dataesr/scientific-paragraphs-categorization huggingface dataset:
CORT treatment Since CORT reduces the number of newborn neurons and alters their morphology  (Schoenfeld and Gould, 2013) , we evaluated hippocampal neurogenesis in the dentate gyrus (DG) of WT and Hdh S1181A/S1201A mice in control and CORT conditions (Figure  3A ). We evaluated the dorsal and ventral regions of the DG separately, as they are thought to play different roles in cognitive and emotional processing. Because the results were similar in these two regions (Figure  S2 ), however, we pooled the data (Figures 4A to  4C ). WT and Hdh S1181A/S1201A mice had a similar basal rate of progenitor cell proliferation, according to the number of Ki67 cycling cells (Figures  4A  and S2A ). CORT treatment reduced proliferation in the WT DG (two-way ANOVA, **p˂0. 01) but had no effect on proliferation in Hdh S1181A/S1201A mice (Figures  4A  and S2A ).
QUANTIFICATION AND STATISTICAL ANALYSIS For western blots, densitometry analyses of Pico and ECL signals were performed using NIH Image J software. Data are represented as mean ± SEM of at least 4 animals per condition. Protein levels are displayed as percentage standardized to α tubulin, p35 and HTT. Statistical significance was assessed using a Mann-Whitney test. In histology experiments, cell counts from at least 4 animals per group were expressed as mean ± SEM values. Statistical comparisons were made using the two-way ANOVA followed by the Tukey's multiple comparison post hoc tests. For the NSF test, a Kaplan-Meier survival analysis was applied in which the Mantel-Cox logrank test was used to evaluate differences between experimental groups. Animals that did not eat during the 10 min test period were attributed the value of 600 sec.
ACKNOWLEDGMENTS We kindly thank  Caroline Benstaali ,  Béatrice Blot ,  Ayma Galland ,  Elodie Martin  and staff of animal facilities (GIN and  SFR-UMS Institut Paris Saclay Innovation Thérapeutique)  for technical support;  Florence Appaix ,  Jacques Brocard ,  Yasmina Saoudi  of the GIN imaging facility platform ( PIC-GIN ) for help with image acquisitions;  Vicky Brandt  for helpful comments on the manuscript;  Heather Cameron  ( NIMH/NIH ) for supplying the GFAP-Tk mice (license agreement L-O 15-2015/0 between the  National Institutes of Health and Université Paris-Sud ). This work was supported by grants from  Agence Nationale pour la Recherche  ( ANR-15-IDEX-02  NeuroCoG in the framework of the " Investissements d'avenir" program , S. H. , F. S. ; AXYON:  ANR-18-CE16-0009-01 , S. H. , F.
Machine learning Data of OR response to the four odorants were collected from the literature. ORs that responded at high concentrations but not in a clear dose-dependent manner were considered ambiguous and discarded. OR sequence alignment was from our previous work  18. Residue positions that contain gaps were removed. The sequences were digitized by assigning three physicochemical scores (features) per residue  40. We removed features that showed low variance (< 10%) or high correlation (Pearson's correlation coefficient > 0. 85). Then, 3 steps of feature selection were applied (Fig. 1A ): i) rational residue selection based on molecular modeling and site-directed mutagenesis; ii) Relief-F filter  41  to identify features that are most relevant to partitioning the given problem; and iii) simulated annealing (SA)  42  , a global search that produces random perturbations to the selected features by substituting one of them with another from the unselected ones.
At each SA iteration, the model performance was evaluated to decide whether to accept the perturbation. The SA was iterated until convergence to obtain an optimized selection of features. RF, RPART and SVM classifiers were built using the Caret package in R  42. Highly imbalanced data (i. e. the classes have very different sizes) were handled by ROSE  43. The data were split into 80% training set and 20% test set. Parameters tuning was carried out by 5-fold cross validation repeated 5 times on the training set, followed by assessment on the validation set to choose the best models. The same procedure was employed to build the final models using all the data. Matthew's correlation coefficient (MCC)  27  and Cohen's Kappa  28  were used as metrics of predictivity for the 2-class and the 3-class classifiers, respectively.
Acknowledgments General: We acknowledge  GENCI-CINES (France)  for providing access to the supercomputer OCCIGEN (grant  A0040710477  to J. G). We also thank  Dr. Sébastien Fiorucci ,  Dr. Jérémie Topin ,  Ms. Priyanka Meesa  and  Mr. Shiyi Jiang  for critical reading and editing the manuscript. Funding: This work received funding from the  French National Research Agency (Agence Nationale de la Recherche ,  ANR , grant  NEUROLF  to  J. G)  as part of the  ANR-NSF-NIH Collaborative Research in Computational Neuroscience ; the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  ANR  (grant No. ANR-15-IDEX-01  to X. C and J. G); the  German Research Foundation (Deutsche Forschungsgemeinschaft , grant  CO 1715/1-1  to X. C); the  Roudnitska Foundation (France , a PhD fellowship to J.
These tests were repeated three times adding up from 90 to 140 7ies for each insecticide concentration and strain. For each insecticide, 8ve concentrations were tested. Drosophila mortality was scored after a 4 h period. We used the Priprobit software (Priprobit 1. 63 © Masayuki Sakuma) to calculate and statistically compare the LC50s (concentration that kills half of the population expressed as the insecticide concentration in the solutions used to treat the tubes)) for each strain. When tested, the cytochrome P450 inhibitor piperonyl butoxide (1 mM 8nal) (PBO)  (Hodgson & Levi, 1999)  was incorporated in 1 mL of corn-meal medium placed in the glass tube already coated with the insecticide solution.
Statistical analysis of the enzymatic activities measurements We compared the enzymatic activities levels pairwise with the Wilcoxon test using the R software (www. R-project. org). We chose this test because of the number of 7ies tested (at least 25 per activity) and because it was senseless to test all the strains together because of their genetic history. In addition, in the supplementary data, we present a comparison of the enzymatic activities levels by visualizing the strain effect size for each activity  (Gardner & Altman, 1986 ) using the DABEST (data analysis with bootstrap-coupled estimation) web application (available at https://www. estimationstats. com, (Ho et al. , 2019) ). This 8gure also includes the values measured with the for S strain. PCRs to detect the accord insertion in the Cyp6g1 promotor were performed as described in  (Daborn et al.
For instance, gregarious locusts have more PKG activity than solitary ones  (Lucas et al. , 2010)   The sequences of the Cyp6a2 and Cyp6g1 primers were designed using the AmplifX software  (Julien N. ). The Accord primers were previously published by  Daborn et al (2002)  22 Figures legends. Insecticides Metabolites Figure 1.
Acknowledgements We want to thank 8rst  Prof. Marla Sokolowski  ( University of Toronto, Canada ) for providing us with the dg2-cDNA, for R , w 1 ;for S , for S and for S2 7ies. We would like to specially mention  Miss Alexia Lebleu  for her very ef8cient technical support and Dr. Robichon for fruitful discussions. Finally, we thank  Dr. Wajnberg  for our discussions around several statistical aspects of the analysis of the experiments. This work was partly supported by a  Santé des Plantes et Environnement research department (SPE, INRAE)  grant ( AO 2005 ).
Acknowledgments: C. L. M. was the recipient of a short-term mission grant funded by the  MedVetNet Association  ( 2016_STM_3 ) for a training period on the isolation of C. botulinum group III strains at the  IZSVe laboratory in Italy under Luca Bano. The authors are grateful to  Luca Bano  for this initial training and his advice on the isolation process.
Imaging and Analysis Immunofluorescence images were captured using a Zeiss Axiovert LSM 710 VIS40S confocal microscope and maintaining the acquisition parameters constant to allow comparison between samples within the same experiment. Acquisition of images for cell counts was done with x20 objective. Images for OPC reconstruction were taken using x100 objective and capturing z-stacks formed by 80-100 single plains with an interval of 0. 3 µm. OPC cell counts were performed in a constant field of view (FOV) of 200 µm x 200 µm and date expressed as OPC density per mm 2. To determine the process domains (cell coverage) of OPCs, a line was drawn around the entire cell process field and the area within the line was measured using ImageJ and data were expressed as m 2. For morphological analysis, single OPCs were drawn in detail using Neurolucida 360 and their morphology was analysed using Neurolucida 360 explorer.
Acknowledgements Supported by grants from the  BBSRC (AB , Grant Number  BB/M029379/1 ),  MRC  (AB, Grant Number  MR/P025811/1 ),  Alzheimer's Research UK (DG-N, AB , Grant Number  PG2014B-2 ),  University of Portsmouth  PhD Programme (AB, ICR),  CNR -Joint Laboratories (MC, LR) , and  Programme Avenir Lyon Saint-Etienne (OR).
Acknowledgements We thank  Luc Buee ,  Alain Buisson ,  Bassem Hassan ,  Subhojit Roy ,  Remy Sadoul , and members of the  Saudou, Humbert and Potier labs  for comments;  Vicky Brandt  for critical reading;  Caroline Benstaali ,  Anne Bertrand ,  Camille Brochier ,  Aurelie Genoux, Felicie Lorenc ,  Jessica Roland ,  Chiara Scaramuzzino ,  Marine Scoazec  and  Gisela Zalcmann  for technical help and/or initial experiments;  Daniel Choquet  for the gift of Super-Ecliptic-pHluorin-APP plasmid,  Y Saoudi  and the  GIN imaging facility (PIC-GIN)  for help with image acquisitions;  G Froment ,  D Ne  `gre, and  C Costa  from  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon ,  UCBL ) for lentivirus production;  Emmanuel Barbier  and  Olivier Montigon  ( UMS 3552/US17  IRMage) for MRI acquisitions; the technical staff of the  PHENO-PARC  platform and the HISTOMICS platform of the ICM for behavioral and histological studies.
This work was supported by grants from  Agence Nationale de la Recherche :  ANR-12-MALZ-0004  HuntAbeta, FS and  MCP ;  ANR-14-CE35-0027-01   PASSAGE, FS ;  ANR-15-IDEX-02   NeuroCoG (FS)  and  ANR-10-IAIHU-06  (MCP) in the framework of the ' Investissements d'avenir' program ),  Fondation pour la Recherche Medicale (FRM ,  DEI20151234418 , FS; DEQ20170336752, SH),  Fondation pour la Recherche sur le Cerveau (FRC)(FS) ,  Fondation Bettencourt Schueller (FS)  and  AGEMED program.
R. R. is supported by the "Centre national de la recherche scientifique" (CNRS). Our projects are funded by the "Fondation ARC pour la recherche sur le cancer", grant number 20171206145 and by the PNR-EST ANSES & ECOPHYTO II (2017). We also receive support from the French government, through the UCAJEDI Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01.
MRI data acquisition Neuroimaging data were collected using a 3T magnet (Skyra, Siemens, Germany) with a 32-channel head coil for each MRI session. Structural images (3DT1-MPRAGE) were acquired using the following parameters: echo time (TE) = 2. 5ms; repetition time (TR) =1690ms; flip angle = 9°; inversion time = 922ms; voxel size = 0. 98x0. 98x1 mm 3 ; 176 slices. CBF data were acquired using a prototype sequence of 3D pseudo continuous arterial spin labeling (pCASL) according to parameters as follows: post-labeling delay (PLD) = 1800ms; bolus duration = 1800ms; TE/TR = 15. 5/4600ms; 42 slices; voxel size = 1. 64 x 1. 64 x 3 mm with interpolation in acquisition plane.
2. 4. MRI data pre-processing Arterial spin labeling images were realigned to the baseline magnetization image acquired with a long repetition time. The label images were then subtracted pair-wise from the time-matched control images using surround subtraction to acquire perfusion-weighted images. To ensure an accurate estimation of the cerebral blood flow (CBF), Asllani et al's partial volume effect (PVE) correction  19  was implemented in MATLAB and applied in native space, using a 7 x 7 x 1 voxel regression kernel. Briefly, it considers that the measured signal is a ponderation of gray matter, white matter and cerebrospinal fluid contribution: ∆M = PGM * ∆MGM + PWM * ∆MWM + PCSF * ∆MCSF Where PGM, PWM and PCSF are segmented posterior probabilities obtained from a high resolution T1 image, ∆M the control-label differences from the ASL sequence in a voxel and ∆MGM, ∆MWM and ∆MCFS the proper differences for each tissue in this voxel.
logi. com). The experiment was presented using Matlab 2018b (www. mathworks. com) and the Psychophysics Toolbox  (Brainard, 1997 ) on a Linux system (Intel Core i5-3330 CPU, a 2 GB Nvidia GeForce GTX 760 GPU, and 8 GB RAM). Millisecond precision of the stimulus presentation timing was ascertained by means of a photodiode test prior to the experiment, following recommendations outlined in De  Clercq et al. (2003). For all critical events, expected on-screen time differed from measured on-screen time by less than 0. 6 ms on average (see Supporting Information, Figure  1  for details).
Analysis The analysis was performed using R (Version 3. 6. 1) and RStudio (Version 1. 2. 1335) with the circular package  (Agostinelli and Lund, 2017). Mixture models were estimated using the MemToolbox  (Suchow et al. , 2013)  under Matlab 2020a (www. mathworks. com). All scripts are publicly available (see Data Accessibility). The error for each trial was defined as the shortest angular distance between the reported and the true gap position, ranging from -180°to 180°with 0°indicating a perfect match (see Figure  1B ). Separately for each participant, SOA and validity condition, the resulting error distribution was modelled using a standard mixture model with parameters sd (precision) and g (guessing; see Figure  1B ). To test for a validity effect (indicating a facilitation at the cued position), paired t-tests were performed to compare g and sd between valid and invalid trials.
For statistical testing, we performed permutation tests to bootstrap a spectral amplitude distribution under the null hypothesis that there is no temporal structure (within or across validity conditions, respectively). A total of 10. 000 permuted datasets were created by shuffling the SOA labels of the original dataset within participants and validity conditions (or collapsed across validity for the exploratory analysis). The same preprocessing and FFT as described above were then carried out for each permuted dataset, resulting in a probability distribution of spectral amplitudes under the null hypothesis. To test for significant rhythms, the observed amplitude in each frequency bin was then compared to this probability distribution. To correct for multiple comparisons, a Bonferroni correction of the alpha level was applied to control for the 10 tests across frequencies in the preregistered analysis and the exploratory analysis, leading to a corrected alpha level of α corr =.
Note that the non-stable, jagged luminance course (resembling a CRT more than an LCD monitor) as well as the (stable) approximately 6 ms rise time between the onset triggers and the first luminance peaks are characteristic for the scanning backlight mode of the Viewpixx/EEG LCD Monitor (www. vpixx. com). (C) Timing deviations across trials for cue duration, SOA and target duration. Note that the deviations in the very first trials (8. 33 ms or 16. 66 ms, corresponding to two cycles of the monitor's 120 Hz refresh rate) are due to internal optimizations within Matlab and are thereby limited to the first trials after starting the experiment. It is important to emphasize that, in a real experiment, these deviations will always fall into the timing-uncritical practice trials at the beginning of each session.
Acknowledgements This study was supported by an  ANR-DFG  grant to  Niko Busch  ( BU 2400/8-1 ) and  Laura Dugué  ( J18P08ANR00 ). We would like to thank  Teresa Berther ,  Davina Hahn ,  Louisa Henkels ,  Annika Herbst , and  Johanna Rehder  for help with data acquisition.
CASE STUDY USING BRAINSTORM SOFTWARE To illustrate the practical use of the Brainstorm software to produce epileptogenicity maps, the authors use data from a patient who was suffering from lesional partial epilepsy (mesial left occipital dysplatic lesion) with bilateral interictal abnormalities and contralateral ictal discharges (above right temporal region) on scalp electroencephalogram (EEG). The same data were used previously {Anonymous:2019uk},  32  in which more clinical information can be found. Stereoelectroencephalographic (SEEG) recordings (90 bipolar derivations in occipital, parietal, and temporal cortices and in hippocampus) showed lesional ictal onset in calcarine cortex with rapid spread to the fusiform gyri followed by bilateral discharges with right temporal lobe predominance (Figs.
SEEG recordings were performed using an audio-video EEG monitoring system (Micromed, Treviso, Italy). Sampling rate was 512 Hz, with an acquisition band-pass filter between 0. 1 and 200 Hz, respectively. Data were acquired using a referential montage with reference electrode chosen in the white matter. For data analysis, the authors used a bipolar montage between adjacent leads of the same electrode to improve sensitivity to local oscillations. Coordinates of virtual bipolar electrodes that are used to construct images were chosen to be at equal distance of 2 successive contacts. The preimplantation MRI was used as the anatomic reference to coregister postimplantation T1 MRI volume where the location of SEEG implantation can be seen.
The seizure-displayed HFOs originated from the dysplastic lesion before propagating to bilateral temporal regions (see Fig. 1 ). Fig. 2  displays the 3D implantation of the SEEG electrodes. This step can be performed with Brainstorm software or with other software solutions, for example, Intranat Electrodes from the authors' group. 34 pileptogenicity mapping successfully suggests a focal HFO onset on the dysplastic lesion (Fig. 3 ) with late diffusion to the rest of the temporal lobe through the ventral visual pathway, and very little involvement of parietal regions (Fig. 4 ). In Brainstorm, rendering can be done in 3D (see Fig. 3 ) or on the cortical surface (see Fig. 4 ). The epileptogenicity maps at the different perionset times were computed using a time window of 2-seconds' duration with a 2-second time step.
We used the top 200 prominent mRNA targets to calculate a signature score (i. e. , a composite expression score of a set of genes using Seurat's AddModuleScore function using standard parameters)  (Butler et al. 2018; Stuart et al. 2019 ). This score is plotted as a boxplot per cell type (WT/KO sample aggregates) (Supplemental Fig. S10 ). We identified the same cell clusters present in AMPAstimulated WT GD1 cells, even if their proportions were altered (Fig. 4C ). This suggests an overall impairment in AMPA response in the absence of FMR1, as cells from each cluster are affected. This variability could be explained by the different levels of ma-turity or developmental profile of the cells comprising each cluster in WT versus Fmr1-KO brains. To evaluate this hypothesis, we measured the levels of the analyzed markers at baseline (Fig.
Both the MINIPULS 3 peristaltic pump (Gilson) and the cytometer flow rate were set to 39 µL/min. Baseline acquisition and sorting were performed with the valve of the buffer syringe opened. Once the valve of the agonist syringe was opened, the valve of the buffer syringe was closed. The agonist solution running in the flow cell was monitored by the appearance of the beads. At this point, the agonist-responding cells started to be sorted. Cells were collected in D-PBS. Data were analyzed using BD FACSDiva v6 and FlowJo softwares (BD Biosciences).
Single-cell gene expression quantification and determination of major cell types Raw gene expression matrices, generated per sample using Cell Ranger (version 2. 0. 0), were loaded and processed in R (version 3. 4. 3) (R Core Team 2018). Samples were analyzed independently within the Seurat workflow using the Seurat R package (version 3. 0. 0) (Stuart et al. 2019). First, cells that had >95% dropouts were removed. Gene expression matrices from the remaining cells were normalized using SCTransform  (Hafemeister and Satija 2019)  from the Seurat package. To reduce the dimensionality of each data set, the resulting variably expressed genes were summarized by principal component analysis, and the first 30 principal components were further summarized using UMAP dimensionality reduction. The three samples from independent analyses were then integrated using CCA.
Acknowledgments We thank  M. Beal ,  M. Drozd , and  M. Grossi  for discussion, help, and encouragement and  M. Capovilla  and  E. Lalli  for critical reading of the manuscript. This study was supported by  Institut national de la santé et de la recherche médicale (Inserm) ,  Centre national de la recherche scientifique (CNRS) ,  Université Côte d'Azur, Fraxa Research Foundation, Féderation Recherche sur le Cerveau ,  Fondation Jérôme Lejeune , and  Agence Nationale de la Recherche   ANR-11-LABX-0028-01 ,  ANR-15-CE16-0015 , and  ANR-15-IDEX-0001. We acknowledge the " Microscopie Imagerie Côte d'Azur" (MICA) GIS-IBiSA multisite platform and the "UCAGenomiX platform " supported by  Conseil Départemental 06 ,  Région Provence-Alpes-Côte d'Azur (PACA) ,  Fondation ARC pour Recherche sur le Cancer (ARC) ,  Canceropôle PACA , and Commissariat aux Grands Investissements  ANR-10-INBS-09-03  and  ANR-10-INBS-09-02.
Data access The single-cell transcriptomic data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE142274. Data processing scripts (R code) and data used in this study are publicly available through the GitHub repository (https://github. com/ucagenomix/sc. castagnola. 2020) and as Supplemental Code.
In conclusion, we show that faces are looked at faster than other stimuli already in children, but that they become special compared to other objects for inhibitory control only during adolescence. This offers normative data to evaluate abnormal interaction between social processing and executive control in abnormal neurodevelopmental conditions such as autism or conduct disorder. Handedness was self-reported as well as information about family and lifestyle including, number of siblings, practice of sport and music, time spent on video-games. In addition, children and adolescents completed the Puberty Developmental Scale to assess their physical development. This is an eight-items gender specific questionnaire targeting changes in secondary sexual characters. For the youngest participants it was filled with the parents.
Data analysis and data exclusion. Data pre-processing and statistical tests were carried out using R (version 3. 3. 1  92. Analyses of variance (ANOVAs) were performed using Type III sums of squares. For all statistical tests, the significance level was set to 0. 05. Gaze parameters were identified with the Eyelink Dataviewer Software (SR Research Ltd. , Mississauga, Ontario, Canada), using velocity and acceleration thresholds of 35°/s and 9500°/ s 2 respectively for saccade detection. For each trial, the first saccade after stimulus onset was analyzed. Trials were discarded if (1) the saccade latency was shorter than 80 ms as these were likely to reflect anticipatory responses  93  , (2) the saccade amplitude was smaller than 2° of visual angle, (3) the saccade direction was classified as 'up' or 'down' , (4) any signal loss, for example due to blinks, occurred before the end of the saccade, or if (5) the distance between the saccade start point to the screen center exceeded 1° of visual angle.