dataesr/scientific-paragraphs-categorization huggingface dataset:
HCP task fMRI analysis The analysis of fMRI data was carried out by the HCP consortium and we describe briefly their pipeline  13. The Story predictor covered the variable duration of a short story, question, and response period (~30 s). The Math predictor covered the duration of a set of math questions designed to roughly match the duration of the story blocks. The grayordinate data for individual task runs were processed in a level 1 analysis. Activity estimates were computed for the preprocessed functional time series from each run using a general linear model (GLM) implemented in FSL's FILM (FMRIB's Improved Linear Model with autocorrelation correction)  64. Predictors were convolved with a double gamma "canonical" hemodynamic response function to generate the main model regressors. The two runs for each task and subject were then combined in a level 2 fixed-effects analysis  13  , which we used as our phenotype.
Acknowledgments Data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
non-moral situations): medial prefrontal cortex activity was augmented and amygdala activity was attenuated in the moral condition only  [41]. Given these results, in Experiment 1 we select pictures related to moral violations (focusing on human rights' violation and animal mistreatment) from the Geneva Affective Picture Database (GAPED;  [42] ) together with threat-related pictures from the IAPS. We aimed to compare the effectiveness of distraction and reappraisal strategies in 14-year-old adolescents and to examine how specific emotions affect both strategies effectiveness.
The pictures and the instructional cue were presented randomly. Three sets of 40 negative pictures were counterbalanced across participants with the look, distraction, and reappraisal conditions. After watching each picture and using the strategy indicated by the cue word, participants then rated their current strength of negative affect on a 5-point scale from 1 (not feeling badly) to 5 (extremely badly) by pressing a key. Stimuli were presented and button responses collected by using E-Prime 2. 0 software.
Third, although the emotion-eliciting stimuli used in experiment 1 have been standardized  [39, 42] , information lacks about the specific emotion elicited by these stimuli. Experiment 1 showed that threat-related stimuli were judged as mainly eliciting fear and anxiety, whereas moral violation-stimuli were judged as mainly eliciting sadness and self-conscious emotions such as pity. Of note is that the stimuli used in this experiment were judged by adults. However, affective properties of the pictures may be experienced differently from the perspective of an adolescent. Database stimuli specifically standardized to elicit specific emotions and in specific population are needed to pursue investigations on emotion regulation. In the same vein, a limitation of experiment 2 is that the CERQ does not allow us to know which specific situations participants were imagining that they were regulating.
8 inhabitants/Km2, per capita gross intern product (GIP) of U$ 9,143,20 and human development index (HDI) of 0. 800, ranked 40th among 5,570 Brazilian municipalities. The population of the remaining 25 counties varied from 1,953 to 110,074 inhabitants (median=23,862), median population density of 75. 0 inhabitants/Km2 (interval 13. 2-308. 4 inhabitants/ Km2), median GIP of U$5,254,80 (interval U$2,234,80-U$20,747,40), and HDI values ranging from 145th to 2,282nd in the national ranking. The sample was composed by individuals aged 16 to 64 years old, and with a first contact with mental health services due to psychotic symptoms in a 3-year period (1st April 2012 -31st March 2015). Statistical analyses were carried out using Stata 13 software. We used the estimated population during the 3-year period based on the 2010 Brazilian Census to calculate person-years at risk.
References: 1. van  Background: The Community Assessment of Psychic Experiences (CAPE) is a self-report questionnaire that has been developed to measure the dimensions of psychosis in the general population. The cross-national equivalence of a questionnaire allows the comparability of a scale across different populations in different countries, i. e. , using different versions of the scale according to the considered language. In this study, our aim was to investigate the equivalence of the CAPE across different countries. Methods: Data were drawn from the European Union Gene-Environment Interaction (EU-GEI) study. Participants (incident case of psychotic disorder, controls and siblings of cases) were recruited across in six countries: United Kingdom, the Netherlands, Italy, Spain, Brazil and France.
We sought to determine whether neighborhood characteristics would also be risk factors for these two prognostic indicators. Methods: Data were collected as part of the Atlanta Cohort on the Early Course of Schizophrenia project, which included patients hospitalized for a first episode of a schizophrenia-spectrum disorder. Diverse variables were obtained from interview-based measures, data from chart reviews, and informant/family member collateral interviews, including gender, family history of psychosis, history of cannabis use, history of incarceration, MOO, patient-level residential mobility, Neighborhood Disorder Scale (NDS), AOP, and DUP. We retrieved 13 neighborhood characteristics.
Procedure The survey was administrated through the online "Key survey tool". To enter the survey, participants were screened for: LC use as a pedestrian, age at which pedestrians are expected to cross unaccompanied (16 and over years old), and current Queensland residency. The survey started with general questions about LC use, knowledge and past experience at LCs, and ended with questions related to the five recorded crossing scenarios which were visualised by all participants in a random order, in a YouTube video format. The survey took approximately 20 minutes to complete. After completion, participants were invited to participate in a prize draw. This study's procedure was approved by the university's ethics committee 5. 2. 4. Data analysis Chi-square and Fisher exact test were used to compare responses of different demographic groups of participants.
RESULTS We acquired electroencephalographic (EEG) data from subjects as they listened to narrative speech in the form of audiobook recordings. To relate the neural data to the semantic processing of this speech, we first wished to parameterize the speech stimuli such that individual words were quantified according to their semantic context. There are many ways to do this. Inspired by the brain's sensitivity to incongruous new words (as seen in the N400), we chose to do it based on quantifying how "semantically dissimilar" each new word was compared to its immediately preceding context. This idea of semantic distance has previously been used in studies of reading-time effects 8 , reading comprehension  19  and brain imaging of speech processing 19. Our specific approach was based on the well-known word2vec model 20 , whereby each word in a speech stimulus is converted to a high-dimensional vector (in our case 400 dimensions) that serves as a proxy for that word's meaning.
Neural signatures of semantic dissimilarity depend on intelligibility The experiments above involved natural speech and its time-reversed counterpart, stimuli that were completely intelligible and completely unintelligible, respectively. To assess how sensitive our semantic dissimilarity TRF might be to gradations of intelligibility we conducted a further experiment involving speech-in-noise. Specifically, we collected EEG data from 21 subjects as they listened to two repetitions of each of fifteen 60-s segments of continuous audio speech which were always mixed with spectrallymatched stationary noise at a signal-to-noise ratio of -9 dB. Based on the well-known fact that visual speech enhances the intelligibility of speech-in-noise  22  , we manipulated intelligibility by allowing subjects to watch a video of the speaker for one of these repetitions.
No evidence of contextual semantic processing for unattended speech Another important test of the behavioral relevance of our semantic dissimilarity TRF would be to determine whether or not it is affected by how much attention a person is paying to natural intelligible speech. Over 60 years ago it was first noted that, when attending to one of two dichotically-presented speech streams, people have a very limited ability to report on the content of the speech in the unattended ear 25 , a phenomenon known as the cocktail party effect. Ever since then, researchers have sought to explain this phenomenon in terms of psychological models  [26] [27] [28]  and neurophysiological data 15,29-.
To test this, we recorded EEG data from 9 subjects who undertook a classic N400 experiment and who listened to the audiobook used in our firstmentioned experiment above. For the N400 experiment, subjects read 300 sentences presented word-byword on a screen, half of which ended with a word that was congruent with the rest of the sentence and half which ended with an incongruent word. N400s were then determined by subtracting the event-related potential to the congruent words from that to the incongruent words. And, using the EEG data recorded during the story, we derived a semantic dissimilarity TRF for each subject as before. Figure  4A, B  show that the two responses display somewhat similar timecourses over midline parietal scalp, as well as similar topographical distributions at a latency of 375-425ms.
We are not convinced of this as an explanation given that our TRF involved representing word meaning as an impulse at word onset, which is a similar assumption to that made when performing averaging of neural data time-locked to word onset. And third, and perhaps most plausibly, the TRF was derived in response to natural, continuous, narrative speech, while the N400 was derived to sentences presented one word at a time. It could be that semantic processing happens more rapidly during natural speech processing, possibly as a result of stronger ongoing predictions in this context  37  , than in the case of individual sentences that may or may not contain incongruent words. The fact that the latency of the TRF negativity was increased for the cocktail party and audiovisual speech-in-noise experiments relative to the first, clean speech experiment, and the fact that working memory has long been linked with speech processing under challenging listening conditions supports this idea of a link between our TRF latency and the efficiency of word processing in the context of preceding words.
34 subjects (28 male) with a mean ± SD age of 27. 3 ± 3. 2 years participated in the cocktail party attention experiment, but data from one subject was not included in the analysis as the recordings from their mastoid electrodes were of poor quality. And 21 subjects (6 female) aged between 21 and 35 years participated in the multisensory speech experiment. Much of the data from these experiments has previously been published in studies examining how EEG tracks the envelope and phonetic content of speech  18, 38, 39.
METHODS Data acquisition and pre-processing. For all experiments, 128-channel EEG data (plus two mastoid channels) were acquired at a rate of 512 Hz using an ActiveTwo system (BioSemi). Triggers indicating the start of each trial were sent by the stimulus presentation computer and included in the EEG recordings to ensure synchronization. Offline, the data were band-pass filtered between 1 and 8 Hz, downsampled to 128 Hz, and re-referenced to the average of the mastoid channels in MATLAB. To identify channels with excessive noise, the time series were visually inspected and the SD of each channel was compared with that of the surrounding channels. Channels contaminated by noise were recalculated by spline interpolating the surrounding clean channels in EEGLAB 40.
Fifteen 60-s videos were rendered into 1280 x 720-pixel movies at 30 frames/s and exported in audio-only (A), visual-only (V), and AV format in VideoPad Video Editor (NCH Software). The soundtracks were sampled at 48 kHz, underwent dynamic range compression, and were matched in intensity (as measured by root mean square; see The "continuous bag of words" implementation  44  was selected, because this was built on British English corpora (ukWaC, the English Wikipedia and the British National Corpus combined) which is both large and probably more reflective of the language exposure of the participants (in Dublin) than US corpora. In addition, word vectors are freely downloadable (see 44 ). Word2vec embodies the "distributional hypothesis" that words with similar meaning occur in similar contexts in an artificial neural network approach.
We did this by calculating a Pearson's correlation between the word's 400-dimensional vector and the average of the vectors corresponding to all the preceding words in that particular sentence, and subtracting this correlation from 1. (Where a specific word was the first word in a sentence, we calculated the correlation between the word and the average of all word vectors in the previous sentence, before, again, subtracting that correlation from 1). This produced a single semantic dissimilarity measure for each word with a value between 2 and 0. We then created a "semantic dissimilarity vector" at the same sampling rate as our EEG data (128 Hz) which consisted of time-aligned impulses at the onset of each word that were scaled according to the value of that word's semantic dissimilarity. The word onset times were determined by performing forced alignment of the speech files and the corresponding textual orthographical transcription using the Prosodylab-Aligner 45.
The method used here to analyze the mapping between the semantic dissimilarity vector and the recorded EEG data is commonly known as a temporal response function (TRF). A TRF can be interpreted as a filter that describes the brain's linear transformation of a stimulus feature, S(t), to the continuous neural response R(t), i. e. , R(t) = TRF*S(t) where '*' represents the convolution operator. The TRFs were calculated by performing regularized linear regression between our stimulus variables and our EEG. Specifically, we performed ridge regression wherein a parameter (lambda) is set to control overfitting (see  21  for a detailed description of this step). In previous work, we have attempt to cast our TRF functions with μV as their unit of measure. However, this relies on a decision to normalize the input stimulus values between some limits and, as such, has been somewhat arbitrary.
In the present work, and in line with previous work from other groups, the EEG data on each channel was z-scored prior to estimating the TRF, meaning that the TRFs are ultimately presented in arbitrary units. The colors in the TRF topographic plots can be interpreted as follows: red at a particular latency indicates that, at that poststimulus lag, the EEG voltage is driven in a positive direction by a unit change in semantic dissimilarity. And blue means the EEG voltage at that poststimulus lag is driven negative by a similar change. Thus, given the same normalization strategy for the various speech stimuli used in this study, the TRF responses can be compared in terms of their amplitudes, despite their description in terms of arbitrary units.
Research was supported by Slovak Research and Development Agency no: APVV-15-0686 and internal funding provided by The University of Texas at Dallas. S91. CLINICAL CHARACTERISTICS OF FORMAL THOUGHT DISORDER IN SCHIZOPHRENIA Joonho Choi* ,1 , Han-Yong Jung 2 , Joo Eon Park 3 , Hoseon Lee 4 , Seon-Cheol Park 5 1 Hanyang University Guri Hospital; 2 Soonchunhyang University Bucheon Hospital; 3 Keyo Hospital; 4 St. Andrew Hospital; 5 Inje University College of Medicine and Haeundae Paik Hospital.
Using a uniquely large genetic-neuroimaging dataset (IMAGEN study  (Schumann et al. , 2010) , we addressed this unsolved issue by applying structural equation modeling  [SEM] ) to examine whether the selective modulation of key components of the reward circuitry -ventral striatum (VS) and orbital frontal cortex (OFC)by dopaminergic functional polymorphisms contribute to the degree of perilous alcohol use behaviour observed at 14 years of age and again at 16 years of age. In particular, functional magnetic resonance imaging (fMRI) data collected from 14 year old adolescence participants performing the monetary incentive delay (MID) task were used to quantify the blood-oxygen-level dependent (BOLD) response of the VS and OFC during the anticipation of large and small rewards. The MID task has been used extensively to investigate changes in neural activity in response to the processing of different stages of reward processing (e.
Participants and Procedure A community-based sample of young adolescents (N=2463) was recruited for the IMAGEN study (for details on the IMAGEN project, see  Schumann et al. 2010). Individuals who provided assent, and whose parents provided informed written consent, completed an extensive battery of neuropsychological, clinical, personality and drug use assessments online and at the testing centers. Participants were excluded if, among other criteria, they had contraindications for MRI (for example, metal implants, claustrophobia). After data quality control, complete and reliable data sets were available for 1840 participants at Time 1 (1666 participants at Time 2). Of these volunteers at Time 2, 1639 had complete neuroimaging data. The demographic information of the participants at time 1 was: mean age = 14.
e. 1 candy  [M&Ms]  for every 5 points scored). The duration of the target was continuously adapted to the performance of the subject, ensuring a successful performance on approximately 66% of all the trials. Immediately following the response, feedback indicated the number of points attained in the recent trial as well as the total points earned during the task. The inter-trial interval varied so that each trial took approximately 10,000 ms (Figure  1 ). Large, small, and neutral conditions were randomized throughout the task Imaging parameters All scanning was performed with a 3T whole body MRI system made by several manufacturers (Siemens, Philips, General Electric, Bruker) at the eight IMAGEN assessment sites (London, Nottingham, Dublin, Mannheim, Dresden, Berlin, Hamburg, and Paris). To ensure a comparison of MRI data acquired on these different scanners, we implemented image-acquisition techniques using a set of parameters compatible with all scanners that were held constant across sites (cf.
Statistical analysis strategy We performed two main sets of analyses using SPSS 17. 0. 1 and MPlus version 6. 12 (Muthen & Muthen 2011). First, a simple regression analysis was performed to identify unique relationships between genetic data (DRD1 rs686 , DRD2 rs12364283 , ANKK1 rs1800497 , and PPP1R1B rs87694 ) and neuroimaging data (medial/lateral OFC and VS), and between neuroimaging data and alcohol misuse at 14 and 16 years. In addition, interactions terms (medial OFC*VS and lateral OFC * VS) were derived from the product of the medial/lateral OFC and VS standardized scores in order to examine whether the interaction between the two reward regions contribute to the prediction of alcohol misuse scores. Type 1 errors were statistically controlled following  Benjamin and Hochberg (1995)  with a corrected significance level of α = 0.
Acknowledgments The IMAGEN study receives research funding from the  European Community 's  Sixth Framework Programme  ( LSHM-CT-2007-037286 ) the  FP7  project  IMAGEMEND (Imaging Genetics for Mental Disorders ) and the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), the  Medical Research Council   Programme Grant  " Developmental Pathways Into Adolescent Substance Abuse " (93558), and the  Swedish funding agency FORMAS. Further support was provided by the  Bundesministerium für Bildung und Forschung  (BMBF grants  01GS08152  and  01EV0711 ), the  Deutsche Forschungsgemeinschaft (DFG)   Reinhart-Koselleck Award  ( SP 383/5-1 ), and  DFG  grants  SM80/5-2 ,  SM 80/7-1 ,  SFB 940/1. This research was also supported by the  German Ministry of Education and Research  (grant  01EV0711 ).
Functional diversity of more and less attractive fishes. To highlight how functional traits were distributed among all species, we selected a set of six traits describing complementary facets of fish biology (the GASPAR database): size, mobility, time period of activity, type of grouping, position in the water column, and diet (see Methods 'Functional space computation' , Supplementary Fig. 3  and 17  ). These traits are all linked to the ecology of the species and thus to ecosystem processes such as regulation of food webs and nutrient cycling  17. To assess functional diversity, i. e. the amount of functional space filled by a set of fish species, we built a multidimensional functional space based on the trait values (see Methods 'Functional space computation' and  18  ). We found that the most attractive fishes (aesthetic scores in the ninth decile of the distribution, n = 18) filled a much smaller part of the total functional space (20% of the total space) than the least attractive fishes (aesthetic scores in the first decile of the aesthetic scores distribution, n = 18, 40% of the total space).
Acknowledgements We thank the volunteers who participated in the online questionnaire. We also thank  Michel Raymond ,  Julien Veyssier  and the  Labex CeMEB  (http://www. labexcemeb. org/)for programming and hosting the questionnaire on the MBB platform. Anne-Sophie Tribot  and  Quentin Carabeux  were supported by a PhD grant from  Fondation de France  (http://www. fondationdefrance. org/). Thomas Claverie ,  Sébastien Villéger  and  Nicolas Mouquet  were funded by the  CNRS.
DATA ANALYSIS Data were analyzed by means of a repeated measures ANOVA, where validity (valid vs. invalid), believability (believable vs. unbelievable), emotion (neutral vs. emotion), and stimulation (tdcs vs. sham) were entered as independent variables and where the dependent measures were accuracy and response times for the reasoning task. Statistical analyses were conducted using SPSS software. An a priori power analysis conducted with Gpower  (Erdfelder et al. , 1996)  indicated that a sample size of 36 would be sufficient to detect a significant interaction effect between emotion and tDCS with a power of 0. 95 and an alpha of 0. 05, based on an effect size of 0. 25.
ESM Data A momentary NA scale was calculated for each subject at each time point by averaging the NA items for every rating-occasion. The resulting scale yielded a multilevel equivalent of Cronbach's alpha (as proposed in Nezlek, 2017) of 0. 98 indicating a good internal consistency. From the momentary NA scale, two central parameters were obtained to capture the two main types of emotion dynamics: (1) the square RMSSD as an indicator for negative emotional instability; and (2) the autocorrelation (or rather, autoregressive effect) as a measurement of negative emotional inertia. These parameters were obtained within a multilevel analysis framework, taking into account the nested structure of the data as proposed in  Jahng et al. (2008)  (see also  Koval et al. , 2015;  see Supplementary Material 1 for more information on the calculation of these parameters as well as Supplementary Material 2 for graphs depicting the example time-series and the distribution of the main ESM-parameters used in this study; further visualizations of Inertia and RMSSD can be found in  Houben et al.