dataesr/scientific-paragraphs-categorization huggingface dataset:
participants. We tested two groups of participants: 24 children who are prelingually deaf with unilateral implants (M age = 9. 24 years, SD age = 1. 21), and 24 NH children with a similar age distribution (M age = 8. 81 years, SD age = 0. 86), as shown in Table  1. We initially recruited 30 implanted children, but six failed to complete the task, owing to a lack of attention or difficulty performing the task. It was not possible to obtain any data from these six participants, and they were therefore excluded from the dataset. None of the 30 children with CIs presented any disorders associated with deafness, and all were monitored by the paediatric cochlear implant programme in Toulouse. All 24 successfully tested children had been fitted with a conventional 22 electrodes CI, except for one who had been implanted with a CI 24 electrode array.
Materials. All participants were assessed with an auditory FST presented via the open-source software TCL-LabX 32 , as described in previous studies  27, 28. We selected 18 sounds from a larger set of 45 taken from a web database (http://www. freesound. org/). They were chosen to cover a broad range of everyday sounds (Table  2 ) that corresponded to four main a priori categories: nonlinguistic human vocalizations, environmental sounds, musical instruments, and animal vocalisations. Having a different number of stimuli in a priori categories enhanced the objectivity of the categorization, as it avoided the natural tendency to create groups containing equal numbers of items (i. e. , the arithmetic strategy of dividing the stimuli into two groups, then each of these into two subgroups is discouraged by the unpredictable number of stimuli in each one).
In both cases, the intensity/volume was adjusted to 65 dB SPL. The hearing deficit of the non-implanted ear was 87-120 dB in our patients so that residual hearing could not influence the results at this level of sound presentation. Using TCL-LabX software, the 18 sounds were represented on the computer screen by 18 numbered and coloured squares that were initially identically arranged for all participants. The task consisted in listening to the 18 sounds and placing them in groups (i. e. , creating categories using any means and criteria). Participants double-clicked on each square with the computer mouse to hear the corresponding sound, and categories were created by dragging and positioning squares together on the screen. Participants were instructed to "Group the sounds that go well together.
F0, an acoustic parameter, is a correlate of periodicity pitch, a perceptual parameter, and for brevity's sake we sometimes refer to these features as frequency, max frequency, and so on. Detailed descriptions of these acoustical properties are provided in Collett et al. 27  and acoustic properties of each presented sound are provided in Supplementary Table  1. The acoustic analyses were identical to Gygi et al. 25  using their MATLAB scripts. statistical analysis of Fst data. Category Analysis. To analyse the categories created by the participants, we used two different functions in R Core Team software. First, multiple correspondence analysis (MCA) was applied to an indicator matrix yielded by the TCL-LabX software. MCA is an extension of correspondence analysis (CA) that allows data containing more than two categorical variables to be summarized and visualized.
It can also be regarded as a generalization of principal component analysis (PCA), when the variables to be analysed are categorical instead of quantitative. The MCA used CA to represent each sound as a data point in an n-dimensional Euclidean space, based on categorical values (i. e. , categories created by the participants). Each dimension was chosen to account for the largest amount of variance possible within the dataset, and dimensions were listed in descending order of explained variance. The MCA also performed an analysis on participants, to determine how strongly individual results coincided with the dimensions, thereby allowing us to determine the similarity of participants' categorization strategies  34. A total of 10 dimensions were used in the analysis, and we retaine those that explained 5% or more of the total variance.
This led to the first five dimensions being used for hierarchical clustering based on principal components. We focused on the two most significant dimensions (Dimensions 1 & 2), as they accounted for the greatest variance in the data. Hierarchical Clustering Analysis. Based on MCA, hierarchical clustering based on principal components (HCPC) computes hierarchical clustering, and then performs k-means clustering. A form of partitioning clustering, k-means clustering is used to split a dataset into several groups. HCPC allowed us to visualize the hierarchical structure of classification in the forms of dendrograms, and thus complemented the MCA visualisation of the subjective importance of categorization features. When using this analysis, it is not possible to account for all the variance (inertia) within the data (i.
5% in an unstable one (Response category 1B). These results show that children who are deaf with CIs have considerable difficulty Figure  5. Word clouds of category descriptors. Word clouds show the words that were most used by participants to describe the categories they created in the FST (using Python package Wordcloud). The size the words reflects the frequency with which they were used by both groups (NH and CI). It should be noted that this figure does not reflect differences in the frequency of word usage between the groups (children with CIs generally used the words less frequently). For example, the word most frequently used by the children with CIs was cough, but they used it 30% less frequently than the NH children. identifying everyday sounds. While not precisely quantified, our previous study showed that age-matched NH children were able to identify all the sounds except for rustling paper  28.
Acknowledgements We would like to thank all the participants and their parents, as well as the staff at the  Paediatric Cochlear Implant and ENT Departments of Purpan University Hospital  for helping to host and arrange the testing. This study was supported by  Purpan Hospital's Clinical Research and Innovation Department (DRCI ,  AOL 2011 ), recurrent funding from the  CNRS  and the  University of Toulouse (Octogone-ECCD lab) , a doctoral subsidy to  E Collett  ( Advanced Bionics SARL, France ), and  Agir pour l' Audition  (# APA-RD2015-6B ).
Relative expression of D1R and D2R mRNA in GPR88-positive cells Image acquisition was performed using the slide scanner Olympus VS120 (Olympus Corporation). Regions of interest (ROIs) were selected using Olyvia software (Olympus) and saved as PNG files. Three brain regions where analyzed: rostral CPu (from 1. 42 to 0. 98 mm from bregma), caudal CPu (from 0. 98 to -0. 58 mm from bregma), and nucleus accumbens (Nacc; from 1. 42 to 1. 10 mm from bregma). For the CPu (rostral and caudal), at least four ROIs were selected: two for the dorso-lateral striatum (DLS) and two for dorso-median striatum (DMS). Counting was balanced between right and left hemispheres. To evaluate expression of D1R and D2R mRNA in GPR88-expressing cells, counting was performed manually using the FIJI (ImageJ) cell counter.
Behavioral experiments For all behavioral measures, mice from different mouse lines and genotypes were tested in a random order and data were analyzed blind to genotype. However, to avoid order-related variability between mouse lines and genotypes, mouse lines were stratified so that each session included both genotypes from all lines. One mouse cohort (N ϭ 8 CMV-CTL, N ϭ 10 CMV-Gpr88; N ϭ 10 A 2A R-Gpr88, N ϭ 10 A 2A R-CTL; N ϭ 10 D1R-Gpr88, N ϭ 14 D1R-CTL) was used to measure marble burying and social interaction. Independent cohorts of mice (N ϭ 21 CMV-CTL, N ϭ 21 CMV-Gpr88; N ϭ 10 A 2A R-Gpr88, N ϭ 17 A 2A R-CTL; N ϭ 13 D1R-Gpr88, N ϭ 12 D1R-CTL) underwent 5 d of open field locomotion followed by a 48-h resting period before 7 d of rotarod motor skill learning.
Social interaction test Social interaction was assessed, as previously described  (Meirsman et al. , 2016a) , in an open field (50 ϫ 50 cm) dimly lit (Ͻ10 Lux) using naive wild-type mice of the same age and weight as interactors. On the first day, all mice were individually placed in the open field arena and left for a 10-min period of habituation. The next day, mice were placed in the open field arena with a wild-type naive interactor and a 10-min session was recorder. Nose contacts were measured manually using video recordings. If an interactor failed to engage in any interaction data from the respective mice were exclude from analysis.
Rotarod The first day, mice were placed on the rod (30-mm plastic roller, Panlab Harvard) at constant speed of 4 rpm. until achieving 90 s without falling from the rod (habituation; data not shown). On the six next consecutive days, mice were placed on the rod accelerating from 4 to 40 rpm in 5 min and the remaining at maximum speed for the next 5 min for four trials every day. Light intensity in the room was inferior to 10 Lux. Mice rested a minimum of 1 min between trials to avoid fatigue and exhaustion. When mice hang on the rod instead of running, they were left for one complete turn but the timer was stopped if the mice engaged in a second consecutive turn. Animals were scored for their latency to fall (in seconds) in each trial. Mean values of four daily trials were used for statistical analysis.
D1R-Gpr88 mice show Gpr88 mRNA deletion in D1Rexpressing neurons To conditionally delete Gpr88 exon 2 in cells expressing D1R, mice carrying two LoxP sites flanking the second exon of the Gpr88 gene  (Meirsman et al. , 2016b)  were crossed with mice expressing the Cre recombinase under the control of the Drd1a gene promoter (Gensat). We first tested whether GPR88 transcript and protein are reduced in the striatum. We quantified Gpr88 mRNA levels by RT-qPCR for CPu and Nacc from D1R-Gpr88 and their control littermates. As shown in Figure  1A , Gpr88 expression was significantly decreased in striatal regions of conditional KO compared to controls (CPu: t (16) ϭ 3. 01, p ϭ 0. 008; Nacc: t (13) ϭ 4. 19, p ϭ 0. 001; Table  1 ). Testing Gpr88 mRNA levels in the hippocampus and amygdala showed a milder but significant reduction in hippocampus There was no significant difference in D1R expression levels across genotypes (data not shown).
Quantitative data We measured the feasibility and acceptability of the MBSR program in terms of attendance, adherence and dropout rate. The Numerical Rating scale (NRS) was used to rate pain on a scale from 0 to 10 where 0 indicates "No pain" and 10 "The worst possible pain" [ 38 ]. The profile of Mood States (POMS) test, consisting of 40 items, clustered into 6 domains (anger, confusion, depression, fatigue, tension, vigour), was used to assess changes in participants' mood [ 39 ]. Questionnaires were self-administered before (T0) and at the end of the intervention period (T1), at 2 (T2) and 4 (T3) months post-intervention.
Qualitative data At the end of the study, the clinical psychologist conducted semi-structured, in-depth interviews with 8 participants at the oncological outpatient clinic. The list of key questions is reported in Table  4. To answer the research question we followed a purposive sampling procedure based on performance status; we stopped interviews upon reaching data saturation. The clinical psychologist gave adequate provisions to protect respondent privacy and maintain confidentiality to participants; he also informed them that they could stop the interview at any time if desired. The questions were open-ended to allow participants to share what was most relevant to them. Interviews were recorded with a Zoom H1 recorder, providing sufficient audio quality voice files that were later transcribed verbatim.
Quantitative data We examined Baseline characteristics and quantitative outcomes using descriptive statistics. We examined changes in NRS measures across the study time-points using the Wilcoxon signed-rank test (p<0. 05). POMS scales were analysed using a repeated measure ANOVA, using R and nlme packages [ 40, 41 ]. Statistical analyses were conducted by STATA software (version 12. 0; STATA, College Station, TX).
Psychologist, physician, and nurse all participated as volunteers without being paid. This may raise concerns regarding feasibility in other settings as well as cost and scalability in other settings. The small sample limits the generalization of our results, which consistently with the IPA approach focuses on the idiographicidiosyncratic character of the data [ 43, 44 ]. The recruitment procedure excluded non-performant patients. Our results pertain to an upper-middle class group composed by Italian women with metastatic cancer and good performance status. Programs delivered in participants with lower performance status should consider online meetings options to make sessions accessible from home. Recruitment procedures limit the outcomes of this study as we included only patients referring to EPC inpatient facility, therefore able to care for most of their personal needs and able to understand study proposal.
Acknowledgments The authors wish to thank all the patients for their contribution to the study. Association  Asia Modena  for providing participants with exercise notebooks and CDs during the study and mindfulness books at the end. Association of  Malati Oncologici  ( AMO Carpi ) for providing study locations. Fondazione Cassa di Risparmio di Modena  for funding part of the research. RF, SP, MPR wish to thank their meditation teacher  Franco Bertossa  for his teaching and for inspiring this work.
METHODS A retrospective analysis was performed of the medicopsychological response of the first two weeks at the Children's Hospitals of Nice CHU-Lenval (Hôpitaux Pédiatriques de Nice CHU-Lenval) to the July 14th, 2016 attack. Qualitative and quantitative data were collected for the period of July 14-28th, 2016 from: real-time and time-stamped (1) hospital admission records, (2) computerized and handwritten logs, (3) handover protocols, and (4) from patients' charts and ORSAN-forms (a standardized disaster plan medical record; see Box 2. 1) for a review. Cross-checking interviews of key informants (on-scene team members) completed data collection when necessary. Collected data concerned clinical consultations (age, sex, date), the medico-psychological emergency cell organization (number and type of professionals) as well as group debriefing (type, date).
ORSAN plan (plan ORSAN): the nationwide French emergency plan to face the sudden healthcare needs due to an accident, a disaster, an epidemic or a serious climatic event. ORSAN stands for Organisation de la Réponse du système de SANté en situations sanitaires exceptionnelles (organization of the health system response in exceptional healthcare situations). Defined in 2014 under its current form, the ORSAN plan had been activated two times before the Nice attack: during the influenza epidemic of winter 2014-2015, and during the November 13th, 2015 terrorist attacks in Paris. When the ORSAN plan is activated, medical information about each patient rescued is recorded on a standardized form (ORSAN form). It comprises personal data (name, age, address, phone number, email), informations about the traumatic event (date, type, implication, duration) as well as clinical evaluation (symptoms, severity).
ACKNOWLEDGMENTS We thank all health care professionals implicated in the medico-psychological emergency unit for its availability and collaboration during this very challenging time after the Nice attack. We notably underline the support by the different intersectors of infant-juvenile psychiatry of the department  Alpes-Maritimes ,  CUMP 06  as well as the different CUMPs of other parts of France (notably CUMP 13, 67, 83 and 93). We are also very grateful to the  Hôpitaux Pédiatriques de Nice CHU-Lenval  and  Agence Régionale de Santé  for administrative support. This study has been financially supported by  Fondation de France  and the  French Society for Child and Adolescent Psychiatry and Allied Professions (SFPEA-DA).
Method Corpus. The data come from the LANGSNAP corpus (http://langsnap. soton. ac. uk/, e. g. ,  Mitchell, Tracy-Ventura, & McManus, 2017). During the initial phase of the project (see Tracy-Ventura & Huensch, 2018, for details on subsequent phases of the project), data were collected six times over a period of 21 months. At each data-collection point, the participants completed two oral tasks, a semi-structured interview and a picture-based narration, and a written argumentative essay. For the current study, we have examined all tasks from three of the six data-collection points. Pre-stay was the first data-collection period in the corpus (May 2011). The data for the third in-stay visit (henceforth, in-stay) were gathered at the end of the academic year abroad, which was a year after pre-stay (May 2012).
Data Coding and Analysis We began our coding by isolating as a separate token every instance of a determiner and/or adjective that modified a referent (K = 16,357). 7 The current dataset consists of modifiers that show overt gender marking and that modify a noun located in the immediate clause or one of the preceding 10 clauses  (k = 11,846). Examples are provided in  (1) , with the noun in bold and modifiers underlined. (1) En vez de vivir en unmasc entornomasc que no sea seguromasc (pre-stay, essay, participant 150) 'Instead of living in anmasc environmentmasc that is not safemasc. Creo que no tuve muchasfem problemasmasc (post-stay, interview, participant 165). 'I believe that I don't have manyfem problemsmasc. ' The dependent variable was whether the gender marking of a modifier was targetlike (the same gender as the noun) or non-targetlike (a different gender from the noun).
For the second and third variables, we explore the role of noun frequency in the use of gender marking. Previous research by  Sabourin, Stowe, and de Haan (2006)  has reported that additional-language learners of Dutch show higher rates of targetlike gender marking with higher frequency nouns on a grammaticality judgment task, suggesting that noun frequency may impact gender-marking behavior. However, there are different ways of operationalizing and measuring frequency (e. g. ,  Linford, Long, Solon, & Geeslin, 2016) ; in the current project, we include two measures. First, the variable noun log-frequency (language) corresponds to the log of the frequency (per million words) with which each noun in our dataset occurred in Modern Spanish, as determined by the Corpus del español  (Davies, 2016-).
Subsequently, we ran one generalized linear mixed model (see the Online Supporting Documentation for the code we used in SAS). This statistical test analyzed multiple factors simultaneously in the data and determined what factors influenced gender-marking behavior. For the dependent variable and the nominal fixed effects listed in Table  1 , the categories of a variable were compared to a reference point. For the dependent variable, targetlike behavior was the reference point. The reference points for the nominal fixed effects were as follows: masculine (noun gender), canonical -o/-a endings (noun ending), determiner (modifier type), grammatical (noun class), singular (noun number), essay (task), and pre-stay (time). Syllable distance, noun frequency (individual), noun log-frequency (language), and initial proficiency are continuous factors so there was no reference point.
After identifying the fixed effects that significantly predicted targetlikeness, we explored interactions between time and the other significant fixed effects. We used a top-down (also called backward selection) strategy to incrementally remove non-significant and spurious interactions. This approach (i. e. , coupling bootstrapping and a top-down strategy) ensures that we do not overfit the model with spurious effects. Because the bootstrapping step considers many distinct datasets, we avoid including variables that may have shown misleading significance from the full dataset.
The need for analyses of longitudinal datasets is not restricted to LCR, having also been a need identified in SLA more generally. As stated by  Ortega and Iberri-Shea (2005, p. 26)  We see evidence that scholars are moving in this direction and believe there is value in continuing to do so. For instance, in an empirical investigation,  Kanwit (2017)  draws connections between concept-oriented approaches and variationism in order to offer new insights on the acquisition of future-time reference in Spanish (see  Edmonds, Gudmestad, & Donaldson, 2017 , for a related discussion). In another example, researchers have brought together psycholinguistics and variationism to further knowledge on language processing (see  Boland, Kaan, Valdés Kroff & Wulff, 2016 , for an introduction to a special issue on this topic).
10. We also considered including a random effect for lexical item in the analysis. However, because our dataset consists of 1223 different lexical items, 494 of which only occur once in the dataset, including a random effect for lexical item was not feasible. Bolker, Brooks, Clark, Geange, Poulsen, Stevens, and White (2009, p. 128 ) explain that computing an integral for all individual random effects becomes "computationally infeasible" when there is a large number of random effects. 11. While we had intended to use R for the entire analysis, we switched to SAS for the regression model for two reasons. By using SAS for the generalized linear model, we were able to explicitly specify a covariance structure regarding the fixed effects that accounts for the fact that observations are correlated both between and within participants.
Population We included all gamblers who self-excluded for the first time over a 7-year period from June 2010 up to October 2016 (n = 4887) on a poker website, Winamax ®, and1:1 matched gamblers who had never self-excluded up to the time of data collection, matched for gender, age and account duration automatically extracted from the account database following a structured query language (SQL) request. For technical reasons we could not match gamblers for the level of gambling involvement in terms of money/time, which were constructed variables not available from the SQL database. From the matched control group, we removed doubloons where one and the same gambler was matched to several self-excluders (n= 436). In France, self-exclusion is a voluntary process; its duration is fixed by the player from 1 day up to a maximum of 3 years.
Measures We collected data retrospectively from different prospective databases systematically recorded by the gambling service provider: (a) Gambler data: self-excluders' basic demographics (gender, age, date of opening of the account), characteristics of self-exclusions (date, duration) and detailed gambling variables in the month prior to self-exclusion: cash game winnings, prize amounts for tournaments, buy-in-plus-rake for tournaments, session characteristics (starting date, end date, duration). Sessions were defined as gambling with no period under 10 min without action. This measure was based on our clinical experience and on the information provided by the provider of no systematic disconnection when leaving from the website or the application on wireless devices especially. This measure was built on the experience of difficulty in extracting and interpretation of sessions duration when taking into account connection time only in a previous study  [14].