dataesr/scientific-paragraphs-categorization huggingface dataset:
BUSCO runs We computed BUSCO  [25]  statistics on the whole proteome dataset. First, we generated a FASTA file for each proteome. Then, we ran BUSCO (version 4. 1. 4) on every individual proteome, using the most specific odb10 reference set for this species. This was determined automatically by mapping datasets to the NCBI taxonomic ID in each species lineage. Indication of the BUSCO set used for each species and all statistics are available in Additional File 1: Table  S1. We divided all proteomes into two sets: complete or incomplete proteomes, based on the number of genes with a complete BUSCO score (not missing nor fragmented). "Complete" proteomes were those with at least 90% of their genes found as "Complete" BUSCO. The complete set was composed of 1942 species, including 253 eukaryotes, 136 archaea, and 1553 bacteria.
Identification of differentially expressed genes DEGs were identified by DESeq2  [61]. A round number of gene counts from the RNAseq data were used as the input for DESeq2 with one data set containing three BS Day 0, Day 1, and Day 3 replicates. The data set was analyzed in four compared groups: day 1 versus day 0, day 3 versus day 0, day 3 versus day 1, and day 3 versus day 0/1. Genes were filtered with expression values of 0 in at least 78% of the samples. Differential expression data was obtained by comparing each group (fold change > 2; p-adjusted < 0. 05) with other parameters kept at the default settings. Ensembl transcript IDs were converted to Ensembl gene IDs or gene names by g:Prolifer (https:// biit. cs. ut. ee/ gprofi ler/ conve rt) and DAVID Bioinformatics Resources (https:// david.
Weighted gene co-expression network analysis To identify gene modules correlated with regeneration in day 3 BS replicates, R-package WGCNA  [62]  was conducted with the 1149 DEGs identified by DESeq2. The co-expression network was constructed with fragments per kilobase of exon per million mapped fragments (FPKM) using the blockwise modules function for day 3 and day 0/1 BS replicates. An adjacency correlation matrix was calculated for the DEGs, and the correlations were weighted to a soft threshold power β, which favors strong over weak correlations. For each pair of genes, a robust measure of network interconnectedness was calculated based on the adjacency matrix. The parameters used were maximum block size-1000 genes, power (β)-20, minimum module size-25, minimum height for merging modules-0.
Identification of hub genes Hub genes were identified by their first principal component, the module eigengene, as a summary of overall module expression, using Cytoscape  [63]  and Network Analyzer  [64]. The hub genes were ordered by descending degrees of connectivity, and genes of highest degree were selected to plot the network. The hub genes with lower degrees of connectivity were identified by Cytoscape cytoHubba using the Density of Maximum Neighborhood Component (DMNC) method  [65].
Functional enrichment analysis GO functional enrichment analysis was conducted using DEG in the WGCNA turquoise and blue modules. The gene function of GO categories was enriched using g:Profiler (https:// biit. cs. ut. ee/ gprofi ler/ gost) (FDR < 0. 05). The top 20 significant GO term elements in the turquoise and gray modules were visualized by the ggplot2 package in R software.
Phalloidin staining Distal body fragments were fixed in 5% formalin and washed in PBS, and actin filaments were stained with 25 μg/ml rhodamine-phalloidin (Molecular Probes, Eugene, OR, USA) at room temperature in the dark  [21]. After staining, the specimens were washed three times in PBS (10 min) and imaged by fluorescence microscopy. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Several of these characteristics (listed in data analyses section) will be used in the regression model as predictors of divergent thinking ability. The tool that we will use to measure divergent thinking ability is the Abbreviated Torrance Test for Adults (ATTA) test, a shortened form of the TCTT. 9 The ATTA was validated in a population of undergraduate and graduate-level students from the Netherlands  14  and measures 4 norm-referenced creative abilities (fluency-the ability to produce quantities of ideas relevant to the task instruction; originality-the ability to produce uncommon ideas or ideas that are new or unique; elaboration-the ability to embellish ideas with details; flexibility-the ability to process information or objects in different ways, given the same stimulus) as well as 15 indicators of creativity.
Data analysis plan We have planned descriptive statistical calculations (ie, mean, median, SD, range) and a multiple linear regression analysis after the completion of data collection and test scoring. We will perform a tabulation of participant characteristics overall and by specialty, and descriptive results of ATTA scores by specialty will be graphically displayed. We will report the estimate for creativity as a mean ATTA score with a 95% CI (median with IQR if not normally distributed). We will use multiple regression to determine associations between the ATTA scores and key characteristics including age, sex, years of surgical training, creative self-efficacy, undergraduate degree, hours spent alone/week, hours worked/week and research output; the results will be reported as an estimate of the association, with the corresponding 95% CI and associated p value.
We will perform an assessment of model assumptions and goodness-of-fit by examining the residuals, as well as a sensitivity analysis treating surgical specialty as a random effect to account for potential clustering or similarity of scores among participants with the surgical specialty. For each independent variable in the regression, at least 10 observations will be required for inclusion into the model; if this threshold is not met, the levels of each independent variable will be collapsed if possible, or excluded. All statistical analyses will be conducted using SPSS V. 26. 0. 18 The statistical analysis plan is summarised in online supplemental table 1.
RESULTS The F-TRACT project  [37, 38]  processed multicenter SEEG data acquired in a large sample of patients with drug-resistant epilepsy. The position and number of implanted electrodes were separately determined for each patient based on prior clinical knowledge. Patients underwent focal single-pulse stimulation and invasive recordings of neural activity at millisecond resolution. Compiling data from a total of 550 participants, 29,055 stimulations and 2. 77 million pairs of recording electrodes, allowed the computation of whole-brain, group-level probability (P) and amplitude (A) matrices of regional activation following stimulation  (Fig 1a ; Materials and Methods). The matrix entries P ij ∈ [0, 1] and A ij ∈ R >0 capture, respectively, the probability of observing a significant excitatory response in region j following stimulation of region i, and the median amplitude of significant responses.
To investigate the role of brain connectivity on stimulus response, we mapped structural and functional brain networks from MRI data of 1,000 healthy participants of the Human Connectome Project  [40]  (HCP; Fig  1b ; Materials and Methods). We generated four grouprepresentative normative measures: structural connectivity (SC), structural connection frequency (F) across HCP participants, functional connectivity (FC) and interregional Euclidean distance (ED). While our primary goal is to understand communication via white matter connectivity, considering functional and spatial factors enabled a comprehensive examination of how stimulus propagation is associated with multiple aspects of brain organization.
A significant response is measured by contact 1, as defined by a z-scored CCEP larger than 5. Contact 2 does not record a significant response. Based on its spatial coordinate, each contact was assigned to a cortical gray matter region. Whole-brain matrices of response probability (P) and median amplitude (A) were computed by combining data from 29,055 stimulations in 550 subjects. (b) Normative, group-representative measures of brain connectivity were inferred from 1,000 healthy participants of the Human Connectome Project using structural, diffusion, and resting-state functional MRI data. (c) Pairs of regions used to model response probability. Violet and copper matrix entries denote regions that are likely and unlikely to be anatomically connected, respectively. Warm-and cool-colored line plots indicate the number of times each region featured as a stimulation and recording site, respectively, and were projected onto the cortical surface on panel (d).
Control analyses We performed multiple control analyses (Materials and Methods) and found that our results remained consistent across a number of methodological choices  (Fig S2) , including replication in an independently acquired and processed normative connectivity dataset (Figs S3, S4). In addition, using ensembles of randomized null networks, we observed that our findings were contingent on the empirical topology of SC, such that rewiring of connections markedly reduced correlations between network communication measures and stimulus response  (Fig S5).
DISCUSSION Here, we used a large dataset of direct electrical stimulation to study inter-areal communication in the human brain. Considering the causal effects of interventional stimulation allowed us to characterize the propagation of electrical pulses without the need for statistical or information-theoretical measures of information transfer between regions. In addition, we leveraged intraand inter-subject variability in electrode placement to derive whole-brain, high-spatiotemporal-resolution maps of stimulus propagation. Together, these factors enabled us to test whether computational modeling of communication dynamics in the human connectome can explain empirical stimulus transmission through the brain.
F-TRACT dataset The F-TRACT project is a continuing effort in collecting, processing and compiling iEEG recordings of drug-resistant epilepsy patients being prepared for resective surgery  [37, 38]. In this study, we used data from 550 patients (50% women, age 31 ± 10 years old) collected across 20 epilepsy centers in Europe, North America and Asia (https://f-tract. eu/consortium). Patients were implanted with intracerebral depth electrodes (stereo-electroencephalography; SEEG) with an average of 87 ± 37 electrode contacts per participant. Corticocortical evoked potentials (CCEPs) were recorded following low-frequency, bipolar direct electrical stimulation with the following parameters: 1 Hz stimulation, biphasic (79% of CCEPs) or monophasic (21%) electrical pulses, 3. 3 ± 1. 2 mA mean pulse intensity, 1.
The position of electrode contacts was computed based on a patient's structural MRI (T1-weighted imaging) and computerized tomography scans. Contacts placed in gray matter were assigned to the corresponding regions of the HCP-MMP1. 0 parcellation  [75]. For contacts placed in white matter, we first computed a 3 mm radius sphere centered in the contact's coordinates and assigned them to the gray matter parcel with highest voxel representation within the sphere. Whole-brain, group-level response probability (P) and amplitude (A) matrices were computed by compiling CCEP data from 2,769,985 SEEG recordings following 29,055 electrical stimulations across 550 individuals. The matrix entries P ij ∈ [0, 1] and A ij ∈ R >0 capture, respectively, the probability of observing a significant excitatory response in region j following stimulation of region i, and the median z-scored CCEP amplitude of significant Note that P and A are asymmetric, such that P ij may differ to P ji.
Normative measures of brain connectivity: HCP dataset Structural connectivity (SC) was inferred from minimally preprocessed, high-resolution diffusion-weighted MRI data from 1,000 healthy young adults participating in the Human Connectome Project. Details of diffusion MRI acquisition and preprocessing are described in  [76, 77]. Whole-brain white matter tractograms of individual participants were mapped using a probabilistic tractography pipeline (MRtrix3 software  [78] , multi-shell multi-tissue constrained spherical deconvolution  [79] , anatomically constrained tractography  [80] , iFOD2 tracking algorithm  [81] , 5M streamlines seeded from the gray-white matter interface; see  [82]  for further details). Cortical gray matter was divided into 360 regions using the HCP-MMP1. 0 parcellation  [75] , while the Freesurfer parcellation  [83]  was used to define 14 subcortical structures (left and right thalamus, caudate, putamen, pallidum, hippocampus, amygdala and accumbens area).
Normative measures of brain connectivity: MICA dataset The Multimodal Imaging and Connectome Analysis (MICA) dataset is an openly available resource (https: //portal. conp. ca/) containing structural and functional brain networks for 50 healthy adults (23 women; 29. 5 ± 5. 6 years old)  [89]. Brain networks were inferred using independent acquisition, preprocessing and connectivity mapping pipelines. We utilized the MICA brain networks comprising 374 regions, delineated according to the same cortical and subcortical parcellations used in the HCP dataset. To account for the nearly fullyconnected nature of MICA structural networks (average connection density 84. 1% across subjects) and prune potential false positives  [84] , we thresholded individual-level SC matrices by keeping only the top-25% strongest structural connections.
90% [control analysis]). • Maximum structural connectivity frequency across HCP subjects for considering two regions unlikely anatomically connected (25% [main text] vs. 10% [control analysis]). • z-scored CCEP threshold for significant responses (z = 5 [main text] vs. z = 4, 6 [control analyses]). • Minimum Euclidean distance between regions pairs (20 mm [main text] vs. 0 mm [control analyses]). Secondly, in Figs S3, we re-examined all the scenarios above starting from structural and functional connectivity from the Multimodal Imaging and Connectome Analysis (MICA) dataset.
As such, predictor variables selected by the lasso regression tend to explain complementary portions of the variance in y. Model training was done using 10-fold cross-validation by splitting X and y into train and test sets. Model fitting was performed exclusively on train sets and prediction accuracy was evaluated exclusively on test sets. To account for sensitivity to particular train-test splits, we repeated the 10-fold cross validation routine 15 times  [95]. Computations were carried out using the cvglmnet MATLAB function  [96]. More specifically, let {X a , y a } and {X e , y e } denote a split of X and y into train and test sets, respectively. The lasso regression computes β ∈ R 8×1 as min β { 1 n ||y a -X a β|| 2 2 + λ||β|| 1 }, where the hyperparameter 0 ≤ λ ≤ 1 determines the sparsity of feature selection.
Control analysis in which response to stimuli were considered significant for z-scored CCEP amplitude larger than 4. (i-j) Control analysis in which response to stimuli were considered significant for z-scored CCEP amplitude larger than 6. (k-l) Control analysis in which no restrictions were imposed on Euclidean distance between pairs of regions. FIG. S3. Control analyses for normative brain connectivity inferred from the Multimodal and Connectome Analysis (MICA) dataset. Each row of plots shows results obtained for a different set of methodological choices. Panels (a-b) shows results obtained considering the parameters utilized in the main text: (i) pairs of regions were included in the analyses of response probability (amplitude) if they had at least 100 stimulation experiments (significant responses); (ii) pairs of were considered to be anatomically connected (unconnected) if they shared a connection in at least 75% (no more than 25%) of participants; (iii) response to stimuli were considered significant for z-scored CCEP amplitude larger than 5; and (iv) pairs of regions were included if they were at least 20mm apart in Euclidean distance.
ACKNOWLEDGMENTS Brain imaging data were provided by the  Human Connectome Project ,  WU-Minn Consortium  ( 1U54MH091657 ; Principal Investigators:  David Van Essen  and  Kamil Ugurbil ) funded by the 16  National Institutes of Health (NIH)  institutes and centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. CS was supported by the  Australian Research Council  (grant number  DP170101815 ). MJ and OD were supported by funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement no. 616268  F-TRACT, the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation under Specific  Grant Agreement No. 785907  and  945539  ( Human Brain  Project  SGA2  and  SGA3 ), and from the  Agence Nationale de la Recherche  grant number  ANR-21-NEUC-0005-01.
We report a comprehensive data set characterizing and quantifying the geometrically necessary dislocation (GND) density in the crystallographic frame (ρ c α ) and disclination density (ρ θ ) in fine-grained polycrystalline olivine deformed in uniaxial compression or torsion, at 1000 and 1200 • C, under a confining pressure of 300 MPa. Finite strains range from 0. 11 up to 8. 6 %, and stresses reach up to 1073 MPa. The data set is a selection of 19 electron backscatter diffraction maps acquired with conventional angular resolution (0. 5 • ) but at high spatial resolution (step size ranging between 0. 05 and 0. 1 µm). Thanks to analytical improvement for data acquisition and treatment, notably with the use of ATEX (Analysis Tools for Electron and X-ray diffraction) software, we report the spatial distribution of both GND and disclination densities.
2 µm). Olivine, diopside, enstatite, and Cr-spinel were acquired during mapping. EBSD data were recorded and cleaned using the AZtecHKL and HKL Channel 5 programs and further treated with the ATEX program (Analysis Tools for Electron and X-ray diffraction;  Beausir and Fundenberger, 2017)  to determine dislocation and disclination densities. The theoretical framework for disclination detection and quantification is already fully detailed in  Beausir and Fressengeas (2013) ,  Cordier et al. (2014) , and  Fressengeas and Beausir (2018)  and will not be reproduced in full here; only the basic principles are recalled below. The noise reduction of raw data is partially performed using HKL Channel 5 Tango (Oxford Instruments) and finalized using ATEX  (Beausir and Fundenberger, 2017)  and can also be completed using MTEX (MATLAB toolbox; see  Hielscher and Schaeben, 2008; Bachmann et al.
Sample Temperature Finite strain Equivalent strain Differential stress T ( • C) ε (%) /γ rate ε/ γ (s -1 ) σ (MPa) Starting material (hot-pressed) we kept the same quality factor, also called the mean angular deviation (MAD < 2 • ) as in previous studies (e. g. ,  Thieme et al. , 2018). ATEX also permits displaying the MAD number variation, and average values range between 0. 38 and 0. 63 • (see Table  S1  and Fig. S1  for an example of the starting material and. cft files available on Zenodo). Grain boundaries can display large values but rarely exceed 1 • (Fig. S1 , Table  S1 ). Finally, the densities of geometrically necessary dislocations and the densities of disclinations are solely calculated along the olivine-olivine boundaries. Potential residual enstatites or Cr-spinel grains are excluded from the data treatment.
This choice is also consistent with the 10 • used to define grain boundaries. Once disclinations are detected, the densities can be calculated, but from a two-dimensional map, only θ 13 , θ 23 , and θ 33 can be obtained. Nevertheless, we emphasis that, to date, ATEX is the only piece of software able to pro-Table  2. For the studied EBSD maps, we report the indexation rate after data treatment, the original step size and subsequent re-step size (italicized and bold), the number of detected grains, the average grain size, and the calculated partial GND dislocation densities. We provide the average of grain density and the median grain density for α ij. The average of each α c ij and θ ij per grain is given in Table  S1  in the Supplement. Roman numerals identify individual EBSD maps acquired for the same sample ("S" stands for small-area maps).
While the extracted densities permit exploring limited statistical parameters, one of the complementary approaches is also to compare dislocation distributions in maps visually. Is one able to see in the maps the dislocation density difference observed in average and median densities between the lowand high-temperature deformation in axial compression? We illustrate the answer with Fig. 11 , which shows two ways to display the GND norm. Figure  11a  and b are identical, and Fig. 11c  and d  are also identical. Only the color scale is different; in blue, we used the MATLAB classic linear color scale "jet" ("rainbow" in ATEX, starting with blue, with a smoothing factor of 2), which is also used typically for KAM (kernel average misorientation) maps, and in green we used a log scale ("linear green" in ATEX, with a smoothing factor of 1).
Acknowledgements. Sylvie Demouchy thanks  Christophe Nevado  and  Doriane Delmas  for providing high-quality thin sections for EBSD and  Catherine Thoraval  and  Thierry Menand  for constructive input. Sylvie Demouchy  wholeheartedly thanks  Claude Fressengeas  for a crucial meeting at the colloque Plasticité in Metz in 2012. The  SEM-EBSD  facility at  Université de Montpellier  is supported by the  Institut National de Sciences de l'Univers (INSU)  from the  Centre National de la Recherche Scientifique (CNRS, France)  and the  Conseil Régional d'Occitanie (France). ATEX is available at http://atex-software. eu (last access:  27 March 2023 ). Financial support. This research has been supported by  Horizon   2020  via the  Marie Skłodowska-Curie Actions  (ITN CREEP; grant no. 642029 ), the  Centre National de la Recherche Scientifique  ( TelluS-SYSTER  project  DOMINO to Sylvie Demouchy ), and the  European Research Council via Horizon 2020  (TimeMan; grant no.
Data availability. All EBSD data (. ctf) used to generate the ATEX maps presented in Supplement S1 are available in an open Zenodo repository (https://doi. org/10. 5281/zenodo. 7486136, Demouchy, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/ejm-35-219-2023-supplement.
e (Top) Western blots of LSD1 levels in MN1 cells expressing Cas9 with or without a specific guide (g4) to silence Lsd1 (n = 5 biological replicates). ** g4 in AR24Q cells p = 0. 0065, ** g4 in AR100Q cells p = 0. 0054. (Bottom) Transcriptional assays in Cas9 and g4 cells expressing AR24Q or AR100Q and treated with DHT (10 nM, 16 h; n = 3 biological replicates). LSD1, PRMT6, and AR were detected with specific antibodies, and β-Tub was used as loading control. Graphs show mean ± SEM; two-tailed Student t-test (a, e-transcriptional assays), or two-way (b, c, e-Western blot), or one-way (d) ANOVA followed by Tukey HSD tests. Source data are provided as a Source data file. downregulated genes, GSE193539) in SBMA muscles compared to WT muscles (absolute log2 fold change >2, corrected p < 0. 01) (Fig.
Cells were washed once with 3% BSA in PBS and with PBS for 5 min. Hoechst 33342 (Thermo Fisher Scientific, #62249, Component G, 5 μg/mL) was added for 30 min at RT in the dark. Cells were washed twice with PBS, and coverslips were mounted on slides. Slides were imaged with a ×20 objective using the Zeiss Axio Observer Z1 inverted microscope, and EdU-positive red cells were quantified using ImageJ 1. 51 software.