dataesr/scientific-paragraphs-categorization huggingface dataset:
Exclusion criteria were: (i) past or present history of neurological or psychiatric disorders (except SAD), including current or past major depressive episodes, current psychotropic drug use and substancerelated disorders (abuse or dependence); (ii) non-compliance with the study protocol or data acquisition failure (such as head movements). According to these criteria, 11 participants were excluded (four due to data acquisition failure). The final sample consisted of 46 participants, with 14 meeting the criteria of SAD (according to MINI) and LSAS scores showing clinical levels of symptomatology (ranging from 30 to 97). Finally, this sample reflected a continuum of participants with clinical and subclinical levels of social phobia, as well as healthy participants. All participants conformed to standard health and safety regulations regarding the use of MRI.
Image acquisition and fMRI statistical analysis Images were acquired during a single session, using a 3-T whole-body MRI scanner (Achieva, Philips Medical Systems, Best, The Netherlands) with an eight-channel head coil. Head motions were minimized with a forehead strap and comfortable padding around the participant's head. For each participant, a T1-weighted anatomical image oriented parallel to the AC-PC was first acquired using a fast field echo sequence (T1-FFE; TR ¼ 253 ms, TE ¼ 2. 30 ms, flip angle ¼ 808, 38 axial slices, slice thickness ¼ 4. 50 mm, no interslice gap, FOV ¼ 240 Â 240 mm 2 , matrix ¼ 268 Â 214 and acquisition voxel size ¼ 0. 43 Â 0. 43 Â 4. 5 mm 3 ). Functional data were acquired using an ascending slice acquisition 2D T2*-weighted EPI sequence sensitive to blood-oxygen-level-dependent contrast in the same axial plane as the T1-weighted structural images (2D-T2*-FFE-EPI; EPI factor ¼ 39, TR ¼ 2000 ms, TE ¼ 30 ms, flip angle ¼ 908, 38 axial slices, slice thickness ¼ 3 mm, no gap, matrix ¼ 80 Â 72, FOV ¼ 240 Â 216 mm 2 and acquisition voxel size ¼ 3 Â 3 Â 4.
5 mm 3 ). To minimize susceptibility artifacts and optimize limbic activity recording, a shim was positioned over the limbic and insular structures, excluding the sinus cavities. The 300 functional volumes were collected during a single functional session (total scan time ¼ 10 min 16 s). The fMRI data were analyzed using statistical parametric mapping (SPM8, Wellcome Department of Cognitive Neurology, Institute of Neurology, London, UK; www. fil. ion. ucl. ac. uk/spm/software/spm8. MRIcro software (www. mccauslandcenter. sc. edu/mricro) was used for image conversion. Five initial brain volumes of the functional run were discarded to eliminate non-equilibrium effects of magnetization. Functional images were spatially realigned to the first volume, slice-time corrected and normalized to the standard space of the Montreal Neurological Institute (MNI) brain.
Brain imaging data Mapping brain responses to emotional faces (main task effect) We examined the activation of selected ROIs (see Methods section) for each contrast of interest (i. e. anger vs shape, happiness vs shape, sadness vs shape and fear vs shape) with P < 0. 05, FDR corrected t-test for multiple comparisons (Table  2 ). In all conditions (neutral and emotional faces), the fusiform gyrus was activated, in accordance with its expected role in the processing of faces. Activation in bilateral amygdala was found in all emotional conditions. The middle frontal was activated bilaterally for anger, fear, happiness and neutral conditions, but only activated in the right hemisphere for sadness. The insular cortex was bilaterally activated in all conditions except sadness. Finally, the ACC was activated bilaterally for fear and happiness.
) for their assistance,  Sylvie Berthoz  for her helpful advice and the cultural department of the  University of Reims Champagne-Ardenne (SUACFrancoise Mittelette)  for its support in constructing the EFE battery. They also wish to thank  Kathleen Smith  for her benevolent proofreading of English. This research was funded by the  Reims University Hospital research program  ( CHU de ReimsAOL -2008Troubles anxieux & emotions). A. C. was supported by a scientific grant from  Champagne-Ardenne Regional Council.
Materials and methods The proposed data are extracted from a review of the scientific literature accumulated during the last decade. A PubMed search was performed, searching for literature from the years 2000-2012, using the following keywords: binocular coordination and dyslexia, dyslexia and eye movements, vision and dyslexia, dyslexia and neglect, as well as space representation and dyslexia. We included publications whose work has been confirmed by subsequent studies, without excluding publications that are original and form a logical basis for currently accepted concepts pertaining to dyslexia. When several publications addressed the same subject, which was rarely the case, the cited publication was the one that was published first, unless it was followed by research involving larger groups of subjects -in this case, data from several publications are cited.
EEG AND EYE TRACKING ACQUISITION Throughout the experiment, participants were comfortably seated in an adjustable chair while their EEG activity was being recorded. Thirty-one active electrodes (Brain Products GmbH) were mounted on an EEG cap (BrainCapTM) placed on their scalp in compliance with the International 10-20 system  (Jasper, 1958). One electrode was affixed under the right eye to record vertical electro-oculographic activity (EOGv) with FP2 on the scalp. The information relating to the bipolar horizontal electrooculographic (EOGh) signal was obtained through the eye tracking system. To get electrical contact and increase the signal-noise ratio, we have used contact gel (SuperVisc Gel, Brain Products, Inc. ) and adjusted individual sensors until impedances were inferior to 5 k. Electrodes were referenced to head (FCz-ground:AFz), and EEG data were amplified through the BrainAmp™ system (Brain Products, Inc.
TECHNICAL IMPLEMENTATION After acquisition, data (EEG raw data, hardware triggers, eye tracker raw data) had to be collected, synchronized and enriched with additional information on the read words, through a joint analysis of the scanpaths and associated texts (section Data enrichment: from fixations to words). Raw data for both EEG (32 channels) and eye tracker (right and left eye positions) were sampled at 1 kHz. Besides, hardware triggers were automatically generated during the experiment to identify the different sequences (fixation cross, text/goal presentation. ) in each trial. These marks were found both in the EEG file and the eye tracker file containing the raw data. Even if both the EEG and eye tracker data were sampled at the same rate, data had to be synchronized to make up for clock drift, jitter and others sources of time distortions or information losses.
This task was carried out through the known sequence of hardware triggers automatically generated during the experiment. After that, the raw data consisted in thirty-seven channels sampled at 1000 Hz: 32 for EEG data, 4 for eye tracker and one logical signal for the blinks detected by the eye tracker. After this global synchronization, eye tracker events (start, end of fixations and saccades) were added to the synchronized data file. The thresholds for saccade detection were a minimum velocity at 30 • / s, a minimum acceleration at 8000 • / s and a minimum motion at 0. 1 • / s. These detections provided 12 eye tracker events (beginning / end of fixations, saccades and blink periods for both eyes).
DATA PREPROCESSING EEG data preprocessing and EFRP analyses were carried out with the Brain Analyzer 2 software (Brain Products GmbH, Version 2. 0. 2). Continuous EEG was first segmented over the whole duration of each text. A low cutoff filter was applied (2 Hz, Time constant 0. 0796, 12 dB/oct. ) as well as a notch filter at 50 Hz (symmetrical 5 Hz bandwidth around the notch frequency, i. e. , 50 ± 2. 5 Hz; 24 dB/oct. ) to eliminate interference from the electricity network. Independent Component Analysis (ICA) was used to remove blink and saccadic movement artifacts from the EEG data  (Makeig et al. , 1997; Jung et al. , 2000). Visual inspection of the FP2 channel, on which blinks are supposed to be maximal, in comparison with channels on which our effects were maximal (e. g. , P4), showed that EOG artifacts were sufficiently removed from the data and that there was no significant impact from EOG artifacts on our results.
For each participant, the number of remaining fixations after reading those words was computed in these two situations. Then, we adjusted thresholds to obtain similar distributions among participants. See section Results on eye tracker data on results of eye tracker data for more details. These two constraints were combined and the two thresholds, respectively set to 0. 06 for the LSA semantic similarity, and 1. 5 per million words for the word frequency. So, the fixated words, whose cosine with the goal was below 0. 06 and word frequency below 1. 5, were tagged as incongruent words. Through the windowing mechanism, as explained above, both the fixations on incongruent words and target words were tagged after scanning the complete eye fixations dataset for all the subjects. These tagged fixations, labeled "Fixations of interest," were specific events for EFRP analyzes, allowing the correct fixation selection before epoching.
RESULTS ON EYE TRACKER DATA Out of the 17 participants, two were excluded from the EFRP analysis because of their behavioral data. One of them had read all the texts up to the end, so we considered that the decisions he made were not made during the reading process. Consequently, the average number of fixations for this participant was too high (over one standard deviation from the average of all participants). A second participant was excluded for the opposite reason; the average number of fixations for this participant was too low (under one standard deviation from the average of all participants). For the fifteen remaining participants, the average number of fixations and standard deviation for the three types of text (HR, MR, and UR) are indicated in Table  1. HR texts are likely to induce positive decisions (keep the text), whereas UR texts should lead to negative decisions (reject the text).
ACKNOWLEDGMENTS This work was supported by a grant from the  ANR Gaze-EEG  "Joint synchronous EEG signal and eye tracking processing for the spatio-temporal analysis and modeling of neural activities," and a grant from the  "Pôle Grenoble Cognition. "A part of the soft development was realized by  Ronald Phlypo  and  Nicolas Tarrin. The authors would like to thank the " Délégation à la Recherche Clinique et à l'Innovation " of the  CHU "Centre Hospitalier Universitaire" of Grenoble  for their role in the ethics committee, and the reviewers for their comments that significantly help the improvement of the manuscript.
STATISTICAL ANALYSES Two-way ANOVA (group × ID) with repeated measures on ID has been carried out on all variables. The sphericity of the data was verified for each analysis with the test of Mauchley and, in case of violation, the Greenhouse-Geisser correction was applied to the degrees of freedom (df). Accordingly, the reported df correspond to the nearest whole number. The effect size was calculated as: η 2 = SS explained/SS total. Post hoc analyses were carried out using Newman-Keuls test. To compare response times observed in CRT and aiming movement tasks, EFs and Brinley functions (BF) were calculated. EFs quantified the relation between the ID and the temporal variables recorded in each task (RT and MT). They were determined by using linear regressions carried out on mean group values in each task.
EFs representing Fitts' law were conducted on MT data and those representing Hick-Hyman's law on RT data. Linear regressions of BF  (Brinley, 1965)  were calculated after plotting mean values of MT and RT observed in young participants (abscissa) against those observed in elderly (ordinate). Thus, it resulted in two BF: one for Fitts' task and one for Hick-Hyman task. Efficiency and BF differed in their purpose and, hence, were complementary. By comparing between the slopes of the different EFs, we assessed information-processing capacities in each age group (in bit/s). BFs, on the other hand, allowed the estimation of age-related slowing ratios, which were measured by the slope values of the regression functions. The comparison between the calculated slopes in each task allowed determining if sensorimotor and cognitive processes presented the same slowing ratios with aging.
Methods Data are from the 1958 National Child Development Study (NCDS) which included all live births during one week in 1958 (n = 18558) in Great Britain. Subsequent data collections (sweeps) were carried out on cohort members aged  7, 11, 16, 23, 33, 42, 46  and 50. At the last sweep, carried out in 2008, 9790 individuals participated in the self-reported questionnaire and face to face interview, representing 53% of the original sample. The NCDS has been described in detail elsewhere  [23].
Cancer in the cohort The outcome variable of interest in this study was selfreported cancer between the age of 33 and 50. We did not use information available on cancer at the age of 23, as we were interested in controlling for health behaviours at this age. Two main types of variables were used across the data sweeps. At ages 46 and 50 respondents were asked to report any medical conditions among which cancers can be identified. At ages 33 and 42 respondents were asked to report if they had 'ever had cancer'. The outcome variable 'cancer' constructed for the purpose of this study was categorised as follows: 'yes' corresponds to individuals who ever reported having had cancer at ages 33, 42, 46 or 50 (n = 444); 'no' corresponds to individuals who explicitly reported not having a cancer at age 33 and 42, and who did not have an ICD coded cancer among their reported medical conditions at the ages of 46 and 50 (n = 5694); 'missing' corresponds to individuals who did not respond to any questions on cancer, and respondents who occasionally responded 'no' or who were missing.
Sources of adversity were divided into six categories: Exposure to adversity was identified by a positive response to any of the above categories. Respondents were excluded if they had missing data for all six categories. Respondents were considered as having no adversities if they answered 'no' all the categories or if they answered 'no' to one or more category and the other categories were missing. ACE was measured by counting the reports of: child in care, physical neglect, offenders, parental separation, mental illness and alcohol abuse. A three category variable was then constructed (0 adversities/1 adversity/2or + adversities).
The respondent's characteristics and birth variables were also included: sex, gestational age at birth, parity, birth weight, foetal distress, problems during pregnancy and breastfeeding (no, one month or less, more than one month). To control for health problems in childhood, a childhood pathologies variable was constructed using data collected at ages 7, 11 and 16 y. It was based both on mother's report and medical examinations including congenital conditions, moderate/severe disabilities, chronic respiratory or circulatory conditions, sensory impairments and special schooling (childhood pathology: yes/no).
Missing data, imputation and statistical analysis Cohort follow-up has been good over time, 84% participated at the age of 16, with a gradual dwindling in participation throughout adulthood (72% at age 23, and 65% at age 42) when participants were most likely to have moved  [23]. Refusal rates have been low, with 7% at age 23 and 13% at age 42 for example  [23]. In the latest data sweep (2008) 54% (n = 9790) of the original sample participated, and for some variables missing data presents a considerable challenge. The sample used for this study is described in Figure  1. To control for possible bias due to missing data, we imputed data for covariates with missing data using the multiple imputation program ICE in STATA v11. Twenty imputations were conducted taking the missing at random (MAR) assumption for the covariates only (mother's education, father's social class, overcrowding, birthweight, gestational age, parity, smoking during pregnancy, mother's age at birth, breastfeeding, child pathologies, educational attainment at 23, social class at 23, malaise inventory at 23, drinking at age 23 and smoking at age 23, age at 1 st pregnancy for women).
Results Descriptive statistics are presented in Table  1  for the subsample (n = 6138) who provided information on whether or not they had had a cancer in the last four data sweeps (ages 33, 43, 46 and 50). Among respondents who answered questions on cancer, 3. 3% of men and 10. 6% of women reported having had cancer by the age of 50. In total 444 self-reported cancers were identified in the cohort reported between the ages of 33 and 50, 80% being reported by women (n = 351). Based on the available information on cancer types reported by the cohort members (see Additional file 1), breast and cervical cancers were most often reported (19% and 15% respectively), followed by skin cancers (11%). Nevertheless, most cancers remained 'undefined '(39%) The majority of men (74%) and women (75%) had had no adverse childhood experiences, based on the prospectively collected information at ages 7, 11 and 16 y.
The strength of the relationship between adversity and cancer was of the same magnitude as that observed between age at first pregnancy and cancer, a well-known risk factor for breast cancer  [30]. The two main weaknesses of this study are in the selfreported nature of cancer incidence, and the amount of missing data caused by attrition in the cohort study. These weaknesses are partly addressed by the conservative nature of our cancer variable, and by using estimates obtained from multiple imputations to account for the missing data. We conducted sensitivity analyses by running the model using imputed datasets, using a casecontrol sub-sample, and using the full cohort dataset without imputation, and found that the relationship between ACE and cancer incidence was stable. Comparisons were made between complete-case analyses and those run on estimates obtained by imputation.
For these analyses we limited the health behavioural variables to those collected at age 23 rather than using variables available afterwards. This enabled to limit any further problems of missing data, but may lead to an underestimate of the contribution of health behaviours in the associations. One important strength of this study is in the prospective nature of the information on ACEs. Most studies examining the links between early adversity rely on retrospective questions, which are prone to recall bias, an important issue raised by Korpimaki et al. using data from a working age population and the cancer registry  [19]. Using a retrospective questionnaire to identify ACE, and limiting their analyses to individuals whose cancer was diagnosed subsequent to answering questions on ACE to address the problem of recall bias, these authors found no association between working-age cancer and reporting ACE.
13 That is not to say, however, that the resulting approach is without flaw (see e. g. ,  Flynn, 2000; Flynn & Widaman, 2008). 14 In the Persée database of scientific journals, where much of L'Année Psychologique has been made available through open access , this article is listed as having been published in 1904. However, that is misleading: the date for volume 11 should read "1904-1905. " (The electronic record for the author names is also incorrect: in the article itself, Binet is named as first author [p. 244]. ).
The test itself was constructed quickly, in just a few weeks, and was based primarily on Binet's work over the previous 15 years. Conveniently, too, the resulting examination typically took only a quarter of an hour to administer. Table  1  provides a list of the sub-tests and their origins, with a view to organizing material now in the public domain that will inevitably come to be included in the new PsycTESTS database. That said, however, it is not the aim of this paper to provide a history of how the items themselves were devised, revised, and retained. Indeed, a full accounting of those efforts would require its own further manuscript. But a few words are necessary to situate the resulting contribution. Thus, for example, the basic skills assumed of an "idiot" were assessed by tasks used by psychiatrists at the time.
Acknowledgments D. Coletti  is supported by  UPMC  Emergence 2011 and by AFM 2012. Z. Li is supported by  ANR  and by  AFM. PRIN 2009 (Project no. 2009WBFZYM 001 ) and  Italian Space Agency (ASI) ,  OSMA  project, grant to  S. Adamo  are also acknowledged. The authors are indebted to  Carla Ramina  for her precious technical assistance. They gratefully thank  Richard Lowry, Ph. D. , Professor of Psychology Emeritus at the  Vassar College  for his web-based, user-friendly tool for performing statistical computation, VassarStats, which They used for statistical analysis. The mice used throughout this study were a generous gift of  Janvier SAS  ( Le Genest St Isle, St Berthevin Cedex, France ).
Data analysis Force signals were time-locked with the GO signal. GF and LF were computed together with their first derivatives (finite difference algorithm). GF and LF onsets were defined when the force exceeded the mean of the baseline value by 2 SD  [26, 36]. We also measured peak of GF and mean GF during the static phase, defined as the interval between 500 and 1500 ms after object lift-off (reported by load force onset). Temporal parameters of the grip-lift task were also measured: (1) reaction time (GO -T0; delay between the GO signal and first fingertip contact with the manipulandum); (2) duration of the preload phase (T0 -T1; delay between GF and LF onsets), (3) duration of the loading phase (T1 -T2; delay between LF onset and the moment LF equals the object's weight). In addition, we calculated the largest coefficient of correlation between grip and load force rates and the time shift for which this condition was fulfilled (cross-correlation).
Statistical analysis There was no statistically significant difference between the movement parameters collected for the left and right SMA sham blocks in Experiment 1 (t-test; all t 7. 1. 32; all p. 230). Therefore, we merged data in these two conditions and used them as control values. Statistical analyses were performed on all the aforementioned parameters using a 2-way repeated measures analysis of variance (ANOVA RM ) with factors TMS CONDITION (left SMA, right SMA or controls) and HAND (left or right hand). In Experiment 2, data were analysed using a 2-way ANOVA RM with factors TMS CONDITION (left or right SMA) and TMS TIMING (0, 50, 100, 150, 200, 250, 300, no TMS). When appropriate, post-hoc t-tests analyses were conducted. Data collected from Experiment 3 were analysed separately for left and right TA muscles.
Mechanical data Mechanical signals were smoothed by a 10 Hz low-pass filter and resampled (one value per degree). On the basis of normal and horizontal components and pedal angle, the total resultant force (F tot ) was calculated by trigonometry and resolved to determine the effective component (F eff , i. e. , the propulsive component applied perpendicularly to the crank arm). The instantaneous index of mechanical effectiveness (IE) was determined as the ratio of F eff to F tot at each point of the pedaling cycle. The overall index of effectiveness on the complete crank cycle (IE cycle ) was determined as the ratio of the linear impulse of F eff to the linear integral of F tot  (Sanderson and Black, 2003; Dorel et al. , 2009). A set of 30 consecutive cycles was extracted from a period of constant power output (between the 9th and 10th minute for the Control condition and after the first 10 s for the independent cranks condition) and averaged to obtain representative profiles of F eff , F tot , and IE.