dataesr/scientific-paragraphs-categorization huggingface dataset:
EMG data EMG signals were filtered with a bandpass filter (4th order Butterworth) between 20 and 450 Hz. Linear envelopes for each muscle were obtained by low-pass filtering the fully rectified EMG signals with an 9 Hz low-pass filter (zero lag). The same 30 consecutive cycles used for mechanical data were extracted and averaged to obtain a representative pattern for each muscle. The mean value over this pattern was considered as the mean EMG activity level. Mean values corresponding to four functional angular sectors of the pedaling cycle were also calculated  (Hug et al. , 2008) : sector 1 represented 330-30 • (top dead center), sector 2 30-150 • (downstroke phase), sector 3 150-210 • (bottom dead center) and sector 4 210-330 • (upstroke phase) (with 0 • corresponding to the highest position of the pedal).
Body Weight, Food Intake and Total Lipids Because enriched housing in Marlau TM cages started at weaning in our paradigm, we first verified whether housing conditions (enriched compared to conventional) altered body growth. We show that body weight gain in rats raised in Marlau TM cage (enriched group, En) was greater than that of rats raised in conventional cages (conventional group, Cv): +12. 9% (p,0. 001, Student's t-test on the slope) from day 28 to day 63 (Fig. 1A ). The body weight gain in En rats was associated with greater food intake (+7. 4% from weeks 1 to 6 in Marlau TM cages, data not shown), with no modification of total body lipid proportion (Fig. 1B ). The body weight gain in En rats was confirmed in the other experiments: +11. 5%, +9. 8%, +10. 8 and +11. 2% in experiments 2, 3a, 3b and 4, respectively.
Morris Water Maze (MWM). A circular tank (180 cm diameter, 60 cm high) was filled with water maintained at 25uC to a depth of 40 cm. The water was made opaque by addition of black gouache, which prevented visualization of the platform by the rat. All visual clues around the room were kept in a constant location from day to day. The pool was divided into 4 virtual quadrants defined as North (N), East (E), South (S), and West (W). A circular platform (10 cm diameter) was hidden 1 cm below the surface of the water, and was kept at a constant position within the northern quadrant, close to the NW border. Rats were tested for a total of 16 trials performed during 4 consecutive days. Each day, rats were subjected to 4 trials, at 2-hour intervals, each trial lasting 90 sec. Rats were videotracked by a camera positioned on the ceiling just above the center of the tank, using software (Videotrack, Viewpoint) that calculated the distance and the time to find the platform.
Data were acquired and analyzed using a Digidata 1440A interface and pClamp 10 software (Molecular Devices). Capillary glass pipettes filled with ACSF and connected to an Iso-Flex stimulus isolation unit (A. M. P. I. ) were used to stimulate presynaptic axons in stratum radiatum (120-150 mm away from the soma). Stimulation at 0. 05 Hz was used to establish baseline synaptic responses. The stimulation strength was set to evoke EPSPs between 5-8 mV. LTP was induced using the theta burst pairing (TBP) protocol, which was delivered within 20 min after whole-cell formation to prevent the washout of LTP induction. Back propagating action potentials (b-APs) were elicited by direct somatic current injection (1 ms, 1-2 nA). The standard TBP protocol consists of EPSPs paired with a single b-AP timed so that the b-AP (,15 ms delay) occurred at the peak of the EPSPs as measured in the soma.
Statistical Analysis Data are expressed as mean 6 SEM of the different variables analyzed. For body weight data, we calculated individual growth taking the body mass at the age of 28 days as reference point. For each rat, the value of each of the weightings has been expressed as a percentage of the value measured at the age of 28 days. Since a significant interaction has been found between ''housing conditions'' (Cv and En) and ''age'' using two-way repeated measures ANOVA, body weight gain was plotted against age and linear regression were established for each group of rats (Cv and En). To determine whether the slope difference was significant, we established for each rat a linear regression between body weight gain and age and tested slope difference between the two groups of rats with a Student's t-test.
Acknowledgments We thank for their assistance:  F. Cohen-Adad  ( scientific documentation ),  M. Le Cavorsin ,  L. Faussurier  and  S. Tre  ´meau (animal care),  H. Koubi  (total body lipid quantification),  C. Moulin  (qPCR),  D. Ressnikoff  (confocal microscopy) and  G. Debilly  from the  Neurochem platform of the University Claude Bernard Lyon  1 (statistical analysis).
Those authors demonstrated that only patients with schizophrenia exhibited a significant relationship between QoL and performance on the hinting task (a verbal ToM task assessing the attribution of intentions behind indirect utterances). Moreover, that relationship was influenced by symptoms; a significant negative correlation was found between QoL and high ToM performance in patients with relatively high symptom levels. The results suggested that a poor QoL could reveal overall symptom consequences on the patient's life, and these consequences might be influenced by ToM skills, because this is related to insight  (Bora et al. , 2007). While these findings are extremely relevant, replication and refinement of the data description are needed. The goal of the present study was to investigate clinical symptoms and QoL items to determine which would reveal an underlying ToM impairment in patients with schizophrenia.
STUDY POPULATION The main study population consisted of 281 patients with schizophrenia (85 females, 196 males) that had been recruited during the baseline of a pharmaceutical protocol that included an antipsychotic medication switch. Inclusion criteria were: (1) aged 18-65 years, (2) exhibited criteria for schizophrenia based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), (3) taking a stable dose of a single antipsychotic therapy for at least 8 weeks, and (4) PANSS score above 45. A seen in the result section, a population of 206 patients was kept in the final analysis. This study was part of a multicenter, pharmacological, longitudinal trial conducted and supported by Lilly France ("Outcome" study, F1D-FP-S029). The authors had full access to all data required for the investigative use of the V-SIR and SCD tools, but remained blind regarding the study treatments (i.
STATISTICAL ANALYSES The statistical analyses were designed straightforwardly to explore whether patient characteristics were related to ToM performance (evaluated through V-SIR and SCD). For hypotheses I and II, we explored the data with Pearson's correlation coefficients. Relationships were tested between ToM and subjects' age, the CGI-S, the PANSS, or the S-QoL. Syndrome analyses were performed with a factor analysis on PANSS items. A varimax rotation was used to introduce the five factors into the model (this number was set empirically, based on the literature;  Van den Oord et al. , 2006). Correlations with these factors were tested. To prevent type I errors, we adopted a stringent 0. 001 threshold for all correlation coefficients examined. To assess the third hypothesis, we used a stepwise linear regression procedure that introduced the total S-QoL as the response variable and age, gender, V-SIR, SCD, and PANSS as the predictors following  Maat et al.
Individuals differ in their ability to deal with unpredictable environments. Could impaired performances on learning an unpredictable cue-criteria relationship in a laboratory task be associated to impaired learning of complex skills in a natural setting? We focused on a multiple cue probability learning (MCPL) laboratory task and on the natural setting of pilot training. We cumulated data on three selection sessions and on the three corresponding selected pilot student classes of a national airline pilot selection and training system. First, applicants took an MCPL task at the selection stage (N = 556; N = 701; N = 412). Then, pilot trainees selected from the applicant pools (N = 44; N = 60; N = 28) followed the training for 2. 5 to 3 yrs. Differences in final MCPL performance were associated to pilot training difficulties.
Logic of the studies The idea was to relate MCPL performance to pilot training outcome. The studies were carried out in an actual pilot selection and training context. The French Air Transport Pilot Training School, "ENAC", offers each year the opportunity to 20 to 80 young students to receive free theoretical and practical airline pilot training. In this organization, pilot selection comprises three steps: Written-academic tests (mathematics, physics, English), cognitive-ability tests and final tests (group exercises completed by individual interviews, and an oral English exam). Among those students, two thirds are eliminated at the first step (written-academic tests). After the final step, around 10% of the sample who took the cognitive-ability tests are selected for training. As the samples of pilot trainees were small, we collected data of three selection sessions.
Two studies were conducted: • First, an individual differences study was carried out on applicant data from three sessions. An MCPL task was added in an actual pilot selection context to assess individual differences in the ability to learn to adapt to unpredictable environments. Based on the MCPL literature, the majority of applicants were expected to perform such a task successfully. Therefore, pilot students who would perform poorly on the MCPL task might have some particular difficulty in dealing with uncertainty or with noisy information. • Second, an external validity study was performed on training data for the three corresponding selected pilot students classes. Final pilot training outcome was collected 2. 5 to 3 yrs after selection and coded as Success (those who succeeded the training without any problem) or Difficulty (those who received additional training hours or who failed).
Individual differences Study on Selection Data MCPL individual differences were studied using three selection sessions. Session 1: Low Uncertainty, Two Cues Session 1 was conducted during the 2006 pilot selection. Participants At the selection stage, 556 applicants took the MCPL task, all aged between 18 and 31 years old (M = 21. 0, sd = 2. 48) and 91. 2% male. Forty four pilot students (90. 9% male, M age = 20. 6) were admitted after the selection process. Task, apparatus and procedure The cue-criterion multiple correlation was high (R e =. 96). Cues were linearly related and individual ecological validities were positive,. 63 and negative, -. 72. The cue inter-correlation was <. 01. The whole task was limited to 10 minutes. The average number of trials completed was 58. 3 (sd = 5. 1). Six cognitive ability tests were administered before the MCPL task: A spatial ability test, a mechanical comprehension test, a perceptual speed test, a numerical ability test, a reasoning test and a divided attention test (see Appendix A for more details).
Session 3: Replication of Session 2 Session 3 was conducted during the 2010 examination. We chose to use exactly the same task as in Session 2, i. e. , with low uncertainty and three mixed cues. As a result, we could cumulate data of these three sessions for the external validity study. Participants At the selection stage, 412 applicants took the MCPL task, all aged between 18 and 30 years old (M = 21. 5, sd = 2. 71) and 91. 7% male. Twenty eight pilot students (92. 9% male, M age = 20. 3) were admitted after the selection process. Task, design and analyses The task was strictly identical to that of Study 2: Three cues, high multiple cue-criterion correlation (R e =. 95) and individual cue-criterion correlations were positive (r P =. 74), negative (r N = -. 69), and almost null (r I =. 08).
Participants We cumulated pilot training data for the three pilot student groups (n = 44, n = 60 and n = 28), so the sample consisted of N = 132 pilot students (87. 8% male, M age = 20. 5 and sd age = 1. 2). All of them came from scientific preparatory classes for competitive admission to elite universities. Among them, 30. 0% had experienced difficulties during practical pilot training leading to complementary flying hours and/or exclusion from the training.
Analyses Firstly, to get a picture of the nature of the relationship between MCPL performance and pilot training outcome, we applied a method described by  Hosmer and Lemeshow (2000). It consisted in creating intervals for the MCPL performance and computing the frequency of occurrence of pilot training difficulties within each group. We chose to use quartiles, so each group size was sufficient to compute representative frequencies (n = 33). Thus, we created four MCPL performance categories labeled "poor", "medium", "high" and "very high". As the tasks used in the three selection sessions were equivalent in level of global uncertainty (R e =. 96), we cumulated MCPL performance and pilot training data of the three classes. Secondly, we investigated the potential role of individual differences in cognitive ability test scores in the association between MCPL performance and pilot training difficulty.
good MCPL performers (44% below cutoff, n = 33 vs. 25% above cutoff, n = 99, p =. 03). On the contrary, there was no evidence of an association between MCPL initial performance and training outcome. Indeed, after applying the same cutoff (ra 1 =. 50), the difficulty rate was not significantly different for the two subgroups (31% below cutoff, n = 72 vs. 31% above cutoff, n = 60, ns). Interaction with other cognitive ability tests. Cronbach's alpha of Z cog were acceptable for the three applicant groups  (. 78,. 75 and. 83, respectively) , revealing acceptable internal consistency of this measurement. The correlation between the Fisher transformed initial and final MCPL performances and the standardized sums of the cognitive ability tests scores on the whole applicant data was low, although significant (at p <.
General Discussion The present studies were conducted in the context of airline pilot selection and training. The data was cumulated from three selection sessions and the three corresponding selected pilot students classes. Initially, Multiple Cue Probability Learning (MCPL) performance was assessed at the time of selection. Then, pilot training outcome (success/difficulty) was collected after a three-year  follow-up. All MCPL tasks used a mix of positive and negative cues, and for two of them, an irrelevant cue. The results showed large individual differences in initial and final MCPL performances, with a majority of applicants achieving high levels of performance. Strikingly, the level of MCPL final performance could be related to the outcome of airline pilot training. Indeed, the frequency of training difficulties (i.
Data collection Piglets from 15 litters were used for the call recordings. Litter size was 12. 1 ± 2. 1 (mean ±SD), there were 43. 7 % males among the piglets included in the experiments. Piglets were weighed, marked with a number on the back and the rectal temperature was measured on Day 1 and Day 7 post-partum (hereafter: pp). The mean body weight on Day 1 was 1548. 7 g ± 414 (mean ±SD) and on Day 7 it was 2565. 3 g ± 804 (mean ±SD). The health status of every piglet was judged for 5 different indicators of disease. Each piglet was observed individually and we noted the presence or absence of each indicator, namely quality of respiration (i. e. presence of coughing, sneezing, and labored breathing), enteric disease (diarrhea), neurological disease (muscle tremor, paddling movements), signs of apathy (animal not moving or reacting to our presence) and skin disease (dermatitis, un-colored skin indicating anemia).
Acoustic analysis To analyze the acoustic structure of S-calls we cut out single calls and conducted a fast Fourier transform (1024-pt FFT; time step: 2. 9 ms; frequency range: 22 kHz; frequency resolution: app. 45 Hz) using Avisoft SASLab Pro 4. 3 (R. Specht, Berlin, Germany). Because the I-calls of piglets have a lower and smaller frequency range  [33]  we reduced the sampling frequency to 11 kHz to have an appropriate range to estimate energy distribution and fundamental frequency (F0). Afterwards we used the same procedure to produce frequency spectra (1024-pt FFT; time step: 2. 9 ms; frequency range: 5. 5 kHz; frequency resolution: app. 11 Hz). We used a custom software program (LMA 2009, available on request from KH), to calculate acoustic parameters best suited to characterize the structure of S-calls and I-calls  [35].
To test whether the produced calls (S-calls and I-calls) had a heterogeneous distribution we used a two-step cluster procedure (SPSS 19) to test for different call clusters. We used the log-likelihood distance measure to establish different vocal cluster (up to 15 clusters) and the Schwarz-Bayesian information criterion (BIC) to decide which cluster solution showed the best fit. The procedure found a two cluster solution as the best division of S-calls. This result was supported by the second independent cluster analysis of Scalls of healthy and sick piglets, which also revealed the two cluster solution as the 'best' solution. For I-calls the two-step cluster procedure found improving cluster solutions, confirming that the one cluster solution is the best description for the existing variation of I-calls.
Statistical analysis To assess which factors affected the acoustic quality of Scalls we used linear mixed models (SPSS 19), with age, weight (i. e. actual body weight on the day of testing, a continuous effect) and the interaction between age and weight as between-subject fixed factors. Piglet identity (subject) was included as a random factor because several calls per piglet were used in the analysis. SSType1 was applied with age as the first factor. In the statistical analysis we included sex of the piglet as a categorical factor. However, this factor was removed from all models based on its non-significant effect in all models. In total we had 59 piglets and 1430 S-calls in the analysis. For the comparison of healthy and sick animals we carried out a separate analysis and added health status as an additional fixed factor.
In this analysis we had 28 piglets with 776 S-calls. Factors affecting I-calls quality were also tested with a linear mixed model (SPSS  19) , with age, weight, duration of isolation (after 2-4 min and 8-10 minutes), and the interaction between age and weight as fixed factor and subject as random factor. In total we analyzed 3422 calls from 46 piglets. In the cases when we repeatedly tested against the same global hypothesis we used Simes correction to adjust for multiple testing.
fMRI Data Acquisition and Preprocessing Images were acquired using a Philips Intera 3T Quaser, a synergy SENSE head coil, 30 mT/m gradients and a standard single shot-EPI with TE = 30 ms, TR = 2s, 37 axial slices of 3 mm thickness, with no slice gap and a 363 mm in plane resolution acquired to cover the entire brain and cerebellum. The slices were acquired in an interleaved spatial order. The first three volumes of each participant's data were discarded to allow for longitudinal relaxation time equilibration. Data were preprocessed with SPM5 (Wellcome Trust center for NeuroImaging, London, UK; http://www. fil. oin. ucl. ac. uk/ software/spm5/). EPI images from all sessions were slice-time corrected and aligned to the first volume of the first session of scanning to correct for head movement between scans.
A mean image was created using the realigned volumes. T1-weighted structural images were first co-registered to the mean EPI image of each participant. Normalization parameters between the coregistered T1 and the standard MNI T1 template were then calculated, and applied to the anatomy and all EPI volumes. Data were then smoothed using a 8 mm full-width-at-half-maximum isotropic Gaussian kernel to accommodate inter-subject differences in anatomy.
To refine this analysis, an additional two-way repeatedmeasures ANOVA was implemented, with 2 correctness (incorrect, 1 st correct) 6 2 learning conditions (TE, LeO). The goal of this analysis was to dissociate the neural systems relative to the processing of incorrect and first correct trials during TE and LeO learning conditions. Finally, we mapped the pMNS by first running two one-sample t-tests on the single participant beta values from the action observation and execution conditions. The thresholded group tmap resulting from the conjunction analysis  [29]  between observation and execution regressors (t = 3. 41, punc,0. 001) was used as a localizer mask for the pMNS. The anatomical location of each activated cluster was assessed using the SPM anatomy toolbox (https://www. fz-juelich. de/ime/ spm_anatomy_toolbox)  [30]  and the Talairach Daemon software (http://www.
talairach. org) [31]. Graphical display was performed using MRIcron software (http://www. cabiatl. com/mricro/ mricron/index. html). To depict the BOLD dynamics across conditions, we extracted the BOLD responses from all voxels in each activated cluster using the MarsBar toolbox for SPM (http://marsbar. sourceforge. net/). The average BOLD response was calculated by temporally aligning the BOLD time series on outcome onset and by averaging them across trials and subjects for each experimental condition (TE, LeO, OBS and EXE; Fig. 3B ). Two separate three-way repeated measures ANOVAs with 2 conditions (observing others vs doing) 6 2 tasks (learning vs. not learning) 6 6 ROIs were then conducted by considering both (i) the peak values and (ii) the D scores (peak value-first point) of the curves shown in the Fig.
The research was funded by a  N. W. O. Vidi  grant, a  Marie Curie Excellence Grant  of the  European Commission  to CK and a  French Agence Nationale de la Recherche  grant to DB. EM is supported by funding from  EC -contract number  27654 , the  NEURODIS Foundation  and the  Institut de Medecine Environnementale ; VG by a  VENI grant  of  N. W. O. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Therefore, a standard of 4 μl of eluted DNA extract was used per reaction. PCR was conducted using the following program: 94°C for 10 minutes, followed by 30 cycles of 94°C for 1 minute, 55°C for 1 minute and 72°C for 1 minute, and a final elongation step at 72°C for 10 minutes. The successful amplification of the samples was confirmed on a 2% agarose gel and all PCR products were then diluted by a ratio of 1 in 30. One microlitre of the diluted product was then added to 8. 7 μl Hidi Formamide (Applied Biosystems) and 0. 3 μl of in-house designed ROX size standard (size standard range = 105 -1007 bp). The mixture was then run on an ABI PRISM 3130×l automated sequencer (Applied Biosystems). Traces were viewed on GeneMapper software 4. 0 (Applied Biosystems).
Cloned PCR products were amplified in both directions using Big Dye chemistry (Applied Biosystems) and sequenced on an ABI PRISM 3130×l automated sequencer (Applied Biosystems). Sequence editing and alignment was conducted using CLC DNA Workbench 6. 1 (CLC bio). Rarefaction analyses were conducted using Analytic Rarefaction 1. 3 (available at http://strata. uga. edu/software) to ascertain the comprehensiveness of our cloning effort.
For example, when both BLAST and the phylogeny agreed that a particular clone belonged to a specific genus, we classified the clone to that genus. However, if there was disagreement between BLAST and the phylogenetic tree in the phylogenetic position of the clone, we classified the clone to a higher taxonomic level (e. g. class or family), with an emphasis on the position of the clone within the tree. Full sequences for each OTU have been deposited in GenBank under Accession numbers KC462595 -KC462734.