dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion The obtained Wöhler curves are presented Fig. 10  and Fig. 11. In both cases, the failure stress is recalculated from the measured thickness and the measured breaking width, in order to approach the real failure stress. To determine the fatigue limit, several models exist, listed by  [21]. The Basquin model is mainly used to model the linear part, in a log-log scale. Stromeyer model include the asymptotic part in the Basquin model. Finally, the Bastenaire approximation, more complete, models the Wöhler curve from the highest to the lowest levels. Using the ESOPE software developed by Arcelor, these models are applied to the curves, the used formulas are summarized in Table  3 , with A, B and C coefficients of the fit, N the number of cycles, S the maximal stress, and E the fatigue limit.
The ESOPE software may plot the interpolation at 50% (the same probability to have an experimental dot to the left or right of the model), framed by the 10% and 90% interpolations. For the study, it is the Bastenaire model that will be applied to both curves, in order to have both the fatigue behaviour of the material and the fatigue limit (Fig. 12  and Fig. 13 ). The fatigue limits are determined around 500 MPa for the 0° direction and 180 MPa for the 45° direction. For the 0° specimens, the fatigue limit at 0° is much higher than what could be assumed from the static tests. However, it can clearly be seen that the scattering is very important on the fatigue tests, and that the material does not seem to be subjected to fatigue damage: it has a brittle behaviour, and samples sharply break.
Data about life-cycle inventory SimaPro 8 (a transparent, robust and reliable LCA software package) (Source: SimaPro) and the database ecoinvent 3 (a comprehensive and consistent life-cycle inventory database, with relevant data and high-quality datasets) (Source: ecoinvent) have been adopted. In Table  2 , the components/materials of the two configurations are presented: (A) indicates scenario A (configuration without PCM) and (B) indicates scenario B (configuration with PCM). Some components are common (regarding the type of the material and the quantity) for both configurations (with/without PCM): These components are indicated with (A, B). Table  2  presents the materials needed for the collectors and the additional components of the system.
Life-cycle impact assessment methods Multiple methods (Sources: SimaPro 8; ecoinvent 3 database) have been utilised in order to assess the environmental profile of the BIST system: 1) ReCiPe Endpoint (H) V1. 10 / Europe ReCiPe H/A: i) Single-score, ii) With characterization results. 2) ReCiPe Midpoint (H) V1. 10 / Europe Recipe H: With characterization results. 3) USEtox (default) V1. 03 / Europe 2004: With characterization results. 4) Ecological footprint V1. 01 / Ecological footprint: Single-score results. Some issues about the adopted methods are following presented: ReCiPe includes impact categories based on midpoint (problem oriented) and endpoint (damage oriented) approach. In the case of midpoint level, 18 impact categories are taken into account (ozone depletion, human toxicity, etc.
Patients We analyzed blood samples collected from 26 vivax malaria patients enrolled in a chloroquine efficacy study  (Popovici et al. , under review). All patients originated from villages within 50 km of BanLung city (Ratanakiri province, Cambodia), presented with fever (or history of fever within 48 hours) and were positive for P. vivax DNA and no other Plasmodium DNA (see  [8]  for details). After providing written informed consent, all patients were treated with a supervised standard 3-day course of chloroquine (30 mg/kg, Nivaquine) and monitored for 60 days. The study was approved by the Cambodian National Ethics Committee for Health Research (038 NECHR 24/02/14) and registered at ClinicalTrials. gov (NTC02118090).
Read alignment We aligned all reads, first to the human genome (Hg38), then to the P01 P. vivax genome (PlasmoDB P01 34  [9] ) using Tophat2  [10]  with the following parameters: -g 1 (to assign each read to a single location), -I 5000 (maximum intron length), -library-type fr-firststrand (for stranded libraries). We removed potential PCR duplicates using the samtools rmdup function. For each annotated P. vivax gene and each sample, we determined the number of reads overlapping any exon using custom perl scripts  [7]. We then transformed the raw counts into normalized transcripts per million (tpm) by dividing each gene count by the gene length (in kb) and by the sum of these values (in millions) in each sample. For further analyses, we only considered annotated P. vivax genes with more than 20 transcripts per million (tpm) in at least 10 samples, resulting in 4,999 genes analyzed.
Identification of genes differentially expressed We also determined which P. vivax genes were significantly affected by chloroquine by testing for differential gene expression using EdgeR and paired analyses  [14]. To account for the large difference in the number of reads obtained from P. vivax mRNAs before and after chloroquine treatment, we randomly subsampled all pre-treatment datasets to the same number of reads as in their corresponding post-treatment datasets. Due to the lower read counts in samples after treatment and the subsampling, only 4,280 genes remained expressed above the threshold described above and were included in the analysis of the effect of chloroquine on parasite gene expression. All analyses were corrected for multiple testing using false discovery rates  [15, 16].
Consistent with previous analyses  [7] , we observed that, in each patient infection, the parasite transcripts derived almost exclusively from trophozoites, regardless of the stage composition determined by microscopy (Figure  1 ). The stage-specific gene expression datasets used for gene expression deconvolution did not include gametocytes and the contribution of sexual parasites to the overall profile of each sample could therefore not be estimated using this approach. Examination of the expression of the gametocyte genes Pvs47 and Pvs48/45  [17]   1 ). To expand our knowledge of P. vivax genes possibly expressed in gametocytes, we then searched for additional genes whose expression correlated with any of these gametocyte genes. Overall, the expression of 1,613 genes were correlated with that of at least one of these known gametocyte genes (Pearson's R>0.
To account for the extensive decrease in parasite reads post-treatment that can lead to statistical artefacts, we randomly subsampled the datasets generated prior to chloroquine administration to the same number of reads as in the post-treatment datasets (see Material and Methods for details). Principal component analysis showed that the parasite RNA profiles did not separate samples pre-and post-treatment (Figure  3B ), suggesting that, despite the large decrease in total parasite RNA induced by the chloroquine treatment, the overall gene expression patterns were not dramatically affected. Indeed, permutation analyses showed that the differences in gene expression between paired samples (i. e. , before and after chloroquine from the same patient) were significantly lower than that between randomly paired samples (i.
This overwhelming transcriptional signal of trophozoite parasites circumvents the limitation of studying asynchronous infections and indicates that analyses of parasite RNA-seq profiles generated directly from infected patient blood are unlikely to be confounded by stage differences (and could be further controlled by using the gene expression deconvolution results as covariates in the analyses). However, these results also imply that differences in gene regulation occurring in rings or schizonts will be more difficult to detect from whole blood RNA-seq and may require other approaches, such as profiling after short-term cultures or single-cell RNA-seq  [32, 33]. In addition, we should caution that gene expression deconvolution relies on the gene expression patterns of reference datasets generated from highly synchronized P.
falciparum in vitro cultures and that these samples might not fully recapitulate the profiles of parasites in vivo  [27]  or the specificities of P. vivax development (though these limitations could be alleviated in the future by using gene expression profiles generated by single-cell RNA-seq analyses of patient infections). The dataset used for gene expression deconvolution did not include profiles from gametocytes and these were therefore not considered in this analysis. However, we observed that the expression levels of putative gametocyte markers were highly correlated with each other across infections but, surprisingly, clustered in two distinct groups. Our co-expression analyses revealed novel putative gametocyte genes and indicated that microtubule associated proteins were overrepresented among the genes from one cluster, while intracellular trafficking and histone remodeling genes were overrepresented in the second cluster.
DNA extraction, amplification (PCR) and phylogeny Extraction of genomic DNA was carried out using the NucleoSpin Plant II Kit (Macherey-Nagel GmbH & Co. KG), according to the manufacturer's instructions. ITS regions were amplified and sequencing with primers ITS1F-ITS4  (Gardes & Bruns 1993 , White et al. 1990 ) and the nrLSU with primers JS1and LR5 (Landvik 1996; http://www. biology. duke. edu/fungi/mycolab/ primers. htm). PCR products were purified applying the GeneJET Gel Extraction Kit (Thermo Scientific, Thermo Fisher Scientific Inc. , MA, USA). Sequencing was performed with an ABI model 3130 Genetic Analyzer (Applied Biosystems, CA, USA). Raw data were edited and assembled in MEGA v. 6  (Tamura et al. 2013 ). All steps of molecular studies were carried out at the Center for collective use of scientific equipment "Cellular and molecular technology of studying plants and fungi" (Komarov Botanical Institute, Russian Academy of Sciences, St.
Phylogenetic analyses: For this study, 51 nrITS and 34 nrLSU sequences were generated (Table  1 ). The new sequences were aligned with additional related sequences downloaded from GenBank (Table  1 ) using MAFFT v. 7 web tool (http://mafft. cbrc. jp/alignment/server/) under the Q-INS-i option for both markers; the alignments were manually adjusted in MEGA v. 6. Phylogenetic reconstructions were performed with Maximum likelihood (ML) and Bayesian (BA) analyses for two datasets (nrITS+nrLSU and nrITS). Before the analyses, the bestfit substitution model for the alignment was estimated based on the Akaike Information Criterion (AIC) using FindModel web server (http://www. hiv. lanl. gov/content/sequence/findmodel/ findmodel. html). GTR model was chosen for all datasets. Maximum likelihood analysis (ML) was run on PhyML server (http://www.
atgc-montpellier. fr/phyml/), under one hundred rapid bootstrap replicates. Bayesian analysis (BA) was performed with MrBayes v. 3. 1 software  (Ronquist & Huelsenbeck 2003) , for two independent runs, each with 3 million generations with sampling every 100 generations, under described model and four chains. To quickly diagnose convergence of MCMC analyses and to get estimates of the posterior distribution of parameter values Tracer v. 1. 6 was used  (Rambaut et al. 2014). Newly generated sequences have been deposited in GenBank with accession numbers listed in Table  1. The alignments have been deposited in TreeBASE (S22969 -nrITS + nrLSU and S22760 -nrITS dataset dataset).
Phylogeny The phylogenetic analyses resulted in two trees for nrITS+nrLSU and nrITS datasets  (Figs 1, 2). The phylogeny derived from the nrITS+nrLSU dataset (1 675 characters, including gaps) (Fig. 1 ) shows that members of Helicogloea s. lato (as advocated by  Baker 1936 ) are spread among three lineages within the class. In general, these phylogenetic reconstructions follow the results of earlier studies (in particular,  Aime et al. 2006 , 2018 , Bauer et al. 2006 ) demonstrating that Helicogloea is polyphyletic and Platygloea vestita (= Helicogloea vestita) belongs to the Atractiellomycetes. Most species of Helicogloea s. lat. included in the analyses form a strongly supported clade with a reference specimen of H. lagerheimii, the generic type (Fig. 1 , pp = 0. 99, bs = 75 %), thus constituting Helicogloea s.
1 , pp = 1, bs = 100 %) only distantly related to both Helicogloea s. str. and Saccosoma. As a consequence, they are separated into the recently described genus, Bourdotigloea. ITS dataset for the corticioid Atractiellomycetes (972 characters, including gaps) included sequences from 54 fungal samples (Fig. 2 ). The three major clades (Saccosoma, Bourdotigloea, Helicogloea) are highly supported and the   DNA data obtained in the present study correspond well to the morphological evidence. All species of Helicogloea s. str. and all but one of Bourdotigloea spp. possess gelatinous, semitranslucent basidiocarps that turn to a vernicose crust after drying, while Saccosoma spp. have sturdier, arid fructifications, unchanged in dry condition (Fig. 3 ). In microscopic terms, presence of clamps and shape of basidia are of crucial value for recognizing crust-like genera of the Atractiellomycetes.
ACKNOWLEDGEMENTS The curators of herbaria TAAM,  CWU, PC, HBG, K, C, S, NY, CFMR, BPI, FH, TENN, TRH, PDD  are thanked for providing specimens for loan. Nicolas Küffer  (Switzerland) and  Ilya Viner  (Russia) kindly shared with us collections from their personal herbaria. Jorma Pennanen  (Finland) collected excellent material of Helicogloea subardosiaca. The research was supported by  Norwegian Biodiversity Information Centre  (the project " A survey of Norwegian jelly fungi ", grant number knr. 44-15 , the authors KHL and VS, and the projects " Wood-inhabiting corticioid homoand hetero-basidiomycetes in Norway ", knr. 59-12 , and " International workshop on corticioid heterobasidiomycetes in Northern Europe ", knr  28-13 , the authors JN, KHL, VS), by  Finnish Academy of Sciences  (project  315927 , the authors OM and VS), by  Komarov Botanical Institute  (project " Biodiversity and spatial structure of fungi and myxomycetes communities in natural and anthropogenic ecosystems ", grant number  АААА-А18-118031290108-6 , the author VM), by the  Estonian Science Agency  (project  IUT20-30 ) and the  European Union  through the  European Regional Development Fund (Centre of Excellence FIBIR)  (the author KP).
The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency. Conclusions: The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software  [1].
Magnetic resonance imaging An axial 3-dimensional magnetization prepared rapid gradient echo dataset covering the entire head was acquired for each participant (176 slices, voxel resolution = 1 × 1 × 1 mm, echo time = 3 msec, repetition time = 2300 msec, flip angle = 9 • ). Additionally, the same participants underwent an fMRI session of resting state. During the resting-state session, participants were instructed to relax, keep their eyes closed, and avoid falling asleep. Functional images were obtained using T2weighted echo-planar imaging with blood oxygenation leveldependent contrast using SENSitivity Encoding (SENSE) imaging, an echo time of 26 msec, and a repetition time of 3000 msec. Each dataset comprised 32 axial slices acquired continuously in ascending order covering the entire cerebrum with a voxel resolution of 2 × 2 × 3 mm; 200 volumes were acquired using these parameters for a total acquisition time of 10 minutes.
White matter tracts disconnection Each patient's lesion was compared with an atlas of white matter tracts  [59] , indicating for each voxel, the probability of finding a white matter tract such as the arcuate fasciculus, the frontal aslant tract, or the uncinate fasciculus in the MNI152 coordinate system. We considered a tract to be involved when the likelihood of a tract being present in a given voxel was estimated above 50%  [23]. This method is freely available as tractotron in BCBtoolkit  [1]. We focused on frontal lobe tracts with a potential effect on executive, semantic, and language functions since all of the patients had a frontal lesion. These tracts included the cingulum, the frontal aslant, and the frontal superior and inferior longitudinal tracts for the executive functions  [60] ; the uncinate and the inferior fronto-occipital fasciculi for the semantic access  [61, 62] ; and the anterior and long segment of the arcuate fasciculi for the phonemic system  [63, 64].
Direct disconnection of brain areas: structural connectivity network This approach used the DWI datasets of 10 participants in the normative group to track fibers that passed through each lesion. For each participant, tractography was estimated as indicated in  [65]. Patients' lesions in the MNI152 space were registered to each control native space using affine and diffeomorphic deformations  [58]  and, subsequently, used as seed for the tractography in Trackvis  [66]. Tractography from the lesions were transformed in visitation maps  [67, 68] , binarized, and brought to the MNI152 using the inverse of precedent deformations. Finally, we produced a percentage overlap map by summing at each point in the MNI space the normalized visitation map of each healthy patient. Hence, in the resulting disconnectome map, the value in each voxel took into account the interindividual variability of tract reconstructions in controls and indicated a probability of disconnection from 50% to 100% for a given lesion (ie, thus explaining more than 50% of the variance in disconnection and corresponding to a large effect size).
2 ). AnaCOM2 is comparable to AnaCOM  [69]  but has been reprogrammed and optimized to work on any Linux or Macintosh operating systems. Initially, AnaCOM is a cluster-based lesion symptom mapping approach that identifies clusters of brain lesions that are associated with a given deficit, that is, the regions that are critical for a given function. In the context of this article, Ana-COM2 used disconnectome maps instead of lesion masks to identify clusters of disconnection that are associated with category fluency deficits, that is, the connections that are critical for a given function. Compared to standard voxel-based lesion-symptom mapping (VLSM)  [8] , AnaCOM2 regroups voxels with the same distribution of neuropsychological scores into clusters of voxels. Then, for each cluster larger than 8 mm 3 , AnaCOM2 will perform a Kruskal-Wallis test between patients with a disconnection, patients spared of disconnection, and controls.
Indirect disconnection of brain areas: functional connectivity network Rs-fMRI images were first motion corrected using MCFLIRT  [73] , then corrected for slice timing, smoothed with a full half width maximum equal to 1. 5 times the largest voxel dimension, and finally filtered for low temporal frequencies using a Gaussianweighted local fit to a straight line. These steps are available in Feat as part of the FSL package  [74]. Rs-fMRI images were linearly registered to the enantiomorphic T1 images and, subsequently, to the MNI152 template (2 mm) using affine transformations. Confounding signals were discarded from rs-fMRI by regressing out a confound matrix from the functional data. The confound matrix included the estimated motion parameters obtained from the previously performed motion correction, the first eigenvariate of the white matter and cerebrospinal fluid (CSF) as well as their first derivative.
The median network resulting from a seed contains, in each voxel, the median of functional connectivity across all the controls. Medians were chosen instead of average as they are less sensitive to outliers and are more representative of the group-level data  [75]. The calculation of the functional connectivity was automatized and made available inside the funcon tool as part of BCBtoolkit. Medians were calculated using the function fslmaths. Visual inspection revealed that several of these restingstate networks shared a very similar distribution of activations. Therefore, an "activation" matrix was derived from the seedbased resting-state networks. This matrix consisted of columns that indicated each seed-based resting-state network and rows that represented the level of activation for each voxel in the cortex.
This activation matrix was entered into a principal component analysis in SPSS (Chicago, Illinois) using a covariance matrix and varimax rotation (with a maximum of 50 iterations for convergence) in order to estimate the number of principal components to extract for each function. Components were plotted according to their eigenvalue (y; lower left panel in Fig. 4 ); we applied a scree test to separate the principal from residual components. This analysis revealed that 3 factors were enough to explain 82% of the variance of the calculated seed-based restingstate networks. This means that 3 factors are good enough to summarize most of the seed-based resting-state network results. Finally, brain regions that had a statistically significant relationship with the 3 components (ie, factor-networks) were detected using a linear regression with 5.
Structural changes in disconnected regions A distant lesion can affect cortical macro-and microstructures remotely. Conscious of this, we attempted to estimate these structural changes and their relationship with category fluency within each functional factor-network. To this aim, we explored the properties of each functional network using the following 2 complementary measures: T1w-based cortical thickness to identify fine local volumetric changes and the Shannon entropy of rs-fMRI as a surrogate for the local complexity of the neural networks  [76]. Each original functional network seeded from each cluster was thresholded and binarized at r > 0. 3 and used as a mask to extract cortical thickness and entropy. Patients' lesions were masked out for these analyses. For the cortical thickness, a registration-based method (Diffeomorphic Registration based Cortical Thickness, DiReCT) was used  [77]  from the T1-weighted imaging dataset.
Shannon entropy was extracted from the previously preprocessed rs-fMRI using the following formula:sum (p * log(p)), where p indicates the probability of the intensity in the voxel  [76]. FSLstats was used to extract the average cortical thickness and resting state fMRI entropy for each cluster and factornetwork. Statistical analysis was performed using SPSS software. In our analysis, Gaussian distribution of the data was not confirmed for the cortical thickness and the entropy measures using the Shapiro-Wilk test. Therefore, nonparametric statistics were chosen to compare cortical thickness and entropy levels between patients disconnected, patients spared, and controls in each cluster and factor-network. Additionally, bivariate Spearman rank correlation coefficient analyses were performed between the cortical thickness or entropy measurement of each functional network and each patient's category fluency performance.