dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Dr. Matthijs Noordzij  ( University of Twente ) for giving us additional details of his 2006 study,  Dr. George Michael  ( University of Lyon ) for his guidance with the case-controls statistics,  Eve Tramoni  ( Assistance Publique des Hôpitaux de Marseille, La Timone, and Institute of Systems Neurosciences ) for the neuropsychological assessment of GL, and  Michel Denis  as well as the two reviewers for insightful comments.
Data The 2010 'Health Barometer' is a French nationwide survey that used a two-stage random sampling frame (household/individual) to measure health perceptions and behaviour of the general population. 26 This investigation was approved by the French Commission on Individual Data Protection and Public Liberties, and all data collected were anonymous and self-reported, with a response rate of 61%. The initial sample of respondents comprised 27 653 individuals aged 15-85 years who answered questions about their migration and smoking histories. Our analyses concerned the subsample of 19 578 individuals born either in metropolitan France or in any of the geographical regions of interest and aged at least 18 years and at most 70 years, the latter cutpoint being used to minimize recall problems and selection biases resulting from differential mortality.
Measures The study outcome was daily tobacco smoking, and information on age at starting and cessation were used to construct an accurate agespecific tobacco-use history for each respondent. 28 To describe life course patterns, we created person-age data with a dichotomous measure of smoking. Three birth cohorts were defined based on age in 2010: born 1940-55; born 1956-70 and born 1971-92. To make the comparisons of smoking meaningful across cohorts, the ages of retrospective follow-up for the older cohorts are restricted to a maximum of age 39, the upper bound of the youngest cohort, considering initiation after those ages as right-censored. The data were therefore pooled by year of age for each individual from ages 10-39 or current age if lower than 39 years. Immigrants were categorized based on place of birth and nationality at birth and mother's and father's place of birth and nationality.
Participants We recruited 70 participants, randomly allocated to the experimental group or to the control group. Due to technical problems (N=2) and inability to realize the complete intervention protocol for personal reasons (N=4), the data of 64 participants (33 male, 31 female, M=22. 11, SD=3. 12, age range = 18 -29 years old) were analysed. The body mass index (BMI) of participants was in the normal range, from 18. 5 to < 25 kg/m 2. In order to meet the inclusion criteria for the study, participants had to be non-smokers, and should not be suffering from sleep disorders (score lower than 5 on the Pittsburgh Sleep Quality Index) or from cardiovascular diseases (self-reported). All participants gave their informed consent for inclusion before they participated to the study. The study was conducted following the Declaration of Helsinki, and the protocol was approved by the Ethics committee of the German Sport University Cologne (Project identification code 42/2015).
Participants Of the 21 healthy volunteers in the behavioral validation study, 2 participants were excluded from the analysis as their performance did not significantly exceed chance level  (Mueller-Putz et al. , 2008). Responses of another participant could not be recorded. Of the remaining 18 participants, 9 were female. The age ranged between 20 and 29 years (M = 23. 22, s. d. = 2. 76). In the fMRI study, caused by the temporal constraints and difficulty of the paradigm, which had the purpose of triggering false positive (FA) responses, 9 out of 50 participants did not achieve a performance above chance level. Due to missing data, two data sets were lost. Furthermore, nine participants did not fulfill the requirements of the fMRI analysis (≥16 valid trials in each combination of condition).
Experimental design In the present study, we used an adapted version of a previously published yes-no paradigm  (Manera et al. , 2011c). Using Psychophysics Toolbox  (Version 3. 0. 11. : Brainard, 1997 ;  The gray silhouettes serve illustrative purposes and were not visible for participants. Kleiner et al. , 2007)  in Matlab R2015a (The MathWorks, Inc. , Natick, Massachusetts, USA), we presented black moving dots on a gray background. On one side of the computer monitor (refresh rate = 59 Hz, resolution of 1024 × 768, viewable region of 375 × 280 mm), moving dots constituted an agent (agent A), who performed a communicative (COM) or an individual (IND) action (Table  1 ). On the other side of the screen, a cloud of temporally and spatially scrambled moving dots was displayed (for details, see  Manera et al.
, 2011c). In 50% of trials, a second agent (agent B) was present within the cloud and reacted to the action of agent A (Signal trials); in the remaining 50% of trials, the dots' motion was scrambled (Noise trials; Figure  1 ). From this 2 (type of action) × 2 (type of trial) design we obtained four experimental conditions: COM Signal, COM Noise, IND Signal and IND Noise. Although being presented simultaneously, agent B's action always succeeded agent A's action without any temporal delay. Movements of the agents were chosen from the Communicative Interaction Database and have been shown to be reliably recognizable  (Manera et al. , 2010 (Manera et al. , , 2011c)). Stimulus duration ranged from 2885 to 3473 ms and the distance from the center of the screen was comparable between actions. To avoid participants' reliance on simultaneous transitions of dots defining agent B's body, we applied a so-called limited lifetime technique  (Burr et al.
Behavioral data analysis Behavioral data analysis was performed in Matlab R2015a. Effect sizes (Cohen's d z ) cited in this paper represent statistics described in  Lakens (2013). For the pre-processing of eye-tracking data, we used the software package edar by Tore Erdmann (https://github. com/toreerdmann/edar; retrieved 26 July 2017) in R (Version 3. 3. 1, R Foundation, Vienna, Austria). Fixation durations represented the cumulative duration of all initial fixations over all trials, i. e. static eye positions longer than 100 ms (Version 1. 5. 0, Eyelink 1000 User Manual) with time points of blinks being removed from the data. If participants fixated each agent for 200 ms or more, i. e. spend at least 200 ms on the side of the screen on which the respective agent or Noise was presented, a trial was declared as 'valid'.
fMRI acquisition and data analysis In the fMRI study, the main part of the experiment took place inside a 3T MR scanner (MR750; GE, Milwaukee, USA). For design efficiency reasons  (Henson, 2007) , we added 35 null trials of 4 s duration to the design prolonging the ITI and representing our baseline trials. The experiment comprised a single functional run of 791 volumes of 40 slices {32-channel head coil, AC-PCorientation, 96 × 96 matrix, 3 × 3 mm voxel size, 3 mm slice thickness, 0. 5 mm slice gap, echo planar imaging [repetition time (TR) of 2000 ms, echo time (TE) of 20 ms, 90 • flip angle]}. By removing the first nine volumes, we controlled for T1 nonequilibrium effects. FMRI data pre-processing and analysis were performed in SPM12 (Statistical Parametric Mapping Software, Wellcome Department of Imaging Neuroscience, London, UK, http://www.
Voxel-wise maximum likelihood estimators were estimated thereby considering the temporal autocorrelation of the data  (Kiebel and Holmes, 2004). On the first level, we included six motion regressors and two regressors capturing the first principal component of confounding signal from white matter (WM) and cerebrospinal fluid (CSF), which on average accounted for 86% (s. d. = 3%) and 79% (s. d. = 5%) of the variance in the signal from WM and CSF, respectively (Caballero-Gaudes and Reynolds, 2017). For this, we obtained a binarized mask from the respective structural images using a 0. 95 threshold in SPM's image calculator (imcalc) and performed a principal component analysis of signals in WM and CSF for each participant. Finally, invalid trials were captured by a regressor of no interest. On the second level, a flexible factorial design was set up, analyzing condition effects on the group BOLD contrast.
Results: The initial database searches identified 311 articles, 149 of which we removed as duplicates. We analyzed the resulting set of 26 unique studies that met the inclusion criteria. Of these, 8 studies were randomized controlled trials, 13 were nonrandomized studies, and 5 were clinical trials with only 1 participant. Most articles focused on clinical populations (19/26, 73%), with the remainder reporting case-control studies (7/26, 27%). Most of the studies used visual immersive equipment (16/26, 62%) with a head-mounted display (15/16, 94%). Two main areas of interest emerged from these studies: virtual work on patients' body image (7/26, 27%) and exposure to virtual food stimuli (10/26, 38%).
Search Strategy We extensively searched PubMed, PsycINFO, Science Direct, the Cochrane Library, Scopus, and Web of Science up to April 2017. We based the list of keywords on two domains: virtual reality and eating disorders. A search strategy was constructed using the Boolean operators AND and OR and applied in the Medical Subject Heading, title, and abstract fields. The keywords and search strategy we used were (virtual reality OR virtual environment OR immersive reality) AND (eating disorders OR anorexia OR bulimia OR disordered eating OR binge eating disorder) in any language, but referenced in the selected databases and meeting the inclusion criteria. To limit the risk of selection bias, we applied no restrictions in terms of article type or clinical population. We did not include serious games, defined as video games with a pedagogical, diagnostic, or therapeutic interest, in the search terms because these do not necessarily rely on immersive VR technology.
Results Figure  1  shows the PRISMA flowchart summarizing the stages of the review. The initial database searches identified 311 articles, 149 of which we removed as duplicates. Following review of the titles and abstracts, we excluded 119 articles. We downloaded the remaining 43 articles for full-text review, following which we excluded an additional 17 articles: 7 articles presented a protocol without results; 3 studies evaluated healthy participants; 2 articles were not entirely in English; 2 articles reported studies already included; 2 studies reported preliminary data; and 1 study described a serious game without VR. We analyzed the resulting set of 26 unique studies that met the inclusion criteria. Multimedia Appendix 1  presents the results of this review in a descriptive way and in the form of a table.
Data and analysis Reachability estimates made for the 31 targets were analysed separately for the pretest and posttest, with all estimates being corrected for actual arm length. The boundary of peripersonal space was determined using a maximum likelihood fit based on the second-order derivatives (quasi-Newton method) to obtain the logit regression model that best fitted the reachable/ unreachable responses (see  [21]  for details). The logit regression model was defined by the equation: y = e (α ± β X) / (1+ e (α ± β X) ), in which y was the (reachable, unreachable) response, X was the crossing distance, and (-α/β) was the critical value of X corresponding to the transition between reachable and unreachable stimuli, thus expressing the perceived boundary of peripersonal space. The goodness of fit in logistic regression was estimated through R coefficient to check how well the model fits the data.
Data and analysis Reachability estimates made for the 31 targets were analysed separately for the pretest and posttest, with all estimates being corrected for actual arm length on a per-participant basis. The boundary of peripersonal space was determined as in Exp. 1, using a maximum likelihood fit based on the second-order derivatives to obtain the logit regression model that best fitted the reachable/unreachable responses. The goodness of fit in logistic regression, the discrimination threshold and reaction time were computed as in Exp. 1. The boundary of peripersonal space, the discrimination threshold and the goodness of fit in logistic regression were analysed using a repeated measures ANOVA on Session (Pretest, Posttest). Reaction time was analysed using a Session (Pretest, Posttest) × Space (Peripersonal, Boundary, Extrapersonal) ANOVA, with repeated measures on both factors.
The target-selection task was analysed as in Exp 1. We computed the location of the targets selected across the manual reaching actions. This was done by plotting the number of times each target location on the touch-screen table was selected relative to the total number of targets (density map, considering each participant's location), pooling the data from the two proximal, the two central and the two distal rows. Statistical analysis was performed using a Participant location (A, B) × Space (Proximal, Central, Distal) Chi-squared test. In order to precisely compare the performances of participants depending on their location (A, B), we computed the number of times each participant selected a target, as well as the number of successful actions (reward-yielding targets), in the proximal and distal hemi-spaces.
This work was funded by the  French National Research Agency  ( ANR-11-EQPX-0023 ) and also supported by  European  funds through the program  FEDER SCV-IrDIVE  to YC. FQ was supported by a PhD grant from the  University of Lille and Region Hauts-de-France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data sources All data were obtained retrospectively. Data related to SH prescription during the stay were obtained from the Computerized Physician Order Entry. Data related to patient characteristics (age, gender), medications taken before hospitalization, reason for admission, length of stay, SH prescription at discharge and type of discharge were obtained from the hospitalization report which is a mandatory document that contains all these data and is inserted in the medical chart at the end of the stay. These data were collected by a trained pharmacist in each center, using a standardized collection grid (Additional file 1). Data related to hospital organization were collected by a trained pharmacist in each center and obtained from the head nurse of each unit, using a standardized collection grid (Additional file 2).
4: c. 1762C > G and NM_018122. 4: c. 563G > A; p. (Leu588Val) and p. (Arg188 Gln), respectively). The variants were predicted as pathogenic using three pathogenicity prediction tools (SIFT  [5] , PolyPhen-2  [6]  and MutationTaster  [7] ). The variant c. 1762C > G (rs972404343) was reported as "likely pathogenic" in the ClinVar database  [8] ; it was absent from both the ExAC and gnomAD databases  [9]. The second variant, c. 563G > A (rs182811621), was not reported in the ClinVar database but had very low allele frequencies in the ExAC and gnomAD databases (0. 00001 and 0. 000004, respectively)  [9]. Sanger sequencing (Fig. 2 ) identified the father and mother as heterozygous carriers of the single variant c. 1762C > G and c. 563G > A, respectively, and validated the presence of both variants in patients 2043 and 2044.
Experimental design. This study has a 2 (BJW spheres: personal/ general) X 2 (self-presentation goals: to convey a positive/ negative image) X 4 (contexts: bank/ trade union/ NGO/ institution) X 3 (country: France/Germany/ Portugal) mixed design, with the former and the latter factors as between-participants, and self-presentation and contexts as betweenparticipants. Procedure. University students were invited to take part in the study on a voluntary basis. In France data were collected during classes at the University of Rouen, in Germany they were collected in the campus of Martin Luther University of Halle-Wittenberg, and in Portugal they were collected in various universities in Lisbon. In the latter two cases the experimenter (a male in Germany and a female in Portugal) approached participants and asked them whether they were interested in taking part in a study.
Following  Alves and Correia (2008, Study 2) , we operationalized the expression of: high BJW-P by marking four items as "completely agree" and three as "agree" (points 1 and 2); moderate BJW-P by marking four items as "somewhat agree" and three as "somewhat disagree" (points 3 and 4); low BJW-P by marking four items as "disagree" and three as "completely disagree" (points 5 and 6). Procedure. The procedure was the same as that used to collect data for the previous studies in Portugal. As far as the study itself is concerned, we again used the judge paradigm but included some changes already presented in the introduction to this study (e. g. , focus on BJW-P at a trade union, between-participants design, inclusion of measured intervening variables). Measures. Manipulation check. In order to check whether participants interpreted our manipulation of BJW-P expression as intended, they responded to the same item as in Study 2.
The case of BJW-P expression at a trade union deserves a few lines. Indeed, Studies 1 and 2 indicated that the expression of BJW-P was neither valued (i. e. , normative) nor devalued (i. e. , counter-normative) in the three countries. In Study 3 we introduced changes that could potentially explain that pattern, among which the same operationalization of high and low BJW-P expression as in  Alves and Correia (2008). With this less extreme operationalization in comparison to that used in Study 2, participants attributed more social value to the target who expressed high versus low (and moderate) BJW-P. Although we did not manipulate orthogonally the two operationalizations, which would allow us to directly ascertain their influence on participant social value judgments, with the data available we think that BJW-P operationalization is the most plausible explanation for the difference.
Stimuli We used 16 Caucasian faces (eight female) and 16 rhesus (16 female) macaque faces to make up the set of face pairs used in the experiments. A total of 12 rhesus macaque faces came from a rhesus cohort in the Laboratory of Comparative Ethology, and four faces came from the PrimFace database (http://visiome. neuroinf. jp/primface). All the faces were frontal views with open eyes and neutral (mouth closed) expression (see Figure  1  for an illustration of the faces used in the experiment). Macaques were presented with opposite sex faces only. Presenting the face of an unfamiliar own-sex conspecific could signal the presence of an out-group individual (i. e. , potential rival), thus creating a situation of increased vigilance resulting in longer looking time  (Pfefferle, Kazem, Brockhausen, Ruiz-Lambides, & Widdig, 2014; Schell, Rieck, Schell, Hammerschmidt, & Fischer, 2011).
Micro-computed tomography. Ectopic bone formation (tibia and fibula) and trabecular bone parameters (tibia and femur) were measured using microCT analysis (Skyscan 1076 Brucker, Belgium) set at 50 kV and 200 mA. Skyscan NRecon program was used to reconstruct the images and then analysed using Skyscan CTAn (Brucker, Belgium)  45. Bone histomorphometry. Bone histomorphometry was performed on the proximal tibial metaphysis from mice as previously described  46. In short, bones were fixed, sectioned using a Leica microtome (Solms, Germany) and tissue sections were stained with tartrate-resistant acid phosphatase (TRAcP) and haematoxylin and eosin (H&E). Histomorphometry was performed on trabecular bone distal from the growth plate (0. 1 and 1 mm distal to the growth plate). Bone histomorphometrical analysis was performed as previously described  46.
Rabbit anti-Semaphorin3A and anti-Nrp1 monoclonal antibodies were purchased from Abcam (UK). Immuno-complexes were visualised by a chemiluminescence detection kit (BioRAD) using horseradish peroxidase-conjugated secondary antibody (Jackson laboratories), and visualised using chemiluminescence on a Chemidoc Imaging system (BioRAD). Bands were quantified using Image Lab Software (BioRAD) and level of actin (Sigma-Aldrich, UK) was used for normalization. Protein expression in conditioned medium. Sema3A expression in conditioned medium was measured by western blot. Cells were incubated for 16 hours with serum free medium. Conditioned medium was collected and concentrated using Pierce Protein Concentrators, 9 K MWCO (Thermofisher). Protein concentration was measured and protein was resolved by SDS page.
3 platform). Image overlays were done using the ImageJ software. [Ca 2+ ] m measurements HEK293T cells were transfected with wild type and mutant mitochondrial GFP-aequorin (mtGA wt , mtGA mut ) for 48 h in a clear bottom, white-walled 96-well plate (Greiner Bioscience, Frickenhausen, Germany). Briefly, mtGA constructs were reconstituted with native coelenterazine (Biotium, VWR Technologies, Darmstadt, Germany) in medium for 2 Cells were then washed with PBS followed by addition of 100 µL internal buffer (140 mM KCl, 1 mM KH 2 PO 4 , 1 mM MgCl 2 , 10 mM glucose, 0. 1 mM EGTA, 20 mM HEPES, 8 mM Na-succinate, 4 mM Na-pyruvate, 4 mM MgATP). Ca 2+ uptake was initiated by addition of 50 mM CaCl 2 or 500 µM carbachol in internal buffer supplemented with 2. 5 mM MgEDTA following background measurement for 5 s.
The qPCR was performed in an Eco Illumina (Illumina, Eindhoven, Netherlands). For analysis, the LinRegPCR software  43  was used to calculate N0 values which were normalized to N0 of Gapdh as an internal control. Data are acquired in duplicates from three independent experiments. The following murine primers were used: Chop (5′ CAT ACACCACCACACCTGAAAG 3′ and 3′ CCGTTTCCT AGTTCTTCCTTGC 5′) and Gapdh (5′ GGAGAGTG TTTCCTCGTCCC 3′ and 3′ ATGAAGGGGTCGTTG ATGGC 5′).
The DNA product is covalently attached to one of the proximity probes to allow detection of hybridized Texas redlabeled oligonucleotide probes. Each red fluorescent dot represents a site of IP 3 R1-VDAC1 interaction. Duolink II mounting medium containing DAPI 18 (Sigma Aldrich, Munich, Germany) was used for mounting, and preparations were analyzed with a Zeiss inversed fluorescent microscope at 63× magnification. Quantification of signals (number of red dots per cell) was done using the BlobFinder software. Experiments were performed in triplicate, n = 25-30 pictures per conditions.
Acknowledgements We would like to thank  Prof. Györgyi Hajnoczky  for kindly providing the ER-mitochondrial linker plasmids,  Prof. Ben N. G. Giepmans  for providing the ER-Flipper-GFP plasmid, and  Dr. Maria Alonso  for providing mitochondrial GFPaequorin constructs. Furthermore, we would like to thank  Prof. Dr. Moritz Bünemann  and  Prof. Dr. Cornelius Krasel  for providing HEK293T cells,  Prof. Frank J. Dekker  and  Dr. Nick Eleftheriadis  for providing CyPPA, and  Klaas Sjollema  for his technical support on live-cell imaging. In addition, we acknowledge  Lena Hoffmann  for performing the osmolarity measurements. This work was supported by a grant from the  Deutsche Forschungsgemeinschaft, DFG  ( DO 1525/3-1 ). A. M. D. is the recipient of a  Rosalind Franklin Fellowship  co-funded by the  European Union  and the  University of Groningen.