dataesr/scientific-paragraphs-categorization huggingface dataset:
ANALYSIS AND INTERPRETATION Full details of self-image norms are provided in the Supplementary Material. By examining the frequency counts we can see which categories are most often used when people describe their identities. The most commonly generated category of self-image was Friend (count = 77), followed by Student (count = 70), Sports player (count = 70), and Occupation (count = 68). Thus, specific social roles are the most commonly generated single categories across the database. The highest frequency traits were Happy (count = 43), Optimistic (count = 41), and Friendly (count = 38). In line with previous research showing that people tend to define themselves in broadly positive terms (e. g. ,  Baumeister, 1998) , the highest frequency categories of self-image traits are generally positive in valence.
However, several negative self-images were also generated multiple times (e. g. , Lazy = 19; Worry = 15; Pessimistic = 10). In terms of mean age of participants, the youngest self-image category generated was Maths (e. g. , ok at maths; Mean age = 18) and the oldest was Serious (e. g. , serious minded; Mean age = 67. 5). By probing the database as a whole, it is possible to identify the most frequently cited self-images for people in given age ranges or for specific genders. For example, within females aged 60-70 the most commonly cited category of self-image was Mother (n = 9). As well as providing a useful resource for researchers interested in the lifespan development of identity (by showing which identities are most relevant at different ages), these age-specific norms may have particular utility for matching to specific clinical samples.
Furthermore, this database may provide a useful resource for researchers who study cultural life scripts: representations of the timing of major transitional life events within a specific culture  (Rubin and Berntsen, 2003; Berntsen and Rubin, 2004). Studies have shown that these normative life events are typically positive and reflect social landmarks such as graduation, marriage, and parenthood  (Berntsen and Rubin, 2004). This database allows investigation of the age of participants who identify with particular life script categories, such as being a mother (mean age 51. 79). Thus, whilst parenthood is a life script event that typically occurs in young adulthood, it nevertheless remains a salient self-image for adults across the lifespan. One issue with the database, as it stands, is that some selfimage categories feature relatively low counts.
For example, Articulate is only generated twice, so the mean age for this category is not as meaningful as that for Caring (with a count of 21). Furthermore, at present, the participants in the database are mostly female younger adults. This must be borne in mind when making comparisons by age and gender. For example, the fact that Sister was generated 65 times and Brother only 12 does not necessarily indicate that males are less likely than females to frame their identity around their siblings-rather, this is probably a reflection of the gender ratio in the database. What is more informative is the within-category gender ratio data. For example, Travel had a relatively even split of 12 males and 13 females, whereas Worry was generated by 14 females and only 1 male. These norms can be used as the basis for further studies on the salience of specific self-images.
For example, one could investigate what proportion of people who are brothers actually generate brother as a self-image. This could be achieved by asking participants to complete an open-ended 20 statements task first, followed by responding yes/no to a standardized set of self-images based on the norms reported here. In this way it will be possible to explore which self-images are most salient for different groups, whilst also being able to account for which self-images are objectively present. As with any self-reported data on personal identity, it is important to acknowledge the impact of environmental cues and social expectations on generation of self-images (see  Markus and Kunda, 1986). Whilst this database cannot address the issue of how true (as opposed to confabulatory) or stable these selfimages are, they nevertheless reflect an accurate snapshot of our participants' views of themselves at a given moment in time.
Study 1 had an age range of 47-66 (N = 16 [11 Female], Mean age 54. 56, SD = 4. 95) and Study 2 had an age range of 39-88 (N = 125 [81 Female], Mean age 52. 94, SD = 10. 19). Note that for Study 2 the sample size in the database is higher than in Study 2 of Rathbone et al. (2008) as we only reported data from participants who completed the online questionnaire. In the database we include self-images for all participants who generated at least one self-image, leading to the discrepancy between sample sizes. Study 3 (reported in Rathbone and Moulin, 2014) had an age range of 17-50 (N = 40 [33 Female], Mean age 19. 63, SD = 5. 01). Studies 4 and 5 (Rathbone and Moulin, 2017) had respective age ranges of 19-61 (N = 40 [30 Female], Mean age 24. 2, SD = 8. 63) and 19-34 (N = 17 [12 Female].
Genome and transcriptome sequencing and assembly A detailed description is provided in Additional file 1: Text S1. Briefly, the C. olivacea MUCL 20566 genome was sequenced using Illumina HiSeq-1 TB Regular 2 × 151 bp 0. 309 kb. Sequenced reads were QC filtered for artifact contamination using BBDuk from the BBMap package (https://sourceforge. net/projects/bbmap/) and subsequently assembled with Velvet 1. 2. 07  [10]. The result -pair library with an insert size of 3000 +/-300 bp in silico that was then assembled together with the original Illumina library with AllPathsLG  [11]. Raw sequences were deposited in SRA (Sequence Read Archive) NCBI database under accession number SRP086489. Strand-specific RNASeq libraries were created and quantified by qPCR. Sequencing was performed using an Illumina HiSeq-2500 instrument.
Genome annotation The annotation of the C. olivacea MUCL 20566 assembly was performed using the Joint Genome Institute pipeline  [14]  to predict and functionally annotate protein-coding genes and other features such as tRNAs or putative microRNA precursors. The SECRETOOL pipeline  [15]  was used to identify putatively secreted proteins, considering the presence of signal peptides, cleavage sites, transmembrane domains and the GPI (glycosylphosphatidylinositol) membrane anchor. Carbohydrateactive enzymes (CAZys) were annotated based on BLAST  [16]  and HMMER  [17]  searches against sequence libraries and HMM (Hidden Markov Models) profiles of the CAZy database  [18]  functional modules. Protein structure predictions were carried out with Phyre2  [19]. Raw sequencing reads, genome assembly, transcriptome assembly, gene predictions and functional annotations are publicly available in the C.
Annotation of transposable elements Transposable elements (TEs) were identified and annotated in the C. olivacea assembly using REPET package  [20, 21] , as well as in the following boletales assemblies available in Mycocosm database (http://genome. jgi. doe. gov/programs/fungi/index. jsf ): Coniophora puteana v1. 0 (ID: Conpu1), Hydnomerulius pinastri v2. 0 (ID: Hydpi2), Serpula lacrymans S7. 3 v2. 0 (ID: SerlaS7_3_2), Pisolithus tinctorius Marx 270 v1. 0 (ID: Pisti1). Briefly, de novo TE detection was carried out with the TEdenovo pipeline  [21]  and the elements were classified with PASTEC  [22]. The resulting TE library was fed into TEannot pipeline  [20]  in two consecutive iterations: the first one with the full library, and the second with an improved library consisting on consensus elements carrying at least one full-length copy after manually discarding false positives (i.
Phylogenetic analyses The predicted proteomes of the following species were downloaded from Mycocosm database (Mycocosm ID in parenthesis): Agaricus bisporus var. bisporus H97 v2. 0 (Agabi_varb-isH97_2), Boletus edulis v1. 0 (Boled1), Coniophora olivacea MUCL 20566 v1. 0 (Conol1), Coniophora puteana v1. 0 (Conpu1), Cryptococcus neoformans var. grubii H99 (Cryne_H99_1), Fomitopsis pinicola FP-58527 SS1 v3. 0 (Fompi3), Gyrodon lividus BX v1. 0 (Gyrli1), Hydnomerulius pinastri v2. 0 (Hydpi2), Leucogyrophana mollusca KUC20120723A-06 v1. 0 (Leumo1), Paxillus involutus ATCC 200175 v1. 0 (Paxin1), Phanerochaete chrysosporium RP-78 v2. 2 (Phchr2), Pisolithus tinctorius Marx 270 v1. 0 (Pisti1), Pleurotus ostreatus PC15 v2. 0 (PleosPC15_2), Rhizopogon vinicolor AM-OR11-026 v1. 0 (Rhivi1), Scleroderma citrinum Foug A v1.
0 (Sclci1), Serpula lacrymans S7. 3 v2. 0 (SerlaS7_3_2), Suillus luteus UH-Slu-Lm8-n1 v2. 0 (Suilu3), Trametes versicolor v1. 0 (Trave1). Species phylogeny was constructed as follows: all-by-all BLASTP followed by MCL clustering was carried out with a dataset containing the proteomes of all the species. The clusters carrying only one protein per species were identified, and the proteins were aligned using MAFFT  [30]. The alignments were concatenated after discarding poorly aligned positions with Gblocks  [31]. The phylogeny was constructed using RaxML  [32]  with 100 rapid bootstraps under PROTGAMMAWAGF substitution model. Phylogenetic reconstruction of Gypsy reverse-transcriptases was carried out as follows: Reverse transcriptase RV1 domains were extracted from LTRretrotransposons of the TE consensus library using Exonerate  [33]  and aligned with MUSCLE.
64% to simple repeats, and 0. 12% to low complexity regions. The estimation of repeat content from low-coverage Illumina data (3. 8X) yielded 6% of the genome size covered by transposable elements (Additional file 2: Table  S1 ). We used transcriptomic information, ab initio predictions and similarity searches to predict a total of 14,928 genes-84. 5% of them having a strong transcriptome support (spanning more than 75% of the gene length). In addition, 88. 3% of the annotated genes had significant similarity to proteins from the NCBI nr database and 46. 6% to the manually curated proteins from the Swiss-Prot database (cutoff e -05 )  [36]. A total of 7,841 predicted proteins (52. 3%) carried Pfam domains and 1,471 (9. 8%) carried signal peptide, of which 470 were predicted to be secreted using the more stringent SECRETOOL pipeline.
olivacea proteins yielding significant tBLASTN hits against the C. puteana genome (13,572 genes, cutoff e-5, Fig. 2c ). For the remaining 1,352 C. olivacea-specific (orphan) genes, only 48 could be functionally annotated based on KOG (Eukaryotic Orthologous Groups), KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology) or InterPro databases.
Analysis of putatively secreted multigene families Using all-by-all BLASTP followed by MCL we clustered by similarity the 1,471 proteins carrying signal peptides in C. olivacea. We used all proteins carrying signal peptides rather than only SECRETOOL predictions in order to obtain larger protein clusters. Up to 60% of the 1,471 proteins grouped in clusters were formed by 2 to 59 genes (Additional file 5: Table  S3 ), showing the same distribution as the whole proteome (p = 0. 6032, Wilcoxon test, 61% of the 14,928 predicted genes were found in clusters containing 2 to 157 members). For further analysis of the secreted genes found in clusters, we focused on the 70 clusters (families) formed by four or more gene members. Using the KOG, KEGG, InterPro and GO databases, we could assign functions to 45 out of the 70 gene families (Table  3 ).
Cytochrome P450, hydrophobins and aspartic-peptidases were the largest gene families. In addition, 17 CAZys clusters were found including glycoside hydrolases (GH), carbohydrate esterases (CE), carbohydrate-binding modules (CBMs) and redox enzymes classified as auxiliary activities (AA). 25 clusters lacked functional annotation, and some of them had a high number of genes (clusters 2, 6 and 7 in Table  3 ). All of these genes belonging to families with unknown function were further analyzed with Phyre2 to predict their protein structure and used for PSI-BLAST (Position-Specific Iterated BLAST) analysis. Using this approach, two gene families were functionally annotated with high confidence (96. 3-97. 4% confidence for individual protein predictions): one as a copper-dependent lytic polysaccharide monooxygenase (LPMO, also known as AA9; cluster 16), and the other as thaumatin-lyke xylanase inhibitor (tlxi, cluster 48).
Two of the orders (LTR and TIRS, which contain long terminal repeats or terminal inverted repeats, respectively) were present in the five species. Class I TEs were primarily responsible for the observed genome size differences-especially the elements belonging to LTR in the Gypsy superfamily, which accounted for more than 15% of the assembly in S. lacrymans and P. tinctorius, but less than 3% in H. pinastri, C. olivacea and C. puteana. Of all the LTR/Gypsy families detected by TEdenovo, we observed that those elements belonging to the Chromoviridae group (carrying a Chromatin organization domain, PF00385, in the N-terminal region after the integrase, Fig. 4 ) were the most abundant LTR-retrotransposons in these five species, ranging from 44 to 83% of the total Gypsy coverage. LTR-retrotransposons in the Copia  superfamily were also particularly abundant in S.
Mitoviruses do not form particles, but form lipid membrane-bound vesicles, and are confined to the mitochondria of their host cells, in contrast to narnaviruses, which replicate in the cytosol  [7]. Several mycoviruses, including some mitoviruses  [7, 8]  have been described to confer hypovirulence to their phytopathogenic fungal host, providing a way to develop biocontrol strategies  [2]. The nucleotide sequence reported in this work has been deposited in the GenBank database under the accession number KY230654. Neofusicoccum luteum is involved in Botryosphaeria dieback, a worldwide grapevine trunk disease  [9]. In the present study, we report the discovery of a novel species of mitovirus infecting N. luteum for which the name of ''Neofusicoccum luteum mitovirus 1'' is proposed. The complete genomic sequence of neofusicoccum luteum mitovirus 1 (NLMV1) was determined and consists of a 2,389 nucleotides long RNA molecule encoding a putative RdRp of 710 amino acids (aa).
Results Double-stranded RNAs were purified and analyzed on agarose prior to random amplification and Miseq sequencing in a multiplexed scheme as previously reported  [11]. After quality trimming and demultiplexing, a total of 136,741 paired reads were obtained, assembled using home-made pipelines, and compared to GenBank database using BlastN and BlastX. A contig of approximately 1. 2 kb, integrating 2. 3% of the total reads, revealed significant similarities with various members of the genus Mitovirus. The 5' and 3' genome ends were determined using purified dsRNA as template and 5' and 3' Rapid Amplification of cDNA Ends (RACE) with internal primers designed from the contig, following the kit manufacturer's instructions (Takara Bio Europe/Clontech, Saint-Germain-en-Laye, France). The complete NLMV1 genome sequence is 2,389 nt long (GenBank accession number KY230654), which is a typical size for a mitoviral genome  [12].
3 Structure-function studies of macromolecular complexes are increasingly moving towards cellular structural biology which requires developing multi-resolution and multi-scale correlative approaches to address the molecular and cellular organization of living cells  1, 2. The strength of such strategies is illustrated for example by correlative light and electron microscopy (CLEM) approaches  [3] [4] [5] [6] [7]. Methods such as protein X-ray crystallography, NMR and cryo electron microscopy (cryo-EM) have had a strong impact over the past decades thanks to their ability of analysing and visualising macromolecules directly in 3D. These well-established 3D techniques encompass dedicated software tools to analyze and also visualize 3D structures, while no equivalent tools exist up to now in the field of single-molecule localization microscopy (SMLM).
Based on Monte-Carlo simulations using an a priori background model (see methods), this type of analysis also indicates whether the experimental distribution is significantly different from the noise model, i. e. if it is clustered or not. Using synthetic data to illustrate the 3D Voronoi tessellation concept (Fig. 2C ; methods), we show that by randomly distributing the same number of points through the same-sized region we obtain a test dataset with Voronoi polyhedrons that should be compared with those of the clustered dataset. This allows determining three characteristic regions which lie between the intersections of probability density functions from the distributions of Voronoi volumes of the two datasets (Fig. 2D ). The smallest polyhedrons correspond to high-density regions that form clusters, while average-sized and big polyhedrons correspond to low-density regions, i.
Additionally, the Voronoi density maps can be conveniently viewed and analyzed with standard tools for processing confocal microscopy or electron microscopy maps because the local density of fluorophores in localization microscopy is proportional to the brightness in classical microscopy. Another advantage of the method is noise reduction when keeping only the points with a density over the threshold determined from the Monte-Carlo simulations (Figs. 3D-F ). The resulting βtubulin dataset demonstrates a strong reduction of background noise without affecting the densely labelled microtubules, even when displayed as a 2D histogram image (Figs. 3E&F). We applied the 3D Voronoi segmentation concept to the cluster analysis of two histone proteins, H2B and CENP-A with distinct spatial organizations (Fig.
4 ). H2B is known to be located inside the nucleus in the form of heterogeneous nanodomains with variable density at euchromatin and heterochromatin regions  31. The distribution of Voronoi cells of an H2B-labelled dataset lies outside the confidence envelope for the noise model indicating statistically significant clustering (Fig. 4A ). Segmented density maps of H2B show bigger and denser clusters at the periphery of the nucleus as expected for heterochromatin (Fig. 4B ), the distribution of H2B cluster sizes being rather broad with a median diameter of 42. 4 nm (Fig. 4C ) in line with previously reported values  31. In contrast, the histone variant CENP-A that replaces the canonical histone H3  24  localizes exclusively at centromeric regions  32  where it plays a key role in determining chromosome location and segregation  23, 25.
It provides automatic threshold determination and noise filtering. In addition, to avoid distortion effects due to non-isotropic distribution of experimental data points we introduced weighting in Z-direction (see methods; taken into account also for Monte-Carlo simulations). As an example, it takes 6 hours to segment a large experimental dataset with a volume of ~200 µm 3 that contains 10 6 localizations using an i7 quad-core single processor with 32 Gb of memory. The calculation includes 50 rounds of Monte-Carlo simulations and interpolation to a 3D density map with a voxel size of 20 nm. Finally, as compared to a 3D implementation of DBSCAN  27  (see methods) which requires multiple trials through individual runs to optimize parameters manually without additional a priori knowledge, our method proceeds in a single run with automatic threshold determination from Monte-Carlo simulations using an a priori background model (into which re-localization events can be included if known) and it proves to be more robust with increasing background densities thanks to noise modelling (Figs.
Standalone software for 3D data processing with a GUI (3DClusterViSu) and a standalone Python-based 3D Voronoi visualization tool with a GUI are available under http://cbi-dev. igbmc. fr/cbi/voronoi3D/repository/archive. zip. (A) Clusters in a 3D dataset (number of clusters = 150, density of localization within clusters = 6⋅10 -4 nm -3 , in the background = 8⋅10 -6 nm -3 ) can be easily segmented using the 3D Voronoi diagram approach described in the current work. (B) The same dataset projected on the X-Y plane and processed with a 2D segmentation method  19  does not allow to determine the correct shape, size and number of clusters. (C) Similar dataset with higher background density (6⋅10 -5 nm -3 ), lower cluster density (4⋅10 -4 nm -3 ) and higher number of clusters (175) becomes undistinguisheble from a random distribution when projected in 2D, but still can be successfully processed in 3D (see later Fig.
2C-H ). In the left column are the clusters determined with the 3D Voronoi approach described in the current work (A), with our previously reported 2D Voronoi approach  19  (B) and using the mean local density as the threshold for binarization of 2D data 25 (C). Figure legends In the middle column are probability density functions of volumes (A) or surfaces (B, C) of Voronoi cells for the processed datasets (blue) in comparison with that of the background model, a spatially randrom distribution (magenta and green). The experimental curve inside the confidence envelope (C) indicates that the experimental dataset is undistinguishable from the background model and so no clusters can be detected with confidence. In the right column are histograms of the equivalent radius of the detected clusters, to be compared with the simulated cluster radius of 30 nm.
Data processing The localization and fitting of single-molecule events were performed in real time during acquisitions in Leica LAS AF software with the "direct fit" fitting method. The localization tables were then exported for further processing in the SharpViSu software workflow 2 and with customized Matlab procedures. To reduce the number of localizations of the same fluorophore and improve localization precision the data were processed by averaging the coordinates of consecutive events within a radius of 50 nm around each localization. The drift was detected and corrected in three dimensions using cross-correlation-based approach. Briefly, the dataset was divided into several consecutive subsets, from each of them a histogram image with pixelation of 20 nm was build; next, the shift between these images was detected with subpixel precision and then interpolated linearly throughout intermediate frames.
The shift value was then subtracted from the coordinates of every frame. The lateral drift was detected using the projection on the XY plane, and the axial drift was detected using the average shift, calculated from the XZ and YZ projections. The procedure was repeated iteratively several times assuring absence of detectable residual drift  2. Regions of interest (ROI) for Voronoi analysis were selected manually allowing faster computations and more homogeneous distributions compared to the entire field of view. The following analysis was performed in Matlab using customized code. Voronoi diagrams (vertices of polyhedrons and connectivity order) were retrieved with 'voronoin' function. Volumes of the cells were determined from the vertices with the function 'convhulln' 3 , the local density in each data point was defined as the inverse value of the volume of the corresponding Voronoi polyhedron.
The spacing of the grid corresponds to the desired voxel size (20 nm in our case). For processing of experimental data acquired with astigmatism, the points for Monte-Carlo simulations were generated to have completely spatially random X, Y coordinates (the 'rand' function), and Z coordinates picked from a normal distribution with standard deviation σ = 300 nm (the 'normrnd' function). The points were distributed over the manually defined field of view (FOV). The distributions were generated for 50 different random sets of points, the boundaries of the confidence envelope were determined as <n> ± 1. 96⋅σ for each bin of the histogram, where <n> is the average number of cells within the range of the bin and σ is the standard deviation of n, calculated from the 50 random datasets. The abscissas of the first and the second intersections between the curves of the experimental and the mean value of the randomized distributions were determined from the two points around the intersections in the linear approximation.