dataesr/scientific-paragraphs-categorization huggingface dataset:
3 ± 0. 8) to form the high monitoring load ones. The resulting syllables were checked for orthographic neighbors using WordGen software  (Duyck et al. , 2004). Only one stimulus had one orthographic neighbor (spald), the rest of stimuli had none. All stimuli were recorded by a Serbian speaker, as Serbian allows pronunciation of all used combinations. Stimuli were also visually presented according to French orthographic rules. As this experiment was part of a larger study that took place over two sessions, any given participant was only presented with half of the stimuli in a counterbalanced manner. Each stimulus was presented 25 times over five experimental runs. The order of the stimuli was pseudorandomized in eight different lists so that the same category (high or low monitoring load) was not presented more than twice in a row and that each stimulus was presented N times before any other stimulus was presented N + 1 times.
Procedure Syllables remained on the screen for 798 ms and were synchronously presented auditorily for 660 ms. They were preceded by a fixation cross for 532 ms and followed by a blank screen during which the participants were instructed to pronounce the presented syllable. The time for pronunciation was 1,530 ms. The jittered interstimulus-interval was 930 to 1,464 ms. In sum, on average a single trial lasted 4. 059 seconds, and the experimental runs lasted 6 minutes each. Figure  2  shows an exemplary trial. Stimulus presentation was controlled by a custom-made software compiled using the LabVIEW development environment (National Instruments) and OptoActive audio system (OptoAcoustics). This software also allowed the recording of vocal productions: Trials were recorded and labeled independently using a multifunction NI PCIe-6353 DAQ (National Instruments) and the FOMRI-III optical noise canceling microphone (OptoAcoustics).
MRI Data Acquisition Data were collected on a 3-Tesla Siemens Prisma scanner at the Marseille MRI Center using a 64-channel head coil. Functional images covering the whole brain, including the cerebellum, were acquired during task performance, using a multiband blood oxygen level dependent (BOLD)-sensitive gradient echo planar imaging (EPI) sequence  (Moeller et al. , 2010)  (repetition time [TR] = 1,224 ms, echo time [TE] = 30 ms, flip angle = 66°, 54 slices with a thickness of 2. 5 mm, field of view [FOV] = 210 × 210 mm 2 , matrix = 84 × 84, slice thickness = 2. 5 mm, multiband factor = 3). Whole-brain anatomical magnetic resonance imaging (MRI) data were acquired using high-resolution structural T1-weighted image (MPRAGE sequence, voxel size = 1 × 1 × 1 mm 3 , data matrix 256 × 256, TR/TI (inversion time)/TE = 2,300/900/2.
The preprocessed data were imported to and analyzed using the Statistical Parametric Mapping software (SPM12; https://www. Fil. ion. ucl. ac. uk/spm/software/spm12/) in MATLAB R2018b (Mathworks Inc. , Natick, MA). Smoothing was performed with an isotropic Gaussian kernel (full-width at half-maximum = 5 mm). Regressors of no interest included 24 head movement regressors, the mean signal of the CSF and WM mask, and 24 aCompCor regressors related to CSF and WM. For the univariate whole brain analysis, we created a general linear model (GLM) for each participant. The GLM included, for each of the five runs, the following regressors of interest: AnyLoad_Incorrect (corresponding to all overtly committed errors), HighLoad_Correct (corresponding to the correctly produced phonotactically illegal pseudowords), LowLoad_Correct (corresponding to the correctly produced phonotactically legal pseudowords).
Regressors of interest were convolved with the canonical hemodynamic response function. Functional data were filtered with a 128 s high-pass filter. For each participant, we estimated the two contrasts: AnyLoad_Incorrect (corresponding to all overtly committed errors) versus HighLoad_Correct and LowLoadCorrect (corresponding to all correctly produced pseudo words) [2 -1 -1] and HighLoad_Correct (corresponding to the correctly produced phonotactically illegal pseudowords) versus LowLoad_Correct (corresponding to the correctly produced phonotactically legal pseudowords) [0 1 -1]. These two contrasts were then explored at the group level with a random effect analysis. All statistical comparisons were performed with a voxelwise threshold of p < 0. 001 and a cluster extent threshold of 25 voxels.
ACKNOWLEDGMENTS Centre de Calcul Intensif d'Aix-Marseille  is acknowledged for granting access to its highperformance computing resources. Work for this study was performed in the  IRM-INT Center  ( UMR 7289 ,  CNRS-Aix-Marseille University ), platform member of  France Life Imaging network. This work, carried out within the  Institute of Convergence ILCB  ( ANR-16-CONV-0002 ), has benefited from support from the  French government (France 2030 ), managed by the  French National Agency for Research (ANR)  and the  Excellence Initiative of Aix-Marseille University  (A*MIDEX).
Acknowledgments The authors would like to thank the patients and their relatives for their patience and cooperation,  Maestro Lorenzo Coppola  and the participating orchestras (notably the  Paris Mozart Orchestra ), the CENIR neuroimaging team at  Paris Brain Institute  (notably  Benoît Béranger ),  Alexandre Guerre ,  Laura Vincent ,  Jean-Jacques Repain  and  Yannick Mahé  at  Sorbonne University.
Data collection After electronically informed consent signature, demographic data and degree of technological readiness, based on the validated Technological Readiness Index, were collected. 27  A standardised MCQ test with 15 questions (four options per question), developed and tested by the authors, was used to assess and compare radiation safety knowledge before starting the MOOC. All correct answers were retrievable in the offered course materials. During the MOOC, performance metrics including test scores and number of test attempts were automatically saved. Radiation safety knowledge was re-assessed with the same MCQ test in a random order after MOOC completion. A post-course survey assessed task performance and task experience but also asked for feedback (open questions). The validated multidimensional conceptual model for evaluating e-learning system success (EESS) was used to appraise task experience.
28  Relevant items were selected and adjusted to the present context. Specific items were added to the 10 evaluation categories (Supplementary Figure  S1 , spreadsheet 1). All items were scored on a five point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Post hoc, mean category scores were calculated according to the EESS model. Knowledge retention was evaluated six months after MOOC completion via the previously used MCQ test locally, but not internationally because of logistical reasons. The electronic data capture tool REDCap hosted at Ghent University  29  was used to collect study survey data During the baseline survey, demographic data and degree of technological readiness, based on the validated Technological Readiness Index was collected online. (Supplementary Figure  S1 ).
Future perspectives Future research should determine whether the proven relevance, acceptability, and knowledge increase also leads to behavioural changes and impacts outcomes. Therefore, a large scale prospective study will analyse radiation safety behaviour and radiation doses in the hybrid room during elective endovascular procedures before and after MOOC implementation. A medical data recorder will enable a detailed post hoc radiation safety behaviour assessment using an objective structured assessment of technical skill derived and validated radiation safety scale. 19, 37  Radiation dose parameters will be automatically collected by the fixed C arm system and supplemented with individual dose estimates using a real time staff dose monitoring tool. 38  Furthermore, this study will assist in determining the optimal timing of refresher courses to optimise radiation safety behaviour.
There are no existing datasets of this size that have collected functional imaging data during language task performance, but resting state functional connectivity is predictive of task-related functional activation  [21] [22] [23]  and also reveals meaningful organisation of the human cortex  [24, 25]. The resting state functional connectivity approach involves identifying similarities between different brain regions in terms of their time course variation in the deoxyhemoglobin to hemoglobin ratio during the resting state, i. e. while participants are awake but not performing any particular task during functional magnetic resonance imaging (fMRI). The task-free nature of resting state fMRI makes it insensitive to choices in task design that can affect lateralization estimates  [26] , and is potentially more useful for studying the language network as a whole rather than circuits activated by one specific task.
This SNP was Figure  1 : Associations with language network connectivity and asymmetry, for genetic variants with population frequencies ≥ 1 percent. A & B: Multivariate GWAS Manhattan plots for language network edges (A) and hemispheric differences (B). The genome is represented along the X axis of each Manhattan plot, with chromosomes in ascending numerical order and their p-to-q arms arranged from left to right. The Y axis of each Manhattan plot shows the pointwise significance of multivariate association, and each dot represents a single variant in the genome. The horizontal dashed line represents the threshold p ≤ 5 × 10 -8 for genome-wide multiple-testing correction. C & D: Genes associated with language network edges (C) and hemispheric differences (D) tend to be most strongly expressed in prenatal brain tissue compared to postnatal brain tissue, according to MAGMA analysis of the Brainspan gene expression database.
Causality cannot be determined in a cross-sectional dataset of the kind used in our study. For example, genetic disposition may affect prenatal brain development in ways that alter functional asymmetries, and this seems likely given that many of the relevant genes are upregulated in the prenatal brain, and that functional asymmetries already exist in neonates  [15]. However, some functional asymmetries may also follow, or be reinforced through, behaviours that are influenced by genetic disposition  [19]. Consistent with this latter possibility, a meta-analysis of neuroimaging studies of dyslexia suggested that reduced left-hemisphere dominance is only present in adults and not in children  [61]. The UK Biobank consists of middle-aged and older adults, but future studies of polygenic risk for dyslexia should test the association with brain connectiviy in younger samples, to help address the developmental/aging questions.
Genetic variant-level QC Four different genetic datasets were prepared, as needed for four different analysis processes: 1. Array-based genotype data were filtered, maintaining variants with linkage disequilibrium (LD) ≤ 0. 9, minor allele frequency (MAF) ≥ 0. 01, Hardy-Weinberg Equilibrium test p-value ≥ 1 × 10 -15 (see  [43] ), and genotype missingness ≤ 0. 01 for REGENIE step 1 (below). 2. Imputed genotype data were filtered, maintaining bi-allelic variants with an imputation quality ≥ 0. 7, Hardy-Weinberg Equilibrium test p-value ≥ 1 × 10 -7 and genotype missingness ≥ 0. 05 for association testing in MOSTest (below). 3. For genetic relationship matrices SNPs were only used if they were bi-allelic, had a genotype missingness rate ≤ 0. 02, a Hardy Weinberg Equilibrium p-value ≥ 1×10 -6 , an imputation INFO score ≥ 0.
Common and rare variant association tests were run conditional upon the LOCO predictor in REGENIE step 2. Functional annotation of variants was conducted using snpEff v5. 1d (build 2022-04-19)  [91]. Physical position in the genome was used to assign variants to genes and were annotated with Ensembl release 105. Combined Annotation Dependent Depletion (CADD) Phred scores for variants were taken from the database for nonsynonymous functional prediction (dbNSFP) (version 4. 3a)  [92]  using snpSift 5. 1d(build 2022-04-19). Variants were then classified for downstream analysis based on their functional annotations to either be included in a 'Strict' or 'Broad' filter or be excluded from further analysis. The 'Strict'-filter only included variants that were annotated with a 'High' impact on a canonical gene transcript (variant types include highly disruptive mutations like frameshifts) outside of the 5% tail end of the corresponding protein (high-impact variants in the 5% tail ends usually escape nonsense-mediated decay) or a 'Moderate' effect on a canonical gene transcript combined with CADD Phred score ≥ 20 (these include likely deleterious protein-altering missense variants).
Data and code availability The primary data used in this study are from the UK Biobank. These data can be provided by UK Biobank pending scientific review and a completed material transfer agreement. Requests for the data should be submitted to the UK Biobank: https://www. ukbiobank. ac. uk. Specific UK Biobank data field codes are given in Materials and Methods. Other publicly available data sources and applications are cited in Materials and Methods. We have made our mvGWAS summary statistics available online within the GWAS catalog: https://ebi. ac. uk/gwas/. This study used openly available software and codes, specifically GCTA (https://cnsgenomics. com/software/gcta/#GREML), MOSTest (https://github. com/precimed/mostest), FUMA (https://fuma. ctglab. nl/), MAGMA (https:// ctg. cncr. nl/software/magma, also implemented in FUMA), PRS-CS (https://github.
Acknowledgements This research was funded by the  Max Planck Society (Germany) , together with grants from the  Netherlands Organisation for Scientific Research (NWO)  ( 054-15-101 ) and  French National Research Agency (ANR , grant No. 15-HBPR-0001-03 ) as part of the  FLAG-ERA consortium  project ' MULTI-LATERAL ', a  Partner Project  to the  European Union's Flagship Human Brain Project. The study was conducted using the  UK Biobank  resource under application no. 16066  with C. F. as the principal applicant. Our study made use of quality-controlled brain images generated by an image-processing pipeline developed and run on behalf of the  UK Biobank. The funders had no role in study design, data collection and analysis, and the decision to publish or preparation of the manuscript. The authors thank  Else Eising ,  Giacomo Bignardi  and  Tristan Looden  for their thoughts on the methodology.
23  To the date, this is the first work that analyzes the impact of COVID-19 in the news media coverage of suicide utilizing Big Data an ML techniques. 24  In this study, we focus on assessing the content and topic classification of the news about suicide and its evolution during and in the aftermath of the COVID-19 pandemic. We pose the hypothesis that the rise in health topics' attention related to the COVID-19 pandemic  4  has put mental health issues front and center. 25  In addition, we extract the causal relation between extracted topics (i. e. , tags). Finding the relationship (it terms causality or rather associativity) in topics is important because it reveals out the way in which suicide is covered in the news media.
Web scraping The key tool applied in this study is the Big Data technique called web scraping (also called web crawling or mining) to extract an immense amount of texts (20Mil tweets and 100K news). In particular, this technique gets a group of servers querying massive quantities of data from public web pages on the internet. We deployed a network of cloud servers and a local server to break up the load of the work: (i) A Mongodb database server: a no-sql database widely used in Big Data projects in order to store large amounts of data. (ii) A cloud server (scraper): this server contains our software coded with the NodeJS programming language. This software searches the web archive of the newspapers and the different twitter accounts, then the nodes save the data in the Mongodb database. (iii) A local server: A server with scripts coded in NodeJS and Python searches our Mongodb database and trains the ML models to classify the subjects in the news and tweets.
Then, this server analyzes the full database and calculates by modeling the probability that each item has of covering the subject of suicide. The previous process gives us two datasets: -A newspaper news dataset: We extracted 100K news from American and British newspapers (from CNN, the Guardian UK, the Guardian US, the New York Times, The Sun US, The Sun UK, L. A. Times and USA Today) from January 2020 to April 2021. These newspapers were chosen because they are some of the most read international news sources available through the Internet and written in the English language. -A tweet news dataset: We also extracted 20Mil tweets from the 100 main news twitter accounts, which include the previous newspaper web pages. These tweets were published from January 2019 to April 2022. Twitter was the social media platform chosen for this study because of the tweets' accessibility.
In newspaper media, every piece of news is classified by tags. This tag or keyword helps describe the text and allows it to be found again by browsing or classification (we used common tags for all the newspapers, such as justice, economy, politics, international, technology, health,. ). The goal was to classify news articles and tweets as either being about suicide or COVID. We therefore extracted over a million tweets with the hashtags: #suicide, #suicided, #selfharm, #teensuicide and #covid, #corona, #covid19, #coronavirus, #pandemic, #lockdown. This corpus of data conformed by newspaper news and Twitter news on one hand and sucide and covid tweets on the other, allowed us to develop the following three ML models: -Multilabel Subject Classification Model: Using the full corpus of tagged news we trained a ML model using Naive Bayes (NB) Multilabel classification.
As in the previously described model, this model takes an input text (if the text is short it uses NB, in another case it uses SVM) and outputs a number between 0 and 1 that represents the likelihood that text has of covering the subject of covid. The models were trained using the python package scikitlearn. Feature extraction was achieved by applying a n-gram vectorizer to the texts. In order to train suicide and covid subject models we used a dataset of hashtags involving those subjects, whereas the multilabel subject dataset was trained using the tags provided by the newspaper news metadata. We applied SVM for short text and NB for longer texts because this combination returned the best results in terms of precision, recall and F1 score. Table  1  summarizes the metrics of the models obtained: The data extraction and analysis process is detailed in Fig.
Causal graphs Causal graphs, also known as causal diagrams or directed acyclic graphs or, are graphical representations of causal relationships between variables. They are used in many fields, including epidemiology, 27 economics 28 or psychology,  29  to understand and model the relationships between variables and to make predictions about future outcomes. In a causal graph, variables are represented as nodes, and causal relationships between variables are represented as directed edges or arrows. The direction of the arrow indicates the direction of causality. For example, if we have variables A and B, and A causes B, then there will be an arrow pointing from A to B in the graph. If there is no causal relationship between two variables, they are not connected by an arrow. Since our dataset consists of a high number of tagged news media items, causal graphs are a useful tool to understand the connections between the different tags.
Temporal fluctuations in suicide and COVID awareness In order to understand the temporal fluctuations of the presence of news (tweets and newspaper) regarding COVID and suicide, we used our covid subject classification and our suicide subject classification models to analyze our datasets described before. Table  1  ML metrics of the models trained for news classification. The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The recall is the ratio tp / (tp + fn) where fn is the number of false negatives. The F1 score can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. This process consists in running the model on each news article and tweet to obtain two probabilities: the probability of the presence of the suicide subject and the probability of the covid subject in a given piece.
18). This gives us an insight about the way these subjects (health, covid, suicide and travel) are related to each other. To complete this analysis, we used casual graphs to explore how the presence of covid probability can be explained from the rest of the probabilities. This analysis can help to understand conditional dependencies between variables. Fig. 4 (A  and B ) shows the result of the NOTEARS structure learning algorithm. 30, 31  This method consists in a very optimized algorithm for learning bayesian networks based on continuous optimization for structure learning. To obtain the graphs (A) and (B) we applied the NOTEARS algorithm over the categorized tweets and news of the dataset during the first three months of the pandemic. After comparing the resulting causal graphs produced by the algorithm we found out that the subgraph (C) was the most common one when focusing on the relationship of suicide-related news with the rest of the categories.
Thematic evolution of Covid and suicide-related news From the full dataset of news (newspaper or Twitter news) we extracted those referring only about COVID or suicide. We did this by selecting those that had a high suicide subject probability (more than 0. 9 using our suicide subject classification model) or by selecting those that had a high covid subject probability (more than 0. 9 using our covid subject classification model). Using our multilabel subject classification model we studied the subjects present in the suicide and covid news in Figure  3  Correlation matrix of all Twitter news from the beginning of the pandemic (a similar matrix was obtained using the newspaper news). We included all the probabilities from the multilabel subject model and the probability of suicide and covid obtained with the other two models.
[42] [43] [44] [45]  This strategy is applied to the general population, where few strategies have been proven effective. 46, 47  On general lines, journalists should avoid explaining and detailing suicidal methods and should focus on the possibility to get help and overcome the suicidal crisis, with the aim to educate the population in mental health Figure  4  Structural Causal Graph of the full dataset of news during the pandemic first three months (A, B) (for computational reasons, similar graphs were obtained the following months). On the left we see the graph corresponding to Twitter news, and on the right, to newspaper news. Graphs A and B were obtained using the NOTEARS algorithm, each arrow represents a causal relationship or association between nodes. (C) subgraph represents suicide and covid nodes in Twitter and newspaper news.
Functional magnetic resonance (fMRI) data These data were obtained from a published source  (Cléry et al. , 2021)  and were acquired on male and female adult awake marmosets. Each animal was recorded while presented with video stimuli of social interactions (playing, grooming etc. ) and non-social behaviors (eating, foraging, etc. ), as well as to the scrambled versions of the same videos (four conditions in total). For each monkey, all four conditions were analyzed and compared, revealing the brain regions that show significantly stronger activation while observing the social condition comparing to all other three conditions  (Cléry et al. , 2021). These areas have been coded as "social" in our dataset. The areas showing instead a significantly stronger activation during the observation of non-social behaviors have been coded as "non-social" and all others as "neither" (Data S1).