dataesr/scientific-paragraphs-categorization huggingface dataset:
Data synthesis This review analyzed psychological and self-regulation mediators and predictors of change in body weight or BMI (primary outcome), physical activity, and dietary intake, separately (Note: we will use the term predictors when studies did not test for formal mediation, and mediators when they did). Intervention effects on the outcomes of interest were included in Additional files 2 and 3. Results were divided according to the length of assessment of the outcomes, into short-term (<12 months from the start of the intervention) and long-term (≥12 months), so that those variables mediating/predicting more sustainable outcomes (the main focus of this review) could be more easily identified. Twelve months has been indicated by an expert panel on obesity as an appropriate threshold between weight loss and the maintenance of the weight lost  [10].
A total of 42 mediators/predictors were identified across outcomes. To facilitate data interpretation, considering the very large number of individual variables, these were grouped together by similarity into categories. Categorization was done through the extraction of information from primary studies on the definition and operationalization of the constructs. The following 12 categories were formed: Self-regulatory skills use, Processes of change, Coping mechanisms, Self-efficacy/barriers, Autonomous motivation, Controlled motivation, Decisional balance, Outcomes expectations/beliefs, Body image/physical self-worth, Cognitive restraint, Eating disinhibition, Perceived hunger. Please refer to Additional file 6 for full details regarding the mediators/predictors identified per outcome. Finally, Tables 2, 3, 4, 5, 6, and 7 show, separately for each mediator/predictor, the number of studies that have analyzed it, the number of times it was tested (some of them within the same study), and the number of times a significant effect was found.
In terms of Data collection tools, 4 studies were rated as weak as they did not provide information on the validity or reliability of the measures used, 11 were classified as moderate, and 17 as strong. Three studies were not rated as they used a larger dataset for which information on psychometric properties of the measures is already provided. All studies scored strong in the use of Appropriate statistical analyses. In terms of Reporting, 30 studies were rated as strong, and 5 studies as moderate. In addition, studies including formal tests of mediation (n = 10) were classified as of moderate (n = 10) quality on the mediation analysis checklist. None of the studies reported conducting pilot studies to test mediation, and in all except two studies, there was no specific information regarding the power of the analysis to detect mediation.
For example, recently, in a large dataset of women in New Zealand, autonomous motivation for eating was associated with less binge eating and slower speed of eating (and a much healthier diet), indicative of improved eating selfregulation  [106]. The literature looking at relations between body image and eating behavior is also fertile in suggesting a close association between improved body image, improved eating regulation, and better weight control (see, for example,  [55, 87] ). In this respect, an attempt was recently made to provide an integrative view of eating regulation and weight maintenance, which also includes an explanation for the etiology and role of body image concerns and disordered eating, while considering motivational and self-regulatory aspects  [80]. It links goals (such as appearance versus health focus) and the predominant approach to eating regulation (such as rigid versus flexible restraint; focus on quality versus quantity) with the satisfaction of the needs for competence, autonomy, and relatedness, resulting in more or less adaptive diet and weight regulation (see Figures one and two in  [80]  for more details).
Acknowledgements This work is part of the  SPOTLIGHT  project and is funded by the  Seventh Framework Program  ( CORDIS FP7 ) of the  European Commission ,  HEALTH  ( FP7-HEALTH-2011 -two-stage), Grant agreement no. 278186. The content of this article reflects only the authors' views, and the  European Commission  is not liable for any use that may be made of the information contained therein.
Procedure Sentences were presented on a computer screen using the e-prime software in a self-paced moving window paradigm  (Just et al. , 1982). Each sentence was first presented with dashes replacing words. Participants were instructed to read sentences by pressing the space bar in order to have the segments appear. Once read, windows disappeared from the screen such that only one window was readable at a time. Participants were told that they would also have to answer yes/no comprehension questions about the content of these sentences. Instructions encouraged both rapid reading and correctness in answering the questions. Order of presentation of the sentences was randomized. The experiment started with four practice trials.
Data Analyses Incorrect grammaticality judgments representing 7. 8% of the data were removed from the response times analyses. Analyses of response times were run both on the full dataset as well as on the data trimmed for outliers, defined as responses slower than 3 s (representing 7. 9% of the data). Since both models provided similar outputs, the model of the complete data set is reported. Log-transformed response times and accuracy proportions were analyzed by way of (generalized) linear mixed-effects regression models with random intercepts for participants and items  (Baayen et al. , 2008) , following the same procedure as for Experiment 1.
Samples and data collection The inner ear is not notably influenced by postnatal growth and development  [35] , so that both juveniles and adults could be sampled and compared directly. We used micro-ct data obtained from dry skulls, or isolated petrous parts (pars petrosa) of the temporal bone, representing 86 juvenile and adult specimens of unknown sex and age. Sex diagnosis can be relatively straightforward in museum specimens representing adult dominant males of the two most dimorphic ape genera (Gorilla and Pongo). However, when museum specimens of unknown sex represent juveniles or non-dominant males, they cannot be sexed reliably. The specimens were distributed among the following 9 hominoid and 13 cercopithecoid contemporaneous species: Homo sapiens (n = 22), Pan paniscus (n = 7), Pan troglodytes (n = 9), Gorilla gorilla (n = 7), Pongo pygmaeus (n = 8), Nomascus concolor (n = 1), Hylobates moloch (n = 1), Hylobates lar (n = 1), Hylobates agilis (n = 2), Papio hamadryas (n = 2), Papio cynocephalus (n = 5), Papio ursinus (n = 1), Papio anubis (n = 2), Mandrillus sphinx (n = 1), Macaca radiata (n = 1), Macaca sylvanus (n = 2), Cercopithecus mona (n = 1), Cercopithecus hamlyni (n = 1), Cercocebus torquatus (n = 1), Colobus angolensis (n = 3), Colobus guereza (n = 4), Piliocolobus badius (n = 4) (for detailed information see S1 Table ).
Measurement methodology All measurements were calculated using Matlab R 2012a (7. 14, Mathworks). We first imported the μCT into the Avizo software package (www. vsg3d. com/avizo) for the 3D reconstruction of the air filled cochlea and oval window fossa. The ECL was measured by placing landmarks at small intervals along the outer circumference of the cochlea, between the cupula (apex) and the point marking the origin of the basal turn, where there is a saddle between the cochlear part and the vestibule, very close to the inferior margin of the round window (Fig  1 ). This method has already been used  [13]  making our measurements directly comparable. The ECL was used as a proxy for the length of the basilar membrane (as suggested in  [13] ) even if this measurement method likely overestimated the true length of the basilar membrane.
The number of turns (TUR) and the curvature (CUR) were also expressed as continuous variables obtained (using Matlab) from the coordinates of the landmarks placed on the outer circumference of the cochlea (Fig  1 ). We first computed the center of the cochlear spiral (noted "A", Fig 1 ) from the local chords defined by the landmarks placed at its two extremities. This method has already been used  [37]. We then calculated the equations of the two circles best fitted to the landmarks placed respectively on the first and last spiral turns and centered on "A" (shown in red and green,  Fig 1). The CUR values corresponded to the ratio between the radii of the larger and the smaller circle (noted respectively "R2" and "R1",  Fig 1). We finally defined two distinct lines joining the center "A" with the two extremities of the spiral.
The TUR values corresponded to the sum of full circle rotations and the angle between the two lines (Fig  1 ). As for ECL values, the TUR and CUR parameters were rounded to the nearest tenth. The OWA was visualized in 3D after extracting an isosurface of its fossa (Fig  1 ). An oblique slice visually considered to best-fit the complete outline of the oval window was reconstructed. The OWA was then measured from its segmentation on this oblique slice. No data on ECL, RECL, TUR and CUR in non-human living hominoids have been published so far.
Non-phylogenetic and phylogenetic interspecific linear regressions We used both traditional (non-phylogenetic) and phylogenetic bivariate and multivariate linear regressions to investigate the relationship between log-transformed mean species values for cochlear parameters and body mass considered as the independent variable (for detailed information see S1 Text). We determined whether the variation in each cochlear parameter was conceived as being tied to, or best expressed as the variation in body mass. The non-phylogenetic regressions (S4 Table) and the Akaike information criterion (AIC)  [48]  were computed using the RStudio free software (Version 0. 96. 331R) while the phylogenetic regressions (S5 Table ) were computed using Bayes Traits (Version 1). In the non-phylogenetic approach, we used two distinct methods to get meaningful interspecies allometric information out of our data: the p-values of linear regressions and the Akaike information criterion (AIC) (S1 Text).
Acknowledgments We thank the  Ditsong National Museum (Pretoria, South Africa), Witwatersrand University (Johannesburg, South Africa) , the  Nuclear Energy Corporation of South Africa, NESPOS  (www. nespos. org), Lunga Bam ,  Jakobus Hoffman ,  Kristian Carlson ,  Stephany Potze ,  Jean-Jacques Hublin ,  Matt Skinner ,  Bernhard Zipfel  for access and help with fossil micro-CT samples. We thank  Francis Duranthon  ( Muséum d'Histoire Naturelle, Toulouse ),  Marie-Dominique Wandhammer  ( Musée Zoologique, Strasbourg ),  Ottmar Kullmer  ( Senckenkenberg Museum Frankfurt am main ) for giving us access to the contemporaneous catarrhine skeletal collection in their care. We also thank  D Ginibrier ,  V Pereda-Loth ,  D Badr  and  L Radoscyz , for their technical assistance. We finally thank  Mark Coleman , another reviewer and  L Bondioli  (associate editor) for their helpful comments which improved the manuscript.
EEG Recordings & Signal Processing The EEG signals were recorded from a g. USBamp amplifier (g. tec, Graz, Austria), using 30 scalp electrodes (F3, Fz, F4, FT7,FC5, FC3, FCz, FC4, FC6, FT8, C5, C3, C1, Cz, C2, C4, C6, CP3, CPz, CP4, P5, P3, P1, Pz, P2, P4, P6, PO7, PO8, 10-20 system)  [32] , referenced to the left ear and grounded to AFz. EEG data were sampled at 256 Hz. In order to classify the 3 mental imagery tasks on which our BCI is based, the following EEG signal processing pipeline was used. First, EEG signals were band-pass filtered in 8-30Hz, using a Butterworth filter of order 4. Then EEG signals were spatially filtered using 3 sets of Common Spatial Pattern (CSP) filters  [35]. The CSP algorithm aims at finding spatial filters whose resulting EEG band power is maximally different between two classes.
• the predictor proposed by Ahn et al. [20]  was computed on electrodes C 3 and C 4 on the data of each trial (500ms to 3500ms after the feedback start): F ¼ w 1 a þ w 2 b w 3 y þ w 4 g with all the w i = 1. • the Blankertz's SMR-predictor  [13]  certainly is the most reliable (correlation of r = 0. 53 with SMR performance over a large dataset, N = 80). It is computed from a 2 min baseline in a "rest with eyes open" state using two Laplacians over the motor cortex, i. e. , C3 and C4. This predictor allows to quantify the potential for desynchronisation of the SMRs at rest, which can be used as an indicator of SMR strength during the performance of motor-imagery tasks. As no 2 min baseline had been recorded with our protocol, we used all the 3 sec. pre-trial time windows of the run (3000ms before the instruction) and computed the predictor on this sequence.
Analyses During each of the 6 sessions, participants performed 5 runs. However, as the classifier was updated after the first run of each session, we only used the 4 last runs (of each session) for the analyses. Thus, we considered 360 trials (15 trials x 4 runs x 6 sessions) per mental task, i. e. 1080 trials (360 x 3 MI-tasks) for each of the 18 participants. EEG data were analysed using Matlab (http://www. mathworks. com/) in order to compute the different neurophysiological patterns that could predict MI-BCI performance according to the literature. Then, these features as well as the psychometric-test results were analysed using SPSS (http://www-01. ibm. com/software/analytics/spss/) in order to find a relevant model of MI-BCI performance predictors. In particular, correlation analyses and (step-wise) linear regressions were computed as descriptive analyses.
Mental-Imagery Task Performance Eighteen participants took part in this experiment. The data of one outlier participant were rejected since, with a mean performance of 67. 21%, he outperformed (by more than two SDs) the group's mean performance over the six sessions ( X group = 52. 50%; SD = 5. 62). Thus, the following analyses were based on the data of 17 subjects. Over the six sessions, participants achieved a mean performance of X = 51. 63% (SD = 4. 39; range: [43. 04, 60. 14]). All the participants obtained performances higher than chance level, this chance level being estimated to be 37. 7% of correct classification accuracy for three classes and more than 160 trials per class and α = 5%  [49]. In the first session, mean performance was X = 51. 72% (SD = 8. 14), in the second X = 51. 18% (SD = 6.
First Predictive Model of MI-BCI Performance: Model ]1 A Step-Wise Linear Regression was used in order to determine a predictive model of each user's average MI-BCI performance obtained across the different training sessions. To reduce  In order to evaluate (1) the stability and (2) reliability of MODEL ]1, step-wise linear regressions were then performed using a leave-one-subject-out cross validation process. During the first step, 17 models were generated, each of them based on the data of all the participants except one (i. e. , the training dataset). This first step allowed to assess the stability of the model by comparing the factors included in each of the models to the ones included in MODEL ]1. During the second step, each of these models was tested on the only participant not included in the respective training datasets (i.
e. , the testing dataset). This second step aimed at determining the reliability of the models. Each model generated from the training dataset enabled to determine a predicted performance as well as a confidence interval for the corresponding testing dataset. This testing dataset used the participant's scores obtained at the psychometric tests that were included as factors in the respective training model. The model was considered reliable when the real preformance fell within the predicted confidence interval. The first step of the the leave-one-subject-out cross validation process revealed the instability of MODEL ]1. Indeed, only 5 out of 17 models included the same factors as MODEL ]1. In 11 out of 17 models, 2 or more factors were different from MODEL ]1. More specifically, the cross validation resulted in 13 different models for the 17 training datasets, with 27 different factors included in the different models.
Among these 27 factors, 17 were present in only 1 or 2 models out of the 17. The second step consisted in testing these 17 models on their respective testing datasets, i. e. , on the only participant not included in each training dataset. Results revealed that the real performance of 9 out of 17 participants fell within the predicted confidence interval, with an absolute mean error (Perf predicted -Perf real ) of 2. 68 points (SD = 2. 37, range: [0. 38, 8. 98]). Second Predictive Model of MI-BCI Performance: Model ]2 In MODEL ]1, the "mental rotation" factor was selected first in the regression and highly correlated with performance (r = 0. 696), which demonstrates its strong influence on the model. While being consistent with the nature of the tasks performed by the participants, this strong influence was likely to hide the effect of other important factors  [53, 54].
resulted in a model, called MODEL ]2 [R 2 adj = 0. 809, p < 0. 001], described in Fig 5 and including 4 parameters: Tension, Abstractness, the Learning Style "Active/Reflective" subscale and Self-Reliance. Tension, Abstractness and Self Reliance were assessed by the 16 PF-5, whereas the "Active/Reflective" dimension is a subscale of the Learning Style Inventory. As was done for MODEL ]1, the stability and reliability of MODEL ]2 were assessed using a leave-one-subject-out cross validation process. Results are detailed in Fig 6 which presents each training dataset, allnXX meaning that the training dataset was composed of all the participants except XX. The factors included in the model as a function of the dataset considered, as well as the R 2 adj value of each model are also shown. The first step allowed to evaluate the stability of MODEL ]2.
The same process as the one introduced in the previous section was used: 17 models were generated from the 17 training datasets, each of them including the data of all the participants except one. Results revealed that among these 17 models, 10 included exactly the same factors as the ones included in MODEL ]2: Tension, Abstractness, the "Active/Reflective" Learning Style subscale and Self-Reliance. In 5 out of the 7 remaining models, only one factor, Self-Reliance, was missing. Finally, one training dataset (alln23) induced a model including all the parameters present in MODEL ]2 plus the Power dimension of the Locus of Control and the Matrix subscale of the WAIS-IV, while in another dataset (alln28), Tension, Abstractness and the Digit Span subscale of the WAIS-IV were included. The second step allowed to determine the reliability of MODEL ]2.
Acknowledgments This research was supported by fundings from  IdEx Bordeaux  and the  Inria Project Lab BCI Lift. The authors also thank  Benjamin Blankertz  and  Laura Acqualagna  for providing her code to compute the Blankertz SMR predictor,  Alison Cellard  for the OpenViBE scenario coding,  Léo Gerville-Reache  for his help to perform statistical analyses,  Morgane Sueur  for her help during the experiments and  John Kelway  for proof-reading the final manuscript.
Introduction Characterization of the meaning of gestures is traditionally based on body-oriented descriptions  [36]  capturing the gestural elements according to a global description of bodily reference points. We aim to show that the meaning of different Gestural Units (GUs) can be defined o n t he b asis o f f orms a long t he u pper limb, using multiple reference points that are not limited to body-orientation, but oriented via each of the segments (hand, forearm, arm), thus facilitating an automatic characterization of gestures to exploit in a humanavatar context. This work takes place in the CIGALE project, whose final g oal i s t o c reate n ovel h uman-avatar interactions in the context of theatrical performances starting from the off-line analysis, characterization and classification of 4 different datasets of gestures we recorded using a motion-capture system; one of these datasets is presented (Sec.
3) and exploited in this work. Once the off-line study is completed, the project will move to the on-line analyses in order to evaluate the actual possible interaction between humans and the avatar, whose behavior has been built exploiting the off-line gesture analysis. After presenting the state of the art from a linguistic and engineering point of view (Sec. 2), we describe the database and the adopted biomechanical model (Sec. 3). Section 4 provides an overview of the linguistic framework within which our semantic characterization of co-verbal gestures is presented. The gesture significant part (stroke  [31] , Sec. 5) segmentation represents a necessary preliminary to such characterization, since it is impossible to characterize the meaning of a gesture without knowing when it occurs. For this purpose, an automatic segmentation is presented and tested against the segmentation of two coders, which serves as ground truth, in line with the highest standard methodes of both domains: robotics and linguistics (Sec.
Segmentation of gestural signals Each gestural signal in our database is composed of a sequence: T-pose, gesture, T-pose. Extraction of the gesture requires automatic segmentation. The generally accepted sequence includes 4 phases  [11, 36] : 1. resting position; 2. preparation (pre-stroke); 3. core (stroke); 4. retraction (post-stroke). This sequence describes the structure of a gesture. However, it is impossible to find an automatic, objective criterion to extract the stroke, the semantically significant part. This is a complex operation even for a human, and remains uncertain  [47]. In our case, segmentation is carried out on the basis of morpho-kinetic properties (as defined by Kendon  [29] ). Indeed, the preparation of motion consists of a ballistic motion that brings the arm(s) to the core of the motion  [8].
Action schema component properties To characterize the action schemas for each of the coverbal gestures recorded, the segmented signals are transformed into kinematic data in accordance with the biomechanical model (Sec. 3. 1). The motion of different degrees of freedom for each joint in the right upper limb (shoulder, elbow, and wrist) is taken into account and normalized temporally on 101 points  [14]. Our starting point for this characterization is the assumption that in human-human communication, the motion of DOF is most visible in prono-supination regardless of the type of gesture (performed on the entire upper limb or only one of its segments). We therefore decided to focus the analysis, initially, on the signals that were temporally aligned with the prono-supination zone, which contains the widest variation.
Conclusion In this study, we have presented a method for the characterization of 12 gestural units involving the upper limb. A motion-capture system was used to build a reliable gesture database to prove that the meaning of different Gestural Units can be defined on the basis of forms along the upper limb, using multiple reference points, that are not limited to body-orientation, but oriented via each of the segments (hand, forearm, arm). For this purpose an automatic segmentation was exploited. Tests of the segmentation protocol demonstrate its robustness in the individuation of the stroke necessary for the characterization of gestures. Simple characterization methods fulfill the requirement to associate each stage with formal semantic tagging. This is so since GUs that share the same poles differ only in the sequence in which these poles appear in the action schema.