dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerous other parameters described in this paper have been optimized manually. Remarkably, they perform well on all datasets described in the paper. It is important to realize that in some configurations only one crack tip can be picked up and in others no crack localization is possible. Once suitable parameters and limitations are established the code can be used to generate possible inspection reports. These contain an assessment of their own quality based on the subjective probability of the report being correct. The probabiltiy is calculated by analysing a variety of images (rather than one) produced by a particular modification of the TFM offered in this paper. It is expected that the human inspectors would still have to examine the AutoNDE reports, particularly the TFM images they contain, to ascertain whether they agree with the preliminary conclusions made by the AI module.
Despite the initial success reported here, just like any other artificial intelligence system, the code can be guaranteed to analyze well only the type of data used for its training, so that, say, the random undulations of the component surface follow the same probability distribution as in the training data set. Also, so far AutoNDE has been trained to process only the regions of interest, which contain one crack or else several cracks parallel to the inspection surface. It is clear that many more data sets are required for testing AutoNDE before it is accepted by the NDT community as a practical tool. To widen the AutoNDE applicability we have plans to automate the choice of the hyperparameters described above too. It is also clear that other methodologies have to be developed for modeling surfaces with undulations that do not obey a normal distribution.
Acknowledgments: The authors are grateful to the  Innovate UK monitoring officers Tom Harris  and  Chris Marshall  for their critical feedback and constant support and to the colleagues from the  Ultrasonics and NDT group, Bristol University  for providing the AMEC data and  Sébastien Robert  for the CEA data acquisition. They are also grateful to  Nicolas Pignet , who undertook translation of the original LabView code into C when a second year ENSEIRB-MATMECA engineering student seconded under the  Internship Agreement  to  Sound Mathematics Ltd. , funded by a  European Union   Erasmus grant. Finally the authors would like to thank anonymous reviewers for many helpful suggestions on improving the presentation of the paper.
In addition, we included two populations of A. sieberi. By using a ploidy-aware genotype calling based on likelihoods, we generated a dataset of 16,107 high-quality SNPs. Classical and model-based analysis was done to characterize the genetic structure within and between the sampled populations, complemented by genome size measurements and chromosomal counts. Finally, to reveal the drivers of genetic structure, we searched for associations between allele frequencies and spatial and climatic variables. Key Results: We found support for a marked regional structure in A. tinctoria along a latitudinal gradient in line with phytogeographic divisions. Several analyses identified interspecific admixture affecting both mainland and island populations. Modeling of spatial and climatic variables further demonstrated a larger contribution of neutral processes and a lesser albeit significant role of selection in shaping the observed genetic structure in A.
Sampling and DNA Extraction Leaf samples of up to 11 individuals (on average 9. 25 individuals) per sampling locality (Supplementary Table  S1 ) were collected from 14 localities of A. tinctoria across three mainland regions of Greece (North, Central, and South), as well as from additionally two A. sieberi localities found on the island of Crete (AT27, AT28; Figure  1 ). These areas roughly reflect four distinct phytogeographical regions of Greece, i. e. , North-East -NE, East-Central -EC, Sterea Ellas -StE, and Kriti and Karpathos -KK  (Dimopoulos et al. , 2016; Strid, 2016). The samples were either silica-or freeze-dried. Voucher specimens and/or living material (seeds) are maintained ex situ at the Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization-Demeter (HAO-Demeter), Thessaloniki, Greece, with specific International Plant Exchange Network (IPEN) accession numbers (Supplementary Table  S1 ).
(A) K = 3, and (B) K = 5. Genetic structure estimates are based on RAD-seq data. For TESS3 and STRUCTURE, 7,935 and 1,000 SNPs, respectively, present across at least 90% individuals were used. The map of Greece was extracted from the WorldClim database  (Hijmans et al. , 2005). with lasers (532 nm; 30 mW Partec, Muenster, Germany, or 100 mW Cobolt Samba, Cobolt AB, Solna, Sweden). The 1C values were calculated based on a linear relationship between the G1/0 mean peak positions on the fluorescence axis of the standard and the objects. Chromosome numbers were analyzed for two individuals of A. tinctoria originating from Northern Greek locality AT03. Actively growing root meristems were harvested from young plants, pretreated with 0. 002 M 8-hydoxyquinoline in darkness for 2 h at room temperature and 2 h at 4°C, and fixed in 3:1 ethanol:acetic acid.
Briefly, root meristems were hydrolyzed in 5 N HCl for 20 min, washed with water, and stained with Schiff 's reagent (Merck, Darmstadt, Germany) for 1 h at room temperature in darkness. Root meristems were then squashed in a drop of 60% acetic acid under a cover slip, and the preparations were analyzed using Axioplan light microscope (Carol Zeiss, Vienna, Austria) equipped with a CCD camera. Images were captured using ZEN software (Carl Zeiss, Vienna, Austria).
The RAD-seq loci from the catalogue have been extracted with the EXPORT_SQL. pl module of Stacks. They have been further used as individual contigs to generate a synthetic reference in FASTA format. Following the pipeline in  Brandrud et al. (2020) , the raw reads were mapped back to this synthetic reference using bowtie2 v. 2. 3. 4. 1  (Langmead and Salzberg, 2012)  with default settings. Further processing (conversion to BAM, sorting by reference coordinates, and addition of read group information) was done using Picard v. 2. 20. 6, 2 and realignment around indels was performed using the Genome Analysis Toolkit  (GATK v. 3. 81; McKenna et al. , 2010).
1. 6 (Li et al. , 2009; Li, 2011) , to extract read counts and to estimate per site error rates with read-counts-from-VCF. py and, respectively, per-locus-err. py scripts provided with EBG  (Blischak et al. , 2018). Finally, we inferred final genotypes as alternative allele counts using the GATK model implemented in EBG.
Genetic Diversity and Population Structure of Greek Alkanna The summary statistics for each sampling locality was assessed by estimating within-population genetic diversity [Hs, analog to the expected heterozygosity (He)] and inbreeding coefficient (G IS , analog to F IS ) in GENODIVE v. 3. 0  (Meirmans, 2020). In addition, nucleotide diversity (π) was estimated with a custom script (Supplementary Notes S1). Finally, we calculated the amount of private alleles for each sampling locality as well as for the genetic groups identified by clustering-based approaches. Private alleles calculations were corrected for a small sample 2 http://broadinstitute. github. io/picard/ size using the factor [(n + 1)/n] following  Brandrud et al. (2020) , where n is the number of individuals in the relevant group.
To further assess genetic relationships among the sampled individuals, we first estimated a pairwise relatedness with a method of moments as implemented in PolyRelatedness v. 1. 8 (Huang et al. , 2016). This was then displayed as a heatmap of coancestry with the heatmap. 2 function in the R package Gplots v. 3. 1. 1  (Warnes et al. , 2009). Next, we carried out a principal component analysis (PCA) based on the covariance of allele frequencies between individuals using GENODIVE (v. 3. 0; Meirmans, 2020). The first three PCs were visualized as scatter plots using the R package Ggplot2 v. 3. 2. 1  (Wickham, 2016). Only SNPs with no missing data were used for the aforementioned analyses (Supplementary Table  S2 ). We further assigned individuals to genetic groups using two clustering-based approaches: TESS3 v.
1. 1. 0 implemented in the R package tess3r  (Caye et al. , 2016) and STRUCTURE v. 2. 3. 4 (Pritchard et al. , 2000). For TESS3, we used a dataset, including a single SNP per RAD-seq locus (in total 7,935 SNPs) with maximum 10% missing data, whereas 1,000 SNPs were randomly selected using a custom script (Supplementary Notes S1) from this dataset for STRUCTURE analyses. TESS3 is a spatially explicit clustering program, and therefore, it is more robust when geographic discontinuities are present in the sampling. Furthermore, TESS3 can accommodate data with thousands of SNPs and remains faster as compared to STRUCTURE. Importantly, like STRUCTURE, TESS3 can analyze polyploid data, which is otherwise currently not possible in other fast algorithms, such as FastStructure  (Raj et al. , 2014)  or Admixture  (Alexander and Lange, 2011).
Spatial vs. Environmental Contribution to Genomic Divergence To disentangle the roles of dispersal limitation vs. environmental drivers in shaping the genomic differentiation of A. tinctoria, we first identified the most important climatic variables for our target species using a gradient forest (GF) analysis in the R package gradientForest v. 0. 1-17  (Ellis et al. , 2012). As a nonparametric, machine learning regression tree approach, GF evaluates the associations between genetic variants and other variables by partitioning allele frequency data at split values along the covariate gradients and defining the explained variability as split importance values. The overall importance of each covariate can then be assessed from GF plots which show the mean importance of the covariate weighted by R 2  (Bay et al.
, 2018; Jia et al. , 2020; Zhang et al. , 2020). For GF analysis, we retrieved 19 bioclimatic variables for our sampling locations from the WorldClim database  (Hijmans et al. , 2005)  at a spatial resolution of 2. 5 arc-minutes. To avoid collinearity among covariates across our data points, the Pearson correlation coefficient between the covariates was estimated using the cor function implemented in the R package stats (R Core Team, 2020). We retained only one variable from all pairs having absolute correlation values greater than 0. 9 using the findcorrelation command implemented in the R package caret v 6. 0-86  (Kuhn et al. , 2016). Finally, a total of seven covariates were used in the GF analysis. The three most important covariates identified by GF were then used in downstream analysis.
Here, CA allows estimating the unique and common effect of geographical and environmental distances on genomic divergence (F ST ) while accounting for collinearity among explanatory variables. We ran MMRR using pairwise F ST as a response variable and geographical and environmental distances as predictor variables (model = F ST ~ GEO + ENV) with 1,000 permutations. Variance partitioning analysis was then performed using CA to quantify the common and unique effect of GEO and ENV using a R-script available at https://datadryad. org/stash/dataset/doi:10. 5061/dryad. q573n5th7  (Cuevas et al. , 2020). In addition, we identified loci associated with environmental variables using BayPass v. 2. 2  (Gautier, 2015). In a nutshell, BayPass can identify loci under selection using an F ST -like statistic (XtX) that explicitly accounts for the confounding effect of population structure.
To do so, BayPass first estimates the covariance matrix of population-level allele frequencies (Ω matrix in  Gautier, 2015) , which can capture even complex demographic histories. Then BayPass detects outlier loci among variants exhibiting the highest XtX values. BayPass can also identify variants associated with population-specific covariates, including climate or phenotypic data  (Gautier et al. , 2018; Leroy et al. , 2020). The program estimates Bayesian factors (BF) by comparing models with and without covariates. Neutral calibrations of the XtX and BF metrics were performed using pseudo-observed datasets (PODS), following the best practices for BayPass  (Gautier, 2015). PODS were generated for 100,000 SNPs using BayPass_utils. R following the recommendations of the BayPass manual. We then used conservative (XtX > 99% quantiles and BF > 99.
6%, Figure  3C ). We then used Bayesian clustering to further investigate these patterns of genetic structure. For that purpose, we used the spatially-explicit TESS3 and the non-spatially explicit, albeit widely used, STRUCTURE programs. Both the cross-validation score as implemented in TESS3 and the use of the Evanno's ΔK statistic support K = 3 as the best K value (Supplementary Figure  S2 ), suggesting that the dataset can be best explained by three main genetic clusters. For the optimal structure (K = 3), the clustering of individuals is consistent with all previously described analyses (i. e. , the presence of three genetic groups), including the likely interspecific admixed nature of AT27 (Figure  1A ). Since heatmap and PCA showed substructure within the Southern-Central group, we further evaluated the individual memberships at K values from K = 4 to K = 5.
Participants We recruited 111 4 th -graders (in three schools of France Lyon area), and 100 college-aged young adults (via social network advertising). Adults were compensated for their participation (10€). All measures, manipulations and exclusions are reported in the following paragraphs. Eight children were excluded from the analyses because they suffered from reading disability, attention deficit or anxiety disorder. Data were entirely missing from four children and three adults due to recording problems. These were excluded from the analyses. Data were partially missing (for one of the two trial types; see below) from two adults and one child. These data were retained (without imputation). For adults, half of the recruited subjects were same-age (± 2 years) pairs of friends, siblings, or significant others (data were pooled across the three types of partner as preliminary analyses revealed no effect of this variable).
Accuracy, reaction times, and effect sizes Using R (RStudio, v. 1. 0. 136) or SYSTAT (v13), accuracy (i. e. , the proportion of correct key presses relative to the total number of key presses) and manual reaction time were conducted through two-sample Student's t tests with the Bonferroni adjustment for multiple comparisons. For RTs, a supplementary 2 x 2 x 2 ANOVA with the within-subject factor Switch (Yes, No) and the between-subject factors Age and Condition was performed to determine whether switch costs (the switch-minus-nonswitch trials difference in RT) were affected by age or peer presence. Effect sizes were reported as partial eta squared values (ηp 2 ) for each ANOVA. In addition, peer presence effect size in children and adults was compared using common language effect size (CL) and Cohen's ds  [43].
Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of INSERM (protocol code 15-282-ter, date of approval: 12/19/2018). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study or their parents. Data Availability Statement: Data supporting reported results can be found on the Open Science Framework website at https://osf. io/pj5r6/. Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 28 June 2021 doi:10. 20944/preprints202106. 0674. v1.
Samples were washed with a solution of Tween-20 (three times five minutes) and mounted and counterstained with DAPI. Fluorescence microscopy (Leica microsystem DM5500B, microscope with a motorized, programmable stage using a CoolSnap HQ camera controlled by Metamorph software) was used. Image J software was used to quantify the relative fluorescence intensity. Fluorescence intensity measurements of Runx2 and OPN were performed for n = 3, considering the expressions of at least 100 cells per each type of surface. As principally expressed in nucleus, only nuclear fluorescence signal was analyzed since both Runx2 and OPN are expressed inside cell nucleus  [18, 36, [44] [45] [46]. Nuclei were delimited using DAPI signal to define regions of interest. Background was determined as the signal obtained from cells stained only with secondary antibody, and subtracted from the fluorescence measured from cells marked with primary and secondary antibodies.
RT-qPCR was performed using a CFX Connect™ Real-Time PCR System (Bio-Rad), using 2 genes of reference: RPC53 and PPIA. Forty PCR amplification cycles were performed for each experiment, consisting of incubating the solution at 95 • C for 5 s, followed by an incubation at 60 • C for 10 s. Cq for the genes of interest were normalized against the reference genes selected using the software BestKeeper: PPIA and RPC  [24, 34, 47]. Relative expression levels were calculated using the comparative method (∆∆Cq). Gene expression was normalized using flat silicon sample as control. For each condition, two replicates were considered.
Acknowledgments: This work was carried out as part of the project  EJD-FunMat , which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  641640. SK gratefully acknowledges partial funding support of  Luxembourg National Research Fund (FNR, Fonds Nationale de la Recherche)  via the project  PLASENS  ( C15/MS/10459961 ), and discussions within  COST Action BIONECA. The authors thank  Murielle Rémy  for the constructive discussions over RT-qPCR and immunofluorescence results,  Jerome Guillot  for discussion on XPS data, and  Didier Arl  for help related to SEM data treatment.
However, for electroweak physics there is a strong interest to vary the polarisation and charge  4. It was considered that the e + p luminosity may reach 1 fb -1 while the tenfold has been simulated for sensitivity studies. A dataset has also been produced with reduced proton beam energy as that enlarges the acceptance towards large x at smaller Q 2. The full list of simulated sets is provided in Tab. TeV and e -p scattering, with varying assumptions on the integrated luminosity and the electron beam polarisation. The data set D1 corresponds to possibly the first year of LHeC data taking with the tenfold of luminosity which H1/ZEUS collected in their lifetime. Set D5 is a low Ep energy run, essential to extend the acceptance at large x and medium Q 2. D6 and D7 are sets for smaller amounts of positron data.
Procedure and Assumptions In this section, PDF constraints from the simulation of LHeC inclusive NC and CC cross section measurements and heavy quark densities are investigated. The analysis closely follows the one for HERA as presented above. The expectations on PDFs for the "LHeC inclusive" dataset, corresponding to the combination of datasets D4+D5+D6+D9, are presented, see Tab. 3. 2. These datasets have the highest sensitivity to general aspects of PDF phenomenology. Since the data are recorded concurrently to the HL-LHC operation they will become available only after the end of the HL-LHC. Therefore, these PDFs will be valuable for re-analysis or re-interpretation of (HL-)LHC data, and for further future hadron colliders. In order that LHeC will be useful already during the lifetime of the HL-LHC, it is of high relevance that the LHeC can deliver PDFs of transformative precision already on a short timescale.
Therefore, in the present study particular attention is paid to PDF constraints that can be extracted from the first 50 fb -1 of electron-proton data, which corresponds to the first three years of LHeC operation. The dataset is labelled D2 in Tab. 3. 2 and also referred to as "LHeC 1st run" Already the data recorded during the initial weeks of data taking will be highly valuable and impose new PDF constraints. This is because already the initial instantaneous luminosity will be comparably high, and the kinematic range is largely extended in comparison to the HERA data. These initial analyses will provide the starting point for the LHeC PDF programme. It may be recalled that the HERA I data period (1992-2000) provided just 0. 1 fb -1 of data which was ample for discovering the rise of F 2 and of xg towards small x at low Q 2 , and still today these data form the most important ingredient to the combined legacy HERA data  [44].
The sets in Tab. 3. 2 comprise D1, with 5 fb -1 , still the tenfold of what H1 collected in 15 years, and D3, which resembles D2 but has the electron polarisation set to zero. Additional dedicated studies of the impact of s, c, b data on the PDFs are then also presented, based on 10 fb -1 of e -p simulated data. Further important PDF constraints that would be provided by measurements of F L and jets are not considered in the present study. These remarks are significant in that they mean one has to be cautious when comparing the LHeC PDF potential with some global fits: F L will resolve the low x non-linear parton interaction issue, see Sect. 4. 2. 3, and jets are important to pin down the gluon density behaviour at large x as well as providing a precision measurement of α s , Sect. 4. 1. To assess the importance of different operating conditions, the impact of datasets with: differing amounts of integrated luminosity (D1 vs.
This provides a 5+1 parameterisation, and the total number of free parameters of the PDF fit then becomes 17. Figure  3. 12: PDF uncertainties at Q 2 = 1. 9 GeV 2 as a function of x for the d and s distributions. The yellow band displays the uncertainties of the nominal "LHeC inclusive" PDF, which was obtained in a 4+1 PDF fit. From the same dataset, results of the more flexible 5+1 fit (see text) are displayed as a cyan band. The red band displays the results, when in addition an LHeC measurement of the s quark density is included. When even further including LHeC measurements of F c 2 and F b 2 , the PDF fits yields uncertainties as displayed by the blue band. Results of the 5+1 PDF fits are shown in Fig. 3. 12, where fits to inclusive NC/CC DIS data are 6 The provision of positron-proton data will enable very interesting tests of charge symmetry, i.
Luminosity and Beam Charge Dependence of LHeC PDFs It is informative to study the transition of the PDF uncertainties from the "LHeC 1st run" PDFs, which exploits only a single electron-proton dataset, D2, through to the "LHeC final inclusive" PDFs, which makes use of the full datasets D4+D5+D6+D9 as listed in Tab. 3. 2, i. e. including high luminosity data (D4), small sets of low energy E p = 1 TeV and positron data (D5 and D6) together with 10 fb -1 of opposite helicity data. Various intermediate PDF fits are performed using subsets of the data in order to quantify the influence of the beam parameters on the precision of the various PDFs. All fits use the same, standard 4+1 fit parameterisation and exclude the use of s, c, b data, the effect of which was evaluated before. The fits do neither include the low electron energy data sets generated for the F L analysis, cf.
In comparison to the analogous HERA fit, it becomes clear, that the vast majority of the gain comes already from the first 5 -50 fb -1. Figure  3. 16: PDF distributions at Q 2 = 1. 9 GeV 2 as a function of x, illustrating the impact of different amounts of integrated luminosity. The blue, yellow and red bands correspond to LHeC PDFs using electron-only NC and CC inclusive measurements with 5, 50 and 1000 fb -1 (datasets D1, D2 and D4), respectively. The yellow band is therefore equivalent to the "LHeC 1st run" PDF. For reference, the dark blue band shows the results of the final "LHeC inclusive" PDF. For comparison, the cyan band represents an identical PDF fit using HERA combined inclusive NC and CC data  [44] , restricted to solely the experimental uncertainties. Note that this, unlike the LHeC, extends everywhere beyond the narrow limits of the y scale of the plots.