dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Due to the changing number of stages and the changes in stage length, running speed was calculated over the whole competition to determine the performance of the athletes. For the analysis of the change in running speed in both sexes and the change in sex difference across years, the results of the annual top 25 female and male finishers were pooled and analyzed. The top 25 finishers were chosen to have study groups that are large enough to find reliable results. For the analysis of the age-related finisher and performance trends, the annual performance of the top five female and male finishers were pooled and analyzed. An additional inclusion criterion was at least five finishes in at least 9 out of the 10 years. The age group had to have at least five representatives in at least 9 out of 10 years with possibly different individuals.
Overall finisher trends Data from 6945 f inishes, including 909 women and 6036 men, have been analyzed from the Marathon des Sables between 2003 and 2012. The number of finishes increased from 37 (2003) to 107 (2012) for women and from 272 (2003) to 687 (2012) for men. Men showed, with a mean number of 603. 6 ± 146. 2 finishes, a higher number of finishes than women, with a mean number of 90. 9 ± 26. 7 finishes (P , 0. 0001). During the study period, the mean percentage of female finishers was 13. 0% ± 1. 2%. Both women and men showed an increase in the number of finishes (Figure  1 ).
In vivo bioluminescence imaging Mice were inoculated intraperitoneally with luciferin (D-Luciferin potassium salt, Xenogen, California), the luciferase substrate, at a dose of 150 mg/kg before any bioluminescence measurements were made. They were anaesthetized in a 2. 5% isoflurane atmosphere (Aerane, Baxter SA, Maurepas, France) for 5 minutes and kept in the imaging chamber for analysis. Emitted photons were acquired for 1 minute by a charge couple device (CCD) camera (IVIS Imaging System Lumina, Caliper, Villepinte, France) set in high resolution (medium binning) mode. The analysis was then performed after defining a region of interest (ROI). The same ROI was used for all animals and all time points. Total photons emitted from the image of each mouse were quantified using Living Image software (Xenogen Corporation, Almeda, California), and results were expressed as number of photons/sec/ROI.
Kelp ecosystems form widespread underwater forests playing a major role in structuring the biodiversity at a regional scale. Some seaweeds such as Laminaria digitata are also economically important, being exploited for their alginate and iodine content. Although some studies have shown that kelp ecosystems are regressing and that multiple causes are likely to be at the origin of the disappearance of certain populations, the extent to which global climate change may play a role remains speculative. Here we show that many populations of L. digitata along European coasts are on the verge of local extinction due to a climate-caused increase in sea temperature. By modeling the spatial distribution of the seaweed, we evaluate the possible implications of global climate change for the geographical patterns of the species using temperature data from the Coupled Model Intercomparison Project phase 5 (CMIP5).
Occurrence Data of Laminaria digitata Because no comprehensive database of the distribution of L. digitata existed, we compiled occurrence data from different sources to optimize our assessment of the recent distribution of this species. We used data from OBIS (http://www. iobis. org/) and the Ecokelp project (http://www. sb-roscoff. fr/ecokelp/images/ marine/ecokelp-36month-report-fin. pdf). We completed these databases with information found in the literature  [8, 9, 12, 27, 28, 29, 30]. Distribution map of occurrence records is presented in Fig. S1.
Large-scale Abiotic Parameters Monthly mean gridded Sea Surface Temperature (SST) data from 1982 to 2009 were taken from NOAA 4 km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder  [31]  (http:// www. nodc. noaa. gov/SatelliteData/pathfinder4km/). Maximum annual SST was calculated for the Northern European region (45uN to 71. 5uN; 11. 5uW to 27. 0uE). Bathymetric data were obtained from the ''Smith and Sandwell Global Seafloor topography''  [32]  (http://iridl. ldeo. columbia. edu/SOURCES/. Sandwell/. seafloor/; NOAA and Scripps Institution of Oceanography). Average Sea Surface Salinity (SSS) data were taken from the Levitus' climatology  [33]. ICES data (http://www. ices. dk) were used to complete this database in coastal regions where there is no assessment of annual SSS (e. g.
some regions of the eastern English Channel). Data on substratum types were obtained from SHOM database (Service Hydrographique et Oceanographique de la Marine). The diffuse attenuation coefficient for downwards irradiance at 490 nm (Kd490) was used as an indicator of the turbidity. Climatological data were downloaded from NASA's Giovanni portal  [34]  (http://gdata1. sci. gsfc. nasa. gov/daac-bin/ G3/gui. cgi?instance_id = ocean_month).
Adequacy between AVHRR data and RCP scenarios. Before using SST data from RCP scenarios, we compared the two datasets for the common period 2006-2009 by applying a normalized Taylor's diagram  [43]. The Taylor's diagram (Fig. S3 ) allows assessing the performance of models by summarized in a single diagram the standard deviation of the data and those of the model, the Root Mean Square Deviation (RMSD) and the correlation coefficient R between the model and the data  [44].
Adequacy between AVHRR Data and RCP Scenarios Since the present distribution of probability of occurrence was based on AVHRR SST data  and projections of future probabilities on SST data coming from RCP scenarios (2006-2100), we compared both observed and modeled SSTs for the common period 2006-2009 to prevent any bias due to the different SST datasets. This comparison was made by the use of a Taylor's diagram. All correlation coefficients with AVHRR data were larger than 0. 9, which indicate that RCP SSTs were close to AVHRR data for the overlapping period 2006-2009. These analyses suggest that any significant changes between observedand projected-SST-based probabilities of occurrence are unlikely to be related to the difference between SST datasets (Fig. S3 ).
Acknowledgments We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. 4 km SST-AVHRR data were provided by  GHRSST  and the  US National Oceanographic Data Center. 4 km AVHRR Pathfinder Project was supported in part by a grant from the  NOAA Climate Data Record (CDR) Program  for satellites. We thank the whole SOMLIT team: technicians, researchers, captains and crews who have contributed to the collection of the SOMLIT samples.
Sequence Analysis BAC-end sequences were analysed through BLASTN and BLASTX searches against sequence databases. Triticeae-specific repetitive elements were searched against both nucleotide and protein subsets of the Triticeae-specific repeat database (TREP, wheat. pw. usda. gov/ITMI/Repeats/) to identify characterised transposable elements (TEs). Genes were identified by searching the Brachypodium distachyon (IBI2010) gene and tRNA, rDNA, mitochondrial and chloroplast DNA sequences (ncbi. nlm. nih. gov/ Genome). Closest Brachypodium homologs of Unigene sequences were identified by BLASTX against Brachypodium proteins (IBI2010). Hybridised Unigene probe matches were screened for TEs against TREP and searched against deletion bin-mapped 1AS-specific wheat ESTs  [43]. All BLASTX searches were performed using BLASTX alignments with a ,1E-10 E-value cut-off.
Anchoring of Genetic Markers Genetic markers (Table  1 ) were gathered from published genetic maps contained in the marker repository GrainGenes (wheat. pw. usda. gov) based on their presence on chromosome 1AS. Initial anchoring of physical contigs to maps was performed using standard PCR screening on MTP 3D-pools using published genetic markers (RFLP and SSRs). Additionally, EST/cDNAbased markers were designed based on low coverage 454 sequencing from flow-sorted chromosome 1AS  [9]. If marker sequence data (e. g. primers or RFLP sequences) were available, these sequences were used in Blastn searches against BAC-end and anchored Illumina sequences (see below). For primers, we required perfect matches for anchoring, while we accepted matches with. 97% for RFLP sequences.
Wheat NimbleGen 40 K UniGene Microarray Hybridisation Forty-nine DNA pools were generated from the MTP clones. The MTP was derived from FPC contigs which cover approximately 86% of chromosome arm 1AS (see results and discusison). The DNA pools were used for hybridisation on a custom 126135 K NimbleGen gene expression array (nimblegen. om/ produts/expression/index. html) containing 40,349 NCBI wheat UniGene EST clusters (ncbi. nlm. nih. gov/unigene). Hybridisation procedures were carried out as previously described  [22]  using identical 126135 K array specifications, but no dye-swap was performed. Normalisation of hybridisation data was performed using the R statistical package (r-project. org). Thresholds from single-dye hybridisation were calculated from the probe data against each pool to eliminate outliers.
01 for t-tests). Hybridised UniGene probe matches were screened for TEs by Blast against the TREP database. They were also searched against deletion bin-mapped 1AS-specific wheat ESTs  [38]. All Blastx searches were performed using a ,1E-10 E-value cut-off. BLASTN searches were run using a 50 nucleotide alignment with. 80% identity and a ,1E-10 E-value cut-off.
Data Deposition The chromosome 1AS BAC library is available through the Centre National de Resources Genomiques Vegetales (CNRGV), Toulouse, France (cnrgv. toulouse. inra. fr/) and the Institute of Experimental Botany, Olomouc, Czech Republic (http:// olomouc. ueb. cas. cz/dna-libraries/cereals). Physical mapping and marker data is available from the Unitede Recherche Genomique Info (URGI) at Institut National de la Recherche Agronomique (INRA), France (wheat-urgi. versailles. inra. fr/Projects/Triticeae-Genome2). The LTC BAC contig information can be accessed through the dedicated Gbrowse browser. (http://urgi. versailles. inra. fr/gb2/gbrowse/wheat_phys_pub/). The integrated sequence model was deposited at at the URGI sequence repository (http://wheat-urgi. versailles. inra. fr/Seq-Repository) in the 1A section.
e. putative chimeric clones). Many Q-clones causing false connections between clones in Qcontigs were identified to be from the same fingerprint sequencing plate, indicating that well-to-well contamination was a major cause of creating non-linear assembled contigs. Similar problems were previously reported when assembling a FPC map in maize  [44]. Q-contigs were then rebuilt with an increased stringency up to 1E-50 in order to disperse the non-linear clusters. After breaking up Q-contigs, the LTC assembly consisted of a total of 505 contigs (Table  2 ). After all contigs showed linear clone patterns, small contigs containing five clones or less and no marker information (see below) were removed from the assembly as they did not provide any more meaningful information. This resulted in a more accurate final dataset of 394 LTC contigs which was used for subsequent analyses (Table  2 ).
The 1AS Backbone Consists of LTC BAC Contigs Complemented with FPC Clones The final dataset of 394 LTC BAC contigs represent over 80% of the backbone of the 1AS physical map (Table  3 ). However, these LTC contigs do not include all BACs, which were part of the minimum tiling path produced by FPC. These excluded BACs contain potentially useful information as they were used for the NimbleGen hybridisation (see below) as well as end-sequenced. To not lose this information, we added to the above dataset of 610 BAC clones that were excluded from the LTC assembly but were part of the minimum tiling path of the FPC assembly. Of these 610 BACs, 144 were singletons while the rest were arranged in 177 small contigs of 2-7 BACs. Thus, the final backbone of the 1AS physical map consists of 394 LTC contigs with a cumulative size of 205 Mbp and 321 FPC BAC contigs or singletons that cover 50 Mbp (Table  3 ).
The total cumulative size of all BAC contigs is 255. 4 Mbp, corresponding to 92,8% of chromosome 1AS (Table  3 ). In summary, we propose the use of both, LTC and FPC software. LTC overall performed better than FPC on 1AS BAC fingerprints, resulting in larger and more robust contigs. However, we also considered it important to include BAC and BAC contigs that were leftover from the initial FPC assembly. Although this leftover FPC dataset consists of BAC singletons or very short contigs, its inclusion nevertheless increased the overall coverage of the 1AS chromosome arm from 82% to almost 99%. Because these additional FPC BAC contigs have an overall low coverage and are therefore less robust than the FPC contigs. Thus, they were flagged in the final backbone with the prefix ''fpc''. Sequence Composition of Chromosome 1AS Inferred from MTP BAC-end Sequencing BAC-end sequencing was performed on each of the 3,414 MTP BACs producing 6,687 sequences with an average size of 703 bp.
The cumulative length of all BAC end sequences (BES) was 4,704 kb (1. 7% of the 1AS chromosome arm). These sequences allowed the quantitative study of the chromosome arm sequence composition. A total of 180 sequences (2. 6%) had homology with Brachypodium genes, 42 of which had their closest homologs in the 1AS syntenic region in the Brachypodium genome. These BAC ends were used to anchor BAC contigs to the Brachypodium reference zipper (see below). As in previous surveys of wheat BES  [11, 45] , a majority of sequences were identified as TEs, with 81. 7% of the total DNA matching known elements (77. 7%) from the TREP database, or novel TEs (4. 0%) identified using protein matches to PTREP. Ribosomal and organellar DNA were present only in minuscule amounts and a total of 16. 75% of all BES sequences could not be classified based on the databases used (Figure  3a ).
In previous genome surveys of diploid Triticeae species, Copia elements of the Angela/BARE1 family were clearly the most abundant with more than 10-12% of the whole genomic sequence  [48] [49] [50] [51] [52]. In contrast, in our dataset, Angela/ BARE1 only contributed 4. 16% to the BES. This could indicate that the TE composition of the hexaploid wheat genome differs from that of diploid Triticeae. Alternatively, it is possible that the.
Evaluation of the Chromosome 1AS Pm3 locus using Physical Contigs and Sequenced BACs To investigate the validity of the physical map at a smaller scale, we used the previously published sequence covering the Pm3 locus (accession number AY146587  [59] ). This sequence has a size of approximately 178 kb and was isolated from wheat cultivar Chinese Spring and should therefore have its exact match in the 1AS physical map. BAC end sequences and positive NimbleGen hybridisation probe sequences were used to align the published Pm3 BAC sequence to four 1AS BAC clones (TaaCsp1A-ShA0068N15, TaaCsp1AShA0060M21, TaaCsp1AShA0041L11 and TaaCsp1AShA0068L13). These were placed in two different physical contigs (ltc132 and ltc5245). Interestingly, those two BAC contigs were placed directly adjacent to each other in the syntenybased anchoring (see above) and the published Pm3 BAC sequence links these two contigs together (Figure  6 ).
A total of 12 UniGene hybridisation probes were found on the two physical contigs. These could be assigned to two gene loci. Three of the probes correspond to the Pm3 gene while a second cluster of 7 probes corresponds to one or more glutenin genes (Figure  6 ). This is in agreement with previous studies, which reported the presence of multiple low-molecular weight glutenins at the Pm3 locus  [60, 61]. To further characterise the glutenin gene cluster, we used the 7 EST UniGene probes that mapped to the glutenin locus in Blastn searches against the IWGSC survey sequences from flow sorted chromosomes. Five of the probes had their best matches in the 1AS Illumina dataset while two originate from chromosome 1BS. Phylogenetic analysis of all probe sequences plus previously published glutenin sequences suggested that a cluster of five genes is present on BAC contig ltc132.
Acknowledgments We are grateful to  Prof. B. S. Gill  ( Kansas State University, Manhattan, USA ) for seeds of the double ditelosomic line 1A of wheat T. aestivum L. cv. Chinese Spring. We thank  Jan Buchmann ,  Christopher Middleton ,  Simone Oberhaensli  and  Stefan Roffler  for their competent support in computer programming and system administration. Assistance of Zden ˇka Dubska, Jarmila C ˇı ´halı ´kova,  Marie Kubala  ´kova,  Romana S ˇperkova  ´and  Jana Dosta  ´lovawith chromosome sorting and BAC library construction is greatly appreciated.
SAXS (small-angle X-ray scattering) SAXS data using purified TorD His , TorA/TorD His and TorA SP /TorD His samples were recorded on the ID14-3 BioSAXS beamline at the European Synchrotron Radiation Facility (ESRF Grenoble, France). Sample-detector distance was 2. 43 m for an X-ray wavelength of 0. 931 Å (1 Å = 0. 1 nm). Then, 50 μl sample and buffer volumes were loaded in a flowthrough quartz capillary cell at 25 • C. The sample volume exposed to the X-ray beam was approximately 10 μl. Samples were checked for radiation damage by using ten successive exposure times of 10 s each. Final exposure time was 100 s for all samples/buffers. The twodimensional diffraction patterns were normalized to an absolute scale and azimuthally averaged to obtain the intensity profiles I(Q), within BSxCuBE (ESRF beamline data collection software).
CD analysis CD analysis of protein samples was provided as a service by the Scottish Circular Dichroism Facility, University of Glasgow. Spectra of protein solutions were obtained using a JASCO J-810 spectropolarimeter. Far-UV CD measurements (185-260 nm) were collected in quartz cells of 0. 02 cm pathlength at 25 • C with a scan speed of 10 nm/min, bandwidth of 1 nm, response of 2 s and data pitch of 0. 2 nm. Before collection of CD data, proteins were buffer-exchanged into CDappropriate buffer (50 mM Tris/H 2 SO 4 , pH 7. 5, 200 mM K 2 SO 4 and 1 mM DTT). Protein concentrations were estimated using a NanoDrop spectrophotometer before the experiment. Near-UV CD measurements (250-320 nm) were obtained in a 0. 2cm-pathlength quartz cuvette using the following parameters: temperature, 25 • C; scan speed, 10 nm/min; bandwidth, 1 nm; response, 2.
0 s; and data pitch, 0. 2 nm. Protein was analysed in appropriate buffer (50 mM Tris/HCl, pH 7. 5, 200 mM KCl and 1 mM DTT). The spectra were corrected for protein concentration and cell pathlength before being analysed by DichroWeb (http://dichroweb. cryst. bbk. ac. uk), an online server which hosts the various algorithms used to estimate protein secondary structures.
5), 200 mM KCl and 1 mM DTT at 0. 5 ml/min; Shimadzu LC Solutions software was used to control the HPLC, and Astra V software was used for the HELEOS-II and rEX detectors. BSA was used for molecular mass calibration. The Astra data collection was 1 min shorter than the LC solutions run to maintain synchronization. Blank buffer injections were used as appropriate to check for carry-over between sample runs. Data were analysed using the Astra V software. MWs were estimated using the Zimm fit method  [30]  with degree 1. A value of 0. 19 was used for protein refractive index increment (dn/dc).
To investigate this further, a construct was designed that would co-overproduce TorD His together with the first 675 amino acids of TorA, including the signal peptide (designated TorA CT ). First, the construct was expressed in a strain lacking endogenous TMAO reductase activity under strict anaerobic conditions, which would induce expression of the MGD biosynthetic machinery. Then a crude cell extract was prepared and the TMAO reductase activity assayed. The resultant data showed that the C-terminally truncated form of TorA was completely devoid of enzymatic activity (Supplementary Table  S1  at http://www. biochemj. org/bj/452/bj4520057add. htm), establishing that this domain of the protein is absolutely critical for function. Next, TorD His was isolated from the soluble cell fraction by IMAC, which resulted in a protein of the expected size of TorA CT co-purifying (Figure  4 ).
A low-resolution model of TorD His , obtained using the program DAMMIN  [37] , is shown in Figure  7(A). The overall shape of TorD His is compact and elliptical with dimensions of approximately 36 Å×50 Å×34 Å. From the crystal structures of S. massilia TorD  [17]  and the TorD family protein DmsD   (A) Two different views of the ab intio model of TorD, generated using DAMMIN  [37]. (B) The electron density for E. coli DmsD (PDB code 3CW0  [39] ) docked into the SAXS-derived shape of TorD His. ( C) and (D) represent two different views of the ab intio model of the TorA-TorD His complex. The X-ray structure of S. massilia TorA (PDB code 1TMO  [32] ) is shown to the right. from E. coli  [38] [39] [40] , scattering envelopes can be calculated  [41]. It is clear from Supplementary Figure  S3  (http://www.
Further evidence for overlapping TorD-binding sites near the Nterminus of TorA come from analysis of the SAXS data obtained in the present study. To investigate the possible arrangement of TorA and TorD in the TorAD complex, rigid body modelling was undertaken using the SASREF program  [47]  and the structures of S. massilia TorA and E. coli DmsD as building blocks. To facilitate this analysis, TorA was split into two parts to give the proteolytically stable N-terminal part (Domains I-III) and the proteolytically sensitive Domain IV identified in the present study. A constraint was then applied that the last amino acid of N-terminal part should be within 10 Å of the first amino acid of Domain IV, in order to incorporate flexibility into the TorA component of the complex, again consistent with the protease accessibility results of the present study.
Gene set enrichment analysis of the T cell transcriptome In order to further investigate the biological basis of the gene expression differences observed between T cells from psoriasis patients and controls, we analyzed the global transcriptome using the GSEA approach  [30, 31]. A total of 55 gene sets were found to be significantly enriched between cases and controls (False Discovery Rate, FDR < 0. 05) (Table  3 ). From these, four gene sets were significant at a FDR < 0. 0001 all belonging to Reactome database  [32]    a group of gene sets related to innate immune system (FDR = 0. 0014) and RIG and NOD-like receptor pathways (FDR = 0. 0005 and 0. 0058, respectively) were also among the most significantly enriched. T cell activation in psoriasis patients also resulted in a significant enrichment of genes involved in PPARαmediated activation of gene expression (FDR = 0.
Microarray analysis and differential gene expression determination Whole genome gene expression analysis of in vitro activated T cells for 17 psoriasis patients and 7 healthy controls was carried out by the HudsonAlpha Institute for Biotechnology using the Illumina HT-12 V4 microarrays (Illumina, USA). Briefly, the Illumina CustomPrep RNA amplification kit (Appiled Biosystems, USA) was used to generate biotinylated amplified complementary RNA (cRNA) from 200 ng of the extracted total RNA. Labelled cRNA was then hybridized overnight to the Illumina microarrays, washed, blocked, stained and scanned using the Illumina BeadStation 500. The GenomeStudio software v2011. 1 (Illumina, USA) was used to generate the raw data from the microarray scanned images. HT-12 V4 microarrays allow the simultaneous profiling of >47,000 transcripts, which include 28,688 well-established RefSeq annotations from the Human RefSeq (Rel 38).