dataesr/scientific-paragraphs-categorization huggingface dataset:
• A selective trigger system to gather signal and signal-like background candidate events. • A cosmic ray background veto system. In order to simulate annihilation signal events in the detector, along with any background sources, a coherent software chain was developed for the HIBEAM/NNBAR program. Expanding on the flowchart of Figs. 1:  1. Signal events are provided by the authors of Ref. [8, 9]  and converted into a MCPLcompatible text format  [7]. 2. The MCPL events are then interfaced with the NNBAR GEANT4 detector simulation. 3. The neutron beam, which is used as the source in PHITS  [21] , is simulated in McStas. For HIBEAM, the ANNI beam simulation is provided by the authors of Ref. [2]  and interfaced with PHITS via MCPL. It should be noted that this versatility allows for any other Monte Carlo transport code to be used, including MCNP6  [10].
Important timing applications will include: 1. Setting short coincidence windows for multiple pion hits with the same origin. 2. Estimating hit positions in the long scintillator staves from the timing of pulses at opposite ends. 3. Hit coincidences to accurately match event data from different subdetectors, for instance, the scintillator and the lead glass calorimeter layers. 4. Cosmic background rejection using time-of-flight measurements from calorimeter and veto data, to reject particle tracks originating outside the inner detector volume. 5. TPC frame selection for matching recorded tracks to calorimeter hits, using the precise calorimeter timing measurements. Pulse shaping and digitization of the calorimeter and veto front-end electronics are foreseen to be implemented with commercial amplifier and ADC devices, along with programmable logic devices (FPGAs) to provide the digital timing, and self-triggered readout to the off-detector systems.
Acknowledgements This work is in part funded by the  European Union   Framework Programme for Research and Innovation Horizon2020 initiative  for the  HighNESS  project, under grant agreement  951782. The authors also gratefully acknowledge project grant support from  Vetenskapsrådet. JB's work on this project was partially supported by the  Visiting Scholars Award Program  of the  Universities Research Association. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the  Universities Research Association, Inc. JB also wishes to thank the  Zuckerman Postdoctoral Scholars Program  for their support in finishing this work.
0 code  [6]. Similar approaches were adopted for several wall materials using different numerical models: OEDGE-ERO2. 0 for berylium in ITER  [7] , EMC3-EIRENE for carbon in LHD  [8] , and SOLPS-ITER-IMPGYRO for W in ITER  [9]. H-mode discharges were not modelled in this work. Nevertheless, it has to be remarked that the presence of edge localised modes during H-mode can have a vital impact on machines equipped with W PFCs  [10]. The paper is organized as follows: in section 2 a representative WEST plasma discharge and the corresponding plasma simulations are presented; in section 3 the set-up of the MC simulations tracking W impurities is described; in section 4 the role of the different PFCs in WEST W contamination from the simulations is discussed followed by an analysis of the influence of light impurities on the capability of heavy wall impurities to reach the separatrix in section 5; in section 6 the role of self-sputtering in plasma contamination in different plasma conditions is discussed; finally, in section 7 a direct comparison with experimental data of the WI emission line from visible spectroscopy is carried out.
ψ N is defined as ψ N = (ψ -ψ ax )/(ψ sep -ψ ax ), where ψ is the magnetic flux, ψ ax is the magnetic flux at the magnetic axis, and ψ sep is the magnetic flux at the separatrix. Poloidal positions of WEST main PFCs are also shown in the figure. The simulations of the plasma during the two phases were performed using the code package SolEdge-EIRENE. The package consists in a coupling between the multi-fluid code SolEdge  [5]  and the MC neutral solver EIRENE  [12]. For this work we used the latest SolEdge version, named SolEdge3X  [13] [14] [15]. At the time of writing, the modelling of complex nonaxisymmetric 3D limiters in SolEdge required further development. Hence, the SolEdge-EIRENE simulations presented in this work were performed in 2D mode assuming an axisymmetric wall. This idealisation strongly overestimates the antenna limiter surface wetted by the plasma in the machine.
Wall impurities erosion and transport simulations set-up To estimate W re-deposition a kinetic description is needed. Also, the high number of possible W ionisation states makes it very demanding to study W transport with a multi-fluid code. For these reasons, a MC impurity tracker was more suitable for this study. ERO2. 0 was the code adopted for the numerical modelling of W erosion and 3D transport across plasma  [6, 22]. SolEdge-EIRENE simulations were used as input to obtain fluid background plasma for ERO2. 0. The SolEdge EIRENE results data provided to ERO2. 0 as input were spatial distributions of n e (considered to be equal to the ion density), T e , the main ion temperature T i , the main ion parallel velocity v , and the magnetic field B. Uniform values of concentration were used for each light impurity ionisation state.
Yellow lines were only modelled in simulations, while green ones were both reproduced in simulations and available in the experiment. The wavelength for which the visible spectroscopy data were analysed is the standard WI line 400. 9 nm. The two targets were simulated separately since this allows to assume different O concentration for each target, so the small effect of photons emitted from the other target was neglected. The self-sputtering was also neglected as it does not influence much erosion, as previously discussed. Since erosion depends on O content, the experimental O visible signal OII 435. 1 nm at the targets (figure  9 ) was used to make an educated guess on the O concentration ratio to set in the simulations. During the first time interval (3-3. 5 s) the OII signal is strongly unbalanced between the targets, with a difference of about one order of magnitude.
Method We mirrored two types of complementary methods to address both the postoperative clinical outcome (ENG) and the postoperative naming outcome (NAM): a predictive machine learning approach (ML), followed by a network analysis using GT. A graphical and general outline of the method is presented below (Fig. 1 ). Codes and data are openly available: https://github. com/ltor-lpnc/ neuropsy-2021. git, for the online repository.
Predictive ML approach We used a similar approach we previously employed  [29]  to estimate the ability of neuropsychological scores to predict the postoperative status and by identifying the most discriminating scores and their interactions. To this end, we developed several Machine Learning workflows trying each time three different algorithms belonging to a different family: (a) a classical Support Vector Machine (SVM) algorithm  [49]  with a Radial Basis Function (RBF), (b) XGBoost algorithm  [50]  and (c) a logistic regression with penalty L2. To avoid sub-optimal results and because we had no reason to privilege a particular algorithm, different algorithms were evaluated. Practically, a binary classification has been conducted on the full dataset (n = 47 patients; no missing values) with a repeated cross-validation (CV) scheme including a feature selection step in each training set.
Binary classification Our purpose was to predict two kinds of outcomes separately (ENG and NAM) with the help of neuropsychological indices. In order to make robust predictions, we restricted the number of features to 9 main neuropsychological indices (as previously used in  Roger et al. , 2020)  to take into account: (1) the patients' sample size and (2) the curse of dimensionality  [51]. We have preliminar-ily standardized the 9 features of interest as required by some algorithms. In practice, we trained a model to try to assign correctly a patient to its own group (ENG+/ENGÀ or NAM+/NAMÀ). We used a classical 5-fold CV scheme repeated 100 times. The CV was stratified, i. e. , samples were randomly chosen in order to get always the same ratio of classes that existed in the original dataset.
We chose the balanced accuracy (BAcc;  [52] ) as a measure of performance to quantify the quality of predictions. The BAcc has the advantage of dealing with imbalanced datasets and to offer an easy to use metrics in a clinical perspective. The BAcc formula is as follows: BAcc ¼ 1 2 TP TP þ FN þ TN TN þ FP The best value is 100% and the worst 0%. BAcc is equal to the arithmetic mean of sensitivity (true positive rate) and specificity (true negative rate). For balanced datasets, the score is equal to classical accuracy.
Feature selection In clinical perspective, it is interesting to simplify the model to the most relevant neuropsychological indices (i. e. , to perform feature selection) in order to make easier and usable the interpretation of the ML approach. Penalized linear models are popular in order to get sparse solutions. Thus, we applied L2-norm on our dataset. Practically, feature selection was done in each training set with the default threshold implemented in scikit-learn v. 0. 24 (i. e. mean of the features importance  [53] ). Selected features were used then to train the algorithm before measuring the performance with the held-out fold. This was repeated 500 times (5-fold cross validation repeated 100 times) to get a good estimate of feature stability and performance. In addition, we estimated the stability which is a very important aspect of results reproducibility.
Black box insight Model-agnostic methods are unspecific and offer interpretability to any machine learning algorithm  [55]. We chose Partial Dependence Plot (PDP) to see if the relationship between the target and a feature or two features is linear, monotonic or more complex. A PDP allows this by showing the marginal effect that one or two features have on the prediction. This algorithm builds the model by averaging the features, except the feature of interest F, and measures the changes in prediction for different values of F. Considering the dataset reduced to the selected features, we performed a random partition (90% for the training set). We obtained ten plots: 4 1D plots (one by feature) and 6 2D plots (one by pair). Details and results about the PDPs are presented in supplementary material (Appendix S2).
Graph construction. As with other studies (e. g. ,  [24] ), a proximity measure (based on Euclidean distance) was used to construct the patients' networks. To avoid a chance that a variable, here a neuropsychological index, creates unrealistic greater inter-sample differences, we standardized the dataset. Then we implemented the proximity measure as: ProximityðNode A ; Node B ; Þ ¼ ddistance max e À distance Node A ; Node B ð Þ ddistance max e À distance min where: ddistance max e is the ceiling of the maximal standardized Euclidean distance observed between two nodes in the dataset; distance min is the minimal standardized Euclidean distance observed; distance Node A ; Node B ð Þthe standardized Euclidean distance between our two nodes/patients of interest. The proximity maximal value is 1 and it corresponds to the minimal distance and the minimal value is close to zero and it corresponds to the maximal distance.
Simple Matching Coefficient is the ratio of both mutual presences and mutual absence with the length of the binary sequence. 3. 2. 2. Cognitive networks 3. 2. 2. 1. General overview. We have adopted a second network's perspective for which neuropsychological indices constitute the nodes of the graphs. The relationship between nodes is, in this case, the similarity of the performance obtained by patients. Two indices, i. e. , two nodes, have a strong link if their sequences of scores have a very similar pattern in the population with TLE. In order to get the most informative network, we included the whole neuropsychological dataset (32 neuropsychological indices; see Appendix S1).
Funding This work has been funded by the  French program  '' AAP GENER-IQUE 2017 " run by the '' Agence Nationale pour la Recherche ", grant ''REORG" [grant number   ANR-17-CE28-0015-01 ]; and by  Neuro-CoG IDEX UGA  in the framework of the '' Investissements d'avenir" program  [grant number  ANR-15-IDEX-02 ].
Whole-genome sequencing and single nucleotide polymorphism calling In addition to the already deposited Illumina whole genome sequencing data for 130 isolates  [30] , we generated raw sequence data for 15 additional isolates (see Supplementary Table  S1  for all accession numbers). Briefly, high-quality genomic DNA from each isolate was extracted following the DNeasy Plant Mini Kits (Qiagen) protocol and pairedend sequencing of 100 bp with an ~500 bp insert size was performed using the Illumina HiSeq2000 platform. The newly generated raw sequences were deposited under the BioProject PRJNA327615. We used Trimmomatic v. 0. 36  [35]  to trim low-quality sequencing reads and remove adapter contamination in each isolate. The refined sequences were aligned to the Z. tritici reference genome IPO323  [32]  using Bowtie2 v.
2. 3. 3  [36]  and duplicate sequences were removed using MarkDuplicates in Picard tools v. 1. 118 (http://broadinstitute. github. io/picard). We used Genome Analysis Toolkit (GATK) v. 4. 0. 1. 2 [37]  for single nucleotide polymorphism (SNP) calling and variant filtration. The GATK HaplotypeCaller was used on each isolate with the command -emitRefConfidence GVCF and -sample_ploidy 1 (Z. tritici is haploid). Joint variant calls were performed using GenotypeGVCFs on a merged gvcf variant file with the option -maxAltAlleles 2. We used VariantFiltration and SNPs were removed if any of the following filter conditions applied: QUAL < 250 (overall quality filter); QD < 20. 0 (avoiding quality inflation in high-coverage regions); MQ < 30. 0 (avoid calls from ambiguously mapped reads); -2 > BaseQRankSum > 2; -2 > MQRankSum > 2; -2 > ReadPosRankSum > 2; FS > 0.
In planta phenotyping We used phenotypic data (Supplementary Table  S2 ) on pathogen virulence and reproduction obtained by Dutta et al. [24]  on a panel of 12 genetically different wheat populations including five landraces (Chinese Spring, 1011, 1204, 4391, and 5254), six commercial varieties (Drifter, Gene, Greina, Runal, Titlis, Toronit) and a back-cross line (ArinaLr34). ArinaLr34 carries the wheat leaf rust resistance gene Lr34, which provides resistance to wheat stripe rust  [39]  and powdery mildew  [40]  but was not previously tested for resistance to Z. tritici. The 1011, 1204, 4391, and 5254 landraces were selected from the Swiss National Gene Bank (www. bdn. ch). Six pots with three seeds of each cultivar were sown per pot and placed on a tray in a 2 × 3 array. The experiment was divided into two phases (6 cultivars at a time) due to space limitations.
, Garden Grove, CA, USA). For each isolate, a 500 µl spore suspension was spread on Petri plates containing potato dextrose agar (PDA, 4 gl -1 potato starch, 20 gl -1 dextrose, 15 gl -1 agar) using a sterile glass rod. Plates were kept at 15 and 22 °C at 70% RH. Each plate was photographed at 8, 11 and 14 dpi with a digital camera. The digital images of each plate from five technical replications were used for AIA in ImageJ following the scripts by Lendenmann et al. [44]  to obtain colony area information for each isolate. Estimates are based on an average of 45 spore colonies. The mean colony radii taking the square-root of mean colony area (√(mean colony area/ π)) were estimated and fitted over the three time points  (8, 11 , and 14 dpi) following a general linear model to obtain growth rates (mm/day) at 15 and 22 °C.
00006, 0. 00017, 0. 0051, 0. 0086, 0. 015, 0. 025, 0. 042, 0. 072, 0. 20, 0. 55, 1. 5 mg/L and a control without fungicide. Each microtiter well was filled with 100 μl of medium at a given concentration of fungicide and 100 μl of spore suspension using the above spore concentration. Three technical replicates were used for each isolate. The plates were sealed and incubated in the dark for four days at 22 °C with 80% relative humidity. Fungal growth was measured with an Elisa plate reader (MR5000, Dynatech) by examining the optical density (OD) at 605 nm wave length. The growth inhibition at different fungicide concentrations was used to estimate the dose-response curves. The dose-response curves were used to estimate the EC 50 value for each isolate using the drc v. 3. 0-1package [46]  in the R-studio  [47].
Statistical analyses We used log-transformed least-square means (LSmeans) for each host×isolate combination and for each in planta trait obtained by Dutta et al. [24]. The host specialization (i. e. , the affinity of isolates for specific hosts to maximize trait performance) index for each isolate was represented by the adjusted coefficient of variation (i. e. , logarithm of adjusted variance/mean) of LSmeans for reproduction among all 12 hosts  [24, 48]. The LSmeans of each isolate for all the in vitro traits was extracted using a one-way analysis of variance. To visualize trait variation among the isolates, we constructed a clustered heatmap with the R package Com-plexHeatmap v. 2. 2. 0  [49]  using the z-scores (mean = 0 and sd = 1) of LSmeans for each isolate. The z-scores were used to reduce variability among different traits as they were expressed in different units.
We excluded the morphological stress response data from the heatmap as clustering did not allow for binary data. The hierarchical clustering was performed following the agglomerative algorithm "complete" based on Euclidean distances. We performed a genome wide association study (GWAS) for all 50 traits and 145 isolates based on 716'619 SNPs. Prior to GWAS, we conducted a principal component analysis (PCA) on the SNP dataset to investigate the population structure using TASSEL v. 20200220  [50]. We constructed a genetic relatedness matrix (GRM) among isolates using all the genome-wide SNPs following the normalized identity-by-descent method  [51]  implemented in TASSEL v. 20200220  [50]. Both PCA and GRM can be used to control the inflation of false positive SNPs due to population structure and individual relatedness in a GWAS.
However, principal components were excluded from the GWAS as the Bayesian information criterion (BIC;  [52] ; Supplementary Table  S4 ) test in R package GAPIT v. 3. 0  [53]  indicated that the inclusion of principal components as covariate did not meaningfully improve the model. Thus, all GWAS were performed using a mixed linear model (MLM + K;  [54] ) where K is the previously estimated GRM used as a random factor in GAPIT. Considering the relatively small sample size (n = 145), we used an MLM instead of a compressed MLM, which groups individuals according to phenotypes following the implementation in GAPIT  [55]. The following mixed linear model was used: y = Xb + Zu + e, where y is the vector of phenotypes, X and Z are the known design matrices, b is the unknown vector of fixed effects (SNPs), u is the vector of random effects (based on the GRM) and e is the vector of residual effects.
We used the stringent Bonferroni threshold (α = 0. 05), false discovery rate (FDR) at 5 and 10% to retrieve SNPs at different levels of statistical confidence using the R package q-value  [56]. For the FDR, we followed the Benjamini and Hochberg  [57]  method to identify significant genome-wide SNPs. We used the physical position of all SNPs above FDR 10% as coordinates to extract the closest genes following the latest annotation of the reference genome IPO323  [32, 58]. For this, we used the "closest" command in BEDtools version 2. 29. 0  [59]. Encoded proteins were assigned to protein families (PFAM) and gene ontology (GO) terms using InterProScan version 5. 36-75. 0 with default parameters  [60]. Protein secretion signals were predicted using SignalP version 4. 01  [61] , Phobius  [62]  and TMHMM version 2.
Here, the variance-covariance structure of the genetic values is described by the GRM and the phenotype is modeled as a function of individual genetic values. Genetic correlation coefficients are estimated as the ratio of the genetic covariance (σ g12 ) and genetic variance of two traits (σ g1 and σ g2 ). We visualized genetic correlation coefficients as a network using the R package "qgraph"  [68]. Phenotypic correlations among all traits were analyzed with the z-scores of the phenotypes based on Pearson's correlation coefficients. Multiple testing adjustments of p values were performed by following the Benjamini and Hochberg  [57]  method at α = 0. 05. As the morphological stress response was recorded as a binary trait, we used a point biserial correlation  [69]  to correlate the trait with other quantitative phenotypes using the R package ltm v.
Study design and sampling A total of 60 administrative neighbourhoods were randomly selected in 5 urban regions across Europe (i. e. 12 administrative neighbourhoods per region): Ghent and suburbs (Belgium), Paris and inner suburbs (France), Budapest and suburbs (Hungary), the Randstad (including the cities of Amsterdam, Rotterdam, The Hague and Utrecht in the Netherlands), and Greater London (United Kingdom). As described in detail elsewhere  (Lakerveld et al. , 2015) , sampling of administrative neighbourhoods was based on two environmental obesity-related characteristics to ensure variability of environmental contexts: residential density and neighbourhood median income, as a proxy for socioeconomic position (SEP). Residential density data were obtained from the Urban Atlas database distributed by the European Environmental Agency, based on a compilation of satellite photographs covering Europe providing high-resolution data on land use  (European Environment Agency, 2002).
This Urban Atlas includes a measure of density of residential areas (calculated as the percentage of coverage of buildings devoted to residential facilities) that is comparable across European countries. Data on residential density were defined using two classeshigh and low residential density -corresponding to >80% and < 50% of areas covered by residential buildings respectively. Median income data were derived from national census databases from all five countries, with two classes used: low and high (i. e. the first and third tertiles). The combination of residential density and neighbourhood-level income classes allowed four neighbourhood types to be defined (high residential density/high SEP, high residential density/low SEP, low residential density/high SEP and low residential density/low SEP).
Modal choicevariable of interest Participants were asked to provide information about the mode of transport they used to travel to/from workplace (commuting purpose) and/or to/from other places (non-commuting purpose). Four transport mode alternatives were proposed for the dependent variable: motorized individual transport, walking, cycling and public transport. Each participant reported on how many days per week and how much time on an average day he/she spent travelling with each mode of transport during the last seven days for commuting and non-commuting travels. We estimated the main mode of travel based on the number of days and time reported (total weekly minutes) by each participant during the previous seven days for both travel purposes. We included participants who provided data on number of days and time for at least one mode of transport for commuting or non-commuting travels (n = 3070 and n = 5058, respectively).
Acknowledgements This work is part of the  SPOTLIGHT  project funded by the  Seventh Framework Programme  ( CORDIS FP7 ) of the  European Commission ,  HEALTH  ( FP7-HEALTH-2011 -two-stage), Grant agreement No. 278186. The content of this article reflects only the authors' views, and the  European Commission  is not liable for any use that may be made of the information contained therein.