dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis We measured both accuracy and reaction time (RT) to examine the hemispheric lateralization pattern, following previous studies  (Jia et al. , 2013; Reilly et al. , 2015). Linear mixed-effects (LME) analyses were performed on the R platform using the lme4  (Bates et al. , 2014) , lmerTest  (Kuznetsova et al. , 2017) , and emmeans packages  (Lenth and Lenth, 2018). The anova function of the R package was used to obtain the p values of the main effects and the interactions in the models. The emmeans package was used to conduct pairwise comparisons with Tukey's correction. Identification accuracy was computed as the relative portion of the correct responses in each ear. The maximal model was first fitted using the following variables and their interactions: group (Jyutping vs. non-Jyutping), stimulus type (nonspeech tone vs.
The same model comparison procedure as in the identification task was applied. In the RT analyses, identification RT was measured from the offset of the stimuli to the time that a response was made. RT in the discrimination task was measured from the offset of the 1 Item was not treated as a random factor in the model because identification accuracy was a set of calculated data which was averaged across items (tone pairs). First, the response to each trial was categorized as either accurate in the left ear or the right ear, or inaccurate. For example, in one trial, the right ear was presented with T1 and the left ear was presented with T3. If a participant's response was T1, this trial was considered as correct in the right ear, and if the response was T3, it was considered as correct in the left ear; if the response was neither T1 nor T3, it was deemed as incorrect.
Identification accuracy was then computed as the relative portion of correct responses in each ear; during this process, item (tone pair) info was not preserved. second dichotic pair to the time that a response was made. In both tasks, trials with null and incorrect responses were excluded from the analysis and RT data of the remaining trials were log-transformed. Linear mixed-effects models were fitted with the fixed effects including group, stimulus type, tone type, ear and their interactions. The random intercepts of subjects and stimulus, along with the random slope of the interaction between categories, stimulus type and ear per subject and random slope of group per stimulus were treated as the random factors. The same model comparison procedure described was applied. Given that these analyses yielded a number of main effects and interactions, for the sake of simplicity, the results are presented in different sub-sections that correspond to the research questions posed in the Introduction.
Funding This work was supported by grants from the  Departmental General Research Funds  ( P0008738 ; https://www. polyu. edu. hk/cbs/web/en/), the  Research Grants Council of Hong Kong  (ECS: 25603916; https://www. ugc. edu. hk/eng/rgc/), the  National Natural Science Foundation of China (NSFC :  11504400 ; http:// www. nsfc. gov. cn/), the  Departmental Reward Scheme for Research Publications in Indexed Journals  (https://www. polyu. edu. hk/cbs/web/en/) and the  Hong Kong Polytechnic Univerity Project of Strategic Importance Scheme  (https://www. polyu. edu. hk/en/rio/about-rio/committees/areas-of-excellence-committee/) to CZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The system identification toolbox in MATLAB is used to compute the identification results. The criterion is as follows: J = 1 N N ∑ k=1 e 2 [k] (9) where e[k] represents the difference between the measured output and predicted output of the model (Figure  5 ): e[k] = δ SW [k] -δSW [k]. (10) In this study, the visual anticipatory and compensatory gain (K p and K c ) are chosen as the parameters to be identified and the other parameters are fixed at their nominal value (the longitudinal speed is fixed at 18 m/s; see Section 2. 5). Although the concepts of anticipation and compensation have certain commonalities with the strategy of the haptic guidance system, it is beyond the scope of this paper to evaluate the relevance of this control architecture, as the sharing level of the anticipatory and compensatory controller have not been manipulated.
Summary Diagram Figure  5  illustrates how the data analysis was conducted in this study. The lateral control process is represented by white boxes. The human driver performs the task with or without the haptic guidance system through the steering column and generates the steering wheel angle as vehicle input. The vehicle interacts with the road and provides ad hoc information to the driver and haptic guidance system. The "Signal Generation" block represents the computational interface, allowing for the state of the vehicle-road model in order to format or even estimate the signals input to the driver model and the haptic guidance system (see Figure  2  and  [26] ). The performance of the lateral control task is evaluated using the metrics in the green boxes and the identified parameters of the LC-SW system in the red box.
Results All data from each scenario were used to calculate the metrics and to identify the two model parameters. To represent the distribution of participants, the results are presented in box plots around the mean value for all participants. The red bar is the median. The blue triangles are mean values. The blue box represents the first quartile and the third quartile data points. The whiskers represent the minimum and maximum data points which are not beyond the interquartile range. The red crosses are outliers. A repeated-measures analysis of variance (ANOVA) with two within-subject factors was used to evaluate the effects of the visibility (F) and haptic guidance (A). The differences between the degrees of the independent variables were considered statistically significant for p-values less than 0.
| Participants Twenty-seven congenitally deaf native signers, 19 hearing native signers, and 36 hearing nonsigners participated in the experiment. The data from four deaf native signers and three hearing signers had to be excluded due to technical difficulties with the stimulation device. All the deaf signers were congenitally deaf and reported severe hearing loss (>85 dB in the better ear), were born to deaf parents and acquired a sign language as their primary and first language. All but one deaf signer (for this participant this information is missing) indicated their sign language skill to be 7 on a scale of 1-7 (1 = not very good at all, 7 = excellent), and all deaf signers communicated with the researchers in German Sign Language (Deutsche Gebärdensprache: DGS). All the hearing signers were born to deaf parents and acquired a sign language as their first language.
| Stimuli The stimuli were generated using the PsychToolbox extensions  (Brainard, 1997)  running under Matlab 2009 (The MathWorks, USA). The visual stimuli were presented using an Asus LCD VG248 computer monitor (refresh rate: 120 Hz), which was mounted in parallel to the tabletop. A mirror mounted below the screen in a 22. 5 angle reflected the display. The stimulus was a white circle (2. 2 or 2. 8 cm) displayed on a grey background. The circle was presented either at the centre of the screen or in the left or right hemifield at 14. 8 (or 19. 26 cm) eccentric to fixation (from the centre of the circle). The luminance of the circle was modulated sinusoidally at 12, 21, or 40 Hz and oscillated between intensities of 0. 3 (dark grey) and 1 (white). Thus, nine different conditions were presented, each repeated seven times, resulting in a total of 63 trials.
No performance feedback was given. The change detection task ensured that participants maintained attention on the stimulus during recording. After each block, participants were asked whether they wanted to take a break or continue with the experiment. To ensure central fixation during stimulus presentation we monitored participants' eye movement with an EyeLink 1000 eye tracker (SR Research, Kanata, Ontario, Canada), which were sampled at 1000 Hz. Trials during which participants failed to maintain central fixation were excluded from the EEG data analysis. This led to the exclusion of 2. 8% of the trials for the group of deaf signers and 5. 8% of the trials for their matched controls. For the group of hearing signers, 5. 8% of the trials were excluded and 4. 9% of the trials for their matched controls.
| EEG acquisition EEG data were acquired with 74 sintered Ag-AgCl electrodes mounted into an elastic cap (EASYCAP GmbH, Herrsching, Germany). Electrodes were arranged according to the standard 10-10 system and connected to a BrainAmp DC Amplifier (Brain Products GmbH, Gilching, Germany; http://www. brainproducts. com). Electrode impedances were kept below 20 kΩ. EEG data were recorded referenced to the left earlobe at a sampling rate of 1 kHz. A 0. 016-to 250-Hz hardware bandpass filter was used during the recording.
| Behavioural data We analysed accuracy using generalised linear mixed models (GLMMs) with a binomial link function  (Jaeger, 2008). As fixed effects, we entered group, frequency, visual hemifield, and their interaction terms into the model. As random effects, we included intercepts for subjects. Significance of fixed effects was assessed with likelihood ratio tests comparing the model with the maximal fixed effects structure and a model that excluded the fixed effect of interest. These comparisons were calculated using the afex package  (Singmann et al. , 2018)  and employed Type III sums of squares and sum-to zero contrasts. Fixed effects were considered significant at p < 0. 05. Post-hoc comparisons of significant interactions were conducted using approximate z-tests on the estimated marginal means  (Lenth, 2018).
| EEG data The analyses were performed using Letswave 6, an opensource toolbox (http://nocions. webnode. com/letswave) running over MATLAB R2017b (MathWorks, USA). Continuous EEG recordings were filtered using a fourthorder zero-phase Butterworth band-pass filter, with cutoff values of 0. 05-120 Hz. Data were downsampled to 250 Hz and then segmented into epochs of 36 s, including 2 s before and after each trial. To remove blink artefacts, independent component analysis (ICA) was applied to data of participants who blinked more than 0. 2 times/s on average during the epochs. Channels that were artefact-prone across more than five trials (less than one channel on average per subject) were linearly interpolated using three neighbouring electrodes. All EEG channels were referenced to the common average of scalp electrodes.
By contrast, the group of hearing nonsigners showed a clear maximum at 12-Hz stimulation, that is, in hearing nonsigners the SSVEP amplitudes for 12-Hz stimulation were significantly larger than for 21or 40-Hz stimulation. In addition, we assessed whether the response to the stimulation frequencies differed between the groups. None of the comparisons were significant. (See Table  S7  for descriptive statistics of the data and Table  S8  for the full results of the LMM. ) To explore whether the lack of a significant group by frequency interaction might be due to the relatively lower power due to smaller group sizes, we compared the group of hearing signers (n = 16) to all the hearing nonsigners (n = 31) who participated in this study. Here, the two-way interaction between group and frequency turned out to be significant (F(2, 90) = 3.
ACKNOWLEDGEMENTS This research was funded by the  German Research Foundation  ( Ro 2625/10-1  to  Brigitte Röder ), and the  German Academic Scholarship Foundation  (attributed to  Anna-Lena Stroh ). We would like to thank  Barbara Hänel-Faulhaber  for her support and  Deniz Främke ,  Antonia Ricke ,  Dagmar Tödter , and  Nicola Kaczmarek  for their help with this study, as well as all the participants, without whom this research would not be possible.
The required sample size was calculated a priori for repeated measures analysis of variance (ANOVA) using the G * power software (version 3. 1. 9. 2; Kiel University, Kiel, Germany)  (44). Values for α were set at 0. 05 and power at 0. 90. Given the pioneer character of this study and since no previous study has evaluated the effects of RDIF on cognitive function in older adults, an effect size f of 0. 4 was reported in the study of Tian et al. (42)  for the verbal learning and memory assessed at16:00 h during Ramadan. A total sample size of 52 was required with at least 26 in each group to achieve the desired power for cognitive functions parameters. Concerning sleep components, based on the results of the meta-analysis of Trabelsi et al. (31) , an effect size f of 0. 41 was computed for the nocturnal sleep duration.
Statistical analysis All statistical analyses were performed using STATISTICA software (StatSoft, version 12, Paris, France). Data are presented as means and standard deviations. The variation ( ) recorded from before Ramadan session to during Ramadan session is calculated as follows: = During Ramadan -Before Ramadan. The percentage of variation ( %) recorded from before Ramadan session to during Ramadan session is calculated by the following formula: % = ( /Before Ramadan) × 100. The normality of the distributions was tested by the Shapiro-Wilks test. Pearson's chi-squared test was used to compare the demographic baseline characteristics of the participants including gender, education level, and health status of the two groups. Because the age and the body mass index (BMI) were not normally distributed, the non-parametric Mann-Whitney U-test was used.
Keywords and database Initial papers' selection was made in August 2021 based on the following keywords for each VRISE: "cybersickness" OR "visually induced motion sickness," "visual fatigue" OR "eyestrain," "muscle fatigue" OR "musculoskeletal discomfort," "stress" OR "acute stress," "mental workload" OR "cognitive load, AND "Virtual reality" AND "work" AND "meta-analysis" OR "Systematic review" OR "Review" in Google Scholar. We used the same keywords listed above to document VRISE at work without adding meta-analysis, systematic review, or review.
Data recording and analysis Data from the first 4 trials of all infants were recoded offline by a second observer with substantive experience coding newborns' looks. Trials where the looking time recorded online and offline differed by more than 5 s (20/128 trials, 15. 6%) were recoded again by another trained observer (usually the experimenter who live-coded during the experiment). For two participants, this third measurement differed by more than 5 s from the two first measurements in at least one trial: these two infants were excluded from the sample and replaced. The two closest measures of looking times were averaged (by virtue of our recoding and exclusion policy these two measures differed by less than 5 s) and entered in an ANOVA with the two within-subject variables of trial pair (first or second) and congruency (Congruent or Incongruent), and the two between-subject variables of auditory condition (AB or ABB) and trial order (Congruent first or Incongruent first).
17 with a power of 80%. This experiment is thus powered to detect large effects only. Lastly, we also ran an exploratory analysis with a third betweensubject variable for sample (first or second), but there was no significant effect or interaction associated with this variable (main effect and interactions, ps > 0. 11, η 2 p s < 0. 10). The data and the scripts for the analyses can be found at https://osf. io/zdvnh/.
Acknowledgments This work was supported by the  European Research Council  [Starting Grant MathConstruction 263179], the  Centre National de la Recherche Scientifique , and  Université Paris Cité. We are grateful to  Pr. Dominique Luton ,  Laetitia de Lorgerie  and the staff of the maternity ward at  Hôpital Bichat (Paris)  for hosting this research. Thanks to  Claudine Baker ,  Nadège Baudhuin ,  Aurélie Coubart ,  Viviane Huet ,  Kristenn Le Saint ,  Sylvie Margules  and  Alice Ragon  for their help in data collection; to  Aurélie Coubart ,  Eugenio Parise  and  T. R. Virgil  for discussions and comments; and to all the families who participated in the research. This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001.
Sleep spindles and slow waves, brain oscillations involved in sleep-dependent memory consolidation, were quantified automatically using the Aseega software (Physip). Subjective sleep disturbances and markers of quality of life (psychological distress, fatigue, and well-being) were assessed by questionnaires. Results: Greater PM performance was observed after sleep than after an equivalent period of daytime wakefulness for both groups (HC and BC). PM performance after sleep did not differ significantly between groups. Yet, BC patients reported greater sleep disturbances than HC which were related with poorer intentions retrieval, greater psychological distress, fatigue and poorer well-being. The frequency of spindles was higher and the amplitude of slow waves lower in BC patients compared to HC.
Sleep stages (N1, N2, N3, and REM) were visually scored by an electrophysiology technician (S. R. ) in epochs of 30 s according to the AASM rules  (Berry et al. , 2017). Standard sleep parameters, including the percentage of each sleep stage, as well as respiratory parameters, such as the apnea-hypopnea index (AHI), were computed. Artifacts (eye movements, ECG, EMG, or movement-related artifacts) were detected visually and rejected (J. P. ), and analyses were conducted on artifact-free epochs. Automatic quantification of sleep spindles and slow waves from the Cz-Pz derivation was performed during N2-N3 and N3 stages respectively, using the ASEEGA software (PHYSIP, Paris, France). The validation of the original algorithm is fully described elsewhere  (Berthomier et al. , 2007). An adapted amplitude slow wave's criterion  (Rosinvil et al.
Statistical analyses Analyses were performed using the R software (version 4. 0. 3), with statistical significance set at p < 0. 05. Normality hypothesis was assessed using the Shapiro test. When the normality hypothesis was rejected, data were analyzed using non-parametric tests. Wilcoxon tests were performed to compare demographic characteristics, scores of depression (BDI), scores of sleep questionnaires (ISI, PSQI), score of alertness/sleepiness (KSS), and encoding performance (i. e. , number of intentions retrieved during the last global cued recall test). The STAI-B scores were compared using student t test. Analyses of covariance (ANCOVA) were conducted with sleep architecture parameters (i. e. , general sleep parameters and sleep stages) as dependent variables, group as independent variable, and the Apnea-Hypopnea index (AHI) as covariate.
Study Design The overall study design is shown in Figure  1. According to our sample size calculation (see below), 200 adolescents and young adults will be recruited in the study. Upon enrollment, baseline data will be collected. Clinical and sociodemographic data will be investigated through the computerized clinical chart and through relatives if necessary. Psychiatric diagnoses will be established according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth edition (DSM-5) during a standardized psychiatric interview. During hospitalization, participants will complete standardized and validated questionnaires. The morning after admission to the emergency unit, a first blood sample will be taken from fasting patients at 7:00 am for biological measurements. Two other blood samples will be collected and stored at -80 • C for future research purposes (one EDTA sample and one PAXgene blood RNA tube).
Outcomes Three months after the initial hospitalization for a first suicide attempt, participants will be contacted by phone to retrieve the primary outcome: the presence or absence of a second suicide attempt (re-attempt) within the first 3 months following the first suicide attempt. The data will be collected by a nurse blinded to the patients' initial characteristics, and will be further controlled by 3 independent reviewers to avoid misclassification. Other information regarding recent life events and received care will be gathered. If the patient cannot be reached, their trusted person, previously designated by the patient, will be contacted. In case of no reply, the primary care physician or medical records will be consulted. A suicide attempt is defined as a non-fatal selfdirected potentially injurious behavior with the intent to die as a result of the behavior.
DISCUSSION The main purpose of the SURAYA study is to assess the incidence of suicide re-attempt after a first attempt in a cohort of young people aged 16 to 25. The study's main strength is the homogeneity of participants: a narrow age group relevant to a critical period of neurodevelopment, and an incipient cohort in terms of onset of suicidal behaviors. There is little research focusing on suicidal behaviors in adolescents and young adults, and we do not currently know whether this group is at a higher risk of early suicide re-attempt compared to a general adult population. Taken separately, most suicide risk factors previously identified are reported to be weak predictors of later suicidal ideation and behavior. In order to gain a comprehensive view of the potential prognostic factors of suicide re-attempts, we carefully collected a wide range of socio-demographic, clinical, and biological data relevant to suicidality; we used both selfand clinician-administered validated scales for more reliability across psychometric evaluations.
2) and the Social-Societal Ethical Committee KU Leuven in Belgium (G-2017 10 954). Each collaborating site was asked to enroll a minimum sample of 100 university students that originated from the nation of testing (e. g. , no international or exchange students). In the end, we collected data from 40 different countries (42 sites), adequately covering all populated continents in the world (i. e. , Europe n = 17; Asia n = 10; Africa n = 4; South America n = 4; North America n = 3; Oceania n = 2). A world map with all participating countries can be found in SI 2, together with the final sample size for each site. On average, each country collected 186 participants (SD = 129), with a total sample of 7,443 participants taking part in the study (M age = 21. 81, SD age = 5. 60). The balance of gender identification consisted of 32% men, 61.
Statistical analyses. All analyses in this article were conducted in R (version 4. 0. 0 72 ). To reproduce our results and figures, researchers can consult the data, code and materials at the Open Science Framework (https:// osf. io/ 3aut4/). All methods were carried out in accordance with relevant guidelines and regulations. Multilevel analysis. To account for the hierarchical structure of the data, we performed our analyses in a multilevel framework, using the lme4 R-package  73. Specifically, we ran various two-level models, with persons (n = 7,443) nested within countries (n = 40). In all models, slopes and intercept were allowed to vary randomly across countries to account for possible national differences in the found effects. For an intuitive interpretation of the model parameter estimates, we group-mean centered all person-level predictors.
Acknowledgements Fund for Scientific Research in Flanders (FWO)  grant  1210621N  (ED). Research Fund of KU Leuven  grant  C14/19/054  (ED, PK). Center for Social Conflict and Cohesion Studies  grant  ANID/FONDAP 15130009  (RG). Interdisciplinary Center for Intercultural and Indigenous Studies  grant  ANID/FONDAP 15110006  and  ANID/ FONDECYT1201788  (RG). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.