dataesr/scientific-paragraphs-categorization huggingface dataset:
A M-Audioâ M-Track 8 soundcard was used to route independent audio channels to each participant via in-ear plugs. The average audio latency from tapping pad to earplug was 17 ms, with a standard deviation of 2 ms. Ableton Live 10 â was used as the main interface for stimuli presentation, with 2 separate MIDI tracks triggering the metronome's audio sample. A Teensy 3. 2 microcontroller was used as a serial/MIDI hub in the setup: tapping onsets were detected with 1ms resolution using analog input of strain gauge sensors installed inside the pads, while metronomes onsets were logged using MIDI messages originating from Ableton. Each class of events (metronomes 1 and 2, finger-taps of participants 1 and 2) was retrieved by means of a predefined ID number. Simultaneous EEG recordings were performed from both partners of the dyads during the whole experiment, but such data are not presented in the present paper.
Additional data were collected prior and during the experiment. Prior to the experiment, demographic data were collected; the Edinburgh inventory  89  was administered to assess the right handedness of the participants; the 28-items version of the Interpersonal Reactivity Index (IRI)  61  was administered as a self-report of empathy and its subscales. During the breaks between experimental conditions, all participants provided subjective self-reports on different aspects of the task by expressing agreement on a scale from 1 (''Completely disagree'') to 7 (''Completely agree'') with a custom-made battery of 11 Likert items. Among these, the sense of ownership and sense of agency experienced during the task were measured by asking to rate the respective following items: ''I felt like the hand that I was seeing belonged to me'', and ''I felt like it was me moving the hand that I was seeing''.
Each JRP is in fact the Hadamard product of the recurrence plot of the first participant and the recurrence plot of the second participant. Computation of JRPs was carried out using the crp toolbox for Matlab â. 58  The 10 trials (i. e. , the metronomes' cycles) of each experimental condition were aggregated by summing the respective JRPs of each trial. This resulted in a 2-D matrix for which every entry contained the amount of recurrences occurring in the corresponding region of the cycle, across all trials. Finally, a 1-D vector recurrence scores was obtained by looping over the columns of the matrix and summing the counts contained in the rows. This vector represents a density measure of the instances of coupled behavior over the course of the metronomes' cycle. The scale of these recurrence scores depend on the size of the JRPs and in turn on the embedding procedure, which makes it necessary to set the same parameters on the whole sample.
In order to improve signal-to-noise ratio and avoid over-sampling in view of our statistical model, the resulting time series were reduced to 64 bins by averaging the recurrence score for equally sized, consecutive time periods. For this segmentation, interval size was equal to the slower metronome's increments, as they provided a regular subdivision intrinsic to the experimental trials. All processing steps presented were carried out in Matlab â. Our approach was preferred over the version for JRQA based on moving windows, for the latter would act as a low-pass filter on our time series and hinder the interpretation of our results. Specifically, a moving window results in a phase distortion of the time series dependent on window size, and is thus not reliable in detecting attractor points over the attractor landscape.
Statistical models The recurrence score was used as response variable in a mixed-effects model with Coupling and Perspective as factors, and Time as a continuous predictor expressed with the indexes of the metronome's steps (from 1 to 64). Given the non-linear time-course observed in coupled conditions, we adopted the method of orthogonal polynomials including linear and quadratic functions of Time into our model. 6, 59, 97  Dyads and interactions between Dyads and the factors were modelled as random effects on all polynomial terms, to account for the individual variability in synchronization skills and individual susceptibility to coupling across the experimental manipulations. The random effects structure was used in order to minimize false alarm rates without substantial loss of power. 98  Informed by our previous study and by the inspection of empirical curves from the present dataset, we limited the polynomial model to the 2 nd order as the most parsimonious solution.
ACKNOWLEDGMENTS The present study was funded by  Bijzonder Onderzoeksfonds (BOF)  from  Ghent University (Belgium) , in the context of a joint-PhD project with the  University of Lille (France)  ( l-SITE ULNE program ; grant number:  01D21819 ). The authors are grateful to  Ivan Schepers  for building the hardware of the finger-tapping device, to  Kevin Smink  for the illustration of the experimental design, and to  Canan Nuran Gener  for her precious help in collecting the data.
Data collection A spirit diagram is included with all the information about the time and events to be carried out in our study (see Table  1  for more information). The type of variables to be collected and their nature can be found below together with instruments to be used. (a) Socio-demographic variables: At baseline, the following information will be collected ad hoc by self-reporting at baseline in a clinical interview (V0): native language, age, gender, marital status, current type of cohabitation, number of offspring (if any), educational level, years of education, employment status, socioeconomic status, country of origin and religious preferences. Any changes in marital, employment status or similar that have taken place during the study period will be recorded at the last visit (V2).
(d) Neuroimaging records: Magnetic Resonance Imaging (MRI) will be performed at baseline, and at 6-month follow-up. Data acquisition will be performed using a full-body MRI scanner (Siemens MAGNETOM Prisma 3T) at the Magnetic Resonance Imaging Core Facility at IDIBAPS. The neuroimage protocol to be used in the present study includes a structural T1-weighted image, resting-state fMRI, diffusion-weighted imaging (DWI), and task-based fMRI related to decision-making (Iowa gambling task). 24  Neuroimaging protocol including all parameters and regions of interest are available for consultation in Supplementary Materials.
Group, time, and group × time interaction will be included in the model as fixed factors. If significant differences are detected between groups, socio-demographic, and clinical variables will also be included as fixed factors. To explore possible differences in neuroimaging records between 2-time-points (V0 and V2) with six months difference (pre-and post-intervention), chi-square or Student's t-tests or ANCOVA analysis will be applied to compare categorical and continuous variables when appropriate. For neuroimaging data, we will also conduct exploratory whole-brain analyses. In all cases, the level of significance will be set at p < 0. 05. When needed, all analyses will be corrected by the Bonferroni model for multiple comparisons. The data will be analyzed using the IBM SPSS statistical package (version 23).
Ethics The study protocol was approved by the Ethics Committee of the Hospital Clinic with the registration number: HCB2022 0659. The study design and the evaluation of the results will be carried out following the CONSORT criteria. All data will be collected following the current Law of Data Protection. All participants must give written informed consent prior to inclusion. All the data collected will be confidential. All Case Report Forms will be anonymized with a code for each subject. Neither the name nor any personal data that could identify an individual will be registered in the database. Only personnel involved in the present project will have access to the data collected. The Ethics Committee of the Hospital Clínic of Barcelona will be made aware of any important changes in the protocol by a written report.
Acknowledgements JR thanks the support of the  Spanish Ministry of Science and Innovation  ( PI19/00394 ,  CPII19/00009 ) integrated into the  Plan Nacional de I+D+I  and co-financed by the  Instituto de Salud Carlos III (ISCIII)-Subdirección General de Evaluación  and the  Fondo Europeo de Desarrollo Regional (FEDER) ; the  ISCIII ; the  CIBER of Mental Health (CIBERSAM) ; the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement  ( 2021 SGR 01128 ), the  CERCA Program. IG gives thanks for the support from the  Spanish Ministry of Science and Innovation (MCIN)  ( PI19/00954 ) integrated into the  Plan Nacional de I+D+I  and co-financed by the  ISCIII-Subdirección General de Evaluación y confinanciado por la Unión Europea (FEDER ,  FSE ,  Next Generation EU/Plan de Recuperación Transformación y Resiliencia PRTR) ; the  Instituto de Salud Carlos III ; the  CIBER of Mental Health (CIBERSAM) ; and the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement  ( 2021 SGR 01358 ),  CERCA Program/Generalitat de Catalunya  as well as the  Fundació Clínic per la Recerca Biomèdica  ( Pons-Bartran 2022-FRCB PB1 2022 ).
Background: Posttraumatic stress disorder (PTSD) can occur after facing with trauma. While PTSD management strategies include rst-line pharmacotherapy and psychotherapy, mind-body therapies, such as yoga, are applied in PTSD population. This overview aimed to summarize the effectiveness of yoga interventions on PTSD symptoms in adults in a systematic review including randomized controlled trials (RCTs). Method: We searched for systematic reviews with or without meta-analysis of RCTs involving adults with PTSD diagnosis or trauma history. The search was conducted until April 2022, through six databases (Cochrane Database, MEDLINE (Pubmed), Scopus, Embase, CINHAL and PEDro). The primary outcome was the evolution of PTSD symptoms throughout the intervention. Secondary outcomes included follow-up, safety, adherence, and cost of the intervention.
Search strategy To ensure screening a maximum amount of items in the literature, and to avoid missing out on any SR, six databases were consulted the Cochrane Database, MEDLINE (Pubmed), Scopus, Embase, CINHAL and PEDro. In addition, grey literature was considered in the present overview. The search was performed up until April 2022. Keywords in titles and abstracts were used to select the SRs: yoga AND posttraumatic stress disorder (see details in Appendix 1).
Data extraction Data extraction was conducted independently by two authors (NL and AP), and disagreements were resolved with a third independent author (MGG). The following information was collected: Metadata: rst author's name, publication date, country, type of study, number of primary studies included Objectives of the study General characteristics of the population: number of participants, age, gender, type of trauma Intervention details: type of intervention, control group, duration, frequency of intervention, scales used, rst outcome measurement, additional outcomes.
Post-COVID-19 symptoms have become a significant global health concern. This study focused on assessing the prevalence, severity, and care preference of post-COVID-19 symptoms, as well as identifying determinants to inform evidence-based policy on post-COVID-19 in Vietnam. A national cross-sectional study was conducted in May 2022 among 12,361 recovered COVID-19 patients, providing the largest dataset on health status after COVID-19 in Vietnam. The study utilized ordered logistic, Poisson regression, Multilevel linear random-effects models, and Multilevel random effects ordered logistic model to identify factors associated with various aspects of post-COVID-19 conditions. Results showed that the average number of post-COVID-19 symptoms was approximately 3, with fatigue and headache being the most common symptoms.
More data on post-COVID-19 conditions should be collected from resource-scarce settings and in LMICs to inform evidence-based interventions and identify multidimensional determinants such as social, financial, and cultural characteristics of each population. Secondly, validated information should be provided more extensively to raise awareness of post-COVID-19 symptoms and avoid potential confusion between post-COVID-19 conditions and symptoms of other diseases. When developing and applying a national strategy for post-COVID-19 conditions, contextual and behavioral characteristics of different settings such as health system capacity, resident behavior, and level of adaptation to guidelines must be considered. At the managerial level, healthcare resources should be distributed more evenly to ensure health access and adequate information in underserved regions, such as the Northern Midlands and Mountains.
Our study's sample size is a strength, as it was drawn from one of the largest post-COVID-19 care initiatives in Vietnam, providing a national reference dataset. However, there are several limitations to our findings. Firstly, because this was a cross-sectional study, we cannot infer causation. Secondly, our data may be under-reported due to the recruitment of participants only from health facilities, which excludes those who did not seek medical attention despite experiencing post-COVID-19 symptoms. This exclusion disproportionately affects older adults. Additionally, our study focused on the young workforce, recognizing their importance in national development, and as a result, the majority of responses were obtained from this age group. Thirdly, it is essential to note that COVID-19 grading criteria were self-reported, which could be subject to recall bias.
Funding Dr. Nguyen Huu Tu  was funded for implementing this project under the  Vietnam Young Physicians Association. Prof. Le Thi Huong  was funded by  Vingroup Joint Stock Company  ( Vingroup JSC ),  Vingroup , and supported by  Vingroup Innovation Foundation (VINIF)  under project code  VINIF. 2020. COVID-19. DA03. Prof. Roger Ho  and  Cyrus Ho  were supported through the  NUS Department of Psychological Medicine  ( R-177-000-100-001/R-177-000-003-001/ R177000702733 ) and  NUS iHeathtech Other Operating Expenses  ( R-722-000-004-731 ). The technical resources were provided by the  Institute for Health Economics and Technology (iHEAT).
Apparatus The participants were tested in a sound-attenuated room at the ''Laboratori de Recerca en Infancia", from the Center for Brain and Cognition at Universitat Pompeu Fabra, Barcelona. The infants sat on their caregiver's lap at approximately 65 cm from a 23-inch screen (resolution: 1920 Â 1080 pixels) on which the stimuli were presented. The caregivers were instructed to close their eyes throughout the procedure. The participants' behavior during the session was recorded using a Sony HDR-HC9E camera (temporal resolution: 25 frames/s). In addition, after a five-point reference calibration, a Tobii TX300 eye tracker recorded the gaze of the infants during the experiment. The presentation of the stimuli was controlled using the Psychtoolbox-3 toolbox in MATLAB (The MathWorks, Natick, MA, USA).
Acknowledgments We thank  Cinta Bertomeu  for working as a primary coder,  Marc Colomer  for his suggestions on improving the manuscript, and all families and infants who participated in the study. J. Bas was supported by: a pre-doctoral grant from the  Spanish Ministerio de Economía y Competitividad   BES-2013-063649  and a post-doctoral grant from  Fyssen Foundation. N. Sebastian-Galles was supported by:  LESOLA  Project  PID2021-123416NB-I00  financed by  MCIN/ AEI / 10. 13039/501100011033 / FEDER ,  UE ;  CECH  Project, number  001-P-001682 , cofunded by the  European Regional Development Fund of the European Union (Programa Operatiu FEDER Catalunya   2014-2020 ) with the support of the Catalan Government; Research support official Id. P-5993 ICREA Acadèmia 2018 Award financed by ICREA; Research group support 2017 SGR 268 financed by  AGAUR.
Participants and procedure Three native speakers of Georgian participated in the study, two female (F1 and F2) and one male (M1). Data collection was limited to the three speakers because of the Covid-19 pandemic. All three were in their twenties and living in southern California at the time of their participation. All three were in their twenties and living in southern California at the time of their participation. They were recruited via word of mouth and an announcement circulated by the Georgian Cultural and Educational Center of Southern California. Data collection was limited to the three speakers because of the Covid-19 pandemic. Data were collected using an Electromagnetic Articulograph AG501 (Carstens Medizinelektronik GmbH). Electromagnetic articulograph (EMA) data allows us to track the movement of the tongue and lips at a millisecond level of temporal resolution.
Two sensors were attached on the lip, one on the upper and one on the lower lip. The Euclidean distance between these sensors at each timepoint was calculated during data analysis, and the resulting variable -Lip Aperture (LA) -was used for labial segments. Sensors were also attached on the upper and lower incisor for reference and jaw movement respectively, and on the bridge of the nose and behind each ear for head correction. Audio was recorded with a Shure SCM262 microphone mixer at a 16 kHz sampling rate, with a Sennheiser shotgun microphone positioned a foot away from the participant's mouth. Kinematic data was automatically synchronized with the external audio data. Participants read the stimuli off of a computer screen placed approximately three to four feet from the EMA. Stimuli were presented in the Georgian orthography, but we present here the IPA transcription.
Analyses Collected data were post-processed; they were head-corrected and rotated relative to the occlusal plane, which was recorded by having participants bite down on a hard plastic plane designed precisely for this purpose. Then, all data were semi-automatically labelled based on velocity criteria using the custom software Mview (Mark Tiede, Haskins Laboratory). Figure  7  shows the relevant timepoints used for the analysis. From left to right, they are: the onset of the gesture; the constriction target achievement; the constriction release; and the offset of the gesture. The figure also shows the point of maximum constriction during the closure, as well as the points of peak velocity for the formation phase (i. e. the interval between gestural onset and release) and the release phase (i.
We use both constriction duration overlap and relative overlap because they measure overlap between different parts of gestures, allowing us to better approach the question of gestural timing. (C 1 Release -C 2 Target) (C 2 Release -C 1 Target) Of the 442 tokens initially measured, 19 were removed from the data set due to issues with sensor tracking or participant speech error, and two outliers that were more than three standard deviations from the mean were removed as well. The final data set had 413 total observations. Statistical analysis was done in R (R Core Team 2018) using linear mixed effects models from the lmerTest package  (Kuznetsova, Brockhoff & Christensen 2017) , followed by posthoc pairwise comparisons with Holm corrections, using the emmeans package  (Lenth 2019). Plots were generated using the ggplot2 package  (Wickham 2009).
Model selection for both measures was done by starting with the maximal model both in terms of random and fixed effects. The maximal fixed effects structure was Sonority (with three levels: Rise, Plateau, and Fall), Order (with two levels: Front-to-Back and Back-to-Front), and their interaction. The maximal random effects structure had random slopes by Word, Speaker, and Frame Sentence. The minimal adequate random effects structure was determined using rePCA from the lme4 package  (Bates et al. 2015) , and the minimal adequate fixed effects structure was determined using drop1 from the basic R stats package (R Core Team 2018). To assess the variability of gestural overlap with respect to sonority shape, we calculated the standard deviation for each measure per word, per speaker, and ran linear mixed effects models on the resulting data set.
Vocoids Vocoids have been reported before in research on Georgian  (Goldstein et al. 2007 ), and we find vocoids in other data collected from these same participants for a different study  (Crouch et al. 2022). For purposes of counting and measuring the duration of vocoids, we defined them as vocalizations that occurred between the release of C1 and the target achievement of C2, that have a voicing bar and a distinct formant structure. Visibility of F2 determined the onset and offset of the measured interval. Vocoids are much shorter than full vowels: the mean duration of all intrusive vocoids in the data reported in this paper is 29 ms, with a standard deviation of 12. 5 ms. Their distribution also differs by sonority shape: 56% of sonority rises produced have intrusive vocoids, as do 25% of sonority plateaus.
Health-related quality of life and daily mini-questionnaire HRQoL was assessed on a daily basis by a short online miniquestionnaire (5 min), which the participants would complete every day in a browser. This daily mini-questionnaire collected data on. • HRQoL and well-being (e. g. fatigue, mood, stress) at the present moment (when answering the questionnaire); • Exposure-related variables (e. g. looking at the phone within 1 h of going to bed the previous night, cordless phone use in the last 4 h, and mobile phone call duration and screen time during the last 4 h); • Important time-varying confounders: (e. g. time spent outdoors, coffee and alcohol intake).
Sleep duration and quality Sleep duration and quality were measured continuously using a Fitbit Inspire activity tracker. This is a small wrist-worn device, which is commercially available and can measure the duration, efficiency and stages of sleep (awake, light, deep and REM). In addition, it registers the number of steps the wearer takes throughout the day. During the first home visit by the study assistant, participants provided the information needed to set up a Fitbit Inspire activity tracker (birth year, sex, height and weight). The study assistant set up the Fitbit application on the participant's phone and configured their personal Fitbit account. We retrieved daily sleep duration and stages data for each account via an Application Programming Interface (API). Sleep efficiency was calculated as the time asleep divided by the total time spent in bed * 100%.
Exposure assessment Daily markers of RF-EMF exposures were collected in the daily questionnaire, during which participants were asked about their call time on cordless phones (in minutes), their total mobile phone call time (in minutes) and their total mobile phone screen time (in minutes) in the 4 h prior to the current assessment, giving an indication of the exposure through the own use of mobile phones. Previous research showed that DECT calls, mobile calls and mobile phone data use are the predominant contributors to personal RF-EMF exposure  (Cabré-Riera et al. , 2020; van Wel et al. , 2021; Eeftens et al. , 2023) , and therefore the best exposure markers for RF-EMF which could be feasibly and repeatedly assessed. We chose 4 h as a typical maximum time frame for short-term human laboratory studies  (Bosch-Capblanch et al.