gel was stained by Colloidal Coomassie Brilliant Blue (CCBB). Alternatively, gels were stained with Bio-Safe Coomassie (Bio-Rad) and then processed for immunoblotting. GS-800 (Bio-Rad) and ImageJ software (http://imagej. nih. gov/ij/) were used to acquire and analyse gels and immunoblot images.
The correlation values between the two studied conditions were 0. 873 to 0. 888. The Pearson coefficients showed that all samples were very highly correlated, but the two conditions revealed some differences as well. Leaf and root identifications were compared in order to understand how many proteins were present in both datasets. The common proteins were 1259, while 1462 have been only identified in leaves and 1187 exclusively in roots (Fig. 3A ). Despite the consistent overlay in the protein identification only 12 were upregulated and only 6 were downregulated both in root and leaf samples (Supplementary Table  S7 ).
ROS and redox regulation are important contributors to the wheat response to X. translucens. The production of ROS is one of the first responses that occurs after pathogen recognition  52, 53. Accordingly, our datasets showed the upregulation of NADPH/respiratory burst oxidase protein D (RbohD) and of extracellular peroxidases, major regulators of the apoplastic oxidative burst around the infection site that are also involved in cross-linking cell wall components  54, 55. In infected leaves, 35 of 45 DEGs encoding ascorbate peroxidases (APXs) were strongly upregulated. APX enzymes play a key role in catalyzing the conversion of H 2 O 2 to H 2 O, using ascorbate or GSH as a specific electron donor. Conversely, at the protein level, there was abundance of only one APX, while levels of three APX proteins decreased.
Overall, these data indicate the necessity of maintaining energy and solute homeostasis for cell protection during pathogen progression both in proximal and distal plant tissues. Secondary metabolism and defense-related proteins. Consistent with previous research, our datasets showed that key enzymes of the phenylpropanoid pathway were triggered during pathogen attack  71, 72. The cell wall is one of the most important barriers against pathogens, and lignification is important in plant defense  73. This lignification was very clear at the transcriptomic and proteomic levels in the present study, as shown in Supplementary Figure  3. Some of the key enzymes involved in the biosynthesis and polymerization of lignin (such as caffeoyl CoA O-methyltransferase, 4-coumarate-CoA ligase, cynnamoyl-CoA dehydrogenase, and laccase) accumulated in the leaves and roots of infected plants.
Acknowledgements This work was supported by the  MIC-CERES  ("Microbial eco-compatible strategies for improving wheat quality traits and rhizospheric soil sustainability") Project ( FC  Project  ID 2013-1888 ;  AF  Project  ID 1301-003 ) jointly supported by  Agropolis Fondation  (through the  "Investissements d'avenir " programme with reference number  ANR-10-LABX-0001-01 ) and  Fondazione Cariplo.
For the construction of pGD20495, a 934 bp PCR fragment encompassing the smb20495 promoter region (full intergenic region) and 26 nucleotides of the smb20495 gene was amplified using the SMb20495-HindIII and SMb20495-BamHI primers,  Genes showing up-or down-regulation in both aerobic or microoxic conditions in two biologically independent replicates are listed. * Indicates the genes common to  Krol et al. (2016). Genes displaying a Clr-box in their promoter are in bold face (Supplementary Table  S3 ). Raw data are available in the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-4780. digested with BamHI and HindIII and cloned in-frame with lacZ in the translational fusion plasmid pGD926 For pGD2177, a 303 bp PCR fragment encompassing the full intergenic region between smc02177 and smc02178 and 32 nucleotides of the smc02177 gene was amplified using the p2177HindIII and p2177BamHI primers, digested with BamHI and HindIII and cloned in the in-frame orientation at the same sites of the lacZ translational fusion plasmid pGD926.
meliloti Rm1021. The protocol was as described before  (Bobik et al. , 2006). Microarray raw data have been deposited in the ArrayExpress database under accession number E-MTAB-4780. For exo cAMP-induction assays of exo and flaB gene expression (Figure  1  and Supplementary Figure  S2 , respectively), overnight cultures of S. meliloti strains at 28 • C in VMG medium were diluted to an OD600 of 0. 12 in 20 ml of same medium and supplemented with 5 mM (final concentration) of 3 -5 -cAMP (Sigma) when cultures reached an OD600 of 0. 3. When exo cAMPtreated cultures reached an OD600 of 0. 6, 15 ml were filtered on Supor R Membrane Disk Filters (Pall) and cells were frozen in liquid nitrogen and stored at -80 • C. All experiments were performed in triplicate. Reverse transcriptions were performed from 1 µg of RNA using the Transcriptor Reverse Transcriptase (Roche), and random hexamers as primers.
0 analysis  (Yu et al. , 2010)  predicted a noncytoplasmic location for all three SMc02177, SMc02178 and SMb20495 proteins without specifying more precisely their subcellular localization. SignalP 4. 1 analysis  (Petersen et al. , 2011)  indicated the presence of a cleavable signal peptide in SMc02178 and SMb20495. As for SMc02178, we experimentally demonstrated the localization of the main portion of the protein in the bacterial periplasm using a combination of lacZ and phoA reporter fusions (Supplementary Figure  S3 ). Contrary to SMc02178 for which sequence inspection gave no structural or functional clues, we identified a FecR domain (IPR006860, PF04773) in SMc02177. In the E. coli full-length FecR protein, the FecR domain interacts with the aminoterminal periplasmic part of the FecA outer membrane protein involved in iron citrate signaling.
FUNDING LZ was supported by a  CSC  scholarship and FS was partly supported by a Post-doctoral AGREENSKILLS fellowship. This work was funded in part by the  ANR " RhizocAMP " ( ANR-10-BLAN-1719 ), the  ANR  " AOI " ( ANR-15-CE20-0004-01 ) and the  Pôle de Compétitivité "Agri Sud Ouest Innovation ". This work is part of the  "Laboratoire d'Excellence" (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ).
| STUDY SPECIES: THE SOUTHERN FULMAR The southern fulmar (F. glacialoides) breeds in the Southern Hemisphere along the mainland coast of Antarctica and on nearby islands, and migrates to sub-Antarctic and subtropical waters during the non-breeding season  (Delord et al. , 2016). It breeds during the austral summer from October to March; a single egg is laid per breeding season. Fulmars feed mainly on krill (e. g. Euphausia superba) and other crustaceans, as well as on small fish (Pleuragramma antarctica) and carrion  (Ridoux & Offredo, 1989). Our study population is located on Ile des Pétrels, (66°40′S, 140°01′E), Antarctica. Mark-recapture data have been collected on this population since 1962. We utilized data from 1964 to 2010 on known-age individuals banded as fledglings (n = 1,165 individuals).
| Statistical models The structure of the MSMR model depends on the number of UH groups (Supporting Information 2). Figure  S1  describes the structure of the MSMR model for g = 2 UH groups and s = 4 breeding states. In that case, there are nine possible states: sg = 8 alive states and one dead state, with 12 associated vital rates pertaining to each group (σ j , β j and γ j for breeding states j = 1, …, s). To determine the number of UH groups and identify the vital rates that differ among groups, we used a multistep model selection approach  (Burnham & Anderson, 2002)  based on the Akaike information criterion (AIC) as described in Supporting Information 3. All analyses were conducted in the e-surge software  (Choquet, Rouan, & Pradel, 2009).
ACKNOWLEDGEMENTS We thank all the field workers who participated in the long-term study since 1964. We acknowledge  Institute Paul Emile Victor  ( Programme IPEV   109 ), and  Terres Australes et Antarctiques Françaises for logistical and financial support in Terre Adélie. S. J. acknowledges support from  Ocean Life Institute  and  WHOI  Unrestricted funds, and  NSF  projects  DEB-1257545  and  OPP-1246407. The study is a contribution to the Program EARLYLIFE funded by a  European Research Council   Advanced Grant  under the European Community's  Seven Framework Program   FP7/2007-2013 (Grant Agreement ERC-2012-ADG_20120314  to  Henri Weimerskirch ), and to the Program INDSTOCH funded by  ERC Advanced  Grant  322989  to  Hal Caswell. We acknowledge  Dominique Besson  and  Karine Delord  for fulmar data management,  Emmanuelle Cam ,  Tom MiIller ,  Jim Nichols  and  Guillaume Peron  for constructive discussions and four anonymous reviewers for helpful comments.
Input data K2 needs four input datasets, the digital elevation model (DEM), the soil map and the land cover map, that are necessary to describe the watershed in term of hydrological and geometric parameters and the precipitation data that are needed at a small time step (5 min) to take into account the short and intense rainfall events, typical of the Sahelian monsoon. The input data used in this study are summarized in Table  1  and described below. Further details and analysis of in situ data can be found in  Frappart et al. (2009)   Guichard et al. (2009) ,  Timouk et al. (2009)  and  Gal et al. (2016).
Precipitation and meteorological data Two sets of precipitation data are used for the Agoufou watershed: -Daily precipitation (DP) from the Hombori SYNOP meteorological station, available from 1930 to 2012 through the Direction Nationale de la Météorologie du Mali, and completed until 2015 by the AMMA-CATCH observatory. This station is 15 km away from the Agoufou lake. -Rainfall at a temporal resolution of 5 min (5M) obtained from an automatic rain gauge network operated over 2006-2010 by the AMMA-CATCH Observatory in the Gourma region  (Frappart et al. , 2009; Mougin et al. , 2009). Rain gauges used in this study (Table  1  and Fig. 1 ) were selected for their proximity to the study site and for the quality of the measurement series (few gaps). In addition, relative humidity, air temperature, incoming shortwave radiation and wind speed derived from the Agoufou automatic meteorological station on a timescale of 15 min are used as input to the grass layer submodel (see Sect.
Derivation of vegetation characteristics Landscape units bear six different forms of vegetation: grassland and trees (GT), grassland (G), sparse trees (T), tiger bush tickets (TB), woody plant (W) and no vegetation (R), with a different combination of herbaceous and woody plants (    (Carlyle-Moses, 2004; Mougin et al. , 2014). The seasonal dynamics of the grass canopy cover (CC) has been simulated with the STEP vegetation model  (Mougin et al. , 1995; Pierre et al. , 2016). It is driven by historical daily precipitation recorded at the Hombori station and meteorological data (shortwave incoming radiation, air temperature, relative humidity and wind speed) recorded every 15 min. For the latter, a mean annual climatology is obtained using data from the Agoufou automatic weather station operating from 2002 and 2010.
Watershed evolution The maps of landscape units were derived from different data (aerial photography and satellite images) of various spatial and spectral resolutions. Delimitation and identification of the landscape units proved easier for the present than for the past. Panchromatic aerial photographs give limited information, and on many occasions the three-dimensional visualization is necessary to clearly identify the units. Photo interpretation for the past aimed to be conservative, meaning that obvious changes only were retained while ambiguous cases were considered as "no change". Overall, despite the uncertainties related to the photo interpretation and mapping of landscape units, that are not easily estimated, the land surface changes of the Agoufou watershed are important and clearly observable.
Man-driven deforestation has been put forward as a cause for thickets clearing in southwestern Niger. For Agoufou, such activity is not reported and remains of dead trees can be observed, testifying natural death of vegetation. The decay of shallow soil vegetation is not at odds with the Sahelian regreening that is observed all over the Sahel and in the Gourma since the 1980s  (Anyamba et al. , 2003; Dardel et al. , 2014a, b; Heumann et al. , 2007; Olsson et al. , 2005). Dardel et al. (2014b)  suggest that the resilience of herbaceous vegetation allows rapid regrowth over most soils in response to rainfall recovery, but that a fraction of the shallow soils may undergo long-term vegetation decay, in a way that impacts runoff but not region-average greening. In the Agoufou watershed, the vegetation changes affecting the northern part of the watershed would not be easily detected by coarse-resolution satellite datasets, as opposed to the herbaceous vegetation growing over the sandy soils of the southern part of the watershed.
Acknowledgements. We thank  Nogmana Soumaguel ,  Ali Maïga ,  Hamma Maïga  and  Mamadou Diawara  for collecting data and  Eric Mougin  for managing the Gourma site and providing the STEP model. We also acknowledge  Shea Burns  and  Carl Unkrich  for their feedbacks on AGWA and  K2. This research was based on data from the AMMA-CATCH observatory and partially funded by the  ESCAPE ANR -project  ( ANR-10-CEPL-005 ). Edited by: Murugesu Sivapalan Reviewed by:  Jean-Emmanuel Paturel ,  Francesc Gallart , and three anonymous referees.
Data availability. Digital elevation model and satellite images have been collected from the USGS database (NASA and CNES data source) which is publicly available from the website https: //earthexplorer. usgs. gov/. Meteorological and ecological data are publicly available from the AMMA-CATCH database (http://bd. amma-catch. org/main. jsf) Aerial photographs are available on request from the Institut Géographique du Mali (IGM), Mali (http: //www. igm-mali. ml/). Long-term precipitation data are available on request from the Direction de la Météorologie Nationale (DMN), Mali.
Notes The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS T. P. acknowledges the  Volkswagen Foundation  (Freigeist Fellowship No. 89592 ) for financial support. H. Y. is a Research Fellow of the  Japan Society of Division for the Promotion of Science. This work was partly supported by  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement  665085 , by  JSPS Grants-in-Aid for Scientific Research  (No. 26248010 ), by the grants of the  Russian Foundation for Basic Research No. 15-29-01290  ofi_m and No. 16-32-00882_mol_a. This work was done in accordance with the  Competitiveness Program of NRNU MEPhI. We acknowledge the kind support by staff members of the  UVSOR-III Synchrotron Facility.
Growth rates to reach a tumor volume of 1 cm 3 ranged from 2 to 6 months in most of the PDX. Total RNA was obtained from the 55 PDX, and gene expression profiling was performed using Affymetrix platform. Subsequently, we selected a panel of 239 RNAs regulated by c-MYC in accordance with the MYC targets v1 and v2 list from Molecular Signatures Database (MSigDB). Figure  1A  represents the hierarchical clustering and heatmap for the top significantly high-expressed genes in MYC-high patients. The dendrogram showing the genetic distance between patients indicates the presence of two major subgroups that we define as MYC-high and MYC-low in red and blue colors, respectively. Interestingly, we observe that 17/55 (30. 9%) patients are characterized by an increase in the expression of 134/239 c-MYC target RNAs (P-value = 0.
76 and FDR = 0. 00). In addition, a complete list of statistically significant enriched signatures using Biological Process, Curated Geneset Enriched, and Hallmarks Enriched tools is presented in Datasets EV1-EV6. To confirm that MYC-high patients give rise to PDX with high proliferative index, we performed an CRCM04 CRCM16 CRCM08 CRCM93 CRCM14 CRCM80 CRCM110 CRCM82 CRCM76 CRCM83 CRCM84 CRCM87 CRCM22 CRCM88 CRCM17 CRCM79 CRCM94 CRCM18 CRCM113 CRCM100 CRCM102 CRCM03 CRCM75 CRCM36 CRCM85 CRCM05 CRCM92 CRCM115 CRCM07 CRCM91 CRCM96 CRCM12 CRCM95 CRCM10 CRCM90 CRCM106 CRCM15 CRCM48 CRCM81 CRCM78 CRCM01 CRCM101 CRCM89 CRCM109 CRCM11 CRCM97 CRCM98 CRCM111 CRCM02 CRCM99 CRCM24 CRCM86 CRCM09 CRCM06 CRCM27 § § § § 0.
Affymetrix data were then confirmed by RT-qPCR on four putative MYC-high and four MYC-low patients. Each transcript was normalized to the 28S ribosomal RNA, the relative quantity was calculated by the ΔΔC t method and the ratios were calculated after normalization. The signature was able to definitively detect all MYC-high and MYC-low profiles by RT-qPCR as shown in Fig  3A  and B. We then assessed the MYC signature on primary cultures derived from the same eight xenografts  (Fig 3C)  and found that the corresponding MYC-high or MYC-low profiles were correctly detected. Therefore, we conclude that the signature based on these transcripts is a reliable for identifying tumor subtypes based on their c-MYC status. We also analyzed whether or not the genetic alterations of the c-MYC gene in PDAC tumors are predictive of the response to BET.
Consistent with these observations, we chose to define c-MYC transcriptional program as a restricted signature that allowed the selection of distinct subtype of PDAC tumors. This MYC-associated signature was established by using selected c-MYC target genes available from the molecular signature database and who are reported to be directly regulated by this transcription factor (MYC target v1 and v2 from MsigDB). From the 239 putative c-MYC-dependent genes, 16 targets were selected according to the fold change between MYC-high and MYC-low PDAC. Those targets usefully identify highly proliferative PDAC with low degree of differentiation and patients with poor clinical outcome as shown in Fig  1C  and D. An efficient signature, like the one reported here, which is easily applicable and low cost, for detecting patients having a MYC-high PDAC is of clear clinical interest, particularly in non-operable patients which represent about 85% of PDAC.
Ethics statements The study was performed according to the principles set out in the WMA Declaration of Helsinki and to the protocols approved by the Department of Health and Human Services Belmont Report. Patients were included in this project under the Paoli Calmettes Institute clinical trial NCT01692873 (https://clinicaltrials. gov/show/NCT01692873). Three expert clinical centers collaborated on this project after receiving ethics review board approval. Patient informed consent forms were collected and registered in a central database. For animal studies, all mice, housed under specific pathogenfree conditions, were 4-week-old males on a Swiss nude background (Crl: Nu(lco)-Foxn1 nu , Charles River, Wilmington, MA). Mice were used at 6 weeks of age, and all animal studies were approved by the Animal Facility and Experimental Platform (Scientific and Technological Park of Luminy, Marseille, France) and the French Ministry of National Education and Research under the reference number: 02859.
Gene expression microarrays Total RNA was purified from xenograft using TRIzol â Reagent (Gibco, Life Technologies) according to the manufacturer. Briefly, 50-100 mg of fresh frozen tissue per ml of TRIzol â was disrupted using a homogenizer followed by a single step of phenol/chloroform purification. Total RNA was quantified using the Nanodrop spectrophotometer (NanoDrop Technologies, Inc), and RNA Integrity Number (RIN) was calculated using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). RNA samples that   Quebec, Canada). Seventeen samples of the cohort were previously published  (Duconseil et al, 2015)  as GEO accession numbers GSE55513 and GSE89792.
Bioinformatics analysis Robust Multi-array Analysis normalized data from microarrays were imported into GENE-E (version 3. 0. 204; Broad Institute, Cambridge, MA, USA) to generate heatmaps. The color intensity on the heatmap reflects global expression within a minimum (25%) in blue and a maximum (75%) in red. Cluster analysis, using Euclidean distance correlation of samples only, and distance for the clustering were calculated using a complete linkage algorithm. Differentially expressed genes were identified using t-test ratio, and false discovery rate was estimated using Benjamini & Hochberg method  (Benjamini et al, 2001). Gene set enrichment analysis (GSEA) was performed using the Broad Institute platform, and statistical significance (false discovery rate) was seated at 0. 05.
GSEA analysis Two categories of pre-defined gene sets in the Molecular Signatures Database (MSigDB, Broad Institute, Cambridge, MA, USA) were selected for analysis named the Hallmarks set, and the C5 set, a Gene Ontology (GO) molecular function gene set derived from the Molecular Function Ontology database  (Subramanian et al, 2005). The gene sets included in the analysis were limited to those that contained between 15 and 500 genes. Permutation was conducted 1,000 times according to default-weighted enrichment statistics and by using a t-test ratio metric to rank genes according to their differential expression levels across the MYC-high and MYC-low groups. Significant gene sets were defined as those with a nominal P < 0. 05. Calculation of the false discovery rate (FDR) was used to correct for multiple comparisons and gene set sizes  (Benjamini et al, 2001).
SNP arrays analysis DNA was extracted for 55 PDX samples using the Blood & Cell culture DNA mini kit (Qiagen) following the manufacturer's instructions. Illumina Infinium HumanCode-24 BeadChip SNP arrays were used to analyze the DNA samples. Integragen SA (Evry, France) carried out hybridization, according to the manufacturer's recommendations. The BeadStudio software (Illumina) was used to normalize raw fluorescent signals and to obtain log R ratio (LRR) and B allele frequency (BAF) values. Asymmetry in BAF signals due to bias between the two dyes used in Illumina assays was corrected using the tQN normalization procedure  (Staaf et al, 2008). We used the circular binary segmentation algorithm  (Venkatraman & Olshen, 2007)  to segment genomic profiles and assign corresponding smoothed values of log R ratio and B allele frequency.
The Genome Alteration Print (GAP) method was used to determine the ploidy of each sample, the level of contamination with normal cells, and the allele-specific copy number of each segment  (Popova et al, 2009). Chromosomal instability index (CIN) was estimated by the mean number of SNP probes with a loss or gained status normalized by chromosomes length. SNP data are available through ArrayExpress (http://www. ebi. ac. uk/arrayexpre ss) under accession E-MTAB-5006.