dataesr/scientific-paragraphs-categorization huggingface dataset:
For that, we drew 10 6 random audiovisual sequences and computed the time-averaged values of MCD CORR and MCD LAG for each sequence, with the parameters reported in  25. We then selected six sequences among these 10 6 to elicit a large range of responses of MCD CORR and MCD LAG values, while maintaining the correlation between MCD CORR and MCD LAG low (Pearson's correlation between MCD CORR and MCD LAG across the six sequences below 0. 2). Pretests were run to ensure that causality and temporal order judgments were indeed uncorrelated. In the present experiment, the two judgments are weakly correlated across participants and sequences (β = 0. 267 ± 0. 08, p < 0. 05), thus allowing contrast analysis on the task. MEG data acquisition. Brain magnetic fields were recorded in a magnetic-shielded room using a 306 MEG system (Neuromag Elekta LTD, Helsinki).
Once cortical models were complete, deformable procedures could be performed including surface inflation and registration to a spherical atlas. These procedures were used with MNE_python  63  to morph current source estimates of each individual onto the FreeSurfer average brain for group analysis. MEG data preprocessing. Data preprocessing was done in accordance with accepted guidelines for MEG research  64. Signal Space Separation (SSS) was carried out using MaxFilter to remove external interferences and noisy sensors. Signalspace projections were computed by independent component analysis (ICA) to correct for eye blinks and cardiac artifacts. Next, raw data were band-pass filtered between 1 and 40 Hz and down-sampled to 250 Hz. All pre-processing steps were done using MNE_python 0. 24. MRI-MEG co-registration and source reconstruction.
The coregistration of MEG data with the individual's structural MRI was carried out by realigning the digitized fiducial points with MRI slices. Using. MRILAB (Neuromag-Elekta LTD, Helsinki), fiducials were aligned manually with the multimodal markers visible on the MRI slice. An iterative procedure was subsequently used to realign all digitized points (about 30 more supplementary points) distributed on the scalp of the participants were digitized, with the scalp tessellation using MNE_python tools. Individual forward solutions for all source reconstructions located on the cortical sheet were next computed using a 3-layers boundary element model  65, 66  constrained by the individual aMRI. Cortical surfaces were extracted with FreeSurfer and decimated to about 10,240 vertices per hemisphere with 4.
Epochs contaminated by artifacts were rejected based on the peak-topeak amplitude (2. 10 -11 T for magnetometers and 9. 10 -9 T cm -2 for gradiometers). Across participants, an average of 98. 9% of epochs were kept in the analyses. Sensors were then interpolated using the function EvokedArray. as_type of MNE_python: each triplet of planar gradiometer, axial gradiometer and magnetometer was mapped onto a single virtual magnetometer, reducing the data space from 306 to 102 sensors without discounting signal or information. Epochs were averaged for each participant across experimental blocks and conditions of interest. In order to focus on multisensory and task-related effects, we first removed unisensory evoked responses from the multisensory brain responses. In order to do this, we fitted a linear regression to the evoked responses elicited by audiovisual sequences in the causality and temporal order judgments blocks using, as predictors, the brain activity evoked by the unisensory (A,V) localizers.
The parameters were all fitted together: the 3 parameters of the Multisensory Correlation Detector were fitted using both tasks whereas the 2 parameters of the mixed-effect logistic regression and the random slopes per participant were task independent. The fit consisted of a particle swarm algorithm followed by Matlab optimization algorithm fminsearch to maximize model's log likelihood. Since both methods are sensitive to starting parameter values, the procedure was repeated 10 times and the chosen parameter values are those providing the best fit (Table  1 ). Reported p-values are Satterthwaite approximations obtained using R (R Core Team, 2000) with the lmerTest  68  and lme4 69 packages. Difference of goodness-of-fit between model units was assessed using a paired t-test on the R 2 across participants.
The residual error ε(n, t) is minimized for each sensor n using least-square regression. Note that we fit one temporal response function per MEG sensor and per Multisensory Correlation Detector component. The parameters of the Multisensory Correlation Detector model were estimated from the behavioral data and left untouched at this stage of the analysis. Overfitting was handled by using a standard procedure of cross-validation: at each iteration, we fitted temporal response functions on all participants and all stimuli except for one stimulus of one participant (78-folds cross-validation). We evaluated the performance of the temporal response function for each component of the Multisensory Correlation Detector separately by Pearson's ⍴ between predicted activity and real MEG recordings. Finally, ⍴ scores were averaged across all folds and tested against 0 with a two-tailed t-test.
The question of how children learn Function Words (FWs) is still a matter of debate among child language researchers. Are early multiword utterances based on lexically specific patterns or rather abstract grammatical relations? In this corpus study, we analyzed FWs having a highly predictable distribution in relation to Mean Length Utterance (MLU) an index of syntactic complexity in a large naturalistic sample of 315 monolingual French children aged 2 to 4 year-old. The data was annotated with a Part Of Speech Tagger (POS-T), belonging to computational tools from CHILDES. While eighteen FWs strongly correlated with MLU expressed either in word or in morpheme, stepwise regression analyses showed that subject pronouns predicted MLU. Factor analysis yielded a bifactor hierarchical model: The first factor loaded sixteen FWs among which eight had a strong developmental weight (third person singular verbs, subject pronouns, articles, auxiliary verbs, prepositions, modals, demonstrative pronouns and plural markers), whereas the second factor loaded complex FWs (possessive verbs and object pronouns).
60. We evaluated the fit of a structural equation model (SEM) to determine the degree of adequacy using different adjustment indices: the index square root approximation error of Steiger Lind (RMSEA), the normalized adjustment index (NFI), the Tucker-Lewis index (TLI), the comparative adjustment index (CFI). These indices are interpreted according to critical thresholds. In general, values below 0. 05 and 0. 08 for the RMSEA are considered to indicate good and acceptable data adequacy respectively. The CFI and TLI should be greater than 0. 95. All statistical analyses were performed under JAMOVI 37 version 1. 6. We used Ωnyx, a graphical SEM software for performing maximum likelihood estimation of parameters in models  38  (http:// onyx. brand maier. de).
Acknowledgements The authors are grateful to the many families who generously contributed their time to these projects over the years. We wish to thank  Brian MacWhinney , whose fundamental insights into measurement of early language skills made this work possible. This paper has also been improved by  Eric Villemonte de la Clergerie  from  National Institute for Research in Digital Science and Technology, Paris , who provided the Metagrammar framework for the discussion of this paper. We acknowledge the support to the  Paris Hearing Institute  from " Fondation pour l' Audition" (FPA IDA09 ).
Data availability The datasets generated for this study are available from the CHILDES site at https:// child es. talkb ank. org/ access/ French/ MTLN. html, Le Normand, M. -T. French MTLN Corpus. https:// doi. org/ 10. 21415/ T58S3M. (  2014 ). This Figure  6. Syntactic tree from dependency grammar (French Meta Grammar http:// alpage. inria. fr/ frmgd emo)  73.
Animals Experiments were performed in multiple laboratories, using protocols that had been extensively validated previously, for optimization of experimental conditions and of dosing regimes. Table  1  shows the comparative data on the animals and drugs used for each group, with the approval of the relevant ethical committees. Adult male Wistar rats (~150 g) and aged male Wistar rats (8 months) were used for the hippocampal slice preparations and the EEG recordings, respectively. Adult male Sprague Dawley rats (300-400 g) were used for platform stress and LTP recordings. All animals were maintained under temperaturecontrolled conditions (22 ± 2 • C), with a normal 12 h light dark cycle and ad libitum access to food and water. Adult male C57/Bl6 mice (2-4 months) were obtained from Charles River UK, and used for platform stress and subsequent ex vivo LTP recordings (Table  1 ).
Male C57/Bl6N mice (Charles-River, Sulzfeld, Germany) were housed in humidity-(60 ± 10 %) and temperature-controlled (23 ± 1 • C) rooms with a 12 h light/dark cycle. Food and water were provided ad libitum. Behavioural testing was performed between 9 am and 2 pm. Normally distributed data sets were compared using the paired Student's t-test and unpaired Student's t-test. Statistical significance between more than two normally distributed data sets was tested by one-way analysis of variance test (or two-way when specified), followed by a Newman-Keuls test to compare individual pairs of data. Non-Gaussian data sets were tested by non-parametric Mann-Whitney test. Indications of significance correspond to p-values < 0. 05 (*), p < 0. 01 (**) and p < 0. 001 (***).
Inhibition of H-PFC LTP Caused by Platform Stress The procedure is fully described previously  [19]. The stress protocol consisted in placing the rats on an elevated and unsteady platform for 30 min. The animal showed behavioural "freezing", defecation and sometimes urination. At the end of stress, rats were anaesthetized (60 mg/kg i. p. pentobarbital) on the platform and immediately after, placed in the stereotaxic frame. High frequency stimulation (HFS) was administered to induce LTP, after preparation and stabilisation of the animals, 180 min after stress; drugs (EGIS 11150, clozapine or haloperidol) were administered. 40 min before HFS. PSP amplitudes were analysed using A/Dvance software, expressed as a percentage change of the mean response over a 30 min baseline period and presented in figures as the mean ± SEM for 2 min epochs.
Stimulation was delivered at 0. 033 Hz and the stimulus adjusted to produce a response 40% of the maximum fEPSP slope. For LTP experiments, maximal, or sub-maximal LTP was induced by a TBS protocol (4, or 3 pulses respectively delivered at 100 Hz, repeated 10 times, at an interval of 200 ms). The stimulus parameters, the acquisition and the analysis of fEPSPs were under the control of LTP software (courtesy of Dr. Anderson and Prof. Collingridge, Bristol University, UK; http://www. ltp-program. com, accessed on 27 February 2022). Statistical analysis of LTP was performed using SPSS statistical software. Comparisons of the PTP fEPSP values were measured using unpaired t-tests and the magnitude of LTP was measured by means of repeated measures linear regression analysis at 50-60 min after the TBS  [42].
The number of correct choices was recorded and statistically analysed. Vehicle or Egis-11150 (0. 01 or 0. 03 mg/kg) was administered p. o. 1 h before the commencement of reversal trials. The first group of eight animals was tested 24 h after the last learning trial and the other groups were examined over the next four days. As the treatment trends were similar on each day of testing the data from the five reversal trials are reported together.
Brain slices electrophysiology and data analysis. Slices were transferred to a recording chamber mounted either on a upright Slicescope Pro 6000 system (Scienti ca, UK) and continuously perfused with oxygenated ACSF (2-3 mL/min) at near physiological temperature (30°-32°C, Heated perfusion tube HPT-2A, ALA Scienti c Instruments Inc, USA). Recordings were performed from FS-PV + neurons located in layer 5 of the prelimbic region (PrL) of the medial PFC. Cells were visualized by infrared differential interference contrast and with a cooled digital CCD OrcaR 2 Hamamatsu camera (C10600-10B, Hamamatsu Photonics, Japan) in combination with an on-line video contrast enhancement. Epi uorescence illumination (CoolLED pE-2 excitation system) was used to visualize tdTomato-positive interneurons (Ex/Em: 555/581nm), using differential interference contrast infrared videomicroscopy.
Recordings were obtained using a Multiclamp 700B ampli er (Molecular devices, CA, USA) and signals were ltered at 2 kHz and digitized at 5 kHz via a DigiData 1440A interface (Molecular Devices, CA, USA). Data were collected and analyzed using pClamp10 software (Molecular Devices, CA, USA). Series resistance and holding current were monitored throughout the experiment. Cells with an access resistance > 25 MΩ at resting potential were excluded from analyses as well as any cell for which a >20% change in those parameters occurred during the course of the experiment. Electrophysiological recording were made using borosilicate glass pipettes (resistance = 3-5 MΩ) lled with either one of the two following intracellular solutions, depending on the experiment : K-gluconate solution (in mM: K-gluconate 120, KCl 10, HEPES 10, EGTA 0.
Acknowledgements We thank  Zoltan Nüsser  ( Hungarian Academy of Sciences, Laboratory of Cellular Neurophysiology, Budapest ) for valuable discussion and advices during the elaboration of the study,  Magalie Martineau ,  Dominique Debanne  ( UNIS, INSERM Aix-Marseille University ) and  Isis Nem Oliveira de Souza (LuMIn, Université Paris-Saclay ) for their critical reading and feedback on the manuscript. Michael Rogawski  and  Sarah Denson  ( University of California, Davis ) provided the SR knock-out mice. Laurent Venance  ( CIRB, College de France, Paris ) and  Desdemona Fricker  ( INCC, University of Paris ) provided the Ai9-tdTomato::PV-Cre transgenic line. The authors also acknowledge the assistance of  Ayma Galland ,  Valérie Domergue  and the personnel of the animal facility AnimEX in mouse breeding and care.
Vagus nerve stimulation (VNS) Therapy ® is an adjunctive neurostimulation treatment for people with drug-resistant epilepsy (DRE) who are unwilling to undergo resective surgery, have had unsuccessful surgery or are unsuitable for surgery. A systematic review and meta-analysis were conducted to determine the treatment effects of VNS Therapy as an adjunct to anti-seizure medications (ASMs) for the management of adults with DRE. A literature search was performed in August 2020 of the Medline®, Medline® Epub Ahead of Print, Embase, and the Cochrane library databases. Outcomes examined included reduction in seizure frequency, seizure freedom, ASM load, discontinuations, and serious adverse events (SAEs). Comparators included best medical practice, ASMs, low-stimulation or sham VNS Therapy. Four RCTs and six comparative observational studies were identified for inclusion.
Data collection and risk of bias assessment General patient/participant demographics were extracted such as age at time of implant, sex, type of seizure and baseline seizure frequency. Outcomes of interest included reduction in seizure frequency, seizure freedom, ASM load, discontinuations, and serious adverse events (SAEs). Quality (risk of bias) assessment of RCTs was conducted using the seven-criteria checklist provided in Section 2. 5 of the National Institute for Health and Care Excellence (NICE) single technology appraisal (STA) user guide for RCTs  [33]. Observational studies were assessed using the quality assessment tool for quantitative studies of the Effective Public Health Practice Project (EPHPP)  [34].
Meta-analysis Evidence synthesis was conducted via pair-wise meta-analyses based on RCT and comparative observational studies. While observational comparative evidence is of lower quality compared with RCTs due to the inherent bias within such studies, their inclusion was deemed appropriate as observational comparative studies provide longer follow-up compared with RCTs. Pair-wise meta-analyses were conducted for the outcomes of interest previously described. For one RCT (PuLsE)  [35] , which reported outcomes up to 2-years post-surgery, the outcome results were restricted to the 12-month time point. The 12-months results were included in the meta-analysis. Outcomes for this study were restricted to 12 months in order to facilitate data comparisons as all other RCTs included in the meta-analysis had shorter follow ups (range: 3.
The SLR searched the electronic databases of Medline®, Medline® Epub Ahead of Print (In-Process & Other Non-Indexed Citations), Embase, and the Cochrane library to identify relevant clinical studies (RCTs, controlled clinical studies, and prospective registries) examining VNS Therapy and other interventions of interest for the management of patients with DRE. Additional searches of congress proceedings from the past 3 years (American Epilepsy Society [AES], Congress of Neurological Surgeons [CNS] Annual Meeting, European Congress on Epileptology [ECE], International Epilepsy Congress [IEC], International Neuromodulation Society [INS] Congress), reference lists of included publications, and Health Technology Assessment (HTA) bodies were conducted to identify relevant evidence. Search terms are listed in the Supplementary Materials.
Precoss-β architecture and oscillating precisions Precoss-β was built by including oscillating state-dependent precisions in a previous generative model version (Precoss)  (Hovsepyan, Olasagasti and Giraud, 2020). The model input consists of a speech reduced auditory spectrogram  (Chi, Ru and Shamma, 2005)  and of its slow amplitude modulations  (Hyafil et al. , 2015) , both extracted English sentences of the TIMIT database  (Garofolo et al. , 1993)  (see  Hovespyan et al. 2020 (Hovsepyan, Olasagasti and Giraud, 2020)  for details about speech input generation). In Precoss-β, the activation of the appropriate syllable unit generates the corresponding auditory spectrogram with a flexible duration determined by eight gamma units (Figure  1 ). Syllable and gamma units represent syllable identity and timing within the syllable, respectively.
external evidence, which, given the predictive nature of speech, should be an optimal processing strategy. We also expected the model performance to depend on the prediction error precision rate, peaking at a frequency that will depend on the two model intrinsic rhythms (~ 200 ms for syllable units, ~ 25 ms for gamma units). In Precoss-β (A) PEP are only modulated in the syllable units, which act as evidence accumulators for each syllable in the input sentence. Therefore, to benefit from the alternation between top-down and bottom-up information flows on the inference process, there should be at least one full PEP cycle per syllable. As the mean syllable duration in our dataset is around 200ms, we expect the preferred PEP modulation frequency to lie within the theta range ~ 5 Hz. Similarly, in Precoss-β (B) PEP are only modulated within the gamma units.
Speech input and syllabification: We have used the same set of 220 sentences from the TIMIT dataset  (Garofolo et al. , 1993)  that we used in  (Hovsepyan, Olasagasti and Giraud, 2020)  for the simulations of the new model -Precoss-β. Briefly, for each sentence, a 6-channel reduced auditory spectrogram was calculated with a biologically plausible model of the auditory periphery  (Chi, Ru and Shamma, 2005). Additionally, slow amplitude modulation of the sentence waveform was calculated following procedures described in Hyafil and colleagues  (Hyafil and Cernak, 2015; Hyafil et al. , 2015). Syllable boundaries in the input sentences were defined with the Tsylb2  (Fisher, 1996)  programme based on the phonemic transcriptions provided in the TIMIT database  (Garofolo et al. , 1993). The programme estimates syllable boundaries based on English grammar rules, using phoneme annotations from TIMIT.
5-fold). 27 The glucoraphanin level in the broccoli seeds is largely determined by plant genotype, although the environment in which the plants are grown (e. g. location, year, drought, pollution and disease pressure) also plays a clear and significant role. 28 Thus, it may be assumed that seeds contain between 0. 5 and 1. 5 g of bioactive components per gram of sprouts. A study published on ClinicalTrials. gov (ID: NCT03390855) showed that a daily consumption of 30 g of raw (not cooked), fresh broccoli sprouts during 10 weeks (70 days) was safe. 29 The daily dose of broccoli seed capsules arbitrarily proposed in food supplements ranges from 300 to 1000 mg (less than 5% of the daily intake of broccoli sprouts). Kimchi, a traditional Korean fermented food, usually contains cabbage and/or radish (Nrf2), garlic, red pepper, ginger and other spices (TRPA1/V1).
The duration of supplemental O 2 use and hospitalisation was also meaningfully shorter in the treatment group. A trial in progress (https://clinicaltrials. gov/ct2/show/NCT050 08003) is evaluating the combination of vitamin D, curcumin and quercetin on the early symptoms. The active comparator will be the standard of care. 4 | SAFETY 4. 1 | Nrf2-containing foods and supplements Cruciferous vegetables such as broccoli are very healthy. No side effects (apart from some diarrhoea) have been reported using the F I G U R E 1 Putative mechanisms of Nrf2-TRP agonists in the management of COVID-19 BOUSQUET ET AL.
Individual patient data We selected patients initially diagnosed with temporal lobe epilepsy (surgical procedure: resection, surgical outcome: seizure-free, Engel score I). The patients underwent comprehensive presurgical evaluation, including clinical history, neurological examination, neuropsychological testing, structural and diffusion MRI scanning, Stereotactic-EEG (SEEG) recordings along with video monitoring as previously described in  Bartolomei et al. (2008) ;  Proix et al. (2017). SEEG electrodes were implanted in the regions suspected to be in the epileptogenic zone. Each electrode had 10-15 contacts (length: 2 mm, diameter: 0. 8 mm, contacts separation: 1. 5 mm). SEEG signals were recorded with a 128-channel Deltamed system (sampling rate: 512 Hz, hardware band-pass filtering: between 0.
Network anatomy The structural connectome was built with TVB-specific reconstruction pipeline using generally available neuroimaging software (see Fig. 1A ). The current version of the pipeline evolved from a previously described version  (Schirner et al. , 2015; Proix et al. , 2016 Proix et al. , , 2017)). First, the command recon-all from Freesurfer package  (Fischl, 2012)  in version v6. 0. 0 was used to reconstruct and parcellate the brain anatomy from T1-weighted images. Then, the T1-weighted images were coregistered with the diffusion weighted images by the linear registration tool flirt  (Jenkinson et al. , 2002)  from FSL package in version 6. 0 using the correlation ratio cost function with 12 degrees of freedom. The MRtrix package in version 0. 3. 15 was then used for the tractography.
Stereotactic-EEG (SEEG) data preprocessing For the selected patients, SEEG electrodes were implanted in critical regions based on the presurgical evaluation. The SEEG data is re-referenced using bipolar montage, which is obtained using the difference of 2 neighboring contacts on one electrode. Bipolar SEEG signal is extracted from 5s before seizure onset up to 5s after seizure offset. The onset and offset times of the epileptic seizure are set by clinical experts. In this study, the log power of high-frequency activity is used as the target for the fitting task. More precisely, the SEEG data are windowed and Fourier transformed to obtain estimates of their spectral density over time. Then, SEEG power above 10 Hz is summed to capture the temporal variation of the fast activity  (Jirsa et al.