dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to do so, we manually placed one dexamethasone molecule on each four extra-cellular vestibules of the X-ray structure of human AQP2 (PDB code: 4nef  50  ). We mimicked the interactions observed on the X-ray structure of the mouse agonist glucocorticoids receptor : the oxygen of the carbonyl oxygen is positioned close to the arginine, the hydroxyl groups close to the polar asparagine and glutamine while the hydrophobic side of the dexamethasone faces the hydrophobic part of the channel ending at the extra-cellular surface with the tryptophan (figure  2. A and B). All of the protein residues involved in the formation of an hydrogen bond along the 60ns of simulation are colored in pink. All representations were made with pymol software  51. Then, we simulated the system for 60 ns and observed that the interaction was stable over the whole course of the trajectory, at least for one of the subunits.
The new interaction site was initially investigated on the basis of the similar features shared with the mouse glucocorticoids receptor X-ray structure (PDB code: 3mne)  44. The interaction site is located on the extra-cellular surface of AQP2, inside the vestibule of each monomer (figure  2 ). The tetrameric assembly hence displays four interaction sites for dexamethasone. To estimate the impact of this interaction on the channel water permeability, three different methods were used (pf, number of water molecules crossing the ar/R constriction and number of water molecules crossing the whole transmembrane section: figure  3 ) in combination with three different atomic systems (initial docking of dexamethasone on the crystallographic structure, reconstructed tetrameric assembly and the same tetramer contextualized inside a compartmentalized system) which all converged toward the same conclusion: there is a significant reduction in water permeability of AQP2 triggered by its interaction with dexamethasone.
Methods : Molecular dynamics simulations All simulations were performed with Gromacs (v. 2018. 1) 31 in a CHARMM36m force field  32. The systems were built with CHARMM-GUI interface  33. A first minimization step was followed by 6 equilibration steps during which restrains applied on the protein backbone and side chains and on lipids were progressively removed before the production phase performed without restrains. Pressure and temperature were kept constant at 1 bar and 310. 15 Kelvin respectively using Berendsen method during equilibration and Parrinello-Rahman and Nose-Hoover methods during production. Lennard-Jones interactions threshold was set at 12 Angströms (Å) and the long-range electrostatic interactions were calculated through particle mesh Ewald method. Three experimental setups were carried out : • First, in order to find an interaction site, four molecules of dexamethasone were manually placed in the extra-cellular vestibules of the tetramer (one dexamethasone per monomer) of human AQP2 (PDB code: 4nef, figure  1.
A). The dexamethasone molecules were extracted from the structure of a mouse glucocorticoids receptor (PDB code: 3mne) and positioned in the AQP vestibules in order to mimic the native interactions described by this structure. Then the tetramer was inserted into POPC bilayer, solvated with TIP3 water and 150 mM of KCl. The system was then simulated during a time course of 60 nanoseconds (ns). The results corresponding to this first experimental setup are displayed on figure  2. • Then, we rebuilt a tetrameric assembly of AQP2 by duplicating the subunit of the previous system which interacted with the dexamethasone over the 60 ns of trajectory. The chosen conformation of this subunit corresponds to time = 48. 6 ns. This starting conformation was chosen based on two criteria : the high number of hydrogen bonds between the dexamethasone and the protein (3 hydrogen bonds, figure 2.
Abbreviations : Menière's disease : MD endolymphatic hydrops : EH aquaporin 2 : AQP2 Angströms : Å PDB code : Protein Data Bank code POPC : 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine TIP3 : transferable intermolecular potential 3 mM : milliMolar KCl : potassium chloride ns : nanoseconds pf : permeability coefficient.
• Abbreviations : Menière's disease : MD endolymphatic hydrops : EH aquaporin 2 : AQP2 Angströms : Å PDB code : Protein Data Bank code POPC : 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine TIP3 : transferable intermolecular potential 3 mM : milliMolar KCl : potassium chloride ns : nanoseconds pf : permeability coefficient.
For (1), we recorded EEG data from native speakers of French while they listened to self-referring sentences uttered by speakers with a banlieue accent or with an upper-class accent. These sentences could contain a mismatch between speaker accent and stereotypical content: a semantic content stereotypically associated with one of the groups (e. g. , listening to rap or playing golf) was said in an accent associated with the other group. The critical comparison was between accent-content congruent and incongruent sentences. We looked at ERPs in the N400 (300-700 ms) and the P600 (700-900 ms) time-windows. Given previous research, and in particular the results of  van Berkum et al. (2008) , we expected to observe an N400 incongruency effect after a mismatch between accent and semantic content (i.
Participants were allowed to add information if they could not fit their case in the proposed categories. Although the wording of the categories is rather vague, participants are familiar with them from school. The INSEE category for Employees includes, among others, some categories of civil servants, police officers, or administrative employees in the private sector. spoke with a discernible banlieue accent. 5  Participants gave written informed consent and were paid 20 euros. The procedure was approved by the local ethics committee (n • CER-PD:2019-14-PELISSIER). The data from two participants were excluded from further EEG analyses due to too many artefacts (>30%) in the EEG signal. The data from 24 participants (12 male) were therefore analyzed for the EEG data, but the data from the other two participants were retained for behavioral analyses.
Voice selection A total of 15 speakers with a perceived banlieue or upper-class accent were recorded in a familiar environment (their university or at home), so as to reduce the risk of the speaker adapting to the experimenter (this was a concern in particular for the banlieue accent). Each speaker recorded all the target stimuli twice and half of the fillers, for a total of 140 10 sentences. The recordings were conducted with an AT2020 USB microphone and ROCme! software  (Ferragne et al. , 2012). All recorded sentences were then cut to remove background noise, and their intensity was normalized to 70 dB with the Scale intensity function in Praat  (Boersma and Weenink, 2016). The speakers who were recorded all gave written informed consent and were paid 5 euros for around 30 min of recording.
However, their experience and familiarity with it may vary depending on their upbringing and the schools they attended. We unfortunately do not have detailed data on this point. 6 As noted by one of the reviewers, the frequency of use of these particular words may still vary between the two groups of speakers, possibly making the incongruency lexical in some cases (e. g. , a banlieue speaker is not expected to use the word "golf" itself). We did our best to use common words that could be used by both groups equally in different contexts, but the lexical aspect could have contributed to the effect in certain cases. some of the critical items, namely proper nouns and acronyms (13 items for the banlieue set and 7 items for the upper class set). 8 Statistical analyses were carried out on the logarithmic transformation of the frequency.
Procedure Participants were seated in a soundproof booth in front of a computer. Stimuli were presented with Psychtoolbox-3  (Kleiner et al. , 2007)  on MATLAB R2016b (The Mathworks, 2016) through Sennheiser CX 1. 00 in-ear headphones (28Ω impedance and 119 dB). A fixation cross was displayed on the screen to reduce eye movements for 500 ms before the stimulus began, during stimulus presentation, and for 1000 ms after it ended. Then (1000 ms after the end of the stimulus), the corresponding image was displayed on the screen for 200 ms. Participants were then invited to judge whether it was congruent with the semantic content of the stimulus, and had to reply within 1000 ms by pressing the left or right arrow on the keyboard with, respectively, the index or medium finger of their right hand.
EEG data recording and pre-processing The EEG was recorded at a sampling rate of 1000 Hz at 24 bits with 64 electrodes placed according to the 10-20 standard, with a BrainVision BrainAmp amplifier and BrainVision pyCorder software, and processed with EEGLAB/ERPLAB  (Delorme and Makeig, 2004; Lopez-Calderón and Luck, 2014). The electro-oculogram (EOG) was obtained from an electrode placed under the left eye. Impedance was kept under 16 kΩ. Epochs from -300 to 1200 ms around the critical point were extracted from continuous data and linear detrended with the EEG linear detrend function from ERPLAB. The data were baseline-corrected (-300 -0 ms), bandpass filtered offline at 0. 1-30 Hz and re-referenced to the average of both mastoids. Artefact detection was performed with the following procedure.
RIDE was applied to the data with the RIDE toolbox for MATLAB  (Ouyang et al. , 2011b)  separately for each condition (Semantic Congruent / Semantic Incongruent / Social Congruent / Social Incongruent), and was set to search for one component in a 200-800 ms time-window. ERPs were time-locked to the beginning of the critical word. Following van  Berkum et al. (2008) , we selected a 300-700 ms time-window for the N400 effect. We additionally investigated possible P600 effects in a 700-900 ms window. The critical results are therefore whether the average amplitude in the given time-window differs significantly between the congruent and incongruent conditions, and whether this difference is present in all the determined surface regions. The dependent variable was therefore the average amplitude across trials for each of the critical conditions separately (congruent vs.
13 The conditions were defined as the following: (  1  incongruent) in the given time-window, 14 as those two congruence conditions were compared. Lateral and medial electrodes were examined in two separate analyses to look at possible differences between hemispheres. Medial electrodes were grouped into 3 regions of interest (ROIs; see Table  4 ): Anterior, Central, and Posterior. Lateral electrodes were divided into 6 standard regions of interest, allowing us to look at effects of anteriority and hemisphere on the potential N400 effect (see Table  4 ). 15 Analyses were conducted on the average amplitude in the given time-window with linear mixed-effect models in R with the lmer() function from the lme4 package version 1. 1-21  (Bates et al. , 2015)  with Congruence (Congruent / Incongruent) and Region (Anterior Left / Anterior Right / Central Left / Central Right / Posterior Left / Posterior Right for the lateral analysis; or Anterior / Central / Posterior for the midline analysis) and their interaction as fixed effects, and a random intercept per participant as well as random slopes whenever possible for Congruence and Region, which were both within-subject factors 16 (see complete models in Appendix B).
We looked separately at the stereotypical incongruences (between stereotypes and accent) and the semantic incongruences. Response times and accuracy data per congruence and type of mismatch are reported in Table  5. There was no     χ 2  (1) = 2. 64, p =. 10. The semantic mismatches were severe enough to interfere with the following task, even though the image only appeared 1 s after the end of the stimulus. The stereotypical mismatches were, however, not strong enough to have the same effect. We then looked at the data for crime-related images only, as our hypothesis was that participants would be slower to reject these images after hearing a Banlieue rather than an Upper-class accent, if they implicitly associated the stereotype associated with the Banlieue accent with a representation of criminal activities.
Response times and accuracy levels by image congruence and speaker accent are reported in Table  6. Analyses were run on the full dataset, including both stereotypical and semantic items, as the effect of mismatch was not included in the models, and as there were not enough crime-related images for each condition to be examined separately. A model run on the logarithmic transformation of the reaction time with Accent as a fixed effect did not reveal a significant effect of Accent on the speed of rejection of crimerelated images (p =. 38). The binomial model on the accuracy data did not reveal any effect of Accent either (p =. 26).
In addition, as we wanted to include the image task as a distractor task in the EEG experiment, it was dependent on the stimuli used for the latter. This means that crime-related images could appear after semantically or stereotypically congruent and incongruent content, which introduces variability. It would be worthwhile in future research to conduct this image task separately from EEG data collection, with better controlled images and with only stereotypically neutral and semantic congruent sentences. Semantic mismatches triggered an N400 effect, maximal at central electrodes. Additional analyses showed that this N400 effect was preceded by a small negativity in the anterior medial and right regionsthis negativity was sustained in the N400 window in the medial area, but not in the right hemisphere.
e. , criminality. The stereotypical mismatches reflected by the EEG data are barely associated with socioeconomic background, and we have no data supporting the association of implicit stereotypes related to criminal activities with the banlieue accent itself. Further research will be needed to (1) establish whether the stereotypical incongruency effect obtained here is lexical or semantic in nature and (2) the nature of the stereotypes implicitly triggered by the banlieue accent of the speaker. Note however that studies with stimuli similar to ours on principle, i. e. , where the mismatch was based on a discrepancy between the voice of the speaker and the content of a self-referring statement  (Lattner and Friederici, 2003; Van Berkum et al. , 2008) , considered the effect as being semantic and not lexical.
Surgery preparation using 3D modeling and printing Chronically implanted devices in large animals require protection components to secure fragile electronics and connectors from hits, bites or even breaking. To do so, customizable pieces adapted to the animal anatomy were developed using 3D modeling following a procedure inspired by a previously proposed method for NHPs  (Chen et al. , 2017). The CT images were imported in the InVesalius software (2007-2017, Center for Information Technology Renato Archer) and segmented to obtain a 3D model of the skull. The MR images were processed with 3D Slicer software (http://www. slicer. org) (Fedorov et al. , 2012)  to segment the brain and visualize the cortical gyrus and sulcus anatomy before implantation in order to make accurate anatomic assessment of the motor cortex for surgical planning.
The 3D models obtained by CT-scan and MRI were then combined using the Blender software (https ://www. blender. org/) to model the full minipig's head. Based on this reconstruction, the coordinates of the craniotomy were planned according to the motor cortex location to access the implantation area (Fig. 1a ). A custom metallic oval chamber (60×50 mm) was further designed to be screwed on the skull during the implantation surgery (Fig. 1b , top). This chamber was adjusted to the minipig anatomy to closely fit its skull surface. Its role was to cover and protect the craniotomy area and house the connector of the implant. To ensure good resistance against possible damages, this chamber was 3D printed in biocompatible TA6V titanium (X3D, Lyon France). To close this chamber, a thick hood was 3D printed in transparent resin using a Form 2 printer (Formlabs, Sommerville, USA) (Fig.
Then, the animal could be let to move and behave freely in the pen under the supervision of an experimenter. However, in order to avoid that the animal damages the headstage against the floor or the walls, an experimenter placed her on her laps or in her arms. This physical contact was also a way to arouse more vocalizations. Synchronous cortical, audio and video data acquisitions were performed with the MCS experimenter software. Neural data was acquired at a 20-kHz sampling rate after 1-5000 Hz bandpass filtering and 16-bits AD conversion at the headstage level. Audio data was acquired at 20 kHz after amplification by a sound card (OctoMic II, RME-Audio, Haimhausen, Germany). Video was acquired at 50 Hz, synchronized with the neural and audio data.
Neural data processing Cortical data was band-pass filtered between 1 and 10 Hz and evoked potentials were computed by averaging single trials locked to the onset of vocalizations (detected by thresholding the audio signal and classified manually) and correcting the baseline with respect to the [-3s, -2s] interval preceding the vocalization onset. We then evaluated the reproducibility of the averaged evoked potential by building a distribution of bootstrap averages  (Yvert et al. , 2002). If N vocalizations were recorded, N trials were drawn with replacement from the original set of N trials, and averaged and baseline-corrected. This procedure was repeated 1000 times. To further assess the statistical significance of vocalization-induced cortical activations, we used the following approach.
A 60-second rest period void of vocal production was considered and used to select N resting trials, which were in turn averaged and baseline corrected. The standard deviations corresponding to both the vocalization and the resting averages were also computed and a Welch t-test was performed to compare both distributions at every time point of the average potential. A threshold was set at 0. 05 and individual electrode values at all time points for which the p-value of the Welch test was below this threshold were considered to correspond to statistically significant activity. This procedure was thus further retained to build spatiotemporal maps of cortical activity using our previously developed NeuroMap software  (Abdoun et al. , 2011)  (freely available at https://sit es. google. com/site/neuromapsoftware/).
Acknowledgements This work was supported by the  French National Research Agency  under Grant Agreement no. ANR-16-CE19-0005-01  (Neuromeddle), by the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreements no. 732032  (BrainCom), no. 696656  ( GrapheneCore1 ), no. 785219  ( GrapheneCore2 ), no. 881603  ( Graphe-neCore3 ), with financial support by the  Bertarelli Foundation , the  Wyss Center for bio-and Neuroengineering  (Grant no. W015-2016 ), the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie Grant  Agreement no. 665667 , and the  SNSF National Center of Competence in Research (NCCR) in Robotics. The authors are grateful to the  Carfil and Ellegaard companies  for providing CT scan data, and to the Clinatec preclinical team for animal housing and care.
The rest of the steps were the same as described in previous sections. Briefly, a homogeneous field was generated by imposing a voltage difference between opposite faces of the cube and the rest of the faces were considered insulators. The plate interface was modeled with the Butler-Volmer equation (2) using the same parameters described previously (parameters for titanium) and the electric potential distribution was calculated by finding the steady-state solution to Laplace equation. To simulate a realistic tES treatment, we used the head model of the subject "Ernie" from SimNibs example dataset  [22]. For this subject, a tetrahedral mesh is readily available with domains for scalp, skull, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM) and eyes (see Figure  2b ). A skull hole representing a craniotomy and a skull plate were added to this healthy head model through local mesh refinement using iso2mesh  [21].
[9]. Overall, 23 participants had missing information for CAIDE calculation. A detailed description of all the data acquired as part of the study can be found in  [29, 30]. From the 701 recruited participants, 647 had an MRI scanning session at baseline (Fig. 1 ). 27 scans were excluded from analysis after visual inspection either due to poor quality, artifacts (e. g. excessive motion, poor contrast) or incidental findings (e. g. meningiomas). An estimated years to dementia onset (EYO) variable was calculated for participants with dementia family history, based on the difference between the age of the participant and the age of parental dementia diagnosis (if both parents had dementia, the younger onset was used) and had a mean of 22. 8 years.
Surface-based analysis Freesurfer version 7. 1. 0 was used for data processing  [32]. The recon-all pipeline was run for every subject with standard settings. The brain masks and surfaces were inspected following recon-all and manual corrections were applied: (a) in the form of erosion of non-brain voxels from the brain mask or non-WM voxels from the WM mask, (b) in the form of filling of areas where the brain was not correctly identified or (c) with the addition of control points in cases where white matter was not successfully identified. Manual corrections were applied for the majority of subjects (87%). We quantified cortical thickness in a vertex wise level and in 68 regions based on the Desikan-Killiany atlas  [33]. Furthermore, the volume of bilateral hippocampi, thalami, amygdala, putamen, caudate, accumbens and pallidum were quantified.
Hippocampal subfield segmentation A dedicated Freesurfer hippocampal subfields segmentation module was applied using information from both the T1 and T2-weighted images. Hippocampal subfield segmentation relies on an atlas constructed based on in-vivo and Fig. 1  Structural scans used in the present analysis per PREVENT-Dementia site. Scans were excluded either due to incidental findings or due to poor scan quality that could potentially impact the structural analyses pipelines. Further exclusions were applied due to quality of the Freesurfer (FS) outcome or missing demographics high-resolution post-mortem scans and on manual subfield delineation from experienced radiographers  [34]. In particular, the Freesurfer algorithm utilizes the constructed probabilistic atlas and the individual voxel intensities to proceed to subfield segmentation using Bayesian inference  [34].
Toward that end, the T2w images were quality checked and a visual rating on the clarity of the molecular layer was recorded ranging from 1 to 3. The dataset was split in three and was rated by three independent raters (MED, CN, NJ). Agreement was evaluated using Cohen's κ for 60 scans. For each hemisphere starting from the slice where the body of the hippocampus was dominant and for another two slices posteriorly, we rated clarity per slice and also overall clarity per hemisphere, as follows: 1-non-clear delineation less than 20% can be seen; 2-more than 20% can be seen but not perfectly discernible and 3-clearly seen. The starting slice per hemisphere, was defined by registration of the headbody-tail segmentations generated by the Freesurfer pipeline to the T2 space using the linear transform generated as part of the main pipeline.