dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, these data are also supported by the cytotoxicity heat damage, which was recorded after having performed the MH treatment. In a parallel experiment, the A431+2D-MNBs_0h-incubation and the A431+2D-MNBs_3h-incubation samples were exposed to MH treatment either immediately after the addition of the 2D-MNBs or after 3h of incubation at 37°. After reseeding the cells, the viability was estimated by means of trypan blue assay at well-defined time points (24 or 48 h) post MH-treatment, and it showed severe toxicity for the A431+2D-MNBs_3h sample rather than for the A431+2D-MNBs_0h-incubation sample (Figure  6c ). This data support the ac measurements indicating that the disassembling of the 2D-MNBs occurring during the incubation for 3 h at 37 °C produced more cellular heat damages than the A431+2D-MNBs-0h incubation that was not kept at 37 °C for 3 h.
■ ACKNOWLEDGMENTS This work was partially funded by the  AIRC  project (contract no. 20790 ) by the  European Research Council  (starting grant ICARO, contract no. 678109 ), by the  Spanish Ministry of Economy and Competitiveness  ( MAT2016-81955-REDT ,  SEV-2016-0686 ), Madrid Region (NANOMAGCOST,  P2018/NMT-4321 ), the European " Seventh Framework Program "  ReBioStent  project (grant agreement no. 604251 ), and by  Neurimp  project (grant agreement no. 604450 ). We thank  Marco Cassani  and  Tiziano Catelani  for helping with polymer staining procedures for TEM images and  Markus Barthel  for helping with the PEG-ligand production following a literature-reported procedure.
Results Transcriptome Analyses Reveal the Spatial Organization of S. sclerotiorum Invasive Hyphae. To test experimentally for spatial organization in S. sclerotiorum invasive hyphae, we analyzed the global transcriptome of S. sclerotiorum mycelium central and apical areas by RNA sequencing (RNA-seq). We harvested in triplicate separate areas corresponding to the central and apical zones of fungal mycelium grown on solid medium in vitro and on Arabidopsis thaliana plants (Fig. 1A ). The first two principal components of the global gene expression dataset accounted for ∼77. 6% of the total variation in read count (Fig. 1B ). The major driver of differential gene expression was growth conditions (in vitro versus in planta, accounting for 53. 7% of total variance), followed by the mycelium area (central versus apical, accounting for 23.
9% of the total variance); these effects greatly exceeded the effects from biological replicates. The amplitude of principal component 2, which separates samples based on colony areas, was largely determined by the samples collected in planta, suggesting that the transcriptional differentiation of mycelium center and apex was stronger in planta. We used gene expression in S. sclerotiorum grown in liquid medium determined by RNA-seq as a reference to determine gene induction folds. We identified a total of 1,133 genes (10. 2% of the genome) induced fourfold either in planta or on solid medium in vitro (Fig. 1C  and Dataset S1). Only 54 genes (4. 7%) were induced in all four conditions, and 218 genes (19. 2%) were induced both in vitro and in planta. A total of 288 genes (25. 5%) were induced in vitro only, and 627 (55.
Division of Labor Drives Metabolic Heterogeneity Along S. sclerotiorum Invasive Hyphae. To assess quantitatively the impact of division of labor on major cell functions in S. sclerotiorum hyphae, we performed a flux balance analysis (FBA) using experimentally determined transcriptomes. To this end, we first reconstructed a genome-scale metabolic model (GEM) of S. sclerotiorum (Fig. 2A  and Dataset S2). Our model includes a metabolic module with 1,277 reactions of the central metabolism, nutrient uptake pathways, degradation pathways, and the biosynthesis pathways of the major components of the fungal biomass. The model also includes a plant cell-wall degradation module with 218 reactions catalyzed by secreted plant cell-wall-degrading enzymes. Altogether, S. sclerotiorum GEM encompasses 1,495 unique reactions associated with 1,039 genes, comparable to validated yeast and filamentous fungi models  (30, 31).
To evaluate the performance of this GEM, we determined experimentally S. sclerotiorum growth on 288 metabolites using Biolog phenotype microarrays and compared it with model predictions (Fig. 2B  and Dataset S3). The model correctly predicted the observed behavior on 252 metabolites (73 used as a C or N source and 179 not used), reaching 88% accuracy, 83% sensitivity, and 78% precision for axenic growth. Next, we used FlexFluxOmics  (32)  to assess metabolic fluxes through S. sclerotiorum GEM based on global gene expression determined experimentally in vitro and in planta. We normalized flux distributions by fixing the biomass production fluxes measured experimentally for each condition. In S. sclerotiorum cells growing in vitro, 536 to 555 reactions supported nonnull fluxes, covering 79 to 86 distinct metabolic pathways.
During A. thaliana colonization, 592 to 610 reactions supported nonnull fluxes in S. sclerotiorum cells, covering 101 to 103 distinct metabolic pathways (Dataset S4). Among these pathways, 16 supported significantly higher fluxes in apex compared with center cells during A. thaliana colonization, including detoxification pathways (cyanide, reactive oxygen species, and nitrile detoxification), biosynthesis of organic acids (oxalate and acetate metabolism and pH homeostasis), carbohydrate anabolism (glyoxylate and citric acid cycles and C1 metabolism), and secondary metabolite trafficking (peroxisomal and toxin transport) (Fig. 2C ). Conversely, six pathways supported significantly lower fluxes in apex compared with center cells during A. thaliana colonization, related to energy and reduced cofactor production (glycolysis and pentose phosphate pathways with the respective upstream pathways, pyruvate metabolism, and arabinose degradation).
sclerotiorum apex and center cells (Fig. 2D ). During in vitro growth, carbon metabolic uptake was similar in apex and center cells (20. 0 ± 0. 5 and 20. 4 ± 1. 9 C mmol•g -1 •h -1 , respectively, P = 0. 73), as well as carbon secretion for virulence functions (0. 80 ± 0. 05 and 0. 67 ± 0. 35 C mmol•g -1 •h -1 in apex and center cells, respectively, P = 0. 56) (Dataset S4). The total carbon metabolic uptake was also similar in apex and center cells during plant colonization (19. 9 ± 0. 7 and 18. 4 ± 3. 4 C mmol•g -1 •h -1 , respectively, P = 0. 52). However, the flux of carbon secretion for virulence was 8. 5-fold lower in center cells (0. 34 ± 0. 06 C mmol•g -1 •h -1 ) than in apex cells (2. 89 ± 0. 34 C mmol•g -1 •h -1 , P = 0. 0047), suggesting that center cells may store carbon and transfer it toward apex cells, as observed in some filamentous fungi  (14, 16, 33, 34).
RNA extraction, sequencing, and reads mapping were performed as described in ref. 64. Normalized read count per gene and differential gene expression (relative to PDB-grown fungus) were calculated using the Bioconductor/DESeq2 (version 1. 8. 2) package. Raw and normalized RNA-seq data have been deposited in the GEO database (accession nos. GSE106811 and GSE116194). Genes were considered significantly up-regulated when induced fourfold or greater compared with the control PDB condition, with a P value ≤ 0. 01 (Dataset S1). Normalized read counts per gene for the top 500 mostexpressed genes were shuffled between the three biological replicates 100 times for principal component analysis (PCA). PCA was performed using the factomineR and factoextra R packages. For each PFAM domain, the number of differentially expressed genes was compared with the total number of genes in S.
The macromolecule secretion module (macromolecule biosynthesis and secretion) was reconstructed based on genomic predictions for protein secretion using WoLF PSORT (70) and SignalP4. 0 (71) , in addition to secreted macromolecules previously reported in the literature (Dataset S4). The cost of secretion was assigned by assuming one ATP hydrolyzed per metabolite unit transported through the fungal plasma membrane when no information was available. The plant cell-wall degradation module was modeled as hydrolysis reactions of plant cell-wall macromolecules. Generic macromolecule complexes describing the repetitive structural and chemical motif of the different plant cell-wall components were designed. Reactions cleaving these components were identified manually from plant cell-walldegrading enzymes annotated in the genome  (52).
The model was converted to Systems Biology Markup Language (SBML) format following the MIRIAM Registry recommendations (Dataset S2). To quantitatively validate the model, we measured S. sclerotiorum growth kinetics in liquid minimal medium containing 50 mM D-glucose as the only carbon source and determined cell dry weight, total protein concentration, and substrate uptake rate by NMR as described in Peyraud et al. (58)  (Dataset S5). Published reports on various Ascomycete fungal species were used to derive S. sclerotiorum biomass composition (Dataset S6). Flux distributions through the model were determined by FBA using FlexFlux  (32) , optimizing for biomass production as the objective function. Exchange fluxes corresponding to substrates available in planta were constrained based largely on data from AraGEM  (72).
Substrate uptake rates were constrained from experimentally measured fluxes when available or the upper bound was fixed to 5 mmol•g (cell dry weight) -1 •h -1 as default value. Fitting of the flux distribution with the transcriptomic data were performed using the Flex-FluxOmics algorithm available at https://github. com/lmarmiesse/FlexFlux/ tree/master/src/flexflux/omics. The relative flux distributions were normalized using experimentally measured growth rates. Phenotype Microarrays. Phenotype microarrays analysis was performed using Biolog plates PM1-3. To produce homogeneous fractions of S. sclerotiorum mycelium, strain 1980 was grown in PDB at 24 °C for 4 d under 180-rpm shaking. The culture was blended for 10 s and filtered through sterile 100-μm filters on a vacuum pump. The flow-through was collected and filtered through sterile 40-μm filters.
ACKNOWLEDGMENTS. We thank  Louis-Henri Fedronic  and  Marielle Barascud  for technical assistance and the  Plant Health & Environment Division  of  Institut National de la Recherche Agronomique  for stimulating discussions and support. This work was supported  European Research Council  Grant  ERC-StG 336808  Project  VariWhim  (to S. R. ) and  French Laboratory of Excellence  Project  TULIP  Grants  ANR-10-LABX-41  and  ANR-11-IDEX-0002-02.
Currently, public databases contain three M. restricta genomes identified at either the contig or scaffold level (whole-genome sequences [WGS] of LFCZ01, AAXK01, and LFDA01) and unassembled next-generation sequencing (NGS) reads of the M. restricta KCTC 27527 strain (8), which limits comparative genomic and in vivo metagenomics studies. We succeeded in completely resequencing and assembling the M. restricta genome at the chromosomal level. Genomic DNA of M. restricta CBS 7877, a strain isolated from normal human skin (13), was purchased from the ATCC (reference MYA4611D5). Sequencing was performed at BaseClear on both the HiSeq 2500 (Illumina) and PacBio RS II (Pacific Biosciences) platforms. The Illumina library was obtained following the Nextera protocol (Illumina). The Illumina 125-base paired-end short reads (4,849,647 reads after quality control with FastQC version 0.
10. 0; 1. 222 Mbp in total; coverage, 167ϫ) were trimmed and de novo assembled using CLC Genomics Workbench version 7. 5. 1 (CLC bio, Denmark). The optimal k-mer size was automatically determined using KmerGenie version 1. 6213 with default parameters (14). For PacBio, the library was prepared using the standard procedure for the PacBio RS II instrument. PacBio reads were processed and filtered using the SMRT Analysis software suite version 2. 3. 0, leading to 858,347 continuous long reads (1. 721 Mbp in total; coverage, 235ϫ). Illumina contigs were then aligned with the PacBio CLR reads using BLASR (15). From the alignment, the orientation, order, and distances between contigs were estimated via SSPACE-LongRead Scaffolder version 1. 0 (16) and gaps filled with GapFiller version 1. 10 (17).
(GenBank accession numbers CP030251 to CP030259 ). Functional annotation was completed using a combination of BLAST-p and Blast2GO  (19, 20)  against Pfam (version 31) and the UniProt/Swiss-Prot (release 2017-04) databases, as included in the Prokka annotation framework version 1. 12. A total of 4,096 protein-coding genes cover 86. 8% of the genome. The mitochondrial sequence is identical to the previously released M. restricta CBS 7877 mitochondrion sequence (GenBank accession number KY911093), with the exception of one repeated region. A previous karyotype study found nine chromosomes for the M. restricta CBS 7877 strain  (21). Here, the nine scaffolds exhibit short tandem repeats (TKA GTG, Ͼ60 bp) considered to be telomeres, as previously reported  (9). Six scaffolds show telomere repeats at both ends, suggesting that these are complete chromosomes, and these hexamer-repeated motifs were not found elsewhere in the assembly.
Six and three lipase coding sequences (CDS) have been identified, harboring the Pfam PF01764 and PF03583 signatures, respectively. Among the cell wall biosynthesis-related proteins analyzed, 20 chitin-chitosan-processing genes were identified by BLAST using a Malassezia globosa protein data set  (19). These activities are of main importance since the M. restricta cell wall contains a very high percentage (20%) of chitosan  (22) , and chitosan is reported to be required for fungal virulence and persistence in mammals  (23). Further investigation on specific factors and functions (such as proteases and glycosyl hydrolases  [9, 12] ) will be essential to a better understanding of the physiopathology of M. restricta.
, 2002; Catani and De Schotten, 2008; Hau et al. , 2016; Chenot et al. , 2018]. In this work, the person performing the task of segmentation (i. e drawing the ROIs, following the protocol) will be referred to as rater. Manual segmentation can be performed in software such as, but not limited to, DTI studio  [Jiang et al. , 2006] , Trackvis  [Wang et al. , 2007] , exploreDTI  [Leemans et al. , 2009] , MITK Diffusion  [Neher et al. , 2012] , FiberNavigator  [Chamberland et al. , 2014] , or MI-Brain  [Rheault et al. , 2016]  (Figure  1 ). 3 Once a bundle of interest is segmented from a tractogram, the analysis varies according to the research question. It is common to report asymmetry or group difference in bundle volume  [Catani et al. , 2007; Song et al. , 2014; Chenot et al. , 2018] , diffusion values within the bundle of interest (average fractional anisotropy, mean diffusivity, etc.
Quantifying reproducibility in tractography When performing segmentation, it is crucial that raters perform the tasks as closely as possible to the dissection plan. Even if a single individual performs all segmentations, the possibility of mistakes or erroneous decisions about landmarks exists  [Boccardi et al. , 2011; Frisoni et al. , 2015; Entis et al. , 2012]. High reproducibility is often an assumption, if this assumption is false the consequence could lead to inconsistent outcomes and erroneous conclusions. To assess the level of reproducibility of raters, identical datasets need to be segmented blindly more than once  [Gisev et al. , 2013; Gwet, 2012; Frisoni et al. , 2015]. Reproducibility of segmentations from the same individual is referred to as intra-rater agreement, while reproducibility of segmentation across raters is referred to as inter-rater agreement.
In the field of neuroimaging, voxels are used as the typical representation of data, while the available representation in tractography is in the form of streamlines (i. e. sets of 3D points in space). Figure  2  is a sketch of both representation. Several similarity measures exist to compare voxel-wise segmentations, e. g Dice Score. Most of them have an equivalent formulation to compare sets of streamlines. However, resulting values can widely vary as the spatial distribution is not the same for both representations. Some measures related to streamlines require the datasets to be exactly the same, e. g Dice score, as streamline reconstructions are sets of discrete points with floating point coordinates and not discrete grids like 3D images. For this reason, comparison of streamlines is more challenging and datasets that do not originate from the same source distance in millimeters is often the only available solution  [Garyfallidis et al.
Study design Twenty-four participants were recruited and divided into two groups: experts and non-experts. The division was based on their neuroanatomical educational background. Participants working as researchers or PhD students in neuroanatomy, neurology or with extended experience in the field performing "virtual dissection" as well as neurosurgeons were part of the experts group (11 participants). The non-experts group was composed of MSc, PhD student or Post-Doc in neuroimaging, but without any formal education in neuroanatomy (13 participants). All participants had knowledge of dMRI tractography in general as well as the concept of manual segmentations of tractography datasets. Participation was voluntary and anonymous, recruitment was done individually and participants from various labs in Europe and the USA were solicited.
The study was performed according to the guidelines of the Internal Review Board of the Centre Hospitalier Universitaire de Sherbrooke (CHUS). 6 Five independent tractograms and their associated structural/diffusion images were used, each was triplicated (total of 15). One was untouched, one was flipped in the X axis (left/right) and one was translated. Then, all datasets were randomly named so the tasks could be performed blindly for each participant. Participants were not aware of the presence of duplicated datasets. Five tractotrams were used to obtain stable averages, duplicated datasets were used to score the intra-rater agreement and the multiple participants to evaluate inter-rater agreement. The decision to separate participants in two groups was made to generate additional data about reproducibility in real-life conditions.
DWI datasets, processing and tractography Tractograms were generated from the preprocessed HCP  [Van Essen et al. , 2013]  DWI data using three shells  (1000, 2000, 3000)  with 270 directions. The B0, fractional anisotropy (FA) and RGB (colored FA) images were computed from DWI to be used as anatomical reference during segmentation. Constrained spherical deconvolution (CSD) using a FA threshold from a tensor fit on the b=1000s/mm 2 was used to obtain fiber orientation distribution functions (fODF)  [Tournier et al. , 2007; Descoteaux et al. , 2007]  (spherical harmonic order 8) from the b=2000s/mm 2 and b=3000s/mm 2 shells. Probabilistic particle filtering tractography  [Girard et al. , 2014]  was subsequently computed at 30 seeds per voxel in the WM mask (FSL FAST  [Woolrich et al. , 2009] ) to make sure sufficient density and spatial coverage were achieved.
Dissection plan and instructions Each participant received their randomly named datasets, a document containing instructions for the segmentation and a general overview of a segmentation as example (see supplementary materials). The segmentation task consisted in 15 segmentations of the pyramidal tract (left and right). Segmentation involved using 3 WM inclusion ROIs (Internal capsule, Midbrain and Medulla Oblongata) and 2 exclusion ROIs (one plane anterior to the precentral gyrus and one plane posterior to the postcentral gyrus). The detailed segmentation plan is available in the supplementary materials  [Chenot et al. , 2018]. Participants had to perform the segmentation plans, following the instructions as closely as possible. The dataset order was provided in a spreadsheet file. Participants had to choose between two software; Trackvis  [Wang et al.
Bundles analysis Once returned by all participants, datasets were de-randomized to match triplicates across participants. The duplicates (flipped and translated) were reverted back to their native space and all datasets (images and tractograms) were warped to a common space (MNI152c 2009 nonlinear symmetrical) using the Ants registration library  [Fonov et al. , 2011; Avants et al. , 2008]  to simplify the analysis. With all datasets having a uniform naming convention and in a common space, the intra-rater and inter-rater reproducibility can be assessed.
Intra-rater and inter-rater Each participant performed the same tasks on each triplicate. The goal of this triplication is to evaluate intra-rater reproducibility. Since all participants had access to the same datasets, inter-rater reproducibility can be assessed too. Computing the average value from all pairwise combinations provides an estimate of the agreement between multiple segmentations of a same bundle. The deviation can also provide insights about the consistency of these segmentations. Measurement values can be between 0 and 1, such as Dice and Jaccard  [Dice, 1945] , meaning they are independent of the size. An alternative to overlap measures are similarity measures, which can provide insights about the distance between two segmentations (in millimeter). Even when spatial overlap between two segmentations is low, both can be very similar in shape  [Descoteaux et al.
Gold standard When multiple raters provide segmentations from an identical dataset, it is of interest to produce a gold standard. For a voxel representation, a probability map can be constructed, where each voxel value represents the number of raters that counted the voxel as part of their segmentation  [Frisoni et al. , 2015; Iglesias and Sabuncu, 2015; Langerak et al. , 2015; Pipitone et al. , 2014]. This can be normalized and then thresholded to obtain a binary mask representing whether or not the voxel was segmented by enough rater. A threshold above 0. 5 is often referred to as a majority vote. The same logic can be applied to streamlines, each streamline can be assigned a value based on the number of raters that considered it part of their segmentation. This can be seen in Figure  6  where increasing the minimal vote threshold reduces the number of outliers and overall size.
Inter-rater evaluation To minimize the influence of intra-rater reproducibility during the evaluation of interrater reproducibility, the triplicate datasets were fused into a single bundle. This was performed to approximate the results as if participant segmentations had no intra-rater variability. This lead to a underestimation of inter-rater variability, but necessary to separate source of variability later in the analysis. Voxel-wise Dice coefficient is on average higher between experts than between non-experts, at 0. 75 and 0. 67 respectively. Streamline-wise Dice coefficient is not statistically different (p -value > 0. 01) at 0. 34 and 0. 32. Voxel-wise distance is on average lower for the experts group than non-experts, 2. 74mm and 3. 85mm respectively. The average density correlation is higher between experts at 0.
Meaning that experts and non-experts alike classified the vast majority of true negatives correctly. Since specificity is near a value of 1. 0, the Youden score is almost equal to sensitivity. All 3 measures take into account the true negatives, which far outweigh the true positives, in our datasets, for this reason they were removed from Figure  11  and shown only in the supplementary materials. Sensitivity is much lower at 0. 59 and 0. 71 for experts and non-experts respectively, as both groups partially capture the gold standard. Precision is higher for experts than for non-experts, meaning that experts were providing segmentations approximately the same size as the gold standard while non-experts were providing much bigger segmentations (that generally encompass the gold standard). This explains the higher sensitivity and lower specificity of non-    The computation of inter-rater reproducibility was performed using the fused triplicate to minimize the influence of intra-rater reproducibility.