dataesr/scientific-paragraphs-categorization huggingface dataset:
MRI Acquisition Protocol MRI data were acquired on a 3T Philips Achieva d-Stream scanner (Best, The Netherlands) with a 32-channel head coil. Visual stimuli were developed with Presentation ® software (Neurobehavioral System Inc. , Berkeley, CA, USA) and delivered through a VisuaStim digital device for fMRI (Resonance Technology Inc. , Northridge, CA, USA). MRIcompatible goggles with two displays were used, with a 60 Hz frame rate and 800 × 600 spatial resolution (4/3 aspect ratio) subtending a horizontal visual angle of 30 •. An MRIcompatible pad was used to record subjects' answers and response times. The MRI protocol included the use of an anatomical T1-weighted (T1W) 3D Turbo Field Echo sequence as a subject morphological reference of MRI data (Field Of View (FOV) = 256 × 256 × 175 mm 3 , voxel size 1 × 1 × 1 mm 3 , Time of Repetition (TR) = shortest (~8.
fMRI Data Processing The fMRI data were processed following the FreeSurfer Functional Analysis Stream (FSFAST, version 6. 0). The preprocessing pipeline included motion correction, slice-timing correction, resampling on the 'fsaverage' template, smoothing, and intensity normalization. Template resampling was performed by exploiting the subject T1W images as an intermediate step, and smoothing was performed using a 3 mm FWHM filter. Outlier volume detection was performed using an ad-hoc software we made by selecting all the volumes with (1) an overall motion from the previous volume larger than 2 mm, or (2) a mean intensity difference from the previous volume larger than 2. 5 times the standard deviation in the whole run. The fMRI runs were excluded from the study if more than 20% of their overall volume was tagged as an outlier or if more than 30% of their volume was tagged as an outlier for a single stimulus condition.
Multivariate Analyses A machine learning-based analysis was performed on the ROI data (controlled for the effects of performance IQ and DSM-IV-I) to investigate whether visual fMRI tasks can be used to discriminate between TRs and children with DD and to detect which ROIs contribute most to the classification. In the classification experiments, we used a multiple kernel technique  [129, 130]  based on the Support Vector Machine (SVM) model to combine multiple contrast maps while preserving the topological information (see Supplementary Figure  S2  for a flowchart of the classification experiment). In the SVM model, we included a weight for each kernel learned during the training (higher weights were assigned to the kernels providing the largest discriminative information). As a different linear kernel was associated with each ROI, the kernel weights quantified the contribution of each ROI to the final classification and could be interpreted as ROI weights.
e. , t-tests) to independently investigate the direction and magnitude of the differences between TRs and children with DD for each contrast. Correlations between the mean activation of each contrast map (i. e. , M-vs-B, P-vs-B, CML6-vs-B, CML15-vs-B and CML40-vs-B) within the significant ROIs and the neuropsychological domains (i. e. , reading, VWM and phonology) in the total sample were calculated using Pearson correlations as implemented in IBM SPSS Statistics for Windows, Version 21. 0 (IBM Corp. Released 2012). To summarize, the whole multivariate analysis can be broken down into four steps: firstly, we performed a classification experiment to discriminate between TRs and children with DD by using whole-brain ROIs. Secondly, we performed an analysis on the selected classifier to identify which ROIs and contrast maps contributed most to the classification.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/brainsci11060722/s1. Figure  S1. fMRI data processing flowchart. Data are represented with parallelepipedons, while operations on data are reported in rectangles. Figure  S2. Multivariate analysis procedure flowchart. Data are represented with parallelepipedons, operations on data are reported in rectangles, dataset splits are reported in rhombuses. Dashed line rectangles cover all blocks included in the cross-validation procedures. Table  S1. Bivariate correlations within reading (Table  S1a ) and within VWM (Table  S1b ) in the total sample. Table  S2. Correlations between the mean activation of each contrast map within each significant ROIs and the neuropsychological domains in the total sample.
Vegetation primary succession We also explored the behavior of the proposed measures on Alpine vegetation sampled by  Caccianiga, Luzzaro, Pierce, Ceriani, & Cerabolini (2006)  along a primary succession. The data set, which has been already used in previous papers on alpha and beta redundancy  (Ricotta et al. , 2016; Ricotta et al. , 2020)  is composed of 59 plots of approximately 25 m 2 sampled at the foreland of the Rutor Glacier (Northern Italy). For each plot, species abundances were measured with a five-point ordinal scale transformed to ranks. Based on the age of the moraine deposits, the plots were then classified into three successional stages: early-successional stage (17 plots), mid-successional stage (32 plots), and late-successional stage (10 plots). For all 45 plant species in the data set, we selected six quantitative traits measured by  Caccianiga et al.
(2006, Table 2) , which provide a good representation of the global spectrum of form and function  (Díaz et al. , 2016) : canopy height (CH; mm), leaf dry mass content (LDMC; %), leaf dry weight (LDW; mg), specific leaf area (SLA; mm 2 × mg -1 ), leaf nitrogen content (LNC; %), and leaf carbon content (LCC; %). All data are available in the data object named 'RutorGlacier' of the adiv package  (Pavoine, 2020)  of R (R Core Team, 2021). We used the Euclidean distance to calculate an interspecies dissimilarity matrix from the six functional traits. Before calculations, all traits were standardized to zero mean and unit standard deviation. The resulting functional distances among species were then rescaled to the unit range by dividing each distance by the maximum value in the distance matrix.
Data availability statement The R code used in this paper is deposited in the Dryad Digital Repository: https://doi. org/10. 5061/dryad. b2rbnzsds (Ricotta, Kosman, Laroche, & Pavoine, 2021). The data of the Alpine vegetation are available online in the R package adiv: https://cran. rproject. org/web/packages/adiv/index. html (R Core Team, 2021). The functional dissimilarity matrix of the artificial data set is available in the supporting information of this paper (Appendix 2).
On top of this, the relation between GPDs and the EMT can also be used to access information about the mechanical properties of partonic systems  [10, 11] , like distributions of pressure inside the nucleon -which is the subject of this article. The potential of studying the mechanical properties of the proton in the formalism of GPDs was first recognised in Ref. [12]. This subject has recently gained a lot of interest and seen vital theoretical progress. In addition, precise data have become available, recently allowing for the first data-driven estimates of a nucleon pressure distribution  [13, 14]. These analyses aim at extracting the so-called subtraction constant from deeply virtual Compton scattering (DVCS) data. The constant naturally appears in the dispersive studies of DVCS amplitudes and it carries information about one of the EMT form factors that is crucial for the understanding of the mechanical properties.
The replica for i = 0 is obtained from a direct fit to the experimental data, while those for i = 0 are obtained from data where the central values are randomly smeared according to uncertainties. In this case i is used to distinguish between the unique values of the random seed used in the smearing procedure. The subtraction constant is evaluated from a pair of i-th replicas (0 ≤ i ≤ 100) using the dispersion relation: C H,i (ξ, t, Q 2 )(t, Q 2 ) = Re H NN i (ξ, t, Q 2 ) - 1 π 1 dξ Im H NN i (ξ, t, Q 2 ) 1 ξ -ξ - 1 ξ + ξ , (74) where the ξ -integration ranges between = 10 -6 and 1 instead of 0 and 1 as in Eq. (  31 ). This corresponds to the kinematic domain probed by the replicas in the global CFF fit to DVCS data. This replacement of the integration range introduces a negligible O(1%) bias, see Ref.
In such a case the systematic uncertainty associated to the choice of a specific functional form is difficult to evaluate and is left as an unknown. The extraction of d uds 1 (µ 2 F ) independently from elements proportional to higher terms in the D-term Gegenbauer expansion, like d uds 3 (µ 2 F ), cannot be performed at this moment. Removing the assumption regarding the symmetry of the quark coefficients d uds n (µ 2 F ) most probably requires a multichannel analysis. Even if the link between the distribution of pressure forces in the proton and the DVCS subtraction constant is well-defined, and even if this link is subject to approximations that can be checked systematically, DVCS data do not allow yet a statistically significant extraction of these pressure forces. Moreover the present need of a prior knowledge of the t-dependence of the D-term drives the pressure profile.
Acknowledgements The authors thank  F. -X. Girod  and  K. Kumerički  for valuable discussions. This project was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  824093 , the Grant No. 2017/26/M/ST2/01074  of the  National Science Centre, Poland  and was realised with the support of the  French Government scholarships programme. The project is co-financed by the  Polish National Agency for Academic Exchange  and by the  COPIN-IN2P3  Agreement. The computing resources of  Świerk Computing Centre, Poland  are greatly acknowledged. We acknowledge further financial support from the  Agence Nationale de la Recherche (ANR)  under the projects No. ANR-18-ERC1-002  and  ANR-16-CE31-0019 , the  P2IO LabEx  ( ANR-10-LABX-0038 ) in the framework " Investissements d'Avenir"  ( ANR-11-IDEX-0003-01 ) managed by the  ANR , and the  CEA-Enhanced Eurotalents Program co -founded by  FP7 Marie Sk lodowska-Curie COFUND Program  (Grant agreement No.
Wild boar is a species with strong societal concern since it has increased in Europe  (Massei et al. , 2015) , leading to damage to crops  (Calenge et al. , 2004; Schley et al. , 2008; Amici et al. , 2012) , to forest production  (Gómez and Hódar, 2008; Barrios-Garcia and Ballari, 2012; Burrascano et al. , 2015) , and to zoonoses transmission (e. g. African swine fever,  Podgórski and Śmietanka, 2018). Our main goal is to gain a good understanding of the hunting process in order to provide relevant management tools. In this collaborative framework built with hunter associations in contrasting areas in France, harvest data has been collected for many years, allowing us to compare the usefulness of our model under various hunting conditions. We developed a Bayesian catch-effort model to assess the demographic status of wild boar populations, and to estimate hunting pressure as a function of the hunting effort (measured as the number of hunters participating to the hunt during a given day) and of catchability, varying according to the habitat type in the hunting area and the time of the hunt within the hunting season.
Thus, in the two departments, hunting notebooks have been given to each hunting team at the beginning of each hunting season. Then, data from logbooks has been stored in a database after some data cleaning and data checking by the Departmental associations of hunters. These notebooks report for each hunting drive, the number of hunters participating to the drive, its date, the name of the person in charge of the organization, the municipality, as well as the start and the end times of the hunting event and the number of culled wild boars. For each day of the hunting season and each MU, we summed the total number of hunters participating to a drive hunt, as well as the resulting number of culled wild boars. Note that we discarded two MU of the Hérault department due to missing data (table  1 ).
Ferrari and Cribari-Neto (2004)  demonstrated that this parameterization ensures that in these conditions, P usi is a random variable with mean P usi and variance (P usi × (1-P usi )) / (φ + 1). This model describes the relationship between the hunting effort, the catchability, and the resulting actual hunting pressure during a given hunting day in a given management unit MU. Now we describe how this general model can be fitted to our dataset. Let D us be the unknown average wild boar density in the management unit u just before the beginning of the hunting season s and A u the area of this unit. The population size N us in this unit at the beginning of this hunting season can be calculated by N us = D us × A u. Consider the first hunting day i = 1 of this hunting season. C usi is the number of animals that have been culled during this day.
For each department, each MCMC iteration generated a sampled vector of parameters (dispersion parameter, initial densities and catchabilities). For each simulated vector, we simulated a replication of the dataset for the department (i. e. we simulated a number of culled animals for each hunting day of our dataset). We then compared summary statistics calculated on the observed datasets to the distribution of these summary statistics calculated on the simulated datasets for model validation. More specifically, we used as summary statistics the total number of culled animals predicted by the model: over the whole department; per MU; per MU and per hunting season; per MU, hunting season and month; per MU and month; per month, per season. The R code for fitting the model and the data are available at https://github.
e. increase, decrease, stable) during the hunting season. The trends observed indicated that not all management units (or MUs) followed the same demographic patterns, providing evidence that at a local scale, patterns might differ from the general trends documented in France and Europe concerning wild boar populations  (Massei et al. , 2015; Saint-Andrieux & Barboiron, 2018). Despite the usefulness of this approach, it has some limitations. First, when hunting events are not frequent enough (e. g. 3-4 hunting events during one hunting season), the model cannot estimate correctly the parameters (e. g. MU # 16 of Hérault was removed from the dataset for this reason). Because catch-effort models are based on the decrease in the number of culls over time as a result of a decrease in the number of individuals in the population (see  Chee and Wintle, 2010) , the parameters of the model will be properly estimated if hunting events are frequent enough to grasp the demographic trends.
Conclusion Our model addresses many challenges. It responds to strong societal demands about the sustainable management of overabundant species such as wild boar by making efficient use of hunting logs dataset integrated in a simple conceptual framework directly accessible to practitioners. This framework can be used both on a large scale (e. g. management unit) and on a smaller scale (e. g. forest). In our case study, the catchability was estimated for drive hunting, independently of our two study departments. However, as soon as the hunting removed enough individuals from the population during one season, the hypotheses on catchability could be adapted to other hunting conditions and hunting practices. Thus, this approach through more detailed hunting logs allows additional information to be gained from conventional studies using hunting bags alone  (Acevedo et al.
Acknowledgements This study was made possible by the funding of the  ANRT  ( Association National de la Recherche et de la Technologie -National Research and Technology Association ), the  Fédération Départementale des Chasseurs  (Departmental hunting federations) of, respectively Ardèche, Hérault and  Haute-Marne ; the  François Sommer Foundation  and the  OFB -Office Français de la Biodiversité (French Office of Biodiversity). This work was also supported by the  Research Council of Norway through its Center of Excellence funding scheme , project number  223257. Thanks to the hunters from department's involved who accepted to gathered data on the field. Data's collected and updated at the Federations level during these last decades allowing us to perform theses analyses. We also thank  Axel Hacala  and  Caroline Vickridge  for revising the English manuscript and two anonymous reviewers for their valuable advice on a previous draft.
For each of the 10 lines selected for age at first reproduction, we monitored 15 groups of 10 larvae fed ad libitum and 15 under food stress (for a total of 3,000 larvae). We recorded the number of surviving larvae, pupae and adults daily for the entire duration of development to adulthood. These data were used to compute development time from egg to adult and larval survival rate. Upon emergence, individuals were sexed and moved to individual boxes. All individuals that survived to adulthood (both males and females) were weighed two days after their emergence. When possible, couples were formed immediately after weighting (or the next day at the latest), by keeping one male and one female in a 5cm-diatemeter box together with the same diet they received as larvae (ad libitum diet: 0. 35g of E.
Statistical treatments All data and R code are available at: https://data. inra. fr/dataset. xhtml?persistentId=doi:10. 15454/V9XCA2 For each variable (i. e. trait), we performed a Generalized Linear Mixed Model (GLMM) analysis with Laplace approximation  (Bolker et al. 2009 ), using the lme4 package  (Bates et al. 2014)  in the R statistical framework (R Core Team). Depending on variable distribution, we ran an LMM for Gaussian variables (i. e. , female and male body mass), a binomial GLMM with a logit link for proportion variables (i. e. , hatching rate, larval survival rate) and a Poisson GLMM with a log link for Poisson distributed variables (i. e. , development time, age at first reproduction, number of eggs laid and age at death of females and males). For all individual variables, we investigated the significance of the following explanatory factors: selection type (selected or control during the experimental selection assay), diet type (ad libitum or stressful diet during the phenotyping experiment) and line (the identity of replicate experimental lines during selection).
Both selection and diet types were treated as fixed effects, while line was treated as a random effect. To account for over-dispersion in some variables, we added an observation-level random effect to the models when necessary, as recommended by  Bolker et al. (2009). We followed a step-by-step procedure of model simplification from the full model (starting with random effects and then fixed effects) based on the Akaike Information Criterion (AIC), and tested the significance of the remaining effects via Likelikood Ratio Tests (LRT). Normality of residuals was checked using quantile-quantile plots. Adjusted means and confidence intervals for each significant fixed effect were calculated using a bootstrap resampling procedure (1,000 resampling of 500 observations each) with the boot package  (Canty & Ripley 2015).
This univariate trait-by-trait analysis was complemented with a multivariate analysis. We include these analyses because no trait is truly independent from all others, and it provides an evaluation of joint responses to selection, acknowledging that the evolution of one trait may affect and be affected by the evolution of the others. Furthermore, the best way to measure the evolution of a syndrome, i. e. of multiple traits, is to account for the correlated nature of phenotypic traits (and the modification of these correlations due to selection). Specifically, we performed a Multiple Factor Analysis (MFA) using the FactoMineR package  (Le et al. 2008). MFA is an extension of a principal component analysis (PCA) where variables can be grouped. For the analysis, we grouped traits by life-history stage.
Dimensions were retained for interpretation when their associated eigenvalue was greater than one. Relationships between variables and PEER COMMUNITY IN EVOLUTIONARY BIOLOGY 13 between individuals were investigated graphically and using dimension descriptors. Additional graphical representations of trait values and correlations matrices were performed for illustrative purposes, but insufficient replication and high correlations between some trait combinations precluded thorough statistical testing of differences in correlation matrices. All figures were plotted using the ggplot2 package  (Wickham 2016).
We tested a potential impact of differences between experiments by re-running all analyses using the difference between observed trait values and the means of controls lines rather than the raw trait data. These analyses yielded the same results as raw-value analyses, indicating a lack of influence of different experimental settings on the conclusion (results not shown, R code and data available in the online supplementary material, https://data. inra. fr/dataset. xhtml?persistentId=doi:10. 15454/V9XCA2). In addition to the evolution of trait values, the relationships between the phenotypic traits were modified by the selection experiments. Multivariate analyses confirmed that selection for a faster age at first reproduction modified the phenotype more profoundly than selection for female body mass.
Acknowledgements AE and BF acknowledge financial support by the national funder  ANR (France)  through the  European Union program ERA-Net BiodivERsA  (project  EXOTIC ), and the  INRA scientific department SPE. AE and JF acknowledge the  UMR CBGP  from its internal grant support. VR acknowledges financial support by the  European Union regional fund (ERDF) , the  Conseil Départemental de la Réunion , the  Région Réunion , and the  French Agropolis Fondation (Labex Agro-Montpellier, E-SPACE  project number  1504-004 ). RAH acknowledges support from  USDA   AFRI COLO-2016-09135 ,  USDA NIFA Hatch  project  1012868 , the  French Agropolis Fondation (LabEx Agro-Montpellier)  through the  AAP 'International Mobility ' ( CfP 2015-02 ), the  French programme investissement d'avenir , and the  LabEx CEMEB  through the  AAP 'invited scientist 2016 '.
Read alignment, variant detection, and ploidy analysis Illumina reads were aligned to the Cryptococcus reference genomes using Minimap2 aligner v. 2. 9  (Li 2018)  with the "-ax sr" parameter. BAM files were sorted and indexed using SAMtools  (Li et al. 2009 ) version 1. 9. Picard version 2. 8. 1 (http://broadinstitute. github. io/picard) tools were used to identify duplicate reads and assign correct read groups to BAM files. SAMtools version 1. 9 and Picard version 2. 8. 1 were then used to filter, sort, and convert SAM files and assign read groups and mark duplicate reads. Single-nucleotide polymorphisms (SNPs) and insertions/deletions (indels) were called using Genome Analysis Toolkit version 3. 6 with ploidy ¼ 1 according to the GATK Best Practices. HaploScore parameters used to filter SNPs and indels included VariantFiltration, QD <2.
Arturo Casadevall (Johns Hopkins University). The cells were washed and incubated with an Alexa Fluor 488-conjugated secondary antibody (10 mg/ml for 1 hour at 37 C; Invitrogen, USA). The cells were finally stained with calcofluor white (5 mg/ml for 1 hour at 37 C; Invitrogen, USA). Alternatively, the cells were counterstained with India ink for capsular visualization. The cells were visualized on a Leica TCS SP5 confocal microscope or analyzed with a FACS Canto II flow cytometer. Data were processed with the FACSDiva software, version 6. 1. 3.