dataesr/scientific-paragraphs-categorization huggingface dataset:
However, 47% of our patients were from cross-sectional and cohort studies, and the case series scored highly across several quality assessment domains. Our novel approach of capturing unpublished data through the Global Covid-Neuro Network meant we included patients, especially from LMICs, that would not otherwise have ever been included in a publication, thus improving accessibility and equity (S4 Table in S1 Appendix). Despite this, just 6 (7%) of 83 studies providing 42 (2%) of the total 1979 patients were from low-or lower-middle income countries. Only 2% of our patients were children. This may reflect some degree of residual selection bias, despite not relying upon published literature. Finally, despite using multivariable analyses with pre-defined exposures and confounders, associations do not equate to causation; determining these would require further research.
Method Procedure The participants were recruited on the Linkedin website from November 2018 to July 2019. Participation was voluntary. Data were collected via an online questionnaire sent to French workers in the industrial sector and distributed by e-mail. A total of 2955 e-mails were sent out targeting industrial-sector occupations (e. g. , production agent, maintenance technician, supervisor, line operator, quality controller, etc. ). The web survey remained accessible for nine months. All the data were collected at the same time before being examined. On the online questionnaire, there could be no missing answers because to move on to the next question, participants had to have checked off an answer to the current question. They gave their written consent to participate at the beginning of the questionnaire.
Mediation Analysis Hayes and Preacher's (2014) SPSS macro was used for testing direct and indirect links based on regression and non-parametric boots-trapping. The bootstrapping method is robust against potential biases resulting from non-normal data distributions  (Preacher & Hayes, 2008). According to the results (Table  2 ) illustrated in Figure  2 , rewards have indirect effects in the relationship between LMX and well-being. Furthermore, the direct effect is not significant (C' =. 07, ns). The effect of LMX on work rewards is significant (A =. 57; p <. 001), as is the effect of work rewards on well-being (B =. 08; p <. 001). Rewards do not have an indirect effect in the relationship between LMX and innovative behaviors. The indirect effect is not significant. LMX has a significant effect on rewards (A =.
Face stimuli To identify facial cues that were consistently modulated across the two populations tested, we computed two prototypical neutral faces, one for each ethnic group, using the following procedure. First, we selected six male neutral faces (three European and three Asian descent) from two databases that were controlled for orientation, position and light exposure [Nimstim database  [21]  for the European descent faces, and an in-lab built database for Asian faces]. We used only male faces to eliminate variability in judgment due to interaction with gender  (47). Second, each image was digitally edited with the GIMP software (http://gimp. org) according to the following specifications: image size (600x737 pixels), inter-ocular distance (176 pixels), left eye location (x = 212, y = 348 from the lower left corner), eyes-nose distance (100 pixels), eyes-mouth distance (193 pixels), eyes-chin distance (332 pixels), perfect vertical symmetry by averaging the original picture with their flipped left-right counterpart.
Here, a pixel is significant if it is either significantly darkened under the different conditions, or if it was significantly lightened under the different conditions. For visualization, dark pixels that were significantly more selected than light pixels are represented with a blue color scale and light pixels that were significantly more selected than dark pixels are represented by a red color scale. The value represented in the color scale corresponds, for each pixel, to the minimal absolute value of the significant t-statistics. Statistical analyses were carried out using Matlab r2018a (MathWorks; Natick, MA). Data were tested for Gaussian distribution (Lilliefors test) and variance homogeneity (Fisher tests). Unless otherwise specified, data were normally distributed, and assumptions for analyses of variance were not violated.
Study design and participants This research recruited a volunteer cohort of adolescents (n = 119 after removals -135 before; age mean = 17. 03 ± 0. 7 years old; 74 girls and 45 boys) from five secondary schools (Strasbourg and Lille, France). Before entering into the study, written consent was obtained from the adolescents and parents/caregivers, if they were under 18 years old. Data collection occurred between October 10th and November 15th, 2020. This period was impacted by the second French lockdown where lifestyles were adapted following school closures  [22]. Schools were randomly selected based on the level of urbanization of their location (varying from rural to urban). In each school, a second randomization has been carried out to select two classes. Gender and BMI parities criteria were investigated to not impact the initial distribution of the physical activity observed  [19, 20].
Inclusion criteria were to be in the last year of secondary school in France, to agree to wear an accelerometer for 1 week and to complete a daily diary for 7 days. For all recruited participants, we collected sociodemographic data including age, sex, height, weight, home and e-mail address and telephone number. This study was conducted according to the guidelines of the Declaration of Helsinki  [23]  and approved by the Ethics Committee of the University of Lille (2020-418-S82) and the NCIF (National Committee for Informatics and Freedoms number 2020-037, approval May 2020).
ActiGraph accelerometers, model GT3X+ (ActiGraphTM, Pensacola, FL, USA), were used to measure PA with a sampling rate of 30 Hz. Participants wore the accelerometer on their preferential hip, fastened with an elastic belt for 7 full days  [24, 25]. Data were reintegrated using a 10-s epoch. Troiano's (2007)  wear time validation algorithm  [26]  was applied, associating non-wear time to all periods > 60 minutes of consecutive counting at zero. The Actigraph output of 1952 counts. min -  was the cut-offs used to define the intensity level of MVPA  [27]. During the week of data collection, participants completed a digital diary on the LimeSurvey platform to obtain information about their daily social activities. Participants completed the diary every night before going to bed, which took about 5 min.
Statistical analysis Statistical analyses were conducted with R software (version 4. 1. 0), with specific packages (tidyverse, outliers, psych, car, rstatix, ez, lsmeans, pwr, Rmisc, ggplot2). Data and residuals of age, BMI, time spent in sedentary behavior, low physical activity and MVPA for each profile (HEPA active, minimally active and inactive) were tested for normality, interdependence and homoscedasticity using Shapiro-Wilks, Levene and Jarque-Bera tests. As these preliminary conditions were not met, non-parametric tests on median and quartile values were used. The threshold of statistical significance was set at 5% (p <. 05). Chi-squared and Kruskal-Wallis tests were used in order to detect profile effects on gender, age, BMI, time spent in each PA categories and time spent in each social occasion.
Selection of Sources of Evidence Articles retrieved from OVID and EBSCO were combined in EndNote software and duplicates were removed automatically by comparing the Author, Year, Title, and Journal fields. Duplicates based on smaller combinations of these fields were then checked manually. The remaining unique articles were randomly ordered and assigned to one of three independent raters (MK, KML, VM) to assess titles and abstracts based on the article selection criteria. All three raters screened the same 200 articles (100 at the beginning and 100 at the end of the selection process) to assess inter-and intra-rater reliability  (Belur, Tompson, Thornton, & Simon, 2018). The ratings of each set of 100 articles were compared between raters and statistical agreement between was computed with the Gwet agreement coefficient (AC 1 ).
Data Extraction Full texts of the selected articles were retrieved, and data were extracted using a pre-developed form by two independent reviewers (MK, PH). Quality control of full extractions was done for approximately 45% of articles by another independent reviewer (CL) and two additional independent reviewers (DRC, KL) reviewed the cognitive domain and network categorizations of all reported findings. The extracted information included demographic details (sample size, diagnosis, sex ratio, and age of patients and controls, if applicable), structural MRI metric (e. g. , volume, cortical thickness, surface area), structural MRI brain coverage (e. g. , vertex, regions of interest (ROIs), whole brain), cognitive measures (cognitive test, cognitive score), and the direct link between the structural and cognitive measures (analysis technique, the value of effect, statistic type and significance).
Meta-Analysis We used the Comprehensive Meta-Analysis (CMA) software (version 2. 2. 021, Biostat, Englewood, NJ) to perform our meta-analyses. We chose Fisher's Z to present our results since the primary outcome involved correlations between brain regions and cognitive domains. Fisher's Z transformations were calculated for each study from the reported statistical effect (i. e. , Pearson's correlation, Spearman's rho, t-tests or p-values) and sample size. Meta-analyses were conducted using a random effects model, which assumes that the true effect size of each of the studies varies and is not due to sampling variance only  (Hall & Rosenthal, 2018). Additionally, the software weights studies based on their sample size which is important to control for effects driven by studies with a small number of patients that may be heterogenous.
Study Selection The flowchart for article selection is displayed in Figure  1. The database search identified 7,259 articles and two additional articles were identified by co-authors. After the removal of 4,929 duplicates, books and conference abstracts, the remaining 2,330 titles and abstracts were randomly assigned to one of three raters. There were 199 articles flagged for full-text screening with the addition of 13 articles from reference lists. Although we included 115 articles in our systematic review (see Table  S2 ), 27 were not included in the meta-analysis as they either represented review articles, did not report relevant values of effect and authors could not be reached, or used analysis techniques that were incompatible with our software (e. g. , multivariate analysis). Thus, 88 original research articles were included in our meta-analyses.
Acknowledgements The authors would like to acknowledge the contribution of  Vanessa McGrory  (VM) and  Cai Li  (CL) during the article selection process and the quality control of data extraction. We would also like to thank  Geneviève Sauvé  for her invaluable help during the analysis of the results,  Gabrielle Pochiet  for her help during the setting of the meta-analysis and  Élisabeth Thibaudeau  for her insight into social cognition.
A comprehensive literature search was conducted using OVID (MEDLINE, PsycInfo, Health and Psychosocial Instruments, and EMBASE) and EBSCO (CINAHL) databases on August 3 rd , 2020. The following keywords were used: (schizophreni* OR psychosis) AND (cogniti* OR attention OR vigilance OR speed of processing OR processing speed OR reasoning OR problem solving OR executive function OR verbal memory OR verbal learning OR visual memory OR visual learning OR working memory) AND (brain structure* OR morphometr* OR volume OR.
Ultrasound vocalizations (USVs) USVs were induced by quick maternal separation in male and female pups at PND 10 as previously described  (12). Each tested mouse was placed into an empty plastic container (11 x 7 x 3. 5 cm), located inside a sound-attenuating isolation box (32 x 21 x 14 cm). USVs were recorded using an ultrasonic microphone (Ultravox Noldus), connected via the Ultravox device (Noldus, Netherlands) and placed 20 cm above the pup in its plastic container. At the end of 4-minute recording session, each pup was weighed, and the body temperature checked. Acoustic analysis was performed using DeepSqueak (Version 2. 6. 2), a deep learning-based software for the detection and analysis of USV (for more details see,  (13) ). The audio le was individually transferred into DeepSqueak, converted in the corresponding sonograms, and analyzed using a Faster-RCNN object detector.
More recently, sleep regularitythe day-to-day consistency in sleep timing and durationhas emerged as an important factor in health  [17]. Irregular sleep is associated with cardiovascular disease  [18] , inflammation  [19] , metabolic disorders  [20] [21] [22] , mental health conditions  [23, 24] , and cognitive impairment  [25]. The data on sex differences are mixed with reports ranging from no sex differences  [26] [27] [28]  to more irregular sleep in female  [29] [30] [31]  or in male individuals  [32, 33]. Chronotype may account for these inconsistencies as 'owls' tend to be more irregular sleepers  [11, 12]. Indeed, in re-visiting three published datasets  [14, 34, 35] , more male individuals were found to be irregular sleepers than females when both were a later chronotype. Finally, while data remain sparse, the adverse health effects of sleep irregularity itself may also differ between the sexes.
This topical research area has applicability in lighting standards, regulations and guidelines  [53, 54] , and various efforts are underway to incorporate scientific data from this research area into building recommendations. This highlights a pressing need to understand sex bias in this field. A preliminary literature search identified 545 papers, which were evaluated against a list of exclusion criteria (see Methods for full details), yielding a total of 180 articles. In this specific analysis, we focused on the reported sex of participants, although in many instances the terms sex and gender were used interchangeably. Each paper was then reviewed by a single reviewer to determine if participant sex and numbers were reported, and where possible, the proportion of female participants was calculated.
In biomedical research, the target population will be, on average, 50% male and 50% female, and it is becoming clear that variations between male and female physiology extend beyond hormonal differences. Therefore, as a rule, sex should be the first variable to be included to significantly increase generalizabilityexcept, as discussed in the NIH guidelines, for cases such as the study of sex-specific conditions or phenomena. We used the web platform Mentimeter to implement polling amongst participants as well as open-ended questions. Prior to participating in the interactive sessions, attendees were informed that their responses would be used for write-up and published as a peer-reviewed article. Attendees were free to not participate in the interactive sessions. No personal data were collected as part of the interactive Mentimeter sessions.
While immersive media have been shown to generate more intense emotions, saliency information has been shown to be a key component for the assessment of their quality, owing to the various portions of the sphere (viewports) a user can attend. In this article, we investigate the tri-partite connection between user attention, user emotion and visual content in immersive environments. To do so, we present a new dataset enabling the analysis of different types of saliency, both lowlevel and high-level, in connection with the user's state in 360 • videos. Head and gaze movements are recorded along with self-reports and continuous physiological measurements of emotions. We then study how the accuracy of saliency estimators in predicting user attention depends on user-reported and physiologically-sensed emotional perceptions.
INTRODUCTION Immersive media and environments are on the rise with increased affordability of virtual reality (VR) equipment and the deployment of popular platforms for 360 • streaming or advanced interaction in the Metaverse 1. This new type of content questions the design of compelling immersive experiences, as well as the technical choices for storage and distribution. Visual quality assessment by humans is key to enable efficient storage and distribution of content with high perceptual quality  [1]. To apply quality assessment-driven processes This work has been partly supported by the French government, through the UCA JEDI and EUR DS4H Investments in the Future projects ANR-15-IDEX-0001 and ANR-17-EURE-0004. This work was partly supported by EU Horizon 2020 project AI4Media, under contract no.
Saliency is hence a key component of quality assessment of immersive media. On the other hand, immersive content have been shown to elicit more intense emotions compared with regular flat-screen presentations  [3, 4, 5]. In this article, we investigate how emotions are associated with head motion to impact the accuracy of content-based saliency estimators. To the best of our knowledge, this is the first article to investigate the tri-partite connection between user attention, user emotion and visual content in immersive environments. Our contributions are: • A new dataset from user experiments recording head and gaze movements, along with self-reports and continuous physiological measurements of emotions. The stimuli are 360 • videos selected to enable the analysis of different types of saliency in connection with the user's state.
RELATED WORK Sensing and analyzing emotions in immersive environments has spurred interest (see, e. g. ,  [3, 4, 5, 6] ), more recently coupled with motion recordings and analysis  [7, 8, 9, 10, 11]. Human emotions are commonly decomposed along two main dimensions: valence, representing the negative or posi-tive nature of an emotion (unpleasant-pleasant), and arousal, representing the intensity of the perceived emotion (calmexcited)  [12]. The first reference database  [7]  providing emotional ratings and motion recordings of 360 • videos is made of 73 VR videos on which 95 users rated valence and arousal using the self-assessment manikin (SAM) tool  [13]  after experiencing each video. Their head positions were continuously recorded. A dataset of self-reported emotions of 19 users watching thirty-six 360 • images is collected by Tang et al.
MATERIAL AND METHODS Dataset We present the dataset we have collected to analyze saliency accuracy in Sec. 4. This dataset is publicly available  2. It is composed of user head and eye movements recorded while watching 360 • videos in a VR headset, along with users' EDA and heart rate (HR) during viewing, and valence and arousal ratings collected at the end of every clip. The user experiment has been approved by the university ethics committee. Stimuli In order to investigate the differences in accuracy of LL and HL saliencies depending on user emotions, we select seven videos from the database provided by Li et al. [7]  that meet two criteria. First, the videos must span an as broad as possible range of (valence, arousal). Video details are provided here 2. Second, HL and LL saliencies must not always overlap.
The reliability of arousal and valence ratings is assessed by the intraclass correlation coefficient (ICC), where a class of measurements corresponds to a stimulus (360 • video). ICC estimates based on mean ratings with a two-way mixed effects model are 0. 96 (95% CI 0. 87-0. 99) for arousal and 0. 88 (95% CI 0. 72-0. 98) for valence. The median root square difference of valence-arousal ratings with the corresponding values available in the original dataset  [7]  is 1. 17 (range: 1-9), showing the agreement between both. Finally, we look at the correspondence between SCR and arousal ratings. Similar to Toet et al. [9] , for each video, we compute the mean (resp. median) of SCR across users. The results show that the video rankings according to mean arousal and to mean SCR are very close (not shown here due to space limitation).
CONCLUSION In this article, we have first introduced a new dataset of user head and gaze movements in 360 • videos with valence and arousal ratings, and continuous physiological measurements of skin conductance and heart rate. The stimuli have been specifically selected to enable a spatio-temporal analysis in relation to content, user motion and emotions. We have presented first results comparing HL and LL saliency accuracy depending on user arousal, showing that the accuracy of HL saliency increases when user arousal increases. Next steps will consist in investigating finer temporal associations of saliency and attention locations with arousal/EDA, as well as structural modeling of possible interacting factors (e. g. , valence, fear, agency) in the production of head and gaze patterns. Also, the accuracy of more refined saliency models such as deep neural networks explicitly or implicitly combining saliency levels will be assessed to better understand motion predictability depending on the user's emotional state.
And another participant would read "I ignore" as well as "I see a stain on my shirt and I scrub it". In addition, TMS latencies were counterbalanced within each list. Latent Semantic Analysis (LSA: lsa. colorado. edu;  Landauer et al. , 1998)  was used to assure that manual action verbs (0. 294±0. 257) and non-manual verbs (0. 295±0. 269) did not significantly differ in their association scores to the preceding sentence fragments (P=0. 868). We also compared various psycholinguistic factors (written frequency, number of characters, and spelling neighbors) between manual action verbs and non-manual verbs using the Lexique. org database  (New et al. , 2004). Corrected t-tests (modified Hochberg's procedure;  Rom, 1990 ) yielded a significant difference only for written frequency (p=0. 004; Mean ratios: 11.
EMG recording The EMG signal was recorded through 10mm-diameter surface electrodes (Contrôle Graphique Médical, Brice Comte-Robert, France) placed over the FDI muscle of the right hand. Before placing the electrodes, the skin was shaved and cleaned in order to reduce noise in the EMG signal (< 20μV). The EMG signals were amplified and bandpass filtered on-line (10-1000 Hz, Biopac Systems Inc. ) and digitized at 2000 Hz for off-line analysis. We also measured the root mean square of EMG signal (EMGrms), to ensure that participants were at rest in all conditions. Throughout the experiment, EMGrms was monitored for the 100 ms preceding every TMS pulse to ensure complete muscle relaxation, using the following formula (MD = movement duration): Data and statistical analysis EMG data were extracted with Matlab (The MathWorks, Natick, Massachusetts, USA) and we measured peak-to-peak MEP amplitude.
All data analyses were performed using the software Statistica (Stat Soft, France). To assess the influence of participants' comprehension ability on their corticospinal excitability modulations by context, we performed Pearson correlations between the context effect (Minimal vs. Rich) and the memory-based, as well as the higher context-level comprehension scores. The data are presented as mean values (±standard error) and the alpha value was set at 0. 05.