dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample We used data from the Millennium Cohort Study (MCS), a longitudinal study that collected information from over 19,000 UK families with children born between 2000 and 2002. To date, seven waves of data have been collected, at ages 9 months and 3, 5, 7, 11, 14, and 17 years, respectively. In this study, as in previous research  12  , only the first sibling of each family was included (4. 9% excluded) to prevent confounding effects of family membership. Participants were also excluded if data relating to their biological mother were unavailable (5. 1%) or all physical health predictor variables were missing (1. 1%). As in previous longitudinal analyses  13  , only participants with non-missing outcome data at age 17 were included in the analysis. The number of participating families varied between waves, and households sampled from disadvantaged areas were more likely to withdraw.
14  As such, standardised weights were applied to ensure the sample was representative of the UK population and to account for attrition rates. Weights were calculated by multiplying the wave one sample design weights by the non-response weights of each wave. As data in this study were taken from all waves, weights from the most recent data sweep were applied. Descriptive statistics for all weighted variables in the current sample for the study (n=8059) are reported in Tables  1  and 2. This study was approved by the Ethics committee of University of [anonymised], in addition to the informed consent procedures obtained in the MCS. 15  Parents gave written informed consent to the open use of the anonymised dataset.
Predictors -Physical Health Conditions At each wave of data collection (age 9 months to 17 years), parents were asked about their child's physical health. This information was gathered in several ways, including asking whether they had ever seen a health care professional about any problems and, if yes, asking to give details. Parents were also asked about specific diagnoses (e. g. , 'has -child's name-ever had asthma?'). Hospital admissions were also recorded along with the reason for admission, such as convulsions, fits or epilepsy. We created variables to identify whether each child had ever experienced the condition. Physical conditions where a diagnosis could be given repeatedly and on a temporary basis, such as infection or diarrhoea, were excluded. We grouped the physical conditions into four clusters, according to similar physiological mechanisms, reflecting their grouping in the ICD-11, as well as existing empirical evidence.
Control Variables -Cumulative Risk Indices (CRIs) and age three SDQ score. Informed by previous evidence (Table  1 ), 20 environmental risk factors for ADHD were identified and controlled for within the available MCS data. 24  Data were collected during interviews with parents at the first data sweep (age 9 months). Individual risk factors were classified as either 'high risk' or 'low risk' (e. g. , caesarean section yes/ no). Continuous variables were assigned a cut-off with reference to previous literature (Table  1 ). Risk factors were grouped and totalled to create five cumulative risk indices (CRIs) as follows: 1) Prenatal risk index, including maternal pre-pregnancy BMI, blood pressure problems in pregnancy, maternal diabetes and smoking during pregnancy, 2) Perinatal risk index, including birth weight, gestation, delivery method, and pregnancy lie/presentation, 3) Postnatal risk (environment) index, including maternal age, number of parents in the household, maternal education, and breastfeeding, 4) Postnatal risk (maternal wellbeing) index, including maternal distress, maternal depression/anxiety, and maternal attachment.
Statistical Analysis Stepwise, multiple regression analysis was conducted using IBM SPSS Statistics for Windows (Version 28. 0, Chicago, Illinois) to analyse the relationship between the physical health clusters and ADHD symptoms at age 17 before and after controlling for cumulative environmental risk, ADHD medication and age three SDQ score. SDQ scores on the hyperactivity/inattention subscale at age 17 were entered as the dependent variable in all four regression models. The.
This study has several strengths. It used a large sample, weighted to represent the general population. Data were obtained longitudinally, allowing analysis of environmental risk before the age of three as a predictor of ADHD symptoms in later adolescence. It is also the first study to control for both environmental risk and medication use while examining the cumulative effect of several physical health conditions. The results further our insight into the pathophysiology of ADHD and point to associations that may eventually inform clinical practice in terms of implemetation of additional support programes. A key area for future research would be to explore the effects of early treatment and support for physical conditions on later development of ADHD symptoms. However, when considering their possible clinical implications, our findings should be interpreted with caution.
Funding for this study: This study received no funding. We are grateful to the  Centre for Longitudinal Studies (CLS) ,  UCL Social Research Institute , for the use of these Henrik Larsson acknowledges financial support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  965381 , the  Swedish Research Council  ( 2018-02599 ;  2022-01119 ) and the  Swedish Brain Foundation  ( FO2021-0115 ). Valerie Brandt  acknowledges financial support from the  Academy of Medical Sciences.
Conflict of interest: Henrik Larsson  reports receiving grants from  Shire Pharmaceuticals ; personal fees from and serving as a speaker for Medice,  Shire/Takeda Pharmaceuticals  and  Evolan Pharma  AB; and sponsorship for a conference on attention-deficit/hyperactivity disorder from  Shire/Takeda Pharmaceuticals  and  Evolan Pharma AB , all outside the submitted work. Henrik Larsson  is editor-in-chief of JCPP Advances. Prof. Cortese  has declared honoraria and reimbursement for travel and accommodation expenses for lectures from the following non-profit associations: Association for Child and Adolescent Central Health (ACAMH),  Canadian ADHD Alliance Resource (CADDRA) ,  British Association of Pharmacology (BAP) , and from  Healthcare Convention  for educational activity on ADHD. Joanne Cotton  was funded by an  ESRC/ UKRI postdoctoral fellowship , Grant number:  G108309  (October  2020 -August 2022 ).
This word was always at the same position within a sentence with regard to a triplet (table  1 ). Across the conditions, these context constraining words were matched with regard to word length (F(2, 606) = 0. 78, p =. 457, with a Mean (SE) of HC: 7. 12 (2. 26); MC: 7. 1 (2. 54); LC: 7. 37 (2. 61)) and word frequency (F(2, 584) = 1. 98, p =. 138, with Mean (SE) of HC: 2. 4 (0. 78); MC: 2. 56 (0. 87); LC: 2. 5 (0. 84); based on the Dutch SUBTLEX-NL database  (Keuleers et al. , 2010). The degree of context constraints was measured in  Terporten et al. (2019)  by using a sentence completion task in which participants had to fill in a missing target word based on a preceding sentence context. Sentences with the same target words filled in by the participants received higher context constraint ratings.
12, p = 0. 264, with a Mean (SE) of Congruent: 5. 79 (0. 14); Incongruent: 6. 0 (0. 13)) and word frequency (t(404) = 1. 29, p =. 199, with a Mean (SE) of Congruent: 3. 07 (0. 05); Incongruent: 2. 98 (0. 04)); based on the Dutch SUBTLEX-NL database  (Keuleers et al. , 2010). A practice stimulus set was also created, including a selection of 50 sentence in total, split in congruent and incongruent sentences from  (Wang et al. , 2017). Half of the sentences were defined as HC, while the other half was defined as LC for each congruency condition separately (see  (Wang et al. , 2017)  for details). For the EEG experiment, six counterbalanced lists were created. Three of these lists contained 80% of congruent target words, while the other three lists contained 80% of incongruent target words. The practice stimulus set was thought to bias participants' expectation of the predictive validity of the context constraints, towards the respective proportion of (in)congruent target words in the critical stimulus set.
Data collection and analysis Data were processed to extract reaction times (RTs) and the  For the learning task, the session was divided into three learning phases (Fig. 3 ): early (first 7 trials), middle (next 7 trials) and late (last 6 trials). Statistical analyses were performed to examine PCR (dependent variable) changes at different learning stages, and investigate the effects of stimulus type, social context, and sex. Contribution of task repetition vs. social presence to the changes in performance was quantified with the following index: Task repetition effect: (A2-P1)/(A2 + P1); Social presence effect: (P2-A1)/(P2 + A1), where A and P represent the PCR for the Alone and Presence conditions, respectively, and 1 and 2 the order in which they occurred. Finally, for the FER task, the mean PCR across all trials was computed for each session (Alone, Presence), and for different trial types (SESF, DESF, SEDF).
An early report suggesting an impact of sleep deprivation on the voice was provided by Morris et al. (23). Based on subjective evaluations by the experimenters of free speech produced by sleep deprived participants, a slowing down of speech and a "flatness of the voice" were noted after deprivation. These observations were extended by  Harrison and Horne (24) , who used a word fluency task and asked raters, blind to the amount of deprivation, to evaluate speech quality on five scales (intonation, errors, volume, fatigue, pace average). Effects of deprivation were found on the "intonation" and "fatigue" scales. More recently, an experiment using more raters on a large recorded database found that indeed, raters could detect sleepy versus non sleepy voices with an accuracy above 90%  (25) , even though the precise degree of self-reported sleepiness was not well estimated by raters.
A more recent study has again attempted to focus on "simple" acoustic descriptors for one of the databases of the paralinguistic challenge, with the explicit aim to facilitate interpretation  (31). Accurate classification was possible with the simpler feature set of about 20 features, with a resulting accuracy of 76%. Here, we aim to extend these findings in several important ways. First, we use our own vocal database, which has been collected in a controlled laboratory setting where the amount of sleep deprivation could be precisely controlled. Vocal recordings were obtained from reading out loud the same texts for all participants, in random order across participants, thus avoiding biases confounding e. g. participant identity and sleep deprivation observed in previous databases  (25, 32).
Machine-learning classification of speech before and after sleep deprivation A standard machine-learning pipeline was used to identify sleep deprivation in vocal recordings  (30, 34, 41). For all recordings, STMs were computed and used as the input feature space. The dataset was first transformed into random train/test splits. We then reduced the high dimensionality of the feature space by means of a principal component analysis (PCA) on the training set (see Methods for more details). The PCA spaces were then fed to a support vector machine classifier (SVM) with a gaussian kernel (radial basis function). We opted for an SVM and not a deep-learning architecture because of the relatively modest size of our dataset, but also because SVMs have been shown to outperform more complex classifiers in similar tasks  (30).
As a preliminary step, we evaluated the consistency of the interpretation masks. Because of our cross-validation technique, 50 classifiers were fitted either for the whole dataset for the population-level classifier or for each participant's classifier. To check internal consistency, we computed the pairwise Pearson's correlation coefficients between all 50 interpretation maps. At the populationl-level, this "consistency correlation" was low (r(22527): M =. 20, SD=. 34, BF10>2 16 ) which is coherent with the large variability suspected across listeners. At the participantlevel, however, consistency correlations were very high (r(22527): M=. 91, SD=. 06, min=. 73; All BF10 are high > 2 16 ). Furthermore, because individual classifiers varied in accuracy, we could check whether the consistency of the interpretation improved with accuracy.
Limitations of the present study There are both technical and conceptual limitations to the present study. We chose to use a controlled protocol to precisely equate sleep deprivation in our participants, but this costly protocol came at the expense of a relatively small dataset compared to the online databases used by machine-learning challenges  (27, 28). This removed biases in the database, such as associating the identity of speakers with the amount of sleep deprivation  (25) , but it also limited our choice of possible machine-learning techniques to perform the classification. We thus used an SVM classifier, and not potentially more powerful deep-learning architectures. We note however that in the studies that compared SVMs with other classifiers types, SVM performed best, including in state-of-the-art studies  (29) (30) (31).
Spectro-Temporal Modulations (STM) The sound files, initially sampled at 44. 1 kHz, were down-sampled to 16 kHz. Spectro-Temporal Modulations (STMs) were computed with our own toolkit (https://github. com/EtienneTho/strflike-model) which is directly adapted from the standard NSL Toolbox  (33). Sounds were processed through a bank of 128 constant-Q asymmetric bandpass filters equally spaced on a logarithmic frequency scale spanning 5. 3 octaves, which resulted in an auditory spectrogram, a twodimensional time-frequency array. The STM were then computed by applying a spectro-temporal modulation filterbank to the auditory spectrogram. We generally followed the procedure detailed in  (34) , with minor adaptations. A 2D Fourier transform was first applied to the spectrogram resulting in a two-dimensional array, also called Modulation Power Spectrum (MPS) (55) whose dimensions were spectral modulation (scale) and temporal modulation (rate).
Classification framework A standard machine-learning pipeline was used  (30, 34, 41). First, at subject/population level the whole dataset was randomly separated into a training set (75%) and a testing set (25%). For all recordings, STMs were computed and used as the input feature space. The STM feature space was sampled with 22 rates * 8 scales * 128 frequencies per 3841 temporal frames corresponding to epochs of 15 seconds, amounting to 22528 features for every sample. We then reduced this high dimensionality of the feature space by means of a principal component analysis  (PCA). At the population level, we trained a PCA on the whole dataset and retained the 250 main dimensions, explaining 99% of the variance. We further checked that the exact choice of PCA dimensions did not affect our conclusions, about the performance but also about the interpretation of the classifiers (see Supplemental Figure  S2 ).
Statistical analysis Qualitative data are expressed as absolute frequencies (n) and percentages. Continuous quantitative variables are expressed as medians (minimum and maximum) considering non-parametric distribution, and analysis was performed using the ManneWhitney U-test. The qPCR results of patients that used an SGCG and their HCWs were compared with samples taken from rooms of patients without an SGCG using Fisher's Chi-squared test. Statistical analyses were performed using SPSS Version 21. 0 (IBM Corp. , Armonk, NY, USA).
EEG-Hyperscanning data preprocessing The EEG data were initially pre-processed in Matlab, where blink, muscles, and head movement artifacts were filtered by optimal projection (FOP) methodology  (Boudet et al. , 2007). Visual control allowed to reject the remaining artifacts (<0. 1% of the data, no difference between the two groups), and noisy EEG channels that were marked as bad (for ASD subjects: average = 1. 80 ± 2. 82, min = 0, max = 10; for TD subjects: average = 1. 06 ± 1. 19, min = 0, max = 6). We then converted the EEG data from Matlab to Python and used the MNE-Python library  (Gramfort et al. , 2013)  for further analyses and statistics on the EEG data. We also used the open-source library Hyperscanning Python Pipeline (HyPyP), based on MNE-Python, that our team implemented (https://github.
Acknowledgments We thank  Robert Soussignan  and  Emeline Mercier  for their help in the manual indexing,  Laurent Hugueville  for his assistance in the setting of the hyperscanning system, and  Florence Bouchet  for her generous help in the EEG preparation. Fundings G. D. was supported by the  Orange Foundation for Autism Spectrum Disorders and the Fonds de recherche du Québec  (FRQ;  285289 ). This study was supported by the  Institute for Data Valorization, Montreal (IVADO ;  CF00137433 ). This study was enabled in part by support provided by  Calcul Québec  (www. calculquebec. ca) and  Digital Research Alliance of Canada  (www. alliancecan. ca). Q. M. was supported by the  Unifying Neuroscience and Artificial Intelligence -Québec (UNIQUE)   postdoctoral excellence fellowship.
Robustness of the model fitting To evaluate the ability of the model-fitting procedure to correctly identify the model that generated a given set of responses, we compute a confusion matrix of the eight models. For each model, we simulate 200 runs of the task (each with 200 passive trials followed by 200 trials in which a prediction is obtained), with values of λ and κ close to values typically obtained when fitting the subjects' responses (for prediction-cost models, λ ∈ {0. 03, 0. 7, 2, 15} ; for unpredictability-cost models, λ ∈ {0. 7, 2}; and κ ∈ {0. 7, 1. 5, 2} for both families of models). We then fit each of the eight models to each of these simulated datasets, and count how many times each model best fit each dataset (Fig. 9a ). To further test the robustness of the model-fitting procedure, we randomly introduce errors in the simulated responses: for 10% of the responses, randomly chosen in each dataset, we substitute the response by its opposite (i.
1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α. Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0. 261 (95% CI -0. 302, -0. 220) to 0. 228 (0. 18, 0. 268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical worth in a large dataset.
All empirical ranges are within the theoretical ones proposed in Box 1. For IV, two individuals have a value that exceeds 2, these outliers correspond to two of the three individuals whose φ value is not within the [0, 1] interval, suggesting a minority of cases with ultradian rhythm in the dataset. When examining how rest-activity fragmentation metrics differ by sex (Table  3 ), we found that men have on average a less constant rest-activity pattern as denoted by smaller IS compared to women (0. 529 vs 0. 546, p = 0. 001). During the day, men tend to transition less from rest to active periods while during the night men are more likely to transition from active to rest period as indicated by lower TP ra,w (p < 0. 001) and higher TP ar,s (p < 0. 001) than in women. Finally, on average they tend to have a less balanced activity behavior than women as shown by lower α (p = 0.
Acknowledgements We thank all of the participating civil service departments and their welfare, personnel, and establishment officers; the  British Occupational Health and Safety Agency ; the  British Council of Civil Service Unions ; all participating civil servants in the Whitehall II study; and all members of the  Whitehall II study team. The  Whitehall II Study  team comprises research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants and data entry staff, who make the study possible.
Measures The questionnaire was in French and comprised brief scales designed to be administered in large online surveys and known to have good validity and reliability across different samples. These scales measured perceived stress, occupational stress, SE, CMSs and EI. The scales were used with the approval of the original authors. Participants were also asked to provide sociodemographic data (i. e. , gender, age, year of study, size of nursing school, number and type of internships completed).
Acknowledgments This work was supported by the  Centre National pour la Recherche Scientifique , the  Institut National de la Santé et de la Recherche Médicale , the  University of Poitiers , the  Nouvelle Aquitaine   CPER 2015-2020 / FEDER 2014-2020 program "Habisan"  and the  Nouvelle Aquitaine  grant  AAPR2020A-2019-8357510  (PI:  M. Solinas ) and the  IRESP  grant  « IRESP-19-ADDICTIONS-20 »  (PI :  M. Solinas ). This manuscript derives from experiments that were performed independently by the groups of  M. Solinas  and  N. Mead  and put together after discussion between  M. Solinas  and  R. Baumeister  in the course of a meeting organized by the  CoCliCo team  of the  Cerca laboratory at the University of Poitiers. We thank  Michel Audiffren ,  Armand Chatard ,  Youna Vandaele  and  Nathalie Thiriet  for helpful comments on a previous version of this manuscript.
Methods We performed a prospective monocentric study in the Department of Pediatric Nutrition and Gastroenterology at Trousseau Hospital (French Reference Center PRADORT [Prader-Willi syndrome and obesity with eating disorders] Reference Centre, Paris, France). The questionnaires were completed by parents, and children when possible, during their rst visit to the clinical department and after signing a consent form to participate in the French Rare Obesity Cohorts With Food Behavioral Disorders: Better Diagnosis for Better Treatment (ObeRare) cohort (NCT04604626). Children with early-onset obesity were divided into 3 groups according to possible etiologies  [1, 6]. The HO group included 15 children with an established diagnosis of genetic (syndromic or not) or lesional origin of HO (PWS [n = 2], X-fragile [n = 2], Bardet-Biedl syndrome [n = 2], 16p11.
Statistical analysis The "Virtual Class" game was completed by the sample of students included in the study. The following formula was used to calculate their scores: Statistical analyses were performed in R software 4. 2. 2 with twotailed tests and a significance threshold of 5%. We aimed to assess the validity of using Virtual Class score to measure perspective-taking via two methods. First, we performed a Pearson correlation analysis of the relationship between the Virtual Class score and the School-Cam score to assess convergent validity. Second, multiple linear regression models were used to assess whether the two score distributions varied similarly according to age and sex. Score ∼ Age + Sex (1) Separate models were constructed to evaluate the interaction between age and sex. Score ∼ Age + Sex + Age * Sex (2) The model assumptions were checked with sensitivity analyses using bootstrap replications (Boot package).