dataesr/scientific-paragraphs-categorization huggingface dataset:
The PstI adapters included a sequencing primer so that the tags generated were always read from the PstI sites. The resulting sequences were filtered and split into their respective target datasets, and the barcode sequences were trimmed. The sequences were trimmed at 69 bp (5 bp of the restriction site plus 64 bases with a minimum Q score of 10). A proprietary analytical pipeline developed by DArT P/L was used to produce DArT score tables and SNP tables. The remaining 69 bp sequences were aligned to the Os-Nipponbare-Reference-IRGSP-1. 0 pseudomolecule assembly  [36]  using Bowtie v0. 12  [37]  with a maximum of three mismatches to recover the position of the restriction site for the DArT markers and the position of the polymorphism(s) within the 69 bp sequences for the SNPs. For the DArT markers, the position given is that of the second base of the 6 base PstI restriction site (5'-C|TGCAG-3') because the mutated base is unknown and can be any of the six.
Imputation of missing data The power to detect significant association is linked to population size. To prevent the loss of detection power, missing data were estimated using Beagle v3. 3, which enables the inference of haplotypes and imputation of sporadic missing data in large-scale phase-known or phase-unknown genotype datasets  [38]. Beagle uses a localized haplotype cluster model. It is a special class of directed acyclic graph which empirically models haplotype frequencies on a local scale and therefore adapts to local structure in the data. The model determines a hidden Markov model that can be used to find the most likely haplotype pair for each individual, given the genotype data for that individual and the graphical haplotype frequency model. The method works iteratively using an expectationmaximization type approach.
Statistical analysis of phenotypic data An analysis of variance was conducted considering all genotype and block effects as fixed. These effects were tested using SAS v9. 2 (SAS Institute, Cary NC, USA), and the means were adjusted for block and sub-block factors. The adjusted means of all accessions are available for download at http:// tropgenedb. cirad. fr/tropgene/JSP/interface. jsp?module=RICE as "Orytage dataset". Broad-sense heritabilities based on genotypic means (h 2 ) were computed from the genotype F value of the variance analysis as (F-1)/F  [44]. An ANOVA was conducted on the adjusted means to assess the phenotypic differences among sub-populations. Phenotypic correlations were computed from the adjusted means using SAS. Principal component analyses (PCA) were run using some or all of the measured traits with XLStat  [45].
The analyses were run on Bioportal (http://www. bioportal. uio. no/). The R Adegenet package  [48]  was used for DAPC. To illustrate the panel organization, an unweighted neighbor-joining (NJ) tree was constructed based on a dissimilarity matrix computed using a shared allele index with DARwin software  [49] ; subpopulation attributions derived from the model-based approach were projected on the tree. An accession was discretely assigned to a subpopulation when more than 80% of its genome composition came from that subpopulation. The percentages of admixtures from Structure results (Q matrix) were used as covariates in the models to correct the association tests for false positives.
Once the model was chosen, the analyses were conducted using the Linux version of FaST-LMM (Factored Spectrally Transformed Linear Mixed Models) that uses an exact method  [54]. In an exact method, the additive genetic and residual variance components (i. e. , the random effects of the mixed model) are re-estimated for each SNP in a model including the marker effect rather than estimated under the null hypothesis. This approach increases the detection power. For each SNP, FaST-LMM computed a p-value, a qvalue corresponding to the False Discovery Rate (FDR  [55] ), the log likelihoods of the null and alternative models and the fixed-effect weight of the SNP with its standard error. The threshold to declare a significant association was set at a probability level of 1. 0 e-04.
QTL map A database of QTLs for root traits had been established previously  [14]. The physical position and confidence intervals of 137 QTLs for LENGTH, DRB, RB and R_S extracted from this database were used to build a QTL map using the Spidermap Excel macro ( http://jframi. free. fr/wordpress/). The type of mapping population (japonica x japonica, indica x indica, or other type) in which the QTLs were detected was also recovered from the database. The positions of markers that were significantly associated with root phenotypes in this study, as well as the positions of genes known to be involved in root development or nutrient uptake in rice in the literature ("EURoot genes" set extracted from TropgeneDB: http:// tropgenedb. cirad. fr/tropgene/JSP/interface. jsp?module=RICE ), were added to the map to assess co-localization.
29 with DEPTH and 0. 33 with LENGTH). In the multivariate analysis (PCA) conducted on all the measured traits, the first two principal components summarized 74. 5% of the variability. The  correlation circle (Figure  4 ) showed that the first axis was mainly a vigor axis, separating plants with small and large biomasses, whereas the second axis divided shoot or shallow root traits (NBT, LLGTH, SB, RB0020) and deep root traits (LENGTH, DEPTH, NBR_30, DRB or R/S). The accessions on the first plane tended to be grouped by subpopulation (data not shown). A second PCA was conducted using only deep root traits (LENGTH, DEPTH, NBR_30, RBB30, DRB and DRP). The first axis summarized 87. 9% of the variation, and the second explained only 5. 0% (data not shown). The scores of the accessions on the first axis of the second PCA (PCA1) that separated shallow rooted and deep-rooted accessions were included among the phenotypic traits used in association mapping.
Discussion We performed an association mapping study for root traits in the rice japonica group using a medium-throughput hydroponic root phenotyping system with glass beads and marker data obtained by GBS. Hydroponics enables an easy observation of the root system but does not permit to assess root growth reaction in response to drought. However, in rice, what is crucially important for productivity under drought stress situations is the presence of deep roots prior to the onset of stress  [3, 56]. Under severe drought stress, root plasticity is limited, with no increase in root distribution in depth, to the contrary of what is occurring in maize  [57]. Therefore the constitutive expression of the genetic potential in the absence of stress, which is what is assessed under hydroponic conditions, is seen as an important element by rice breeders  [58].
The allele at this meta-QTL may be fixed in the japonica subspecies, as could be suspected from the low polymorphism observed around its position in the japonica panel. Because the LD in this panel spans long distances and because the marker density is 22. 5 kb on average, the Figure  7. Relative positions of significant markers, genes and QTLs for corresponding root traits. Significant markers are in black and genes in blue on the chromosome bodies. QTLs in orange and pink were detected in japonica x japonica and indica x indica mapping populations respectively. QTLs in grey were detected in other population types (indica x japonica, japonica x indica or japonica x aus). MRL = Maximum root length DRB= deep root biomass; RB = root biomass; R_S =root to shoot ratio. Data were extracted from the rice module from TropgeneDB for the root genes ("EURoot genes" set) and the QTLs (http:// tropgenedb.
cirad. fr/tropgene/JSP/interface. jsp?module=RICE). The QTL numbers correspond to their ID in this database. doi: 10. 1371/journal. pone. 0078037. g007 resolution of association mapping is much broader than the single-gene level. An analysis of the genes near the significant markers suggested several possible candidate genes based on data from Arabidopsis or other evidence. For instance, the multicopper oxidase domain-containing genes are known to play a role in root development in low phosphate situations  [66] , glutathione-S-transferases have been reported to be involved in meristem maintenance and the growth of lateral roots  [67] , and several LRR-LRKs have been found to be associated with a root mutant phenotype in rice (Dievart, personal communication). However, further evidence is needed to demonstrate that these genes are indeed involved in root growth in rice.
Acknowledgments. We thank two anonymous referees for their very careful reading of the manuscript. The first author thanks  Jean-Christophe Breton  for useful conversations. Ivan Nourdin is partially supported by the  ANR  grants  ANR-09-BLAN-0114  and  ANR-10-BLAN-0121. David Nualart is supported by the  NSF  grant  DMS-1208625.
Acknowledgments This work is part of an official  French-Moroccan Palaeontological Convention  between the  Museum National d'Histoire Naturelle/Centre National de la Recherche Scientifique  ( MNHN/CNRS, Paris ), the  Office Cherifien des Phosphates (OCP, Casablanca) , the  Ministere de l'Energie, des Mines, de l'Eau et de l'Environnement (MEMEE, Rabat) , and the  Universities Cadi Ayyad  ( UCAM, Marrakech ) and  Chouaı ˆb Doukkali  ( UCDJ, El Jadida ). We thank  E. Gheerbrant ,  P. Janvier , and  C. de Muizon  for their advices on the manuscript,  O. Selloum  for field data concerning the specimen,  R. Vacant  for the preparation of the specimen,  C. Letenneur  and  C. Lemzaouda  for the illustrations, as well as  S. Bailon ,  L. Pierre ,  J. -F. Voisin  and  L. Vives , for the access to the collections under their care (all MNHN, Paris).
Mkn 421 is a nearby active galactic nucleus dominated at all wavelengths by a very broad nonthermal continuum thought to arise from a relativistic jet seen at a small angle to the line of sight. Its spectral energy distribution peaks in the X-ray and TeV γ-ray bands, where the energy output is dominated by cooling of high-energy electrons in the jet. In order to study the electron distribution and its evolution, we carried out a dedicated multi-wavelength campaign, including extensive observations by the recently launched highly sensitive hard X-ray telescope NuSTAR, between December 2012 and May 2013. Here we present some initial results based on NuSTAR data from January through March 2013, as well as calibration observations conducted in 2012. Although the observations cover some of the faintest hard X-ray flux states ever observed for Mkn 421, the sensitivity is high enough to resolve intra-day spectral variability.
g. [4, 5]. In order to provide insight into the radiative processes, the distribution of radiating particles, constraints on the particle acceleration, and thus the structure of the relativistic jet, we conducted a multi-wavelength study of Mkn 421 focused on the X-ray and TeV γ-ray bands. Coordinated simultaneous observations were carried out from December 2012 to May 2013 with the MAGIC and VERITAS ground-based Cherenkov-telescope arrays, and the Swift and NuSTAR orbiting X-ray observatories. The campaign was supported by coordinated (but not necessarily simultaneous) observations by ground-based optical, infrared and radio observatories, and the orbiting Fermi γ-ray observatory. In this report we present a preliminary analysis of the NuSTAR data from January through March 2013. NuSTAR (Nuclear Spectroscopic Telescope Array) is a hard X-ray (3-79 keV) observatory launched into low Earth orbit in June 2012  [6].
NuSTAR Observations In order to maximize the strictly simultaneous overlap of observations by NuSTAR and ground-based TeV γ-ray observatories during the dedicated campaign, the observation times were arranged according to visibility of Mkn 421 at the MAGIC and VERITAS sites. Coordinated observations in 2013 were performed on January 10, 15 and 20, February 6, 12 and 17, and March 5, 12 and 17. A typical NuSTAR observation spanned 10 hours, resulting in 15-20 ks of source exposure after accounting for orbital modulation of visibility and intervals of high background radiation. In addition to these dates, NuSTAR observed Mkn 421 for pointing calibration on 2012 July 7 and 8 (70 ks in total 1 ) and on 2013 January 2 (10 ks). The data 1 Due to sub-optimal pointing, the count rates for this observation have increased systematic uncertainties, estimated to be 4%.
The Fermi-LAT, MAXI and Swift-XRT count rates were taken from publicly available data  [7] [8] [9]. Binning is weekly for the Fermi-LAT and MAXI data, per-observation for the Swift-XRT data and per-orbit for the NuSTAR data. Note that the uncertainties on data points from the latter two are too small to be seen in this plot. The NuSTAR count rates are plotted separately for its two modules, FPMA and FPMB. The dotted vertical lines mark mid-points of the NuSTAR observations and the dashed horizontal lines denote long-term median count rates. were reduced using the standard NuSTARDAS pipeline 2  , version 1. 2. 0. Figure  1  shows the observations listed above with publicly available light curves in X-ray  [7, 8]  and GeV γ-ray bands  [9]  in order to provide a broader context.
even though the lowest and the highest observed fluxes span approximately an order of magnitude. Modest count rates are apparent from both MAXI and Swift-XRT data in comparison with long-baseline light curves and the intense flaring episodes covered in the literature, e. g. [11]. Very high fluxes in X-ray and γ-ray bands have been observed later in the campaign, peaking in mid-April 2013  [12] [13] [14]. The data from this flaring period will be presented in a dedicated publication. With the possible exception of the lowest-flux states, the observed count rates are not consistent with being constant during any of the observations -as demonstrated by light curves in Figures  1  and 2. In order to quantify the variability on timescales shorter than the observations, we divide the data into individual NuSTAR orbits, as they represent the most natural, although somewhat arbitrary, way of partitioning the data.
96 ± 0. 13; 04013-p. 3 The Innermost Regions of Relativistic Jets and Their Magnetic Fields   [11, 15]. Lower Three Panels: The green points with errorbars (1σ uncertainties) are best-fit parameters of the broken power law model fitted to Mkn 421 spectra of individual NuSTAR orbits. For the orbits where the lower bound on E b is below 4 keV (i. e. close to the lower end of the NuSTAR band) the break energy was fixed to 7 keV and the uncertainty is estimated to range between 4 and 12 keV. The grey data points are for the same model (also 1σ uncertainties) fitted to 2001 RXTE data, from  [15]. ii) the observations with higher mean flux systematically prefer values of Γ lower than Γ ; iii) the observations with lower mean flux systematically prefer values of Γ higher than Γ ; and iv) the best-fit χ 2 increases with flux, owing to curvature apparent in the higher-flux spectra.
Summary and Conclusion The data and the analysis presented here are preliminary, but indicative of the new results uncovered by NuSTAR in the hard X-ray band. Its high sensitivity enabled probing the flux state significantly fainter than previously possible with comparable spectral and temporal resolution. The observed variability ranges over an order of magnitude, including instances of flux-doubling over a half-day period and occasional variability on ∼10-minute timescales. In this low-flux regime we find that the hard X-ray spectrum does not cut off steeply, nor show any sign of an increase towards the inverse-Compton component of the SED, but rather saturates at Γ ≈ 3. The spectrum hardens with an increase in flux, which can be phenomenologically described with a broken power law model: the break energy E b ≈ 7 keV separates the soft and the hard power law slopes, both of which change with flux.
2 Figure 4. 24 Figure 4. Top Panel: Comparison of the spectral variability ranges observed with NuSTAR up to the end of March 2013 (dark green), and up to the end of April 2013 (light green), to the ranges previously studied two selected examples from the literature [11, 15]. Lower Three Panels: The green points with errorbars (1σ uncertainties) are best-fit parameters of the broken power law model fitted to Mkn 421 spectra of individual NuSTAR orbits. For the orbits where the lower bound on E b is below 4 keV (i. e. close to the lower end of the NuSTAR band) the break energy was fixed to 7 keV and the uncertainty is estimated to range between 4 and 12 keV. The grey data points are for the same model (also 1σ uncertainties) fitted to 2001 RXTE data, from [15].
5% tri-fluoracetic-acid, and allowed to dry for five minutes. Measurements were performed with a Microflex spectrometer (Bruker). Spectra were recorded in the positive linear mode for the mass range of 2,000 to 20,000 Da (parameter settings: ion source 1 (IS1), 20 kV; IS2, 18. 5 kV; lens, 7 kV). A spectrum was obtained after 675 shots at a variable laser power. The time of acquisition was between 30 seconds and 1 minute per spot. The four ph8 spectra were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against the main spectra of 3,769 bacteria including the spectra from A. finegoldii, A. onderdonkii, A. shahii, A. senegalensis and A. timonensis used as reference data, in the BioTyper database. The method of identification included the m/z from 3,000 to 15,000 Da.
For every spectrum, 100 peaks at most were taken into account and compared with spectra in the database. A score enabled either an identification, or non-identification, from the tested species: a score > 2 with a validly published species enabled a presumed identification at the species level, a score > 1. 7 but < 2 enabled a presumed identification at the genus level; and a score < 1. 7 did not enable an identification. For strain ph8, the obtained score was 1. 1, suggesting that this isolate was not a member of a known species. We incremented our database with the spectrum from strain ph8 (Figure  4 ). Finally, the gel view allows us to highlight the spectra differences with other of Alistipes genera members (Figure  5 ).
Genome sequencing information The organism was selected for sequencing on the basis of its phylogenetic position and 16S rRNA similarity to other members of the genus Alistipes, and is part of a study of the human digestive flora aiming at isolating all bacterial species contained within human feces. It was the seventh genome of an Alistipes species and the first genome of Alistipes obesi sp. nov. The EMBL accession number is CAHA00000000 and consists of 59 contigs. Table  3  shows the project information and its association with MIGS version 2. 0 compliance  [21].
Genome annotation Open Reading Frames (ORFs) were predicted using Prodigal  [34]  with default parameters but the predicted ORFs were excluded if they spanned a sequencing gap. The predicted bacterial protein sequences were searched against the GenBank database and the Clusters of Orthologous Groups (COG) databases using BLASTP. The tRNAScanSE tool  [35]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [36]  and BLASTn against GenBank. Signal peptides and transmembrane helices were predicted using SignalP  [37]  and TMHMM  [38] , respectively. ORFans were identified if their BLASTP E-value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05. Such parameter thresholds have already been used in previous works to define ORFans.
To estimate the mean level of nucleotide sequence similarity at the genome level between Alistipes obesi strain ph8T and other members of the Alistipes genera, we compared genomes two by two and determined the mean percentage of nucleotide sequence identity among orthologous ORFs using BLASTn. Orthologous genes were detected using the Proteinortho software  [39]. We compared A. obesi strain ph8 T with A. finegoldii strain AHN 2437 (GenBank accession number CP003274), A. indistinctus strain YIT 12060 (ADLD00000000), A. putredinis strain DSM 17216 (ABFK00000000), A. senegalensis strain JC50 T (CAHI00000000), A. shahii strain WAL 8301 (FP929032) and A. timonensis strain JC136 T (CAEG00000000).
Oxidase, nitrate reduction, indole formation, urease, arginine dihydrolase, α-and βglucosidase, 6-phospho-β-galactosidase, arginine arylamidase, proline arylamidase, phenylalanine arylamidase, leucine arylamidase, pyroglutamic acid arylamidase, tyrosine arylamidase, glycine arylamidase, histidine arylamidase, glutamyl glutamic acid arylamidase and serine arylamidase, mannose and raffinose fermentation activities are absent. Cells are susceptible to imipenem, ciprofloxacin, metronidazole, nitrofurantoin and rifampicin, but resistant to penicillin G, amoxicillin, amoxicillin-clavulanic acid, erythromycin, vancomycin, gentamicin, doxycycline, ceftriaxone and trimethoprim/sulfamethoxazole. The G+C content of the genome is 58. 6%. The 16S rRNA and genome sequences are deposited in Genbank under accession numbers JN837494 and CAHA00000000, respectively.
EXPERIMENT I MATERIALS AND METHODS The data reported here were collected as part of a validation procedure for a scale unevenness quantification method published elsewhere  (Jabusch et al. , 2004). Eight pianists (six female) were recruited from the student/teacher pool at the Hanover University of Music and were 24. 3 (SD 2. 4) years old. All but one were right-handed (M = 57. 2, SD = 66% right-handed according to the Edinburgh handedness inventory). None of the participants reported any neurological condition. Participants played on a MP 9000 MIDI keyboard (Kawai, Krefeld, Germany). The keyboard's digital music interface (MIDI out) signal was captured on a PC using a commercially available sequencer software  (Musicator Win, version 2. 12; Music Interactive Technology, Bergen, Norway). Participants were requested to play two-octave C-major scales beginning with the C (131 Hz) one octave below the middle C and ending with the C (523 Hz) one octave higher than the middle C.
42 ms (SD 0. 89), whereas those originating from different pianists have a distance of 7. 24 ms (SD 0. 54) (Figure  4 ). ANOVA with distance as dependent variable shows a significant main effect of self vs. other [F (1, 7) = 108. 18, p < 0. 001, η 2 G = 0. 79] but no effect of hand  [F (1, 7) = 0. 55, p = 0. 48] nor playing direction [F (1, 7) = 0. 30, p = 0. 60] nor any interaction effect [all F (1, 7) < 1. 1]. As a result, we designed the simplest possible classification algorithm as follows. Our algorithm is given a database of the irregularity traces for the first measurements of each of the eight pianists. Then it is presented each of the follow-up irregularity traces, without the player label, and its task is to match each pianist to one of the traces in its database. Our algorithm simply chooses the irregularity trace that matches most closely.
Participants performed three thresholding blocks, one for each of the sample fingerprints. At the beginning of each block, we deployed 500 hypothetical psychometric curves with their midpoints linearly spaced over the factor levels from 0 to 5, crossed by the five false alarm rates of 0, 10, 20, 30, and 40%, yielding a total of 2,500 hypothetical psychometric curves maintained online in parallel. The slope parameter of these curves was set to four, since no prior experimental data exists and the slope has been shown not to influence the resulting thresholds all that much  (Gu and Green, 1994). This yielded the following equation for the psychometric curves: p(yes) = a + (1-a) × (1/(1 + exp[-k × (x -m)])) , where x is the stimulus level (i. e. , the factor), a is the false alarm rate, m is the mean of the psychometric curve, k is the slope parameter (4), p(yes) is the probability of responding "irregular.