dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In the present study, we used the BINS datasetwith data from 56,968 respondents across 65 nations and 40 languagesto conduct the most comprehensive assessment to date of the measurement invariance of the BAS-2. Overall, the present results provided evidence of scalar invariance of the BAS-2 (after combining some response categories) across all nations, languages, gender identities, and age groups represented in the BINS. Thus, the BAS-2 appears to be measuring the same latent construct of body appreciation across all four of these respondent categories in the BINS dataset (i. e. , mean differences in the latent BAS-2 construct capture all mean differences in the shared variance of the BAS-2 items across national groups, languages, gender identities, and age groups). These results are consistent with some previous assessments of measurement invariance conducted across a much smaller subset of national groups  (Aimé et al.
Measurement invariance across national groups and languages The results of our analyses supported full scalar invariance of the BAS-2 across all 65 national groups and 40 languages represented in the BINS. This is especially notable in view of the fact that full scalar invariance is often an unrealistic goal for datasets with a larger number of groups  (Marsh et al. , 2018). Indeed, previous cross-national studies Note. All ps of χ 2 and Δχ 2 tests (model comparisons) were <. 001, except where noted otherwise. Gender identity compared groups of women, men, and other gender identity, age compared groups of participants with 18-24 years, 25-44 years, ≥ 45 years of age. a Response categories 1 and 2 of all items except Item #6 were combined for analysis. b Response categories 1 and 2 of all items except Items #6, #7, #8 and #10 were combined for analysis.
, 2022). Additionally, our results showed that those who identified their gender in another way had significantly lower body appreciation than both women and men. Although this finding is interesting and consistent with other work showing that those who identify as transgender or gender-expansive are less likely to experience positive body image compared to cisgender individuals  (Jones et al. , 2016 ; see also  Barnhart et al. , 2023) , it should also be remembered that respondents who identified as another gender in the present study were relatively small in number (0. 6% of the total dataset) and were likely heterogeneous in their experiences (i. e. , widely differing experiences across nations). Certainly, this is an aspect of our study that is worthy of further investigation, particularly as some research has suggested that those who are further along in consolidating gender identities may have more positive body image  (McGuire et al.
Constraints on Generalisability Although our work provides one of the largest cross-national databases on body image currently available, our findings should be considered in light of a number of constraints on the generalisability  (Simons et al. , 2017). First, our findings may be limited in terms of their generalisability because of sampling and recruitment constraints. Most important in this regard is the fact that our samples were recruited opportunistically and, as such, cannot be considered representative of their respective nations. Similarly, although we strove toward operational equivalence in the BINS  (Swami et al. , 2022) , small differences in recruitment and survey completion were unavoidable (e. g. , while a majority of surveys were completed online, a small number were completed offline using paper-and-pencil format).
Additionally, while the BINS dataset provides a snapshot of body appreciation across a diverse set of nations, it remains the case that the project was under-represented in several world regions, especially Africa, Central Asia, the Caribbean, and Central America. A further point to consider is that specific conditions during the period of data collection may have varied substantially across nations. Indeed, the period of data collection was extended (i. e. , over about two years) and occurred in the shadow of the COVID-19 pandemic, which may have introduced nation-specific biases. For instance, there is evidence to suggest that pandemic-related experiences, such as COVID-19 stress and anxiety (e. g. ,  Czepczor-Bernat et al. , 2021; Swami et al. , 2021) , negatively impacted body image outcomes across diverse populations (for a review, see  Schneider et al.
, 2022). This makes it difficult to know to what extent our data are temporally reliable and whether specific pandemic-related experiences (e. g. , being in lockdown, severity of the pandemic, national and international responses to the pandemic, all of which were not measured in our survey) may have affected our findings. Still, given consistency of factor structures across groups, any biases in results are likely to be reflected in latent group differences and possibly in correlates of body appreciation. Additionally, we also cannot rule out common-method biases, given that the BINS dataset consists of self-reported data. On this note, there was some evidence of ceiling effects in our BAS-2 data, which suggests that the response scale may not be sufficiently fine-grained to differentiate high levels of body appreciation.
[5]  Relatively easily done on a national level,  6  the PV system may become more complex to manage in multi-country trials, in particular for academic sponsors and paediatric research. In the latter, PV activities have to comply with additional paediatric guidelines,  7  requiring, for example, the submission in EudraCT of the final results within 6 months instead of 12 months in adult trials. 8 serm-ANRS MIE has been involved for a few years in different consortia aiming to support European multinational trials, including with academic sponsors outside of our institution. Our PV department was first commissioned to provide a centralized PV system (CPVS) in 2018 for the conect4children (c4c) consortium, a pan-European collaborative paediatric network whose challenging goal is to speed up and facilitate the conduct of high-quality clinical trials in children.
| METHODS To support our safety responsibilities, the complex safety processes had to be harmonized  12  : Key safety standardized operational procedures (SOPs) were prepared based on the trial specificities and shared to the appropriate actors in the trial. In parallel, we implemented a network of local safety officers (LSOs) to tackle the challenging heterogeneous local safety requirements and thus ensure the CPVS is compliant. Serious adverse event (SAE) processing and assessment were centralized at our department and entered in a unique, secure and validated PV database. When necessary, we ensured that all operational study teams had and maintained proper training throughout the trial. In each trial, the system was adapted either or both to the paediatric-specific regulations, Covid-19 pandemic context and the sponsor (whether Inserm-ANRS MIE or not).
centralization of SAE processing in a unique database guaranteed the full control of the safety data by the PV function, avoiding unrecorded, incomplete, misplaced or deleted information, or generation of duplicates, and the continuous evaluation of the risk-benefit profile of the study drugs. Centralized SAE processing drove the implementation of internal operational procedures to harmonize the safety case processing and coding. The procedures for each trial were adapted to the trial based on: the trial design (e. g. , double-blind vs. open arms); the investigational medicinal product (IMP)-specific safety profile, such as the need to collect adverse events of special interest (AESIs); and the targeted population and pathology (paediatrics, severe Covid-19 pneumonia). Regulatory compliance was also ensured with the designation of a unique RPPV (especially when ANRS MIE was not part of the sponsor's institution), which avoided missing submissions and assured all safety data were transmitted to the relevant NCAs and ECs.
Experience of the CPVS per trial (data processed by 30 April 2022). Safety issue 1 Number of SUSARs 2 5 Number of SAEs 24 59 Number of countries with LSOs/Total countries a 2/2 16/16 Number of recruits by 30 April 2022 31 196 Phase II: Open phase to define the minimum effective dose of acetaminophen to close the ductus arteriosus before or at Day 7 in preterm infants of 23-26 weeks of gestation Phase III: Blinded phase to define the increase in surviving without severe morbidity at 36 weeks of post menstrual age in preterm infants born at 23-28 weeks of gestation treated with acetaminophen Trial title Prophylactic treatment of the ductus arteriosus in preterm infants by acetaminophen Sponsor (country) Inserm (France) Bambino Gesù Children's Hospital-OPBG Trial name EudraCT number NCT number TREOCAPA 2019-004297-26 NCT04459117 cASPerCF 2019-004511-31 NCT04966234 Consortium conect4children TERZIT A B L E 2.
As we previously showed  19  , such behavioral responses are visible on surface EMG sensors measuring corrugator (frowning) and zygomatic (smiling) isometric contractions (see Fig. 2 , Extended Data Fig. 1  and Supplementary Figs. 1-11 for examples). At the end of each nap, participants reported the following: (1) their mental content during the nap, (2) whether they had a lucid dream during the nap and (3) whether they recalled having actively performed the lexical task while sleeping. They also underwent an old/new task (Supplementary Results). Each nap was labeled as lucid or nonlucid according to participants' postnap subjective reports, and all REM-sleep trials from this nap were labeled as lucid or nonlucid accordingly. Participants were also instructed to signal their lucidity (if any) with a 'mixed code,' by frowning and then smiling once.
These objective dream-lucidity signals typically matched participants' subjective reports upon awakening (Extended Data Fig. 2 ). We assessed responsiveness to task stimuli across sleep stages in the two groups, by visually inspecting the corrugator and zygomatic of stimulus integration during sleep complicates the exploration of the neurophysiological basis of this complex and variable phenomenon. Obtaining behavioral responses that serve as real-time indicators of subjective reports could enable us to analyze brain dynamics associated with sensory and cognitive integration in a trial-by-trial manner. Because behavioral responses have long been assumed to be possible only during wakefulness, they are either rejected from the analysis  8, 20  or not collected at all in sleep studies. The rare studies that measured behavioral responses in sleeping participants discovered manual behavioral responses during N1 sleep (sleep onset)  3, [21] [22] [23] [24]  , but not in deeper sleep stages.
This neurophysiological profile combined with the subjective report of having performed the task during sleep (Fig. 4 ) suggests that NP consciously processed the stimuli when in lucid REM sleep. Several signatures of conscious processing have been described in the literature, including the late P3b component in evoked related potentials  [40] [41] [42]  or the square-like shape pattern in the temporal generalization method  43, 44  (such a pattern reflects a late, stable and sustained processing stage that has been previously related to conscious access  [43] [44] [45]  ) Given our unbalanced dataset, we primarily used the temporal generalization approach to explore consciousness of external stimuli in lucid REM sleep (see Supplementary Fig. 14  for stimulus-locked ERPs). Briefly, this analysis tests how stimulus-induced brain activity differs from baseline activity; it consists in training a linear classifier at each time point to differentiate stimulus-present versus stimulus-absent epochs and testing its performance for all the other time points (for example, training the classifier at t = 2 and testing its ability to correctly classify at t = 1,2,3,4,5,…, obtaining thus a whole matrix of   7  and 8.
Note however that our visual scoring was consistent with an automatic detection of responses based on EMG signal variance (Supplementary Note and Extended Data Figs. 3  and 4 ). Second, we only investigated responsiveness during daytime naps, thus preventing us from fully assessing behavioral responsiveness during N3 sleep (not enough trials) and more generally during night-time sleep. Third, our ten-electrodes montage with a mastoid reference was not ideal for computing the wSMI connectivity measure  46  , making the results of this analysis difficult to interpret. Fourth, we used postnap subjective reports to determine lucidity instead of the gold standard, objective signal of lucidity  26, 28, 47. Nevertheless, we also collected an objective lucidity signal (successive corrugator and zygomatic contractions) that substantially matched participants' subjective reports upon awakening, confirming the reliability of subjective reports in determining participants' lucidity.
They were required to pause their medication for the day of the experiment to facilitate sleep onset. We recruited patients with narcolepsy type 1 (n = 17, with clear cataplexy or hypocretin deficiency) and type 2 (n = 13, no cataplexy or hypocretin deficiency). Among the 30 participants, three (two women) were discarded from the analyses because of technical issues affecting the recordings. In total, data from 27 participants with narcolepsy (21 frequent lucid dreamers) were analyzed in this study.
Stimuli Stimuli were French words and pseudowords pronounced by a female voice, taken from the MEGALEX database  61. All stimuli were controlled for their duration (690 ms), and the words were controlled for their frequency and valence. Five distinct lists (one for each nap session) of 60 stimuli (30 words and 30 pseudowords) were created for each participant in a randomized fashion. Participants heard each stimulus only once during the day. Stimuli were presented through speakers using the Psychtoolbox extension  62  for MATLAB (MathWorks). Stimuli were played every 9-11 s (random uniform jitter) after a 60-s OFF period (without stimuli). Button-press responses in the old-new recognition task were collected through a regular keypad.
Electrophysiological recording EEG (ten channels: Fp1, Fp2, Cz, C3, C4, Pz, P3, P4, O1, O2, referenced to the right mastoid (A2 electrode); 10-20 montage), EOG (two channels, positioned above the right superior canthus and the left inferior canthus), EMG (one channel on chin muscle for sleep staging, one channel on zygomatic and one channel on corrugator muscles for recording participants' behavioral responses) and electrocardiography (EKG, one channel) were continuously recorded during the nap sessions. All signals were recorded simultaneously at a 2,048 Hz sampling rate. EEG data were amplified through a Grael 4K PSG:EEG amplifier (Medical Data Technology, Compumedics). https://doi. org/10. 1038/s41593-023-01449-7.
Sleep scoring and identification of muscular responses Sleep scoring. Sleep stages were scored offline by a certified sleep expert according to established guidelines  29  using Profusion software (Compumedics, Medical Data Technology). For scoring, the EEG and EOG signals were filtered between 0. 3 and 15 Hz, the EMG and EKG signals were filtered between 10-100 Hz and 0. 3-70 Hz respectively. A 50 Hz notch filter was applied on all channels. Sleep scoring was visually performed on 30-s time epochs, each scored as wakefulness, N1, N2, N3 or REM sleep, according to the American Academy of Sleep Medicine international rules. Detailed information on the sleep characteristics can be found in Supplementary Table  12. Micro-arousals were scored when α rhythm was present for more than 3 s and less than 15 s (if longer, the epoch was scored as wake) and, in REM sleep, when there was an increase in chin muscle tone in addition to the α rhythm.
Automatized detection of spindles and slow waves Raw EEG files were band-pass filtered between 0. 1 and 45 Hz, with 50 and 100 Hz notch filters. Data were downsampled to 250 Hz. Trials were then segmented from -1,000 to 8,000 ms relative to stimulus onset. We analyzed N2 trials of HP using a previously validated automatized sleep scoring algorithm (YASA  64  ). For each trial (from -1,000 to 8,000 ms relative to stimulus onset) we assessed whether at least one spindle or slow wave (independently for each one of these two sleep graphoelements) was present during the duration of the trial, in at least two different channels. We then computed for each sleep graphoelement, each participant and each condition (responsive versus nonresponsive trials), the proportion of trials containing graphoelements (spindles or slow waves).
Prediction of responsiveness using a decision tree algorithm We aimed at predicting, independently for each sleep/wake stage, if a given trial would contain a response or not based on the EEG markers computed during the 1,000 ms time period preceding the stimulus presentation. We used a random forest algorithm, a classification algorithm consisting of many decision trees. This algorithm implements bootstrapping and feature randomness when building each tree, which ensures the construction of an uncorrelated forest of trees. Because the different trees in the forest are uncorrelated, their global prediction by committee is more accurate than that of any individual tree. Random forest has shown to be among the best currently used machine learning classifiers, in a very wide range of different datasets (n = 112) from several research fields  66  , outperforming other choices as SVM classifiers.
As before, we computed balanced accuracy and F1 scores. To obtain a distribution of scores in the absence of cross-validation, we repeated the fitting and testing steps ten times (note that the random parameters of the random forest classifier allowed us to obtain a distribution of-closely relatedscores in this manner). As mentioned above, we computed two scores to measure the performance of our classifier, both measures being well adapted to unbalanced datasets  67  as ours (with more nonresponsive trials than responsive ones during sleep): • The balanced accuracy score corresponds, in binary classification problems, to the mean of the sensitivity-also called recall ('How many relevant items are retrieved?') and the specificity ('How many nonrelevant items are correctly identified'). In terms of true positives (TP), false negatives, true negatives (TN) and false positives (where, in our case, TP are responsive trials correctly identified by the classifier, and TN nonresponsive trials correctly identified by the classifier), the balanced accuracy score can be computed by the following formula: • The F1 score corresponds, in binary classification problems, to the harmonic mean of the precision ('How many retrieved items are relevant?') and the sensitivity.
Decoding of stimulus-related brain activity We aimed at assessing brain responses to stimuli in function of participants' sleep/wake stages and of their responsiveness to the task using a multivariate pattern analysis with the temporal generalization decoding method  43. The idea of this analysis is to test, for a given time point after stimulus presentation, how different the multivariate pattern of activity across electrodes was at this specific time point compared to the pattern at baseline (before stimulus presentation), for the different conditions. To reduce computation time, we first downsampled our data to 100 Hz (decimation factor of 2. 5). To ensure a correct features/number of trials ratio, we restricted our analysis to three centroparietal electrodes (Cz, Pz and P3), and, for each condition (sleep stage/responsiveness), we only included in our analysis the participants who had at least 15 trials of the given condition.
Statistical analysis Most statistical analyses were conducted in R 68 using the lme4 (ref. 69), emmeans  70  , BayesFactor  71  , car  72  and DHARMa 73 packages. For the machine learning analysis, statistics were conducted in Python 64 using the numpy  74  , scipy  75  , pingouin  76  and scikit-learn  77  packages. All statistics were corrected for multiple comparisons using the FDR Benjamini-Hochberg procedure. FDR corrections were applied separately to each group of statistical tests (each panel in the Figs. 3-6, and 8). For example, one correction was performed for Fig. 3a , combining NP and HP and all sleep stages. Assumptions of the generalized linear models were evaluated using DHARMa 73 package in R  68. For linear mixed models, the distributions of residuals as well as Q-Q plots were visually inspected but were not formally tested.
Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. 3a , indicating that response rates found by the algorithm followed the same trend as the manual scoring. https://doi. org/10. 1038/s41593-023-01449-7 Extended Data Fig. 7  | Timing of statistical differences in the time-frequency analysis. Differences in power across time in delta, alpha, low beta/sigma and high beta bands, in responsive (R) and unresponsive (NR) trials during N2 and REM sleep in participants with narcolepsy (upper panel) and during N1 and N2 sleep in healthy participants (lower panel). Time-frequency analysis was performed over Fp1 and O1 electrodes. The error bands depict 95% confidence intervals. Significant differences are indicated by yellow shade (FDR corrected p-value < 0.
Acknowledgements This study was funded by the  Agence Nationale de la Recherche  ( ANR-20-CE37-0001-01  grant to D. O. ) and a research grant from  Société Française de Recherche et Médecine du Sommeil  (to D. O. ). B. T. and E. M. M. received Ph. D. grants from the  French Ministry of Higher Education  and the  Institut national de la santé et de la recherche médicale (INSERM) , while J. B. M. received a grant from  Assistance Publique -Hôpitaux de Paris (APHP)  and  Sorbonne University  (' Poste d'accueil APHP' ). The  National Reference Center for Narcolepsy  benefits from a recurrent grant from the  National Program on Rare Diseases  ( PNMR-3  grant to I. A. ) from the  Health Ministry , which encourages research on narcolepsy. L. N. was supported by a  UNIM grant. The authors would like to thank  T.
Data and Methods For our analysis, we use spectral envelopes derived from the 20 samples-per-second (sps) VBB waveform data for the LF family events cataloged during the entire mission. All the events and waveforms (InSight Mars SEIS Data Service, 2019a, 2019b) are publicly available with the release of the V14 catalog (InSight Marsquake Service, 2023). A breakdown of the catalog, including the HF events, is provided in Table  S1  of Supporting Information S1. Our method builds on the visual alignment approach of  Giardini et al. (2020). In the visual alignments, the envelopes of both low SNR and high-quality events are compared, and weaker events are assigned a relative distance using the envelope shape similarities, theoretical travel time curves and the events with pick-based distances as anchors.
, 2021). Therefore, MQS resorts to identify the glitch windows manually. We mask the glitches (as reported by MQS) by replacing the original envelope values with the mean of adjacent data points. The spectral envelopes start 90 s prior to the energy onset as marked in the V14 catalog. The end times are manually assigned for the few events that have a very long duration (>1 hr); otherwise, we use the signal end from the catalog (InSight Marsquake Service, 2023) assigned by MQS operators with the aid of spectrograms. For shape similarity metrics, the point-wise distances in Figure  2a  are computed using the ‖L 1 ‖ norm because of its stability and relative insensitivity to noisy time series. The final similarity values (Figure  S3  in Supporting Information S1) are normalized with the warping path length to account for differences in the duration of the time series.