dataesr/scientific-paragraphs-categorization huggingface dataset:
During the experience, two raters evaluated and annotated the emotional response of the user. Immediately after the virtual learning experience, the participants were asked to fill in the UES (user engagement scale) questionnaire  [20]. The two raters analyzed the 3D videos and selected frames of interest according to their annotations during the users' experience; this was necessary to construct the dataset for the automatic expression classification. The aim of involving facial expressions in the monitoring of emotional activation was to measure the involvement and the engagement of sample users interacting with the virtual environment with a real-time acquisition of facial expressions that are supposed to be spontaneous. The virtual learning environment involved in this study was developed as a supporting tool for educational purposes within the context of the Erasmus+ project "EVA-A European Entrepreneurship VET Model and Assessment Framework for Ethnic Minorities".
The two raters analyzed the 3D videos and selected frames of interest according to their annotations during the users' experience; this was necessary to construct the dataset for the automatic expression classification. The aim of involving facial expressions in the monitoring of emotional activation was to measure the involvement and the engagement of sample users interacting with the virtual environment with a real-time acquisition of facial expressions that are supposed to be spontaneous. development: an SVM (support vector machine) method can classify the level of user engagement based on facial analysis; (4) evaluation: assessment of the proposed solution on a group of sample users during the navigation in a virtual learning environment. A scheme of the followed DSR approach is shown in Figure  1.
A sample of 12 participants (six males and six females) were asked to navigate the virtual learning environment; the participants (students, postdoctoral students, PhD students, and researchers) with an age from 20 to 30 years and without any payment for their participation, took part in our research. During the navigation, the participants were equipped with the Intel ® RealSense™ SR300 structured-light depth camera; this way, learners' body posture and facial expressions were monitored and recorded in real time in the form of depth maps (Figure  2 ). During the experience, two raters evaluated and annotated the emotional response of the user. Immediately after the virtual learning experience, the participants were asked to fill in the UES (user engagement scale) questionnaire  [20]. The two raters analyzed the 3D videos and selected frames of interest according to their annotations during the users' experience; this was necessary to construct the dataset for the automatic expression classification.
The virtual environment was created using 360-degree computer-generated panorama images produced using a 3D computer graphics software toolset. Blender was applied because it is one of the best free and open-source alternatives currently available on the market. Thanks to its Cycles Render Engine which allows generating ray-tracing production-quality images, it was possible to obtain the best photorealism possible and enhance the immersivity and effectiveness of the images. Then, different high-quality, royalty-free 3D models, commonly available on the internet on repositories such as Google SketchUP Repository, were used to assemble the virtual environment. Then, we employed Google VR (virtual reality) view, which allows embedding 360-degree VR media into websites. It is based on WebGL technology, allowing our virtual environment to be effectively cross-platform.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 14 3D computer graphics software toolset. Blender was applied because it is one of the best free and open-source alternatives currently available on the market. Thanks to its Cycles Render Engine which allows generating ray-tracing production-quality images, it was possible to obtain the best photorealism possible and enhance the immersivity and effectiveness of the images. Then, different high-quality, royalty-free 3D models, commonly available on the internet on repositories such as Google SketchUP Repository, were used to assemble the virtual environment. Then, we employed Google VR (virtual reality) view, which allows embedding 360-degree VR media into websites. It is based on WebGL technology, allowing our virtual environment to be effectively cross-platform.
Selected facial depth maps were post-processed to frame only the face of the subject; then, each depth map was imported into Matlab ® (version: MatlabR2019b), and facial shells were obtained. Figure  5  shows an example of a facial depth map given in output by the sensor and an example of a facial shell obtained in Matlab ®. Geometrical descriptors were computed on the facial shells, as they are discriminative features to perform facial analysis  [22]. Previous articles assessed the reliability of facial analysis performed using the implementation of geometrical descriptors in a face recognition application  [23]  and the discriminative capability of these features with a neural network approach  [24]. To classify the data in the three classes of engagement, an SVM (support vector machine) classification method implemented in python was used, relying on that proposed in a previous work by Violante et al.
Sample To test our hypotheses, we use data from a large multi-country collaborative project involving 5,728 students working in 804 GVTs across 132 universities. On average, each team had 7. 1 members, with an average of 5. 2 countries represented per team. The average age of our study participants was 23. 9. The average work experience in the sample was over three years, 31% of participants were employed at the time of the study, and 5% reported owning their own business or holding managerial positions, thus "doubling up" as corporate employees with considerable work experience. Furthermore, 41% of the participants were MBA and EMBA students. Aware of concerns around student samples in business research (e. g. , editorials by  Bello, Leung, Radebaugh, Tung, & Van Witteloostuijn, 2009; Wheeler, Shanine, Leon, & Whitman, 2014) , we considered the generalizability concerns.
Measures The goal of the study was to simultaneously test and compare the effects of many different indicators of diversity on team effectiveness. Accordingly, for personal diversity, we selected demographics (age, gender), differences in skills and attitudes more relevant to work in GVTs (communication and technical skills plus cultural intelligence), and perceived differences (aka "psychic distance", c. f. ,  O'Grady, Lane, 1996). For contextual diversity we focused on the main commonly accepted economic, political, cultural, and institutional characteristics of each participants' home country. All variables used in the present study were derived from individual-level indicators aggregated to the team level. To eliminate the threat of common method bias  (Podsakoff et al. 2003) , the data are drawn from multiple sources.
Specifically, task outcomes were measured by professors evaluating the outcome (report) for each team. Psychological outcomes were measured based on participant survey responses at the conclusion of the project. Contextual diversity was based on archival, publicly available data based on team members' country affiliation. Finally, personal diversity was measured based on survey responses at the beginning or during the project. Thus, all data were drawn from different sources or at different points in time, which should eliminate concerns about common method bias. To capture team diversity, we used the standard deviation approach  (Thomas, 1999). For each indicator, we calculated diversity by squaring every member's distance from the team's average and then taking a square root of the sum of squares.
Personal Diversity Seven personal diversity dimensions were captured in our dataset and included in the analysis: Age diversity was the standard deviation of the team members' ages. Gender diversity was operationalized as gender heterogeneity in the team. A perfectly gender-homogeneous team would be one with 100% male or female members, while a perfectly mixed team would be 50-50% male-female. We calculated the index as 1. 0 minus the absolute distance from 0. 50 gender ratio. This way, the values would range from 0. 50 = perfectly homogeneous (100% male or female) to 1. 0 = perfectly heterogeneous (50-50% male-female). Each team member rated all other team members' English proficiency with an interrater reliability of 0. 74. Working language skills diversity was measured as the standard deviation of English proficiency skills among the team members.
Contextual Diversity Whereas all of the personal diversity measures were based on team member personal data, all of the contextual diversity measures were based on secondary data with each team member assigned a score based on his or her home country. National variety was measured using  Groves and Feyerherm's (2011)  variety index. It is superior to simply counting the number of nationalities represented on the team as it takes into account the distribution of countries within the teams (i. e. , even distribution or clear majority/minority). We used the student's nationality to operationalize this index, which appropriately captures the national variety regardless of the country of study. Economic and social diversity were represented as standard deviations of a series of indicators that are typically used to describe economic and social institutions.
Post-Hoc Robustness Check As a robustness check, we repeated the analyses by creating personal and contextual diversity composite indices and used them in our analysis. Since the diversity variables in our dataset are measured on different scales, we first standardized the data with a mean of 0 and standard division of 1. The personal and contextual diversity composite indices were computed as formative averages across the variables in each block  (Diamantopoulos & Siguaw, 2006). Table  5  provides correlations among the composite diversity indices and GVT outcomes. -------------------------Insert Table  5  about here -  ---------------------------- We revisited our hypotheses by analyzing the relationship between the diversity types and GVT performance measures using both bivariate correlations and multivariate regression analysis (Tables  5  and 6 ).
Primers were designed for the mouse Penk gene using the GenBank mRNA sequence (accession number NM 001002927. 2). The sequences were:5 0 -CTTTGCACCTGGCTGCTGGC-3 0 (forward) and 5 0 -GGGAACTCGGGCCTGGACAC-3 0 (reverse). Reverse transcription polymerase chain reaction (PCR) was used to generate a DNA template with sequences encoding the T7 and SP6 polymerase promoter sites incorporated onto the ends of the primer sequences. 35 S UTP-labeled RNA probes were transcribed from the cDNA template using a Riboprobeâ in vitro transcription kit (Promega) and the appropriate polymerase (T7 for the antisense probe, SP6 for the sense probe). The probes were purified using Mini Quick spin columns (Roche) to remove unincorporated nucleotides. Probes were denatured at 95 C for 3 min (40 ng probe/slide with 50 mg tRNA/slide).
QUANTIFICATION AND STATISTICAL ANALYSIS All experiments were conducted on female mice. Unless otherwise stated, statistical analysis was performed using PRISM software (GraphPad). Individual tests and n values are reported in the Results and in the relevant figure legends. All data are presented as mean ± SEM per section. In all statistical tests, a value of p < 0. 05 was accepted as significant. The number of TH and met-enkephalin immunoreactive cells in diestrus, late pregnant and lactating mice in Figure  1  was compared by one-way ANOVA followed by a Tukey's multiple comparison test with 4 to 5 animals per group. A one-way ANOVA was similarly used to compare levels of Th mRNA and Penk mRNA expression between these three groups. In Figure  1  the level co-expression of TH and met-enkephalin expression during diestrus and lactation were compared using an unpaired Student's t test with 3 to 4 animals per group.
A two-way ANOVA, followed by a Tukey's multiple comparison test, was also used to compared the number of TH immunoreactive cells expressing pSTAT5 or pERK1/2, (Figure  4 ) with 3 to 6 animals per group. In Figure  5  an unpaired Student's t test was used to compare the relative level of met-enkephalin immunoreactivity in the median eminence and the relative levels of phosphorylated TH measure by western blot. The data obtained from Ca 2+ imaging in Figure  6  was analyzed using custom scripts in R (R-Project).
ACKNOWLEDGMENTS This work was funded by the  Agence Nationale de la Recherche   ANR-DAT-NET 2007  (to A. O. M. );  France-BioImaging   ANR-10-INBS-04  and  ANR 12 BSV1 0032-01  (to P. M. );The  Royal Society New Zealand, Marsden Fund   UOO1406  (to S. J. B. ) and  New Zealand Heath Research Council Program  Grant  14-568  (to D. R. G. );  Diabetes UK R. D. Lawrence  ( 12/0004431 ) Fellowship; and  MRC  ( MR/N00275X/1 ) Project and  Diabetes UK  ( 17/0005681 ) Project Grants (to D. J. H. ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (starting grant  715884  to D. J. H. ).
Participants The MAPT-AV45 sleep ancillary study (www. clinicaltrials. gov NCT00672685) included 143 community-dwelling participants aged ≥70 years (median age: 73 years-old; range [70 to 85]; 87 females) with spontaneous memory complaints, but free of dementia  (16) (17) (18) (19). Sociodemographic characteristics, health status, and neuropsychological test results were available for all patients who also underwent sleep assessment and 18 Fflorbetapir-PET brain imaging. The MAPT-AV45 study and the sleep ancillary study were approved by the Toulouse ethics committee (Comité de Protection des Personnes-Sud-Ouest et Outre-Mer I et II). The methods were carried out in accordance with the approved guidelines. Each participant signed legal consent forms. Informed consent was obtained from all subjects.
Statistical Analysis The sample was described using percentages for categorical variables, and medians [range] for quantitative variables, because the Shapiro-Wilk test indicated the data distribution was mostly skewed. The negative and positive amyloid PET groups were compared using univariate logistic regression models. Sociodemographic and clinical variables associated with brain amyloid load at p < 0. 10 in the univariate analysis were included in logistic models to estimate the adjusted odds-ratios (OR) and their 95% confidence intervals (CI) for sleep parameters. For all analyses, the significance level was set at p < 0. 01. Analyses were performed using the SAS statistical software (version 9. 4; SAS Inc, Cary, North Carolina).
Statistical Analysis Categorical variables (socio-demographics, presence/absence of parental suicidal attempts, insecure/insecure attachment) were compared between groups (borderline and control sample) using chi-square analyses. To compare the mean scores of the borderline and the control sample on the number of parental suicidal attempts and on attachment styles we used a oneway anova. Bivariate associations between attachment styles and the overall severity of borderline symptomatology were examined with Pearson correlations in the borderline sample only. Finally, to test whether attachment styles and the number of parental suicidal attempts predicted the severity of borderline symptomatology we conducted a linear regression analysis with a stepwise procedure. All analyses were conducted using SPSS (25th version).
Statistical analyses The clinical scores will be analyzed using an intention to treat (ITT) analysis: all patients allocated to RT or TAU will be analyzed. The collected data will be used to describe the 2 cohorts: the RT group and the TAU group. Variables will comprise socio-demographic parameters, duration of PTSD, psychiatric comorbidities, pre-morbid disorders, and co-prescriptions. As described above, the primary outcome for effectiveness analysis is based on the delta (T3 minus T0) of PTSD symptom severity (PCL-S). A first indicator will be the mean PCL-S difference between T3 and T0. The delta of EQ5D-5 L (T3 minus T0) examines efficiency from the health economic perspective. A regression model will be used to calculate a second indicator based on the PCL-S values (t1-t6) collected between T0 and T1, allowing for the evaluation of PTSD symptoms evolution over time and its variation between individuals.
A third indicator will be the rate of patients considered as treatment responders (PCL-S score < 44) at Week 7 (T1), Week 13 (T2) and Week 52 (T3). Mean and SD will also be computed for the delta of CGI score between T1 and T0. Missing data will be replaced using multiple imputation methods, provided that the data is missing at random or completely at random. Additionally, in order to take into account the difference between the features of both cohorts (RT and TAU), we will use a propensity score of being allocated to RT to adjust the different model. For the secondary criteria, these propensity scores will be applied to the other scales used for the assessment. Costs will be estimated from the point of view of society for the follow up period (1 year) without discounting, due to the study duration.
Acknowledgements We sincerely thank the centers who participated in the Paris MEM study:  Pitié Salpêtrière Hospital  ( Dr Girault );  Ville Evrard Hospital  ( Dr Januel ),  Tenon Hospital  ( Dr Abgrall ),  Corentin Celton Hospital (Dr Louville), Lille University Hospital  ( Dr Ducrocq  and  Pr Vaiva ),  Nice University Hospital (Pr Benoit) ,  Marne La Vallée Hospital  ( Dr Marc ),  Sainte-Anne Hospital (Pr Krebs), Bicêtre University Hospital (Pr Hardy), Saint-Antoine University Hospital  ( Dr Mouchabac ),  Poitiers University Hospital (Pr Jaafari), Saint Maurice Hospital  ( Dr Cabié ), and  Rouen University Hospital (Pr Guillin ). We thank  Deniker Foundation  for its supportive network. We particularly wish to thank the  AP-HP Assistance public -Hôpitaux de Paris  for supporting this work.
Approximately 40,000-60,000 industrial chemicals are sold worldwide, with 6000 representing 99% of their total volume (ICCA & UNEP, 2019 3,4 ). Recently over 600 compounds, some of which are produced in high volumes, have been included in a database of potential EDCs, with supporting evidence of adverse effects in either humans or rodent models  (Karthikeyan et al. , 2019). Importantly, the vast majority of chemicals have yet to be assessed for their potential impact on human health or the environment  (Applegate, 2008; Krimsky, 2017). Moreover, regulatory agencies are unable to keep pace with the rate of new compounds being introduced (600-1000 compounds per annum in the US alone)  (Krimsky, 2017; Vandenberg, 2016).
Acknowledgements We thank  Gérard Benisti  for supplying the photos that were edited for Fig. 2. We thank the  MNHN  and the  CNRS  for the annual funding provided to the unit. SC is a PHD student implicated in EU  H2020 HBM4EU (n°733032 ). ML worked in EU H2020 project-EDC-MixRisk (n°634880) and lately in ATHENA  EU H20202 (n°825161 ).
An indirect way of detecting durational categories in a dataset of durations is testing whether the distribution of durations is not uniform but multimodal, where each mode approximates the prototype of one category Frontostriatal brain circuitry Neural pathway(s) connecting cortical frontal lobe brain areas with the striatum, including the putamen and caudate nucleus Grouping Process of building basic patterns of sound events based on acoustic features, such as stress, loudness alternation, pitch variation, durational relationships, etc. Hierarchical temporal structure Clustering of signal events in time, such as peaks in the amplitude envelope, where smaller clusters are nested within larger clusters across timescales Iamb "Metrical form in speech alternating a weak (unstressed) syllable with a strong (stressed) syllable" (Inter-event) interval "Temporal duration encompassed by two events.
We explore the scope of this method based on its strength to describe temporal structures in four datasets: random, isochronous, hierarchical, and speech. We start by describing the datasets and then discuss this technique. The datasets consist of time series of events represented by pulses. The isochronous series has a pulse every 0. 2 seconds. The random series is a Poisson process with a rate λ = 12 pulses per second. The hierarchical series is composed of triplets of double pulses. All these artificial sets-random, isochronous, and hierarchical-are 10-s long with a sampling rate of 1 kHz (i. e. , a temporal resolution of 1 millisecond). Additionally, the isochronous and the hierarchical series are jittered with Gaussian noise with a standard deviation of 0. 005 seconds. The speech dataset comes from "The north wind and sun dataset" corpus, consisting of recordings of the fable in 18 different languages.
For our analysis, we use the position of the syllable centers annotated by Jadoul and colleagues. 158 This annotated speech dataset contains the syllable centers of all 18 languages. The distribution of the logarithm of the IEI (log-IEI) highlights the typical IEI in the time series (Fig. 5 ). The isochronous signal has an event every 0. 2 s, so its distribution of log-IEI shows a single peak at 0. 2 s (Fig. 5 ). The distribution of the random series ranges over various time scales around 1/λ. The hierarchical series presents three peaksthe shortest corresponds to the IEI within the double pulses, the middle one corresponds to the IEI between the double pulses within the triplet, and the third one corresponds to the intervals between the triplets. The IEIs of the speech dataset are spread with 50% of the IEIs between 0.
19 and 0. 25 seconds. The downside of the distributions of IEI is that they are not sensitive to high-order temporal  structures, as randomizing the IEIs would yield the same distributions. The advantage of these distributions is that they are easy to interpret and can be compared using the sKL-divergence (Fig. 6 ). 157 he Kullback-Leibler divergence measures the similarity between two probability distributions. The divergence is smaller the more similar two probability distributions are, being zero only for identical distributions. Comparing the distributions of our datasets using the sKL-divergence, we obtain 210 distances. By projecting these distances to a two-dimensional space, we observe that the distributions of the speech datasets are more similar to each other than each is to one of the other datasets (Fig.
6 ). This approach is described in detail in Ref. 157. The IEI-related distributions can be visualized and used for computations by employing either the IEI itself, or its logarithm (as we do here). Using the logarithm is advantageous because it scales the IEI according to their magnitude, thereby dealing with different time scales simultaneously. This logarithmic scaling may also be quite plausible neurobiologically, at least for single intervals and musical rhythm. 3, 160 However, sometimes, one may prefer to work with the IEI directly, for instance, for dealing with negative intervals arising from overlapping calls from different signalers. 161, 162 The fact that this method can work with both IEIs and their logarithm makes it flexible to work with different types of datasets.