dataesr/scientific-paragraphs-categorization huggingface dataset:
(TIF) S2 Fig. Partialamino acid sequence alignment of several members of the eukaryotic flavincontaining monooxygenases. A. nidulans FMOs predicted in the A. nidulans genome database at Broad Institute, www. broadinstitute. org) were selected according to their similarities to human FMO3. FmoB (ANID_04110) showed highest similarity and was subsequently used as query in the local BLAST search. Six additional FMO-type proteins appeared and three of them had predicted domain structures and lengths similar to FmoB. Similarity scores were 2. 8e -39 for ANID_08206 (designated FmoA), 6. 9e-38 for ANID_03043 (designated FmoD) and 1. 4e -27 for ANID_02197 (designated FmoC). Conserved FMO motifs are shown by horizontal black bars, the first conserved GxGxxG motif (Rossman fold, x = any amino acid) for FAD binding, is followed by the FMO-identifying signature FxGxxxHxxxY/F and finally by the less conserved NADP-binding motif GxGxxG (Rossman fold).
bars refer to 5 5 μm. (B). Comparison of niaD mRNA levels between the control strain (alcA p -FnirA) and the msr double deletion mutant (alcA p -FnirA msrABΔΔ). Numbers below the niaD hybridization panel are relative values compared to the niaD IND expression in the control strain (alcA p -FnirA) that was arbitrarily set to 100. (C). NirA is fully functional in thioredoxin mutants. Transcription of the NirA target gene niiA was tested in strains carrying a deletion in the single A. nidulans thioredoxin gene (trxAΔ, accession number ANID_00170) under noninduced and induced conditions and compared to wild type (WT), nirA c 1 and a strain in which the trxA deletion has been complemented by a functional ectopic copy (27). Growth conditions were as in B, except that induction by nitrate was proceeding for 2 hours instead of 20 minutes.
1) First, we analyzed the pair-wise F ST values between each invasive population sample and each South American sample. 2) We then computed the mean individual assignment likelihood  13  (denoted LiRs) of each invading population sample i, to each possible South American source population using GENECLASS2  14  (ver. 2. 0). The most probable source of a target invasive population's sample i was determined as the South American population whose sample displays the minimum corrected F ST values with i and the maximum mean individual assignment likelihood of i. 3) We also plotted a neighbor joining (NJ) tree  15  based on the genetic distance described by Cavalli-Sforza & Edwards  16  using the POPULATIONS software version 1. 2. 30 (http://bioinformatics. org/,tryphon/ populations/). It is expected that the source of a target invasive population's sample i is located in close proximity to i in the tree.
GC-TOF-MS analyses Metabolites for GC-TOF-MS were extracted from 30 mg of FW and analysed as described previously  (Osorio et al. , 2012)  with minor modifications as detailed in Supplementary text. Chromatograms and mass spectra were evaluated using Chroma TOF 1. 6 and TagFinder 4. 0 software  (Luedemann et al. , 2008). Quantities of metabolites are expressed as relative intensity, based on peak integration, and relative to the internal standard.
Statistical analyses Metabolite contents expressed on a DW basis were used for statistical analyses. Principal component analysis (PCA, correlation matrix) was performed using R scripts in the BioStatFlow web application (http://bit. ly//biostatflow). One-factor analysis of variance (ANOVA) was performed using MultiExperiment Viewer, version 4. 8  (Saeed et al. , 2003). K-means clustering (MultiExperiment Viewer version 4. 8) was performed on the means of the biological replicates (data mean centred and reduced to unit variance, Pearson correlation distance) after ANOVA filtering (P<0. 05). To visualize co-regulations between leaf and fruit compositional changes, partial correlation graphs were used. We chose a Gaussian graphical model (GGM) approach based on pairwise Pearson correlation coefficients conditioned against the correlation of all other metabolites, since GGMs are much sparser than total correlation networks  (Krumsiek et al.
, 2011). Network cartography was done on log 2transformed data, with false discovery rate correction (q<0. 001), and Fruchterman layout, using R scripts in BioStatFlow (NETGRAPH;  Opgen-Rhein and Strimmer, 2007)  and Cytoscape software version 3. 0  (Shannon et al. , 2002;  http://www. cytoscape. org/).
Fruit composition is less affected by the experiment, condition, and harvest time compared with the leaf Complementary analytical techniques for biochemical profiling provided data on 70 compounds in leaves and 56 compounds in fruits (Supplementary Tables  S1-3  and Fig. S3  at JXB online). To visualize leaf or fruit data, we performed PCA for the two experiments and conditions. Leaf samples harvested during the day and night periods were not clearly separated on the PC1×PC2 plan (Fig. 1A ). The PCA scores plot revealed that leaf composition was similar in the two experiments for the control condition and hardly distinguishable from that of the shaded condition in Exp. 2. However, the leaves of the shaded condition in Exp. 1 clearly separated from all other samples along PC1. Since the shading condition was applied in early July, the closeness of the control and shaded leaf samples in Exp.
Several leaf and fruit diurnal patterns seem to be coordinated The links between leaf and fruit metabolic patterns were visualized with networks reconstructed using GGM. Since carbon availability was different in the control and shaded conditions, as highlighted above (Tables  1  and 3 ), a specific network was reconstructed for each condition. First, the metabolite networks based on the data of the two experiments for the control condition was reconstructed (Fig. 6A ). GGM yielded two networks implicating more than two variables, and comprising 56 out of the 126 variables considered, with 60 edges. Each of these two networks comprised compounds from leaf and fruit. The smaller network comprised a majority of leaf secondary metabolites and only three compounds determined in fruit, including malate.
Acknowledgements We thank  Pierre Gaillard  and  Jacques Longuesserre (INVENIO) ,  Emilie Labadie , our colleagues of the BFP ( UMR1332 ) and  PSH (UR1115) Research Units  for their helpful contribution and support during culture, harvests, and sample preparation, and  Dr Vanessa Zhendre  for NMR annotations. This work was supported by the project  Eranet-EraSysBio+ 'FRuit Integrative Modelling', MetaboHUB - ANR-11-INBS-0010  and  PHENOME-ANR-11-INBS-0012  projects and  INRA.
Additional MIRC-only observations were performed to track the orbital motion of the companion, and these were fit together with new and existing radial velocity measurements of both stars to derive the complete orbital elements and distance. We also used the MIRC data to reconstruct an image of the Be disk in the near-IR H-band. VEGA visible broadband and spectrointerferometric Hα observations were fit with analytical models for the Be star and disk, and image reconstruction was performed on the spectrally resolved Hα emission line data. Results. The hot subdwarf companion is clearly detected in the near-IR data at each epoch of observation with a flux contribution of 1. 5% in the H band, and restricted fits indicate that its flux contribution rises to 3. 3% in the visible. A new binary orbital solution is determined by combining the astrometric and radial velocity measurements.
The derived stellar masses are 9. 6±0. 3M and 1. 2±0. 2M for the Be primary and subdwarf secondary, respectively. The inferred distance (186 ± 3 pc), kinematical properties, and evolutionary state are consistent with membership of ϕ Persei in the α Per cluster. From the cluster age we deduce significant constraints on the initial masses and evolutionary mass transfer processes that transformed the ϕ Persei binary system. The interferometric data place strong constraints on the Be disk elongation, orientation, and kinematics, and the disk angular momentum vector is coaligned with and has the same sense of rotation as the orbital angular momentum vector. The VEGA visible continuum data indicate an elongated shape for the Be star itself, due to the combined effects of rapid rotation, partial obscuration of the photosphere by the circumstellar disk, and flux from the bright inner disk.
Spectroscopic orbital determination from radial velocity measurements A combined analysis of the astrometric and radial velocity orbits provides the key step to determining the stellar masses and distance. However, a comprehensive spectroscopic orbit was last determined by  Božić et al. (1995) , so it is worthwhile to reassess the spectroscopic orbital elements based on observations that are more contemporaneous with the interferometric observations. Here we present new radial velocity measurements for the Be primary and for the elusive sdO secondary. Božić et al. (1995)  showed that the wings of the Hα emission line that are formed in the inner disk close to the Be star display radial velocity variations of the Be star. Thus, we collected the available Hα spectroscopy from the Be Star Spectra Database 1  (Neiner et al.
Two different calibrators were used for the VEGA observations: HD3360 and HD25642. They were identified using the SearchCal service 3   (Bonneau et al. 2006 (Bonneau et al. , 2011) )  and the CDS Astronomical Databases SIMBAD and VIZIER 4. We used the SearchCal spectrophotometric uniform disk angular diameter determinations in the R and H bands, which led to θ UD-R = 0. 284 ± 0. 020 mas and θ UD-H = 0. 287 ± 0. 020 mas for HD3360 and to θ UD-R = 0. 439 ± 0. 031 mas and θ UD-H = 0. 447 ± 0. 031 mas for HD25642. In addition to these calibrators, MIRC calibration employed the additional calibrators 7 And (0. 65 ± 0. 03 mas), θ Cas (0. 57 ± 0. 04 mas), and HD 33167 (0. 50 ± 0. 04 mas). The data processing followed the standard MIRC  (Monnier et al. 2007 (Monnier et al. , 2012) )  and VEGA  (Mourard et al.
6 km s -1 as explained in the text. The width of the solid line illustrates the errors on our orbital solution estimated from 100 bootstrap fits. The RV is negative at zero phase because we have chosen an astrometric orbital ephemeris of the secondary relative to the primary, which is opposite in sign to the radial velocity convention, where the velocity of the primary star is measured relative to the center-of-mass. is 3σ larger than that found by  Božić et al. (1995) , but we use this revised period and epoch (T RV min = RJD 56110. 00 ± 0. 12) to reference the orbital phase in the rest of this paper. We fixed the period to P = 126. 6982 and assumed a circular orbit for our final solution. To assess the errors in the orbital parameters, we carried out a bootstrap analysis, for which we resampled the astrometric and RV data to create 100 synthetic data sets.
Visible broadband data and model fitting Using the VEGA wide spectral band measurements, we first performed a model fitting using the software LITpro 5   (Tallon-Bosc et al. 2008 ). We first used the three bands containing continuum and then, separately, the band containing the Hα line as defined in Table  3. In a first approach, we just used a simple uniform disk model. We found a diameter in the continuum of 0. 343 ± 0. 006 mas and in the line of 0. 423 ± 0. 012 with a reduced χ 2 of 9. 3 and 14. 4, respectively (Table  8 , first section). The larger equivalent diameter in the line indicates the presence of an extended structure, corresponding to the line emission associated with the disk. The residuals clearly indicate that the size of the object varies with position angle, and thus we used a second model with the addition of an elongated Gaussian disk.
Spectrally resolved image reconstruction around Hα We used the MiRA software  (Thiebaut & Giovannelli 2010)  combined with an improved self-calibration method  (Millour et al. 2011)  to reconstruct wavelength-dependent images of ϕ Persei in the visible wavelength range. The data set contains the broadband V 2 defined in Table  3  and the wavelength-dependent differential visibilities and phases, but no closure phases, as previously said. Therefore, the self-calibration method had to be modified. The different steps of our method, and the additions made in this work, are presented in Appendix A. The so-called SELF-CAL software and its documentation are available online 6. The image reconstruction leads to an (x, y, λ) image cube that covers a field of view of 6. 4 mas with 64x64 pixels, a wavelength range between 646 nm and 666 nm with a spectral resolution of ≈ 1640.
9 × 1. 8 mas, and ≈ 20 • position angle of the minor axis, a significant amount of unconstrained flux (≈ 15% of the continuum flux, throughout all wavelengths), which could be linked to the companion star or to image-reconstruction artifacts. The images in the emission line are very different from those in the continuum, showing emission that sweeps across the "disk" feature. In the upper part of Fig. 6 , we compare the spectral images and the maps deduced from the model presented in the previous section. We also provide a set of reconstructed images from simulated data based on the kinematic model. To extract information from these images from milliarcsecond integral-field spectroscopy, we multiplied the images by the system spectrum (as MiRA provides total-fluxnormalized images) and selected different zones with binary masks to extract their spectra.
Acknowledgements. The  CHARA Array  is operated with support from the  National Science Foundation  through grant  AST-0908253 , the  W. M. Keck Foundation , the  NASA Exoplanet Science Institute , and from  Georgia State University. VEGA is a collaboration between CHARA and  OCA-Lagrange/UJF-IPAG/UCBL-CRAL  that has been supported by the  French programs PNPS  and ASHRA, by  INSU  and by the  Région PACA. We acknowledge the use of the electronic database from the  CDS, Strasbourg  and electronic bibliography maintained by the  NASA /ADS system. Part of this material is based upon work supported by the  National Science Foundation  under Grant No. AST-1009080  (DRG) and grants  NSF-AST0707927  and  NSF-AST1108963  (JDM). We finally warmly thank  Dietrich Baade , our referee, for his accurate review.
IV A. X To improve the modeling of hadron interactions inside and outside the target, we use data from the NA61/SHINE experiment  [18, 19]  collected at 31 GeV/c and several other experiments  [20] [21] [22]. The hadron production data used for the oscillation analyses described here are equivalent to those used in our previous publications  [3, 11] , including the statisticslimited NA61/SHINE dataset taken in 2007 on a thin carbon target. The NA61/SHINE data analyses of the 2009 thin-target and T2K-replica-target data are ongoing, and these additional data will be used in future T2K analyses. We incorporate the external hadron production data by weighting each simulated hadron interaction according to the measured multiplicities and particle production cross sections, using the true initial and final state hadron kinematics, as well as the material in which the interaction took place.
The remaining uncertainties due to the uncertain position and calibration of the other beam monitors are significantly smaller. As described in Sec. IV A, the neutrino beam direction is determined with the INGRID detector, and therefore the assigned uncertainty on the off-axis angle comes directly from the INGRID beam profile measurement. To account for the horn current measurement that drifts over time and a possible scale uncertainty, 5 kA is assigned as a conservative estimate of the horn current error. In the flux simulation, the horn magnetic field is assumed to have a 1/r dependence. Deviations from this field, measured using a Hall probe, are used to define the uncertainty of the horn field. Horn and target alignment uncertainties come from survey measurements. Systematic uncertainties in modeling particle multiplicities from hadronic interactions come from several sources: experimental uncertainties in the external data, the uncertain scaling to different incident particle momenta and target materials, and extrapolation to regions of particle production phase space not covered by external data  [11].
NEUTRINO INTERACTION MODEL Precision neutrino oscillation measurements rely on having an accurate neutrino interaction model. The model is used to evaluate the selection efficiencies of the different signal and background interactions as well as the estimate of the neutrino energy from the detected final state particles. Finally, the model forms the basis to account for differences in the predicted neutrino cross sections between different T2K detectors due to their different target nuclei compositions. All of these factors and their uncertainties are incorporated into the model for the T2K experiment through a set of systematic parameters x listed in Tab. VII, and their covariance V x. This section describes the interaction model in NEUT, the primary neutrino interaction generator used by T2K, explains how we use data from external experiments to provide initial constraints on the model before fitting to T2K data, discusses remaining uncertainties not constrained by external data sources, and discusses uncertainties based on differences between the NEUT model and those found in other interaction generators.
B. Constraints From External Experiments To establish prior values and errors for neutrino-interaction systematic parameters x and constrain a subset for which ND280 observables are insensitive, neutrino-nucleus scattering data from external experiments are used. The datasets external to T2K come from two basic sources: pion-nucleus and neutrinonucleus scattering experiments. To constrain pion-nucleus cross section parameters in the NEUT FSI model, pion-nucleus scattering data on a range of nuclear targets are used. The most important external source of neutrino data for our interaction model parameter constraints is the MiniBooNE experiment  [37]. The MiniBooNE flux  [38]  covers an energy range similar to that of T2K and as a 4π detector like SK has a similar phase space acceptance, meaning NEUT is tested over a broader range of Q 2 than current ND280 analyses.
Constraints From MiniBooNE Inclusive π Measurements To constrain single pion production parameter errors, we use published MiniBooNE differential cross-section datasets for CC single π 0 production (CC1π 0 )  [71] , CC single π + production (CC1π + )  [72] , and NC single π 0 production (NC1π 0 )  [73]. Because the modes are described by a set of common parameters in NEUT, we perform a joint fit to all three data sets. The selection of NEUT simulated events follows the signal definition in each of the Mini-BooNE measurements. For the (CC1π 0 , CC1π + , NC1π 0 ) selections, the signals are defined as (ν µ , ν µ , ν) interactions with (1,1,0) µ -and exactly one (π 0 ,π + ,π 0 ) exiting the target nucleus, with no additional leptons or mesons exiting. In all cases, there is no constraint on the number of nucleons or photons exiting the nucleus.
The errors given in Tab. V also include the variance observed when refitting using the 16 FSI 1σ parameter sets and scaling the errors when fitting multiple datasets following the approach of Maltoni and Schwetz  [74]. The "W shape" nominal prior is kept at the default of 87. 7 MeV/c 2 and in the absence of reported correlations from MiniBooNE, the uncertainty is estimated as the difference between the best fit and default values. The correlations between M RES A , x CC1π 1 , and x N C1π 0 are given in Table  VI.
NEAR DETECTORS Precision neutrino oscillation measurements require good understanding of the neutrino beam properties and of neutrino interactions. The two previous sections describe how we model these aspects for the T2K experiment and how we use external data to reduce model uncertainty. However, if only external data were used, the resulting systematic uncertainty would limit the precision for oscillation analyses. In order to reduce systematic uncertainty below the statistical uncertainty for the experiment, an underground hall was constructed 280 m downstream of the production target for near detectors to directly measure the neutrino beam properties and neutrino interactions. The hall contains the on-axis INGRID detector, a set of modules with sufficient target mass and transverse extent to continuously monitor the interaction rate, beam direction, and profile, and the off-axis ND280 detector, a sophisticated set of sub-detectors that measure neutrino interaction products in detail.
Event pileup, defined as more than one neutrino interaction occurring in a module in the same beam pulse, occurs in as many as 1. 9% of events with interactions at the current beam intensity. The new algorithm handles pileup events correctly as long as the vertices are distinguishable. For the full dataset, 4. 81 × 10 6 events are selected as candidate neutrino events in INGRID. The expected purity of the neutrino events in INGRID is 99. 58%.
The ratios of observed to predicted event rates, using the nominal values for the beamline and neutrino interaction systematic parameters, are: N data 250kA N MC 250kA = 1. 014 ± 0. 001(stat) ± 0. 009(det syst), (3) N data 205kA N MC 205kA = 1. 026 ± 0. 002(stat) ± 0. 009(det syst), (4) The uncertainties from the neutrino flux prediction and the neutrino interaction model are not included in the systematic errors. The profiles of the neutrino beam in the horizontal and vertical directions are measured using the number of neutrino events in the seven horizontal and seven vertical modules, respectively. The observed horizontal and vertical profiles are fitted with separate Gaussian functions and the profile center is defined as the fitted peak positions. Finally, the neutrino beam direction is reconstructed as the direction from the proton beam target position to the measured profile center at INGRID using the result of accurate surveys of the proton beam target and the INGRID detectors.
Y = 0. 011 ± 0. 012(stat) ± 0. 105(det syst) mrad, (5) θbeam respectively. The neutrino flux uncertainty arising from possible incorrect modeling of the beam direction is evaluated from this result. This uncertainty, when evaluated without ND280 data, is significantly reduced compared to the previous analysis, as shown in Fig. 6. The horizontal and vertical beam width measurements are given by the standard deviations of the Gaussians fit to the observed profiles. Figure  7  shows the history of the horizontal and vertical beam widths with the horns operating at 250 kA which are found to be stable within the statistical errors. The ratios of observed to predicted widths, using nominal values for the systematic parameters, are: W data X W MC X = 1. 015 ± 0. 001(stat) ± 0. 010(det syst), (7) W data Y W MC Y = 1.
The curvature due to the magnetic field provides measurements of particle momenta and charges and, when combined with ionization measurements, allows for particle identification (PID). The tracker is downstream of a π 0 detector (P0D  [86] ) and all of these detectors are surrounded by electromagnetic calorimeters (ECals  [87] ) and side muon range detectors (SMRDs  [88] ). Data quality is assessed weekly. Over the entire running period, the ND280 data taking efficiency is 98. 5%. For the analyses presented here, only data recorded with all detectors having good status are used, giving an overall efficiency of 91. 5%.