dataesr/scientific-paragraphs-categorization huggingface dataset:
5. 2). Here, VIF were assessed for the final model and consisted in averaging data from multiple sensors collapsed over a particular variable at hand. None of the VIF values exceeded 1. 1, indicating that multicollinearity was unlikely to have had a major influence on the reported findings. Note that  Rogerson (2001)  recommended maximum VIF value of 5 and the author of fmsb recommended value of 10. Before entering empirical variables in the model, we calculated normalized values or z-scores: trials in which a given variable deviated more than 3 z-scores were removed from further analysis. This normalization was computed separately for every MEG sensor and every EEG electrode. For single-trial analyses of β power in FOJ, we focused on the maximum power within the 0. 4 s to 0. 8 s period following the R1.
, 2012). Second, participants can monitor their attentional state, i. e. have an appropriate metacognition of an internal variable, which depended on the lateralization pattern of α oscillations  (Whitmarsh et al. , 2014). According to this view, the post-R2 α decrease observed here could be seen as a reorienting of attention following the generation of durations that were too long. Specifically, in an attentional gate model of time perception  (Zakay & Block, 1995) , long durations would correspond to not enough attention paid to temporal production, hence more attention needed for the next trial. However, the lack of association between α power during the self-generation of durations and produced duration in this dataset  (Kononowicz et al. , 2017)  and in previous studies  (Kononowicz & Van Rijn, 2015)  do not fit well with this interpretation.
Second, we used the Axiom Analysis Suite 2. 0 software  [29]  to control the quality of the remaining markers on a large dataset of 3418 genotyped individuals from the six French rainbow trout lines (including the 302 individuals of the current study). Edits consisted in discarding 7711 SNPs with probe polymorphism or a call rate lower than 97%; 2995 SNPs for which no homozygous individual was observed for the minor allele on the full genotyped set; and 1689 SNPs that were monomorphic in all lines. Third, regarding the genotypes for the remaining 38,425 SNPs, we performed a final quality control using PLINK v1. 9 software  [30]  on each line set considered in the current study. We discarded SNPs with a very significant deviation from Hardy-Weinberg equilibrium (p value < 0. 0001) in one or several populations.
Genetic structure of the population Observed heterozygosity (Ho) and expected heterozygosity (He) for a population under Hardy-Weinberg equilibrium were derived with the PLINK v1. 9 software  [30]. Levels of genetic variation in the different lines were compared using a Kruskal-Wallis non-parametric test on Ho values. Genetic differentiation between populations was measured with pairwise F ST estimates  [31] , using the VCFtools v0. 1. 13 software  [32]. In addition, a principal component analysis (PCA) was performed with the R package Adegenet  [33]  to visualize the genetic structure of the lines.
Linkage disequilibrium To estimate LD, we used the squared correlation based on genotypic allele counts (number of non-reference alleles at each locus) using the PLINK v1. 9 software  [30]. This r 2 value does not necessitate phasing, it is very similar to but not identical to the r 2 estimate derived from haplotype frequencies  [34]. Pairwise LD between adjacent SNPs and pairwise LD between all SNPs in a 60-Mb long window were derived for each chromosome and line. Mean r 2 values were calculated for each chromosome and line by considering the following average distances between SNPs: 10 kb with a 20 kb-window; 50 kb and 100 kb with a 50 kb-window; 1, 3, 5, 10 and 30 Mb within a 100-kb window for each distance.
There is no clear consensus in the literature on how to choose these parameters and most studies used Plink default values. In a recent study  [16] , a gene-dropping simulation was performed and inbreeding estimates based on ROH and pedigree data were compared to true inbreeding. Inbreeding based on ROH was estimated using different software and different threshold parameters using 50 K chip data. While pedigree inbreeding underestimated true inbreeding, using ROH with a minimum window size of 20 to 50 SNPs provided the closest estimates to true inbreeding regardless of the software  [16, 88]. In our study, we observed that inbreeding estimates were similar for window sizes of 30 and 50 SNPs, and estimates were almost identical when considering recent inbreeding (results not shown). Nevertheless the results were sensitive to the maximum gap between two successive SNPs (1000 kb vs.
Acknowledgements We thank the staff of the  INRA experimental facilities (PEIMA, Sizun)  and the French breeding companies who are members of  SYSAAF (Aqualande, Bretagne Truite, Les Fils de Charles Murgat and Viviers de Sarrance)  for their participation in the fish rearing and for providing biological samples for genotyping individuals. We are very grateful to  D. Laloë  and  G. Restoux  for valuable advice on principal component analysis and N e estimation, respectively.
Physiological trait measurements for plant phenotyping Plant and leaf physiological data are part of a larger dataset presented in  Blanchet et al. (2018). Specific leaf area (SLA) was determined according to  Allinne et al. (2009). Both leaf osmotic potential (OSM_POT) and leaf osmotic potential at full turgor (OSM_POT_100) were measured as described in Poormohammad  Kiani et al. (2007). To assess carbon isotope discrimination (CID), samples were oven-dried, ground, weighed and analysed using a continuous low isotope ratio mass spectrometry at the Stable Isotope Platform SHIVA (University of Toulouse, France).
1 H-NMR analysis of major polar compounds Polar metabolites were extracted from lyophilized powder (40 mg DW per biological replicate) with an ethanol-water series (80/20, 50/50, 0/100 v/v) at 80 °C as described in  Deborde et al. (2009)  with modifications. This three-step extraction process (ethanol-water series) was chosen to take into account the diverse affinities and solubilities of leaf major polar compounds (i. e. sugars, organic acids, aminoacids) for ethanol or water, in order to obtain an accurate view of these compounds in leaf extracts. The 1D (cpmg and single-pulse) spectra were processed using the NMR-ProcFlow application v1. 1  (Jacob et al. 2017 ; http://nmrpr ocflo w. org/). For the cpmg dataset, this resulted in 479 normalized variables corresponding to spectral regions (named Unk_ppm:number in Online Resource 2) which included compounds that were annotated later on.
LC-ESI-QTOF-MS untargeted analysis of semi-polar metabolites Liquid chromatography-electrospray-ionization-time-offlight-mass spectrometry (LC-ESI-QTOF-MS) profiling of aqueous methanol extracts containing 0. 1% formic acid was performed with extracts obtained from 20 mg DW lyophilized powder. An Ultimate 3000 HPLC (Dionex, Sunnyvale, CA, USA) was used to separate metabolites on a reversed-phase C18 column using an acetonitrile gradient in acidified water. Metabolites were detected by using a hybrid quadrupole/time-of-flight mass spectrometer (micrOTOF-Q, Bruker Daltonics, Bremen, Germany). Electrospray ionization in positive mode was used to ionize the compounds. A quality control sample (QC) was injected after each set of ten samples. The MS data were processed using XCMS  (Smith et al. 2006 ) and R scripts for filtering.
A total of 1519 features were detected and reduced to 540 metabolic variables after filtering. The corresponding MS-based variables were named using their nominal masses in dalton and retention time in seconds in Online Resource 2 (MxxxTyyy). Metabolite identification was performed using the accuratemass data and Orbitrap (Thermo Fisher, Villebon-sur-Yvette, France) MS and MS/MS data of a representative sample extract.
Statistical analyses All statistical analyses were performed using the R Software (http://www. r-proje ct. org/), the R package mixOmics  (Rohart et al. 2017 ) and the BioStatFlow online tool (biostatflow. org) which is based on R scripts. Two-way ANOVA with FDR correction was performed to highlight line status or water-treatment effects and interaction. The parameters used for partial least squares-discriminant analysis (PLS-DA) in BioStatFlow were adjusted to a tenfold cross-validation (CV) to generate the model (and calculate the Q 2 ) and 200-randomized permutations to estimate the robustness of the generated model. Some graphical outputs for PLS-DA were produced by mixOmics, using the same parameters than with BioStatFlow. An additional R script from  Fu et al. (2017)  was used to perform least absolute shrinkage and selection operator (LASSO) and sparse partial least square (sPLS) selection.
Sunflower leaf metabolic contents measured by targeted and untargeted approaches In total, 27 metabolites plus starch and protein content were targeted and quantified in sunflower leaf. Major soluble sugars (i. e. the ones with the highest content), organic acids and chlorophylls were quantified with spectrophotometric analyses. FAMES and free amino acids were measured by using GC-FID and UPLC-fluo, respectively. These data are presented for the different conditions in Fig. 1  and Online Resource 3-Fig. S1. We targeted these compounds because they are (1) often considered as putative metabolic markers  (Fernandez et al. 2016 ) and (  2 ) valuable candidates for a high-throughput metabolic marker approach, as they are easy and cheap to measure. The concentrations of these 29 compounds were summed to estimate their contribution to leaf biomass.
This yielded about 45% of leaf dry mass. Glucose was found to be the major soluble sugar. Its concentration (32-45 mg g -1 DW) was in the same range as that of sucrose, but 8-10 times higher than fructose depending on the chosen conditions. Glutamate, alanine and serine were found to be the most abundant amino acids. In leaves, linolenic acid (C18:3) was the most abundant fatty acid (7. 5-18. 6 mg g -1 DW), followed by linoleic acid (Fig. 1 ). 1 H-NMR profiling was performed on polar extracts to further analyse metabolites from primary metabolism in the millimolar range. Four hundred and seventy-nine regions were observed in the 1 H-NMR cpmg dataset, of which 20 compounds were annotated (Online Resource 4). Eleven identified compounds were measured and quantified with the 1 H-NMR quantitative single-pulse dataset, but only nine of them were kept in the final dataset to avoid redundancy with targeted spectrophotometric measurements.
When summed, these compounds represented an additional 5% of the leaf dry mass (Online Resource 4). LC-ESI-QTOF-MS analysis of semi-polar extracts was performed to analyse specialized metabolites. The most intense peaks that were detected in the sample extracts, based on their intensity in the XCMS table generated by a relative area under curve (AUC) approach, were tentatively annotated. Orbitrap-MS data were used in order to gain precision on mass measurement and to perform MS/MS. Online Resource 5 shows the annotation table generated using a representative spectrum of a leaf extract with annotation of the most intense peaks. The two most intense peaks were annotated as mono and di-caffeoyl quinic acid. With a retention time around 17-20 min, several methylated flavonoids were also detected.
Elimination of redundant metabolic variables Since a single metabolite can be encompassed within several 1 H-NMR buckets or MS-based ions, we first reduced this full data set by hierarchical clustering (BioStatFlow, Pearson correlation, average linkage as aggregation method). Clusters were generated with a correlation threshold of 0. 85. Within each cluster, MS-based metabolic variables corresponding to adducts or isotopes were eliminated while the one with the highest AUC was kept. For 1 H-NMR buckets, we used a similar process in order to keep buckets bearing the highest AUC. After this curation process (Fig. 2 ), the new dataset comprised 588 variables (Online Resource 7). We then tested the discrimination potential of this curated data set on our sample groups using an unsupervised statistical approach.
PCA was first carried out (Fig. 3 ). The first two components displayed in Fig. 3a  (water treatment) and Fig. 3b  (line status) explained 25% of the total variability. The separation of our sample groups was incomplete, although slightly better for DS. We then performed a supervised method (PLS-DA) on this 588-variable dataset for each type of sample group. Each PLS-DA analysis was able to discriminate WW from DS samples (Online Resource 3-Fig. S2a ), and B from R lines (Online Resource 3-Fig. S2b ), in the 2D space based on the first two latent variables. Predictive ability (Q 2 ) and proportion of variance (R 2 ) explained by the model were higher than 0. 9 and 0. 8 in both cases (Table  1 ), respectively. Each model was considered as valid as it bore Q 2 and R 2 values above 0. 4 and 0.
Metabolic variable selection process To select variables, we compared three different methods for each condition (DS or line status), a generalised univariate method (one-way ANOVA) and two multivariate ones (sPLS and LASSO penalty;  Fu et al. (2017) ; Fig. 2 ). The 588-variable data matrix (Online Resource 7) was submitted to these methods and subsequent PLS-DAs were performed. We compared the Q 2 and R 2 to assess the quality of the variable selection process for each resulting PLS-DA model (Table  1 ). Since our objective was to find the smallest possible variable set, we analysed datasets of different sizes (90, 50 and 20 variables for water treatment; 35 and 20 variables for line status). We dimensioned the first selected data set size according to the numbers of variables with a p for line status).
Comparison with physiological variables for DS markers Physiological markers are used to assess the impact of DS on plant. In our experiment, SLA, OSM_POT and CID were measured in young and mature leaves at the end of DS. To test the quality of our PLS-DA model built with selected metabolic variables, we compared its discriminative capacity with a PLS-DA model built with this physiological dataset comprising six variables extracted from a larger dataset published in  Blanchet et al. (2018). Unsupervised PCA computed with this dataset showed poor separation of DS and WW samples (Fig. 5b ). Furthermore, the PLS-DA model built with these physiological data displayed a Q 2 = 0. 68 and an R 2 = 0. 54 (Table  1 , Fig. 4b ), but was less efficient than those built with the minimal set of eight metabolic variables (Q 2 = 0.
Biomarkers of water treatment The discrimination of WW and DS samples using metabolic variables was more efficient than the discrimination of line status. Amino acids were clearly the best DS markers in our dataset, displaying a 5-to 10-fold increase in DS sunflower leaves (Fig. 1a ). Increases in amino acids under DS in sunflower have already been documented, although to a lesser extent and in a cultivar-dependent manner  (Manivannan et al. 2007). This feature has also been detected in other crops such as barley  (Lanzinger et al. 2015)  and wheat  (Bowne et al. 2012). Conversely,  Moschen et al. (2017)  found that the concentrations of several leaf amino acids were decreased under DS in sunflower  (Correia et al. 2005). These contradictory results regarding amino acid responses might be due to water-stress intensity, sampling stage or differences in nitrogen nutrition.
In the present study, the use of Heliaphen high-throughput phenotyping platform allowed the application of a precise and reproducible drought scenario that is available for more thorough understanding of the impact of DS on leaf metabolism. Nevertheless, higher concentrations of individual amino acids such as proline and glycine have been detected in DS leaves  (Moschen et al. 2017). Amino acids, and especially proline, might participate in osmotolerance under DS, although the case is highly debated for the latter  (Szabados and Savouré 2010). In our dataset, other metabolites appeared as good markers of DS samples, i. e. glycine-betaine and myo-inositol. Glycine-betaine is accumulated in various plants under abiotic stress  (Giri 2011). Generally, plants accumulate amounts of glycine-betaine that are too low to significantly impact the sap osmotic potential.
2017). Fernandez et al. (2016)  argued that ideal metabolic markers should be easy and cheap to analyse. For this purpose, we tested the discriminant capacity of a small metabolic marker set composed of eight biochemical variables: total free amino acids, citrate, glycine-betaine, myo-inositol, sucrose, glucose, total proteins and starch. An unsupervised PCA clearly separated WW and DS samples when these eight biochemical variables were used (Fig. 4a ), but not with the physiological dataset consisting in six common indicators of DS measured at plant level. Indeed, SLA, OSM_POT and CID (measured in both young and mature leaves) are often used to characterise the water-stress status of a given crop (Fig. 4b ). This was confirmed when comparing Q 2 values for PLS-DA models computed with each of these data sets (0.
In addition to dislocation modeling, PF models are also widely employed to model microstructure evolution and solute diffusion  [30, 31]. For example, in the case of Ni-based superalloys, Kundin et al. [32]  simulated g 0 coarsening with the help of such models. To this end, they accounted for the lattice misfit between g and g 0 as well as the corresponding interface energy. To calibrate their model, they used the Thermo-Calc database for Ni alloys. The energy based formulation of PF provides a straightforward way to couple different types of ordering (e. g. , phase, lattice plane stacking, chemical). For example, Zhou et al. [33]  modeled dislocation shearing of g 0 precipitates by coupling dislocation glide and atomic reordering via a long-range order parameter mediating the formation of a SESF as the dislocation enters the precipitate.
Model identification For simplicity, the phase mass density 9 a and molar volume U a of a ¼ g,g 0 are assumed constant in this work. With   Energetic properties of a ¼ g; g 0 containing Co are calculated atomistically at 0 K for c Co ¼ 0; 0:01; 0:02; …; 0:2 using the NieAleCo EAM potential of  [3]  in LAMMPS  [37]. These properties include ε a 0 Co , ε a 0 CoCo and h a 0 Co in (3), C a in (  4 ), H a Co in (  5 ), 4 a 0 ab in (  6 ), and the stacking fault energy j sf a in (2). Determination of these, the Co mobility M a Co in  (1) , and the dislocation mobility m a a in (  9 ), is discussed in what follows. When there is no chance of confusion, we dispense with the subscript "Co" to simplify the notation in what follows.
APT specimens were prepared from site-specific liftouts from rafted g 0 precipitates and analyzed with a Cameca LEAP 5000XR instrument. The specimens were analyzed in laser mode at a base temperature of 50 K, laser pulse energy 45 pJ, and repetition rate of 125 kHz. Data reconstruction and processing was performed using Cameca IVAS 3. 8. 2. Fig. 7  displays a typical APT reconstruction of a rafted g 0 precipitate containing the trace of compositional variations related to the presence of structural defects  [1, 48, 49]. As shown in Fig. 7  (bottom left, bottom right), Co and Cr segregate to defects in the rafted g 0 precipitate. In particular, in both profiles (indicated by arrows) in Fig. 7  (bottom), up to 10 at. % Co enrichment in the defects is evident. On the basis of APT alone, it is not possible to determine the nature of the defect to which this segregation occurred.
Acknowledgements Financial support of the modeling and simulation work reported on here in Subproject M5 of the  Priority Program 1713  " Chemomechanics" of the Deutsche Forschungsgemeinschaft (DFG)  is gratefully acknowledged. We thank  SAFRAN Helicopter Engines, France , for providing MC2 alloy. P. K. , B. G. and D. R. are grateful to the  MPG  for the funding of the  Laplace  project. P. S. thanks the  EPSRC  for financial support through the  associated programme grant Light-FORM  ( EP/R001715/1 ) and the  Airbus -University of Manchester Centre for Metallurgical Excellence  for supporting aspects of this research. Uwe Tezins  and  Andreas Sturm  are also acknowledged for their support on APT experiments.
This function is concerned with installation of location systems and importing the primary information into the R. IO-DIAG application. These information are: identifying zones of the experiment, patient's information, and functions that could be executed in certain zones. The second function is the location data gathering. After obtaining the primary event logs (cf figure 2(a)) a series of interpreting rules have been devised in order to extract the corresponding activities at each zone of a hospital. A more detailed illustration of this function is included within  (Namaki Araghi, 2018)  article. After receiving the interpreted data, the list of un-linked activities would go within the process modeling function. Thanks to several process mining algorithms, a cartography of process models will be discovered and presented by declarative modeling languages or the OPC modeling language.
The effective pixel size of the detector varies from 1. 25 μm to 1. 80 μm (depending on the sample), yielding a view of approximately 2 5 mm × 2 5 mm. The tomographic slice reconstruction of the synchrotron X-ray microCT images was performed using the SYRMEP Tomo Project software developed at Elettra  [85]  and powered by ASTRA tomography toolbox  [86]  and TomoPy  [87]. To improve their liability of quantitative morphological analysis and enhance the contrast among the different mineralogical components of the solid phases and the porous phase, a single-distance phase-retrieval algorithm was applied to the white beam projections using Paganin's algorithm  [88]  based on the Transport of Intensity Equation (TIE). After reconstruction, 3D images were filtered, segmented, and analyzed using the Pore3D software library, customdeveloped at Elettra  [89].
Aims: We hypothesized that a genome-wide analysis of blood gene expression could offer new prognostic tools and lines of research. Methods: Sixty-nine patients were enrolled from an ancillary study of the clinical trial NCT00999583 that tested the effect of erythropoietin (EPO) after OHCA. Blood samples were collected in comatose survivors of OHCA at hospital admission and 1 and 3 days after resuscitation. Gene expression profiles were analyzed (Illumina HumanHT-12 V4 BeadChip; >34,000 genes). Patients were classified into two categories representing neurological favorable outcome (Cerebral Performance Category [CPC] = 1-2) vs unfavorable outcome (CPC>2) at Day 60 after OHCA. Differential and functional enrichment analyses were performed to compare transcriptomic profiles between these two categories.
5 and corrected p-value<0. 05 were considered as differentially expressed as compared to control conditions. Such a high fold-change threshold provides very robust results, despite the rather low number of patients. Functional enrichment analysis was then made using the QIAGEN Ingenuity® Pathways Analysis™ software (www. qiagen. com/ingenuity) using a fold-change>1. 3 and a p-value<10 -3. Indeed, functional enrichment generates hypotheses using wider gene profiling, requiring the use of lower foldchange. Analyses were performed using R (http://www. r-project. org) and Bioconductor (http://www. bioconductor. org). The logistic regression analyses were also performed using R.
Patients and samples characteristics As shown in Among these 69 patients, 181 blood samples were collected from the 207 expected samples (3 samples planned for each patient). Missing samples were related to early death (n=14) or lack of sampling in surviving patients (n=12). Among the 181 available samples, 7 samples were excluded for technical reasons after quality control. We ultimately analyzed transcriptomic data from 174 blood samples (64, 62 and 52 at Day-0, 1 and 3, respectively).