dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to further identify the iPRC1 epitope, the interaction of iPRC1 with ICL3 was assessed in presence of competing decapeptides covering ICL3 Nt part (ICL3-Nt), middle part (ICL3-Mid) and Ct part (ICL3-Ct) (Fig. 3D ). A significant inhibition of interaction was obtained with the ICL3-Mid peptide, suggesting that iPRC1 epitope likely encompasses amino acids 558 NPNIVSSSSD 567. In the 3D structure of the activated FSHR (PDB: 8I2G), the NPNIV motif appears as a flexible loop that is constrained between the extra-membrane helices formed by 552 IYLTVR 557 in Nt and by 563 SSSDTRI 570 in Ct. Hence, being the most exposed, this pentapeptide is the most likely epitope of iPRC1.
4B ). The selectivity of iPRC1 was tested on two other Gs-coupled class A GPCRs, structurally unrelated to the FSHR, the AVPR2 and the beta 2 adrenergic receptor (ADRB2). The AVP-induced cAMP production in HEK293A cells transiently expressing AVPR2, and stimulated for 15 min with 0. 25 nM AVP (Fig. 4C ), or the ISO-induced cAMP production in HEK293A cells endogenously expressing ADRB2 (Fig. 4D ) were not altered by the co-expression of T31 as expected, nor of iPRC1. These data show that iPRC1 negatively regulates the FSH-induced Gs protein-dependent signalling, and this effect is specific of the anti-FSHR intra-VHH selected in this study. As suggested by the docking data, the reduced cAMP production in response to FSH might be due to iPRC1 partially preventing Gs protein interaction with the receptor.
Importantly, iPRC1 recognized the receptor whether it is FSH-stimulated or not. Hence, iPRC1 is not specific of an inactive or an FSHactivated conformation of the FSHR. G proteins are recruited to the FSHR notably through the ERW motif present on ICL2, but also through a basic motif on ICL3  [21]  (Fig. S4 ). We showed that iPRC1 does not bind the PTH1R, a GPCR that contains a basic motif similar to the FSHR's. Even though these data suggest that this motif is not part of iPRC1 epitope, iPRC1 might interfere with G protein interaction with the FSHR through steric hindrance. This hypothesis is supported by our docking model that overall suggests direct steric clashes with Gas. Consistently, iPRC1 specifically reduced the FSH-induced cAMP production in HEK293 cells transiently expressing the FSHR, but not the AVPR2 or the ADRB2 in the presence of their respective ligands.
Acknowledgements The authors would like to acknowledge the  Merck Company  for kindly providing purified recombinant human FSH,  Pierre Martineau  ( Institut de Recherche en Canc erologie de Montpellier, France ) for precious technical indications and for kindly providing the pCANTAB 6 phagemid vector and the KM13 helper phage. This work was funded with the support of  Institut National de la Recherche Agronomique et de l'Environnement (INRAE) , of the  MAbImprove Labex  ( ANR-10-LABX-53 ) and of  R egion Centre Val de Loire   ARD2020   Biom edicaments SELMAT grant  and  APR-IR INTACT grant. PR was funded by a joint fellowship from  R egion Centre Val de Loire  and  INRAE PHASE Department  and by the  APR-IR INTACT  grant. OV was funded by the  APR-IR INTACT grant. AV, CaG and VJ were funded by the  SELMAT grant  of  R egion.
Western blotting Protein lysates from the VMH were subjected to SDS-PAGE, electrotransferred to polyvinylidene difluoride membranes (PVDF; Merck Millipore; Billerica, MA, USA) with a semidry blotter and probed with antibodies against β-actin (1:5000; A5316), α-tubulin (1:5000; T5168) (Sigma; St Louis, MO, USA); pACCα-Ser 79 (1:1000; 3661; Cell Signaling; Danvers; MA, USA); ACCα (1:1000; 04-322; Merck Millipore; Billerica, MA, USA)  [13, 14, [16] [17] [18] [19]. Autoradiographic films (Fujifilm; Tokyo, Japan) were scanned and the band signals were quantified by densitometry using ImageJ-1. 44 software (National Institutes of Health; Bethesda, MD, USA)  [13, 14, [16] [17] [18] [19]. Values were expressed in relation to α-tubulin (BAT) or ACCα (VMH). Representative images for all proteins are shown with all bands for each picture derived from the same gel, although they may be spliced for clarity; a black line was inserted when samples were not loaded side by side.
Immunohistochemistry Adipose tissue depots were fixed in 10 % buffered formaldehyde and paraffin embedded. For the hematoxylin-eosin processing, the BAT and sWAT sections were first stained with hematoxylin for 5 min, washed and stained again with eosin for 1 min. Detection of UCP1 in sWAT was performed using anti-UCP1 antibody (1:500; ab10983; Abcam, Cambridge, UK)  [14, 16, 18, 19]. Images were taken with a digital camera Olympus XC50 (Olympus Corporation; Tokyo, Japan) at 20×. Digital images for BAT and sWAT were quantified with ImageJ 1. 44 software  (National Institutes of Health; Bethesda, MD, USA).
To control the specificity of our treatment, we assayed the effect of SF1-AMPKα1-DN sEVs in non-neuronal SF1expressing tissues, such as pituitary, adrenal, and testis. As formerly demonstrated in a diet-induced obesity model (DIO)  [14] , our data showed no changes in pACCα content (and therefore in AMPK activity) in those organs (Supplemental Fig. 3A-B ). Overall, those results demonstrated that the specificity of SF1-AMPKα1-DN was restricted to SF1 cells of the VMH. To address whether increased BAT function was due to an activation of SNS tone, we analyzed BAT neurotransmitter content. Our data showed that both wildtype and db/db mice treated with SF1-AMPKα1-DN sEVs had higher levels of norepinephrine (NE) in the brown fat (Fig. 2C-D ), which is indicative of higher adrenergic tone. No changes were found in other neurotransmitters analyzed, such as dopamine (DA) or serotonin (5-HT) (Fig.
Acknowledgments Funded by:  Ministerio de Ciencia y Universidades  co-funded by the  FEDER Program of EU  (CD:  BFU2017-87721 ; RN:  RTI2018-099413-B-I00  and  RED2018-102379 -T; ML:  RTI2018-101840-B-I00 ,  PID2021-128145NB-I00  and  PDC2022-133958-I00 ). " la Caixa" Foundation  ( ID100010434 ), under the agreement  LCF/PR/HR19/52160022  (ML); EuroNanoMed III (RA & ML:  EURONANOMED2019-050-ENAMEP );  European Research Council  (RN:  ERC Synergy Grant - 2019-WATCH-810331 ).
Unsupervised classi cation trees were generated using R package "Party"  [12]. The relevance of the groups identi ed by the classi cation trees were then tested using ANOVA with post-hoc Tukey for multiple comparisons of means. Then, we performed supervised classi cation methods using the R package "mlr3''. [13]  In sum, we split the sample in 2 groups, 80% of the individuals for training and the remaining for validation. As the MIA + individuals were under-represented, we used an oversampling method with a 3. 5 ratio. The sample characteristics before and after oversampling are in Supplementary Fig. 1. For classi cation, we used both a logistic regression classi cation learner and a linear discriminant analysis classi cation learner. The learning parameters used by the model were then evaluated using the package R package «iml»  [14]  with three components: feature effects, Shapley values and feature importance.
Unsupervised analysis with a linear model regression learner con rmed this association (mean absolute error = 20. 2; mean qquare error = 562. 7; root mean square error = 23. 7, R-square = 0. 16) (Fig. 2A ). According to feature importance, MIA appeared to predict the intensity of communication de cit, just after the intensity of the global autistic symptom severity itself (Fig. 2B  and Supplementary Fig. 4 ). Finally, we explored whether there was a causal link between the MIA and the impairment intensity of socio-adaptive skills. We used structural equation modelling and applied a path analysis to our dataset. We found, with a good model performance (Fig. 2C ), that MIA in uenced directly the socio-adaptive skills of children, independently of pregnancy complications or global severity of autism symptoms (Fig.
Funding: This work was supported (in part) by the  Fondation FondaMental, Créteil, France  and by the  Investissements d'Avenir  programs managed by the  ANR  under references  ANR-11-IDEX-0004-02  and  ANR-10-COHO-10-01. Autors' contribution: PE and AM contributed to data collection. PE, HP and NT analysed and interpreted the data. PE wrote the rst version of the article and revised it after its revisions by co-authors. VV, EH, AAm, AAn, PA, JMB, SB, OB, MB, AC, NC, RC, DDF, CD, MG, FGB, FG, AK, ML, AL, FL, CL, EM, NR, CMS, MS, EZ, participated in the inclusion of children and in the revision of the article and approved its nal version,.
In vivo Electrophysiological Recordings The signal was amplified, and low-filtered at 5 kHz with a homemade amplifier. Respiration was recorded with an airflow sensor (described in  (51) ). Positive and negative values corresponded to expiration and inspiration respectively. All signals were then digitalized at 25 kHz (NI USB-6211, National Instruments, Austin, TX, USA) and stored on a computer using Neurolabscope, a homemade software.
Cycle by cycle interpolation preserved the ratio, i. e. , for a 0. 33 ratio, interpolation attributed 33% of the 2000 points to the inspiration period and the remainder to the expiration period. This rescaling allowed the construction of respiration-triggered MP average (Figure  2C  bottom) or respiration-triggered AP histogram (Figure  2C top ). We made one exception to construct Figure  4B  as we wanted to compare the preferred phase of depolarization between cells, so we rescaled all recordings with the same ratio 0. 4 in order to align all cell respiratory phases. The average inspiration-expiration ratio of our dataset was 0. 41±0. 01. In this section, we will describe how we detected respiration-related oscillations (RRo) in oscillatory signals (membrane potential and LFP). We proceeded in 5 steps (Figure  1A ).
4±0. 9 mV, N = 8 cells) (Kruskal-Wallis test, p = 0. 01, H(2) = 9. 1, post hoc Mann-Whitney U test S1-mPFC: p = 0. 002 and S1-HPC: p = 0. 02). To pursue, we characterized the respiratory phase of the MP RRo. As already suggested by the illustrative examples in Figure  2 , the MP RRo phases were not unique. Indeed, while MP exhibited maximal depolarized values around the transition phase between inspiration and expiration in S1 and HPC examples (Figure  2C2  and 2C4), MP of mPFC and V1 examples exhibited hyperpolarized values at this same respiratory phase (Figure  2C1  and 2C3 ). On the whole dataset, we extracted, for each cell the phase of the most depolarized value of the median MP RRo (see Figure  2 , panels C-bottom for examples of median MP RRo). Figure  4B  shows for each structure the preferred phase for each cell.
These results suggest that respiratory rhythm can entrain brain activity down to the membrane potential. In addition, we recorded in a preliminary study two prefrontal neurons whose membrane potential RRo was suppressed by ipsilateral nostril obstruction (Supplemental Figure  S6 ). If extended, such results would definitively show that MP RRo in mPFC are entrained by respiratory nasal airflow. Contrarily to anesthetized animals, awake animals display various pattern of respiration. It has been shown that respiratory modulation is stronger for low frequency respiration  (52) (53) (54) , more precisely for short and deep inspirations  (25, 55). During anesthesia, respiration tends to have a slow frequency and the inspiration tends to be shorter than the expiration. In our dataset, the mean respiratory frequency was 1.
ACKNOWLEDGMENTS We would like like to thank  Marc Thévenet  and  Belkacem Messaoudi  for providing technical assistance. AUTHOR CONTRIBUTIONS C. A. and N. B. conceived and designed research; M. J. and M. Z. performed experiments; M. J. , N. F-T. , S. G. , C. A developed the RRo detection method; M. J analyzed data; M. J, N. F-T. , N. B. and C. A. interpreted results of experiment; M. J. and C. A. prepared figures. M. J. , N. B. and C. A. drafted manuscript; M. J. , M. Z. , N. F-T. , N. B. and C. A. edited and revised manuscript; M. J. , M. Z. , N. F-T. , S. G. , N. B. and C. A approved final version of manuscript.
The amount of synaptic decrement (LTD) depends on the activity which arrives through the MFs. This activity is convolved with the kernel defined in Eq. (  27 ). This LTD mechanism computes on those MF spikes arriving after/before the PC spike arrival within the time window defined by the kernel. The amount of LTP at the MF-MVN synapses is fixed  (Ito, 1982; Lev-Ram et al. , 2003; Luque et al. , 2011a)  with an increase in synaptic efficacy, each time a spike arrives through an MF to the target MVN. The article source code implemented in EDLUT is available at the URL: https://www. ugr. es/~nluque/code_open/CODE_EC_regul ated_GABA_Olivary_VOR. rar.
Funding This work was funded by the  EU  to FN NEUSEQBOT [91774] and to  Human Brain  Project Specific Grant Agreement 3 to ER [ H2020-RIA. 945539 ]. The  Andalusian Regional Government (Spain)  and  EU  to  ER CEREBIO [P18-FR-2378 ] and to  NRL [A-TIC-276-UGR18 ]. The  Spanish Ministry of Science and Innovation  to  NRL SPIKEAGE [MICINN-PID2020-113422G A-I00] ref [MCIN/ AEI/10. 13039/501100011033 ] and to  ER INTSENSO [MICINN-FEDER-PID2019-109991GB-I00 ]. It was also funded by the  French Government  through the Chair SILVERSIGHT to AA [ ANR-14-CHIN-0001 & ANR-18-CHIN-0002 ], the  LabEx LIFESENSES  to  AA  [ ANR-10-LABX-65 ], and the  IHU FOReSIGHT  to  AA  [ ANR-18-IAHU-01 ]. Funding for open access charge:  CBUA/Universidad de Granada.
The analysis of MSE is performed on the dataset from  Nourhashemi et al. (2020). Moreover, we test whether power spectral density (PSD) and/or SATs are related to MSE. We hypothesize that these are the two major contributors to MSE. In fact, the discontinuous and non-stationarity of EEG signal in infants is characterized by SATs which can be changed with age by occurrence, duration and amplitude, and which can impact MSE calculation. Moreover, since entropy is sensitive to spectral power, the MSE results with regard to the correlation between PSD and MSE in adults should be interpreted with caution  (Kosciessa, Kloosterman, & Garrett, 2020). After 1) determining the development of MSE, PSD and SAT over time in simultaneous EEG -fNIRS recordings in preterm infants between 27 and 35 weeks of GA, we 2) explore the relationship between MSE, PSD, SATs and GA by performing a stepwise regression.
Participants, recordings and datasets: The participants' particulars, recordings and datasets are described in  Nourhashemi et al. (2020). In short, the study consists of 32 preterm neonates (12 females, with a mean gestational age (GA) of 29. 3 weeks at birth, and ranging between 27 and 34 weeks GA), who were recorded in a supine position (mean GA at test of 31. 3 weeks). The ethical committee of the Amiens University Hospital approved this study (CPP Nord-Ouest II-France IDRCB-2008-A00704-51). All the parents gave their informed consent in accordance with the Declaration of Helsinki, and agreed to their data being used for further research. The neurophysiological recordings were performed using the simultaneous, multimodal approach as described in Nourhashemi et al. (Nourhashemi et al. , 2020).
This includes EEG and Continuous Wave (CW) fNIRS. The multimodal recording probe was constructed so as to be placed on the infant's forehead to record the spontaneous physiological data (for further details on the recording system, see  Nourhaschemi et al. (2020) ). For the current analysis, we included one frontal EEG channel out of the eight available, corresponding to Fp1 in the international 10-20 system. The choice of channel was simplified by the fact that this was the only channel where EEG and fNIRS sensors were placed on the same spatial position. The datasets length varied between 12 and 55 minutes, with a mean of 32 and standard deviation of 10 minutes. The EEG sampling frequency was of 1024Hz. The fNIRS sampling frequency was 5Hz. Before preprocessing, two datasets were excluded since no simultaneous EEG -fNIRS recording was possible due to technical problems, resulting in N = 30 datasets included for further analyses.
Preprocessing EEG datasets were initially linearly detrended. Data were then band-pass filtered between 1 and 30 Hz. After filtering, data were cleaned from high amplitude artefacts using the artefact blocking (AB) algorithm  (Mourad, Reilly, de Bruin, Hasey, & MacCrimmon, 2007). In the AB algorithm, a threshold was calculated for each participant, corresponding to the median value of the data time-series plus three standard deviations of the signal. Finally, data were downsampled to 256Hz. With the CW-fNIRS, we acquired the concentration changes in oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and calculated the cerebral Tissue Oxygenation Index (TOI). In addition and offline, we calculated the difference between HbO and HbR, which is a proxy for oxygen consumption (HbD). These data were also initially detrended with a linear fit.
Parameters estimation Before proceeding with the estimation of our parameters of interest, we divided the EEG datasets into non-overlapping one-minute windows, resulting in N = 15360 data points per-window. We divided the datasets in non-overlapping windows of N = 2000 data points in the fNIRS signal. This number was selected to ensure that there were enough data-windows for each dataset, and that these sufficed to perform MSE analysis up to scale 20  (Angelini et al. , 2007; Da Sortica Costa et al. , 2017). The MSE and PSD were calculated for each of the windows defined in the EEG, HbO, HbR, HbD and TOI signal. Outliers were identified using the generalized "ESD many-outliers" procedure  (Rosner, 1983)  and removed using the algorithm by  Taliaferro (2021). The values of these parameters were averaged across windows within subject.
Despite the fact that dividing a dataset into separate windows decreases the data-points for the parameters' calculation, this procedure is more proficient in reducing noise and artefacts that are common in neurophysiological recordings in newborns. SAT detection (see paragraph below) was applied to the EEG dataset as a whole, but not to the single data-windows. A list of the parameters calculated is depicted in Table  1. (Goldberger et al. , 2000; Silva & Moody, 2014). For the calculation of scales 1-20, we used all the default parameters. The WFDB toolbox uses for MSE computation the codes from  Costa, Goldberger, & Peng (2002)  and  Costa, Goldberger, & Peng (2005). The frequencies in the signals covered by our complexity scales were calculated by dividing the sampling frequency by the scale length.
Spectral analysis In the EEG data, we calculated PSD using the Fast Fourier Transform algorithm ('fft') implemented in Matlab. We determined the mean power in four frequency ranges, namely delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). Finally, we calculated the proportions of PSD in each frequency band in relation to the overall power. We therefore had four parameters for further analysis: proportion-delta, proportion-theta, proportionalpha, proportion-beta.
Step 2. Stepwise regressions We investigated the relationship between MSE, PSD, SATs and GA by performing a stepwise regression analysis in which one of the MSE variables (MSE-EEG, MSE-HbO, MSE-HbR, MSE-HbD and MSE-TOI) is taken as the dependent variable. The other variables, such as the proportion-delta, proportiontheta, proportion-alpha, proportion-beta, proportion-SAT, GA and the MSE parameters which are not taken as dependent variable are included as possible predictors. We used the "stepwiselm" function in Matlab with default parameters to calculate the stepwise regression, where terms are added or removed iteratively. The default starting model is 'constant', which begins with only the constant term. The function evaluates available terms and adds the best one to the model if its associated F-test has a pvalue of 0.
Step 3. Mediation analysis We also performed a mediation analysis to further investigate the relationship between MSE, PSD, SATs, and GA. This involved the use of the MediationToolbox (Tor Wager, 2022) implemented in Matlab. GA was considered as predictor, one of the five MSE parameters (MSE-EEG, MSE-HbO, MSE-HbR, MSE-HbD, MSE-TOI) as outcome variable, and one of the proportion-delta, proportion-theta, proportion-alpha, proportion-beta, proportion-SAT as mediator factor. With five mediation models for each of the five MSE parameters, a total of 25 mediation models were calculated. We applied Holm correction for multiple comparisons considering N=20 possible p-values. This number results from multiplying the number of p-values of each model (4) for the number of possible mediator factors for each of the MSE parameters (5).
Additionally, we focused our analysis on a single channel in a frontal position in which the EEG and fNIRS sensors were placed on the same spatial location. A multi-channel recording would facilitate a topographic description of MSE in relation to SAT and PSD. Furthermore, a follow-up clinical evaluation of the neonates involved in our analysis would enable us to determine whether our MSE values have a predictive value for the neurological outcome. The original work by  Nourhashemi et al. (2020)  is currently the only simultaneous EEG-fNIRS dataset available in preterm infants. Here, the behavioral states (BSs), such as sleep or wake, were not registered. BSs have already been shown to have an impact on MSE and on the power of the different frequency bands in the EEG recordings of premature infants  (Janjarasjitt, Scher, & Loparo, 2008b; Knyazev, 2012; Wel et al.
Acknowledgement This study was conducted with the support of the Procope Mobility program of the French Embassy in Germany, which is designed to enhance scientific cooperation between France and Germany. The work was also partially funded by the ANR-DFG French-German collaboration (PR 496/11-1). We also thank the International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction (IMPRS-MMFD) and the Joachim Herz Foundation.
Objective The objective of this study is to explore the boundary between science and belief based on the case of chlorine dioxide, a toxic bleaching agent for textiles or paper that also has disinfectant properties (water, surfaces) presented as a "miracle cure" particularly effective against COVID-19. The aim is to measure the spread of misinformation and fact-checking of a "miracle cure" presented as scientifically proven to the French-speaking people using Twitter. First, the two primary forms of chlorine dioxide promoted will be described: the Miracle Mineral Solution (MMS) and Chlorine Dioxide Solution (CDS). Second, the content of messages posted in French on Twitter about chlorine dioxide and the links between users (tweeters) will be analyzed. Using these data and academic articles published on chlorine dioxide, this study aims to explore how a belief perceived as irrational (ingesting a bleach derivative to treat COVID-19) can be legitimized by a scientific discourse and what consequences this has in the dissemination of "miracle cures" on a social network such as Twitter.
Materials and Methods To measure the diffusion of chlorine dioxide as a "miracle cure" against COVID-19, the terms "chlorine dioxide" and "COVID" were searched in French-speaking messages posted on Twitter between 1 December 2019 and 30 November 2021 and extracted using the Twitter API V2 Academic Research with a Python script request ("dioxide de chlore" AND COVID lang: fr). The following data were downloaded: tweet content, tweet ID, author ID, and creation date. A total of 1,252 messages were collected containing 596 unique tweets. An analysis of tweets per user was conducted to identify bot accounts, and fake users were removed from the collected data. As "retweeting rapidly disseminates messages across user networks, creating a wider sphere of influence"  (Harrigan et al. 2021) , several data were collected and aggregated to create a new indicator, a "diffusion metric", to measure the dissemination of messages.