dataesr/scientific-paragraphs-categorization huggingface dataset:
81, SD = 1. 33; adults: 0-3 channels, M = 1. 77, SD = 0. 81;  Nolan et al. , 2010). For preprocessing of the infant data, six channels  (23, 55, 61, 62, 63, 64)  on the boundary of the electrode net were also excluded from analyses since they were heavily susceptible to eye, face and head movements. Then, independent component analysis (ICA) was performed on an identical copy of the dataset. Before ICA, this copied dataset was highpass filtered at 1 Hz and segmented into 1 s epochs. In the copied dataset, noisy segments were rejected using a combined voltage threshold of ± 1000 μV and spectral threshold (range -30 dB to +100 dB) within the 24-40 Hz frequency band to remove EMG-like activity. After ICA decomposition, independent components (ICs) were transferred from the ICA copied dataset to the original dataset, which was used from then on.
Artifactual ICs were removed from the original dataset using the EEGLAB ADJUST plugin (infants: 7-31 components, M = 15. 68, SD = 6. 94; adults: 4-21 components, M = 12. 91, SD = 4. 61;  Mognon et al. , 2011)  and via visual inspection of individual ICs. The data were then segmented into 3 s (from -1. 5 s to 1. 5 s) epochs time-locked to four event markers: execution baseline, execution object contact, observation baseline, and observation object contact. In both execution and observation conditions, time 0 therefore represents the time at which the hand first contacted the toy, and the time window from 0 to 1. 5 s is when the grasp is completed and the toy is brought to the body and shook. After segmenting data into 3-second epochs, a voltage threshold of + /-200 µV was used to remove epochs contaminated by artifacts (infants: 1-40 epochs, M = 9.
Burst analyses This analysis included all participants with at least 5 trials following preprocessing (infants: 30 participants, trials per participant, M = 13. 73, SD = 6. 87; adults: 22 participants, trials per participant, M = 36. 59, SD = 6. 37). All source code for these analyses is available at https://github. com/danclab/dev_burst_analysis. These analyses were run using EEGLAB v14. 1. 1  (Delorme and Makeig, 2004)  and FieldTrip v20190329  (Oostenveld et al. , 2011)  on MatLab 2015b, and lme4 v1. 1. 26 (Bates et al. , 2015)  on R v3. 6. 1 (R Core Team, 2020). Fig. 4. Infant and adult beta bursts differ in duration and amplitude. A) Infant beta bursts in C3 and C4 have longer duration than adult beta bursts. Each dot shows the duration of an individual beta burst, color-coded by subject.
The periodic components were than averaged within the C3 (E16, E20, E21, E22) and C4 (E41, E49, E50, E51) electrode clusters, located centrally in the left and right hemispheres, and then across subjects within each age group. For each cluster, spectral peaks in the periodic component from 5 to 40 Hz were identified using the MatLab function findpeaks, and for each peak, the frequency band limits were determined by computing the full width half maximum (FWHM) of the peak power. Lagged coherence was computed for all data from each subject (e. g. from both the observation and execution conditions) from 5 to 40 Hz in 1 Hz increments and 2-7 cycles in increments of 0. 1 cycles  (Fransen et al. , 2015). We used overlapping epochs with lag-and frequency-dependent widths. Fourier coefficients were then obtained for each epoch using a Hann-windowed Fourier transform.
Beta amplitude was computed by bandpass filtering the raw data (6th order two-pass Butterworth filter, reducing the order when instability is detected), and taking the amplitude of the Hilbert transformation of the bandpass filtered data. The data were padded with 1 s on either side using the DC offset before applying the filter and Hilbert transform. The relative threshold for beta burst detection was computed separately for C3 and C4 for the infant and adult participants. This was defined as a multiple of the standard deviation of beta amplitude above the median amplitude. We determined the standard deviation multiple that maximized the mean of the nonparametric correlation between the number of beta bursts detected in each trial (number of times beta amplitude crossed the threshold) and the average beta amplitude, averaged across participants.
, 2019; Seedat et al. , 2020) , which have the advantage they can model bursting dynamics over a wide frequency range, but require specification of the number of hidden states. Another approach involves analyzing the signal cycle-by-cycle in the time domain  (Cole and Voytek, 2019). This avoids the assumption of the Fourier and Hilbert transforms that the signal is sinusoidal, but also introduces a new set of hyperparameters, and it is not clear how to set these empirically. Finally, empirical mode decomposition has recently been used to identify beta bursts, which has greater temporal resolution than Fourier-based methods  (Echeverria-Altuna et al. , 2021b). An interesting avenue for future research is to compare these methods for extracting beta bursts from developmental datasets. We determined the infant beta band at 12 months via examination of the PSD and lagged coherence, but the PSD includes both periodic and aperiodic activity.
Conclusion In this paper, we illustrated how to apply techniques for assessing the rhythmic versus transient nature of frequency-specific activity, specifically, lagged coherence, as well as methods for identifying and extracting bursts of activity within a frequency band. We then used these techniques to show that infant sensorimotor beta activity is dominated by short bursts, and that these bursts consist of more cycles and have a higher amplitude than adult beta bursts. In future developmental research, such methods will enable probing of the functional role of highly dynamic cortical activity across frequency bands. In terms of the beta band, applying the methods described here to developmental datasets coupling neural data with behavioral kinematics will be critical for determining the role of cortical beta activity in motor development, including how aberrant beta activity may relate to atypical motor outcomes.
The mutant mice were more sensitive than the wild-type mice in the tail flick and hot plate tests, acetone, and von Frey tests for sensitivity to heat, cold, and touch, respectively, although with sexual dimorphic effects. The newly developed bioinformatic pipeline, Gdaphen is based on general linear model (GLM) and random forest (RF) classifiers as well as a multifactor analysis of mixed data and shows the qualitative and quantitative variables contributing the most to the pain phenotype. Using Gdaphen, tail flick, Hargreaves, hot plate, acetone, cold plate, and von Frey tests, sex and genotype were found to be contributing most to the pain phenotype. Importantly, the mutant animals displayed spontaneous pain as assessed in the conditioned place preference (CPP) assay. Altogether, our results indicate that Scn9a R185H mice show a pain phenotype, suggesting that the SCN9A R185H mutation identified in patients with SFN having chronic pain contributes to their symptoms.
Establishment of the Genetic Animal Model The Scn9a R185H mouse line was generated by using the CRISPR/Cas9 system. The CRISPOR online software was used to select high specificity-score sgRNAs with a low number of predicted off-target sequences. A single-stranded oligonucleotide was used that contained the CGT to CAT change encoding for the R185H mutation with 2 additional silent mutations, one to mutate the PAM sequence and the other to generate a new BspHI diagnostic restriction site (Figure  1A ). Before injecting sgRNAs into eggs, the efficiency of sgRNA was tested in vitro on the targeted DNA by polymerase chain reaction (PCR). After checking sgRNA efficiency in vitro, sgRNA, Cas9, and ssODN were microinjected into the cytoplasm and into the pronucleus of the fertilized oocytes of C57BL/6NCrl.
Image Acquisition and Analysis Dorsal root ganglia and sciatic nerve images were acquired with the Leica fluorescence microscope using a 20 × (DRG) and 40 × (sciatic nerves) dry objective, the 20 × and 40 × resolution were achieved with a digital zoom factor. Image acquisitions in the sequential mode (single excitation beams: 405, 488, and 633 nm) were used for marker co-localization to avoid potential crosstalk between the different fluorescence emissions. Images were acquired with the LCS (Leica) software using randomly selected sections. The ImageJ software cell counter (approximately 4 non-adjacent sections per condition and per animal) was used to count on-screen neurons expressing a given fluorescent marker manually and blindly. Only neurons from L4 to L6 DRGs with a visible nucleus were considered.
Thermal Gradient Ring The analysis of thermal preference in mice was performed with the thermal gradient ring (UgoBasile, Gemonio, Italy)  (Touska et al. , 2016)  at INCI. All mice were adapted to the equipment on day 1 with the ring adjusted to RT for 30 min. Mice were recorded on day 2 for 4 consecutive periods of 15 min for a total of 60 min using the 15-40 • C ring gradient. Behavior was videotaped with camera and analyzed with the ANYmaze Software (V5, UgoBasile. Gemonio, Italy). Temperature preference was calculated as the percent of time spent above the surface at a given temperature. Temperature preference was averaged for each 15-min period. The temperature preference over the first 30 min was calculated as the mean of temperature preference from the first two 15-min periods.
Von Frey Test The evaluation of sensitivity to touch by the von Frey test was performed on different cohorts at IGBMC (I) or INCI (II). At IGBMC, mice were placed in the same transparent plastic boxes and habituated as described for the acetone test. A series of eight von Frey filaments (with the bending force of 0. 008 to 2 g) (Bioseb, Vitrolles, France) was applied to the hind paw using the up-and-down method. Each paw was scored on two successive testing sessions. The withdrawal threshold was calculated by the Excel program generously provided by A. Basbaum (University of California San Francisco). The mean of the sensitivity of the two hind paws was calculated. At INCI, the mechanical threshold of hind paw withdrawal was evaluated using von Frey hairs (Aniphy Vivo-Tech, Salon-de-Provence, France).
Gdaphen Analysis for the Identification of the Variables Contributing the Most to the Genotype or Sex Discrimination The Gdaphen R pipeline was applied for the identification of the major variables contributing to the genotype or sex discrimination as described previously by  Chidiac et al. (2021). Gdaphen is a public R package (Muniz et al. , available on github https://github. com/munizmom) developed to identify the explanatory variables from the experimental data. We considered 18 variables, genotype, and sex as well as the following 16 behavioral variables: hot plate 48 • C latency to the first response (s), hot plate 48 • C latency to jump (s), hot plate 48 • C coping reactions (nb), hot plate 52 • C latency to the first response (s), hot plate 52 • C latency to jump (s), hot plate 52 • C coping reactions (nb), hot plate 56 • C latency to the first response (s), hot plate 56 • C latency to jump (s), hot plate 56 • C coping reactions (nb), tail-flick latency (s), Hargreaves latency (s), acetone duration of paw reactions (s), acetone paw reactions (nb), cold plate 5 • C paw lifts (nb), von Frey threshold (g), and tail pressure threshold (g).
Statistical Analysis Results are expressed as means ± SEM. Statistical analyses were performed using GraphPad Prism 9 software. Results were analyzed using two-tailed unpaired Student's t-test to assess differences between two groups for normally distributed data or by Mann-Whitney test if the data did not pass the normality test. Genotype and age effects, and genotype and sex effects were analyzed using two-way ANOVA. The genotype effect in each sex was analyzed by one-way ANOVA followed by Dunnett's multiple comparison test, when appropriate. A P-value less than 0. 05 was considered as statistically significant. The detailed statistics are presented in Supplementary Tables 6-14. P-values for genotype differences are shown in figures when significant or close to significance.
Therefore, we analyzed several predicted off-target sites that were based on the number of mismatches to the target sequences and the homologous genes by the CRISPR design tool. 1  No evidence of Cas9-mediated deletions or base-pair changes in any of the seven mostly predicted off-target sites (Supplementary Table  4 ) was found after comparing with wt sequences in Ensembl genome database 2  (Supplementary Figure  2 ). Thereafter, the heterozygous F1 (53-11) was used as the founder to generate the Scn9a R185H mutant line. When evaluating Scn9a transcript expression in the Scn9a R185H mice, the two-way ANOVA showed a strong effect of genotype, and there was no effect of sex on expression in both the DRG and spinal cord. Scn9a wt and Scn9a R185H transcripts were equally expressed in the DRG and spinal cord of wt and homozygous mutant mice, respectively, and in both sexes (Figure  1C  and Supplementary Table  5 ).
ACKNOWLEDGMENTS We wish to thank  Pr. Giuseppe Lauria  and PAIN-Net members for all discussions. We thank  Laurence Schaeffer  and  Valerie Erbs  in the genetic engineering team,  Sylvie Jacquot  and  Amélie Jeanblanc  in the ICS genotyping team for their help to create and genotype the mutant mouse line. We thank  Loïc Lindner  and  Pauline Cayrou  for their help in ddPCR design and training. We thank the animal caretakers,  Sophie Brignon ,  Charley Pinault ,  Dalila Ali-Hadji , and  Gilles Duval  at  PHENOMIN-ICS  and IGBMC animal facilities, and the Chronobiotron  UMS3415  facilities for animal care. We wish to thank  Quentin Leboulleux  and  Aurélia Cès  and the  ComptOpt plateform (Comportement et Optogénétique ,  INCI CNRS UPR 3212 ) for experiments on thermal ring, and ongoing pain.
FUNDING This work has been funded by the  Molecule-to-Man Pain Network , a  European Commission Multi-Center Collaborative Project  through the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 721841-Pain-Net. This work has been supported by the  National Centre for Scientific Research (CNRS , contract  UPR3212 ), the  French National Institute of health and medical research (INSERM) , the  University of Strasbourg (Unistra) , the  French state funds through the "Agence Nationale de la Recherche " under the frame of  Programme Investissements d'Avenir labeled   ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ,  ANR-10-INBS-07 PHENOMIN  and  ANR-17-EURE-0022  (Euridol), and the  Region Grand Est  through " Fonds Régional de Coopération pour la Recherche " ( CLueDol  project).
BEFORE YOU BEGIN The whole experiment runs over two days, the measurement conditions and experimental set up are programmed on the broadband dielectric spectrometer (BDS) using the software WinDETA such that the experiment can be followed remotely. The user intervention is needed to load the samples on the nanomembrane. Here, the buffer and the protein samples are loaded the first day which hence requires an early start to: (i) prepare the samples, (ii) perform the measurement of the membrane and water controls, (iii) load the protein sample and (iv) start the protein measurement to check that everything is working fine. The BDS needs to be booked in advance, as the machine will be running over two days. Nitrogen gas is used for the temperature control so check there is enough to cover the two days experiment.
Note: The WinDETA software can be installed on any computer to control the experiment remotely. Note: For the temperature control, a flow of pure nitrogen gas was used in a closed cryostat, providing water-free and oxygen-free experimental conditions. Program protein experiment on WinDETA-WinDETA is programmed for changing temperature and frequency, as well as for the temperature ramp. The details are indicated in the step-by-step method and it depends on the protein under investigation. The seven frequencies are measured almost simultaneously at each temperature point. The dielectric measurement is started from WinDETA and stop automatically once the program is finished. Electrostatic dipole networks-The amino acid network (AAN) of CtxB 5 is built using the atomic coordinates of CtxB 5 X-ray structure (PDB: 1EEI) freely accessible on the RCSB website (https://www.
STEP-BY-STEP METHOD DETAILS The broadband dielectric spectrometer Novocontrol Alpha analyzer is equipped with a WinDETA software where experimental conditions are programmed and from which the machine can be controlled remotely (materials and equipment). Below is described the detailed of the different steps that take place during the two days experimental course but once the sample is loaded, the experiment programmed on WinDETA and the dielectric measurement started using WinDETA, the user does not need to perform anything. The change of temperatures, frequencies, incubation times is programmed and controlled by the WinDETA software.
Note: Functional slow dynamics is also not detected simply because the function of the protein is not triggered in our experiments. Slow and collective motions that take place during the thermal unfolding are monitored. 7. Peak and unfolded state identification. a. Export the data from WinDETA to Excel (or else). b. Sort the data in Excel by temperature of the thermal treatments and frequencies. c. Plot the dielectric loss e'' measured after each thermal treatment as a function of temperature and at a given frequency (Figure  4A ). Here the cooling measurement is plotted at 1 Hz as an example. d. Count the number of peaks to determine the number of different CtxB unfolded states observed during the experiment. On Figure  4A , two peaks P1 and P2 are detected at 60 C while three peaks are detected at 140 C.
10. Dipole contribution to the dielectric signal (only if PDB of protein is available). Under ionized buffer conditions, electrostatic dipoles are the main contributors to the dielectric signal and the contribution of non-ionized dipoles is negligible in comparison. a. If proficient in protein structure program: determine electrostatic dipoles from the protein PDB using PDB viewer (or else). b. Download PDB: 1EEI from https://www. rcsb. org/. c. Select opposite charged residues within 5 A ˚of one another as electrostatic dipoles. d. Determine the structural level of the electrostatic dipoles to infer the frequency range they are expected to contribute to the dielectric signals (see background 6d). e. If proficient in python, determine electrostatic dipoles from the electrostatic network model (materials and equipment).
f. Download the code from GitHub (see key resources table) and run it on the PDB of the protein of interest (1EEI for CtxB 5 ). g. Determine the structural level of these electrostatic dipoles to infer the frequency range there are expected to contribute to the dielectric signals (Figure  4D ). For CtxB 5 with PDB: 1EEI, the electrostatic model (or the structure analysis) reveals 5 electrostatic dipoles involved in 1D structure (first neighbors along the protein sequence), 11 in 2D structures, 7 in 3D-structures and 11 in 4D-structures per monomer. This is consistent with the relaxation peaks observed over the frequencies from 1 Hz to 1 MHz and the detection of slow collective motions of 2D, 3D and 4D structures.
Data and code availability Two codes are used during this study to produce two different networks: the amino acid network (AAN) and the electrostatic dipole network (EN). The codes to produce the AAN and EN are already published. The AAN is produced using the code available at: https://github. com/lorpac/amino_acid_network. To create the full AAN, the option ''dim: 'all'" should be used in the configuration file (analysis_ config. json). The EN is produced using the code available at: https://github. com/lorpac/amino_acid_network/ blob/master/electrostatic_network. ipynb. To create the full EN, the option ''dim: 'all'" should be used in the configuration file (electrostatic_network_config. json).
Building a cartographic diagnosis Using geographic information systems and cartographic tools, we mapped the current extent of official knowledge on land use. The cartographic diagnosis of the study area, was built during a scientific and technical partnership, funded by AFD in 2017-2018, and involving SAED, the University of Gaston Berger, CIRAD, and ISRA. In this context, partners pooled geospatial information which included a geo-database from SAED that referenced all hydro-agricultural developments. The database included irrigation scheme and georeferenced plots. Within this database, we extracted areas officially listed as registered irrigated lands (under SIF for "Système d'Information Foncier"). If POAS served to clarify the rules of access and sharing of natural resources, and thus ease the climate of social tensions and conflicts between users, they did not constitute a real tool for planning, securing land tenure and supporting decision-making in land use planning.
The SIF intends to provide a rural cadaster, indicating land allocation status. SAED also gave access to georeferenced POAS, including cattle tracks. The geodata used for the identification of current and future agro-industrial schemes in the study area comes from a partnership between CIRAD, the Land Matrix Initiative (https://landmatrix. org) and the Senegalese Observatory on Land Governance. To complete land-use datasets, we used BaseGéo Sénégal (www. basegeo. gouv. sn), which is an online and free geospatial database that gave access to broad classes of land use. Under the "Agence Nationale de l'Aménagement du Territoire "(National Land Use Planning Agency), those classes give us an account of the extent of irrigated and rain-fed agriculture. It also includes other base geographic layers, such as the location of roads, localities, hydrography, and protected areas.
Historical pastoral activities were identified through bibliographic work. We used digitalization tools to overlay historical cattle displacements depicted by  Bonnet-Dupeyron (1950). Complementary sources of information regarding current displacements were gathered using Google Earth Pro. Satellite imagery was used to digitizing all visible tracks. Although all of them are not used by cattle, they show that human-led activities do exist in these areas. All of these sources of information were used to build a Geographic Information System (GIS), within which thematic layers were combined, intersected and overlayed to highlight territorial dynamics. The GIS and subsequent maps were designed with ArcGIS software.
Fieldwork and rapid appraisal of campsites The study area, commonly called delta throughout this article, is composed of 11 communes and includes 8484 square kilometers. Initial field work included gathering information on pastoral activities in each commune in appointments with local government councilors. Decentralization was initiated in 1972 and reinforced in 1996 with increased autonomy regarding land management. Decentralization policies incrementally provided enhanced responsibilities to rural councils, but unfortunately, they were not backed up by institutional frameworks that could ensure sustainable and equitable, human and financial capital for the communities  (Piveteau, 2005; Sané, 2016; Wilfahrt, 2018). These initial interviews revealed that no data on pastoral activities were available at the local level, and there was no recent census.