dataesr/scientific-paragraphs-categorization huggingface dataset:
In a first attempt, we described each map's organization through eight-dimensional vectors whose entries were given by synaptic weights averaged over the eight structural microzones (g weights_zonewise (M), zone-wise synaptic weights in Fig. 5 ; see Supplementary Fig. 1 ). Figure  5a  shows a bi-dimensional visualization of the distributions of single-map vectors in our dataset, obtained via a non-linear dimensionality reduction algorithm (t-Stochastic Neighbor Embedding technique, t-SNE, see Methods section) to visualize clusters of maps with similar synaptic weight profiles. When maps were labeled by conditions, 2D projections showed strong scattering and overlapping centers of gravity (i. e. , group medians of t-SNE projection coordinates, Fig. 5a ), indicating a poor separation of behavioral groups.
As shown by Supplementary Fig. 8b , we also constructed random forest classifiers to discriminate maps at different developmental stages (cf. Fig. 2 ). Maps from the control groups of both the datasets of Fig. 2  (developmental) and 4 (adaptive) were correctly classified in a common class by the classifier. This fact confirms that our approach robustly operates even when maps are (down-)sampled to the lower spatial resolution of Fig. 2. Individual connectivity maps reflect individual-specific behavioral features. The large variability between maps may be due to experimental "noise" or reflect on the contrary fine levels of behavioral differences across individuals in normal or adaptive conditions. To investigate this hypothesis, we tried predicting individual-level specificities in adaptive locomotor behavior from graph descriptions of the global synaptic maps (g global (M)) and behavioral performance either on the wheel (short/long training) or on the force pressure corridor (adapted sham/cuff).
e. , prediction performed by models on data points for which they were not trained on, see Methods section): the Pearson correlation between actual and GLM-predicted values (R-value; top right insets of Fig. 6b, c ); and the Mean Square Error of prediction (MSE; bottom right insets of Fig. 6b, c ). Both these metrics were evaluated over the whole pooled short and long training groups or separately over the two groups. For the larger pooled dataset, both metrics were significantly better than chance expectations. R-values for pooled data reached 0. 81 ± 0. 04 (actual) vs 0. 02 ± 0. 08 (shuffled, Fig. 6c ) for total distance and 0. 3 ± 0. 07 (actual) vs -0. 04 ± 0. 06 (shuffled, 95% CI, permutation testing, Fig. 6b ) for motor training slope. The MSE for the pooled total distance data was 1.
They thus reflect structure at many different and nested intermediate scales  77  , that overlap only imperfectly with traditional anatomical subdivisions (Supplementary Fig. 7 ) and that reveals a mixture of "segregation" and "integration", two notions more commonly invoked in reference to neuroimaging data  78. As a matter of fact, individual-specific adaptive behavior can be predicted by graph features even when averaged over all the sites in the map, thus fully ignoring anatomy (Fig. 6 ). At the same time, the discrimination between conditions is improved by using lateralized (ipsi vs contra) with respect to global graph metric averages but averaging to the level of individual microzones does not yield additional improvement (Fig. 5d  vs Supplementary Fig. 8 ). It is thus likely that the number of available data for classifier training puts a limit to the amount of information that can be extracted without overtraining  79  and that more precise characterizations could be still achieved by using larger datasets.
Force signals of the left and right side of the body were acquired simultaneously using WinWCP 4. 2. 2 freeware (John Dempster, SIPBS, University of Strathclyde, UK). The balance index (BI; Fig. 3 ) corresponds to the log of the ratio of the integrated force signal from each side of the body. All recordings contained at least five consecutive strides, otherwise they were discarded. A unique datapoint was established as the average of minimum three trials for each mouse in each session. For further technical details (i. e. , apparatus dimensions, scripts, and wiring diagrams) see https://github. com/ludo67100/cerebellarMaps/tree/main/Balance_supplementary. In cuffed, sham, and control animals, the BI was determined before and after surgeries every 2-3 days for 1 month. A time-course was established, and we estimated the cumulative gait imbalance during the early days (AUC early for the area under the curve; see Fig.
8, Zeiss, Germany; Fig. 1a ). The developmental dataset was performed with an orthogonal grid of 32 sites (40 × 40 µm per site; low-resolution) while synaptic maps recorded after locomotor adaptation were obtained with an orthogonal grid containing 96 sites (20 × 20 µm per site, high-resolution). A single grid covers 320 µm of the GC layer in the mediolateral axis. One synaptic map is composed of 2-4 adjacent fields of view (yielding 64 to 128/192 to 384 sites in total for low/high resolution respectively) covering up to 1280 µm along the mediolateral axis (see Fig. 1 ). Two photostimulations of a given site were separated by 60 s minimum. A single site of the granular layer was photo-stimulated between 5 and 10 times in total (yielding 5-10 recordings for averaging and analysis, Supplementary Fig.
Zebrin bands were identified using intrinsic Venus fluorescence. Recorded cells were labeled using Alexa 555-Streptavidin (Thermo-Fisher, 1/1000, 3 h at room temperature). Ipsi-and contralateral P1+, P1-, P2+, and P2-Zebrin bands and distance between the recorded PC and the midline (P1+) were measured in each experiment. In adult CD1 mice, Zebrin band lengths in lobule III/IV/V were (µm, average ± SD, n = 100): P2-contralateral 416. 6  Recorded PCs were located at 52. 42 ± 29. 8 µm from the midline of lobules III/IV/V, corresponding to the cluster 1 defined in Valera et al. 17. Data processing and analysis. Data processing and analysis of synaptic maps and graph parameters were performed with customized scripts and routines written in Python 3. 6 with the following packages: Pandas 1. 3, Scipy 1.
6, statsmodels 0. 12, Sci-kit learn 0. 24, Numpy 1. 19, Neo 0. 10 88 , Orange 2. 7 89 , bctpy 0. 5. 2. Plots were generated with Matplotlib 3. 4 and/or Seaborn 0. 11. Python-based code is available in the following repository: https://github. com/ludo67100/CereballarMaps-GraphProperties. Reconstruction of synaptic maps and profiles. For each experiment, the absolute positions of the recorded PC and photo-stimulation sites were normalized to the size of the corresponding P1 -ipsilateral Zebrin band (% of P1 -). Median profiles and average maps were therefore obtained from spatially aligned maps within a global referential defined by the Zebrin band patterning, highly conserved between individuals (Supplementary Figs. 1  and 3 ; see also Apps and Hawkes)  39. Each GC site was photostimulated 5-10 times and EPSCs were averaged.
1c  in which a block structure is better visible than before reordering of nodes. The resulting graph can be represented using a linear layout, in which the positions of nodes reflect exactly the ordering x of the original columns, or a "force-spring" embedding, in which nodes belonging to the same module are also positioned to be spatially closer between them, to emphasize the existence of modules in the graph visualization. Graph-based features. All the absolute graph properties were calculated using the Brain Connectivity Toolbox for python (https://pypi. org/project/bctpy/) designed by Rubinov and Sporns  90. We characterized the GC-PC maps using graph properties such as: (a) Modularity index -modularity index maximizes the number of withingroup edges and minimizes the number of between-group edges using the Louvain community detection algorithm.
The sklearn. manifold. TSNE function in python was used. Centroids of point clouds corresponding to maps obtained in similar locomotor adaptation conditions have been obtained by computing the median projected coordinates of all the maps in the considered point cloud. Subtype Classification using random forest classifier. The absolute graph features of three different resolutions (g global (M), g bilateral (M), and g zonewise (M)) and zone wise synaptic weights (g weights_zonewise (M)) were used to train and test four random forest classifiers in order to classify the different locomotion contexts (control, short and long training, early cuff and sham, and adapted cuff and sham). A random forest classifier uses multiple decision trees and combines their results to enhance the classification performance.
The classifier used 150 individual tree estimators per tree ensemble, each with a maximum depth of 30. The data was divided into 100 stratified trials, with training:testing ratio of 80:20%. Proportion of correctly and misclassified samples (evaluated as generalization performance, i. e. , applying the classifier on the testing subset not used for training) were quantified in a confusion matrix and the accuracy was calculated as a percentage of samples that were correctly classified. To compare the accuracy score with chance level scores, random forests were also trained on shuffled data, using identical hyper parameters but by shuffling target prediction labels. The sklearn. ensemble. RandomForestClassifier function from the sklearn package in python was used. We also used an analogous procedure to train a classifier to discriminate maps from different developmental stages.
Acknowledgements This work was supported by the  Centre National pour la Recherche Scientifique (CNRS) , the  Université de Strasbourg , the  Agence Nationale pour la Recherche  ( ANR-2015-CeMod, ANR-2019-MultiMod ,  ANR-2019-NetOnTime ) and by the  Fondation pour la Recherche Medicale  to PI (#  DEQ20140329514 ). JB and DB acknowledge support by the  Agence Nationale pour la Recherche  ( SENCE ,  ANR-18-CE37-0018-03 ). DB was supported by the  University of Strasbourg Institute for Advanced Studies , allowing his stay in Strasbourg (fellowship  USIAS-2020-044 ). LS was funded by fellowships from the  Ministère de la Recherche  and  Fondation pour la Recherche Médicale  (# FDT201805005172 ). We thank  Nathalie Petit-Desmoulières  and  Dr. Michel Barrot ,  Dr Elise Savier ,  Dr. Sophie Reibel-Foisset  and the staff of the animal facility (Chronobiotron, UMS 3415  CNRS and Strasbourg University ) for technical assistance.
g. fiche, bec, fasse, pote). This task provides a baseline for each participant's pronunciation of the L1 French vowels most closely corresponding to the two English vowels (/ɪ/ and /ɑ/) heard in the pseudowords. It also provides a baseline for each participant's pronunciation of the vowels represented by the French grapheme-to-phoneme correspondences (<i> $ /i/, <o> $ /ɔ/); recall that we hypothesize that our French participants use L1 GPCs to form phonological targets. All four tasks were controlled by E-Prime 2. 0 software (Psychology Software Tools, 2016), using an AKG C520 head-worn microphone and Roland Rubix22 audio interface for the audio recordings (Tasks 1 and 4). At the end of the test session, each participant completed a language background questionnaire.
Following  Baayen, Davidson, and Bates (2008) , each model was run twice, with a first model on all data points and a second model without the residuals of the previous statistical model that were larger than 2. 5 standard deviations. The output of the second model is reported. Extreme values, defined based on density plots, were disregarded prior to running the statistical models. The Box-Cox test  (Box & Cox, 1964)  was run on each dependent variable to decide on the most relevant transformation (choosing one of the following options: natural logarithm, inverse (À1/response times) or no transformation). Unless otherwise specified, the analyses were pre-registered (https://osf. io/cdh7n). The data and scripts used in these analyses are publicly available (https://osf. io/krsqt/).
Response latencies The data set was restricted to correct responses to the first presentation of the picture, excluding responses produced with dysfluencies or errors (440 responses) and responses over 2500 ms (15 responses). A total of 425 responses were available for this analysis. Following the Box-Cox, the naming latencies were log transformed. Response latencies were significantly shorter in the Audio-Ortho (1241 ms, SD = 506) than in the Audio condition (1359 ms, SD = 514); (b = 0. 1, SE = 0. 03, t = 3. 65, p < 0. 001), providing support for the hypothesis that the presence of orthographic input in learning facilitates the retrieval of words from the production lexicon. Response latencies were numerically shorter in the Multiple talker condition (1251 ms, SD = 400) than in the Single talker condition (1337 ms, SD = 408) but this difference was not significant (b = 0.
Acoustic analyses We performed acoustic analyses to assess the pronunciation of the vowels in the newly learned words, using four formant-based measures. In these analyses, the data from all four repetitions of Task 1 (Picture naming) were considered, including only items that had been coded as correct responses in the accuracy coding (1981 items). For each item, the beginning and end of the word and of the vowel were labelled, using  forced alignment  (Kisler, Reichel, & Schiel, 2017 ) and hand correction. The analyses were performed in Praat  (Boersma & Weenink, 2020) , using scripts to semi-automate the process. The first two formants (F1, F2) were extracted from the vowel mid-point and hand corrected, using a Praat script adapted from  Styler (2015). Items with unclear formants were excluded (33 items), leaving 1948 data points for these analyses.
Euclidean distance. For the analysis of Euclidean Distance (ED), one outlier (a value above 4) was removed, leaving 1947 data points for the analysis. We fitted a linear mixed-effects model with the logarithm of the ED values as dependent variable and learning Modality as fixed effect. We also included Vowel as a covariate. As predicted, ED in the Audio-Ortho condition is smaller (1. 02, SD = 0. 76) than in the Audio condition (1. 34, SD = 1. 1; b = À0. 24, SE = 0. 051, t = 4. 90, p < 0. 0001). Moreover, ED is also smaller for <i> than for <o> (b = 0. 28, SE = 0. 067, t = 4. 16, p < 0. 00065). Analyses of Dispersion score and Euclidean distance provide support for the hypothesis that orthographic information provides phonological targets to which the productions converge. We performed an exploratory analysis to examine whether input from multiple talkers led to smaller Euclidean distance for words learned with both audio and orthographic input.
Acknowledgments This research was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation , project number  317633480 -SFB 1287 , Project  B05 ,  PI. Audrey Bürki ) as well as by the  University of Potsdam International Cooperation Program (KoUP)  and the  Kommission für Forschung und wissenschaftlichen Nachwuchs  at the  University of Potsdam. This research was supported by the  Institute for Language, Communication , and the  Brain  (https://www. ilcb. fr). We thank  Jasmin Sadat  for her assistance with data collection and coding, and  Nadéra Bureau ,  James Sneed German ,  Alain Ghio , and  Catherine Perrot  for finding solutions at key points in the project. We thank two anonymous reviewers for their insightful comments.
056) increase in 5-hydroxyindole-treated rats compared to the control group (Figure  1a ; Table A in S1 Table ). Next, the microbiota diversity was determined by Shannon's H and Simpson's index, both indices are used to measure similar parameters of alpha diversity. 19, 20 The diversity index did not differ between the treated and untreated groups (Figure  1a ; Table A in S1 Table ). The data highlight that 5-hydroxyindole has a negligible effect on the richness and no effect on the diversity of the cecal microbiota. As a general exploratory analysis, principal component analysis (PCA) was performed, explaining 21. 3% and 16% of the variance, respectively, and showed no significant difference between the 5-hydroxyindole and vehicle-treated groups (PERMANOVA: P value =. 175, stratified P value = 1; Figure  1b ; Table B in S1 Table ).
Microbiota analysis Paired-end reads were trimmed of their barcodes and sequencing primers and subsequently merged using FLASH (v1. 2. 11). 46 Fastp was used for quality control and read filtering,  47  while further VSEARCH was employed to detect chimera sequences by searching them against the e2154544-12 Greengenes database. 48 Filtered high-quality reads were then subjected to read denoising using DADA2 to obtain amplicon sequencing variants (ASVs), while making use of the QIIME2 software. 49, 50 Sequences with less than 5 counts were removed and the remaining ASVs were classified with Classify-sklearn in QIIME2 leveraging the Greengenes database. For downstream analysis, QIIME2 was further used to assess alpha diversity. We used the phyloseq and microbiome packages in the statistical programming language R to process our data further.
System setup and settings of the MD simulations The Ca V 1. 1 complex was embedded in a membrane composed of 1-palmitoyl-2-oleoyl-sn-glycero -3-phosphatidylcholine (POPC) lipids. The simulation box with dimensions of 16 × 16 x 22 nm 3 was built using the INSANE program. 61 The poreforming subunit α1 was positioned in the membrane according to the data derived from the OPM database. 63, 64 The orientation of the principal z-axis of this subunit was set to be parallel to the normal of the lipid bilayer. The system was solvated using water solution with 0. 15 M concentration of NaCl, mimicking physiological conditions. A total of 30 copies of one of the ligands were randomly placed in the solvent, which is an equivalent to a water solution of around 11. 1 mM. For each ligand, a separated box was built.
1 (Build: Feb 12,  2015). Quantitative analysis of the organ bath recordings was performed as reported before. 7, 70 In short, each 15 min recording segment was selected in BioPac Student Lab 4. 1 and FFT analysis was done with following settings: data were padded with zeros, mean was removed, and magnitude was displayed with linear transform, signal was processed using Hamming window. Afterward, the maximum amplitudes of the dominant frequencies obtained from FFT analysis were selected and analyzed in GraphPad Prism 7.
Data was recorded BioPac Student Lab 4. 1 (Build: Feb 12, 2015). Quantitative analysis of the organ bath recordings was performed as described above. Data were normalized in GraphPad Prism 7, where 0% and 100% was defined as the smallest and the largest mean, respectively, in each data set. Results were presented as percentage of a maximum response. The normalized data was fitted using the nonlinear regression. For nifedipine concentration-response curves, sigmoidal dose-response (variable slope) was used with the bottom and top constrained to 0 and 100, respectively. Every other concentration-response curve was fitted with nonlinear fit (Log(Gaussian)). Automatic outlier elimination option was selected with ROUT analysis (Q = 1%).
KCl-induced response Potassium chloride (KCl; 30 mM) was added to the tissue mounted in the organ bath to achieve the maximum KCl-induced response and contractions were recorded for 10 min. Subsequently, 100 µM 5-hydroxyindole, 4-hydroxyindole, 6-hydroxyindole, 7-hydroxyindole, 5-methoxyindole or 5-aminoindole was added and contractions were recorder for additional 15 min. Data was recorded BioPac Student Lab 4. 1 (Build: Feb 12, 2015). Quantitative analysis of the organ bath recordings was performed as described above.
Statistical analysis and nonlinear regression models All statistical tests and nonlinear regression models were performed using GraphPad Prism 7. For alpha diversity, Mann Whitney test was used. For pairwise comparison (beta diversity) between groups, unpaired t test with Welch's correction was used. Data are presented as mean ± SEM. Data were evaluated with FDR < 0. 05 for significance and P value <. 05 for marginal effect (see main text for more details). For correlations between cecal microbiota, 5-hydroxyindole treatment and TGTT, Spearman correlation was used. Data were evaluated with FDR < 0. 05 for significance and P value <. 05 for marginal effect (see main text for more details). For concentrationresponse curves, the nonlinear sigmoidal doseresponse (variable slope) or log(Gaussian) regression was used.
Acknowledgments We thank  Amber Bullock ,  Host-Microbe Interactions group ,  Groningen Biomolecular Sciences and Biotechnology Institute (GBB) ,  University of Groningen, Groningen ,  The Netherlands  and  Prof. Dr. Gertjan van Dijk ,  Department of Behavioral Neurosciences, Cluster Neurobiology, Groningen Institute of for Evolutionary Life Sciences (GELIFES), University of Groningen, Groningen, The Netherlands , for their help with the collection of the cecal samples.
Liquid analysis (Porapak elutions and solvent extractions) -Liquids were analysed with a GC-MS QP2010 Plus (Shimadzu, Kyoto, Japan). Mass spectra were recorded in electronic impact mode (EI) at 70eV over a m/z mass range from 38 to 350. The temperature of the transfer line and the ion source were programmed to 250 °C and 200 °C, respectively. The injections were made with an injector temperature of 250 °C, and a 1:4 split mode ratio. Analyses were performed using a 30 m x 0. 25 mm x 0. 25 µm Optima 5-MS (Macherey-Nagel, Düren, Germany) fused silica capillary column with a constant helium flow set close to 1. 0 ml. min -1. The oven temperature was programmed as follows: 40 °C (held 5 min. ) to 250 °C at 6 °C. min -  , and finally to 300 °C at 14 °C. min -  and held 2 min. GC-MS Solution software (Shimadzu, Kyoto, Japan) was used for data processing of these analyses, with the NIST 2011 as spectrum database.
The carrier gas used was helium at 1 ml. min -1. Oven temperature program was as follow : held at 40 °C for 3 min. , then increased to 220 °C at 5°C. min -1 and finally to 250 °C at 10 °C. min -1 , and held for 2 min. Mass spectra were recorded in electronic impact mode (EI) at 70eV over a m/z mass range from 38 to 350. The temperature of the transfer line and the ion source were programmed to 250 °C and 200 °C, respectively. Xcalibur™ software (Thermo Scientific™, Milan, Italy) was used for data processing and the NIST 2011 as spectrum database. SPME fiber analysis -We analyzed SPME fibers as described in  Mozūraitis et al. (2020)  with a GC-MS QP2010-SE (Shimadzu, Kyoto, Japan). Volatiles were desorbed into the injector at 225 °C for 1 min in split mode (1:4 ratio). Analyses were performed using a 30 m x 0.
25 mm x 0. 25 µm J&W DB-Wax silica capillary column (Agilent, Santa Clara, USA) with a constant helium flow at 1 ml. min -1. The oven temperature was programmed as follows: 40 °C (held 1 min. ) to 150 °C at 5 °C. min -  , and finally to 220 °C at 20 °C. min -1 and held 9 min. Mass spectra were recorded in electronic impact mode (EI) at 70eV over a m/z mass range from 38 to 350. The temperature of the transfer line and the ion source were 250 °C and 200 °C, respectively. GC-MS Solution software (Shimadzu, Kyoto, Japan) was used for data processing of these analyses. We injected a solution of standards made of the five compounds (acetoin, sulcatone, octanal, nonanal and decanal, ≥98%GC, 99%, 99%, ≥98% and ≥98%GC, respectively, Sigma-Aldrich, Darmstadt, Germany) to identify both their specific ions and their retention time.