dataesr/scientific-paragraphs-categorization huggingface dataset:
Music-related genes A total of 334 genes were selected through a thorough scrutiny of the literature and datasets (Supplementary Table  S2 ). A narrow selection  10. 3389/fnagi. 2023. 1063536 Frontiers in Aging Neuroscience 08 frontiersin. org of 127 candidate genes was highlighted based on transcriptomic/ epigenomic analyses (see below). Six of those were particularly prioritized as candidate genes, namely, SNCA, SLC6A4, ASCC2, FTH1, PLAUR, and ARHGAP26, because: (i) they were repeatedly found to be associated with musical aptitude in previous work and have emerged in transcription-based studies, and (ii) they occur consistently in the literature on AD. Among the biological processes in which the 334 music-related genes are involved, we found that learning / memory was the most significant pathway (P-adjusted = 3.
Epigenomic commonalities between AD and music-related genes There are 8,497 DMPs (FDR p-value <0. 05) when contrasting AD patients vs. controls. 6,432 (75. 7%) CpGs appeared as hypomethylated and 2,065 (24. 3%) as hypermethylated in AD samples. A total of 5,928 (69. 8%) of these 8. 4 k CpGs could be annotated to genes, resulting in 4,097 unique genes. Of the positions annotated to genes, 345 (4%) CpGs are annotated and related to more than one gene. 2 www. string-db. org For most of these genes, there are multiple DMPs associated (2. 76 ± 1. 28) gene-set and pathways enrichment analysis with the 8. 4 k DMPs points to the following pathways depending on the database used: (a) KEGG: cytokine-cytokine receptor interaction, JAK/STAT signaling pathway, osteoclast differentiation, chemokine signaling pathway; (b) Reactome: neutrophil degradation, signaling by interleukins, nucleotide-binding domain, leucine rich repeat containing receptor (NLR) signaling pathways, platelet activation, signaling and aggregation, and (c) GO: leukocyte activation, cell activation, positive regulation of immune system process, immune effector process, cell migration, cell motility, localization of cell, immune response, locomotion.
Gene expression commonalities between Alzheimer's disease and music-related genes After data normalization and merging, a total of 19,145 genes were common when examining the three datasets of AD meta-analyzed in the present study. A quality control check of the normalized data through a principal component and clustering analysis highlighted 12 outlier samples belonging to the dataset GSE63061 that were finally disregarded for downstream analysis (GSM1539649, GSM1539651, GSM1539652, GSM1539653, GSM1539655, GSM1539644, GSM1539646, GSM1539647, GSM1539648, GSM1539650, GSM1539722, GSM4187601). A total of 1,837 genes were found to be upregulated and 1,717 downregulated, all statistically significant in AD patients when compared against healthy controls (Figures  4B, C ; Supplementary Table  S5 ).
Consensus co-expression analysis between Alzheimer's disease and music conditions To further investigate possible molecular links between AD and music-related genes, we generated a consensus co-expression network using AD patients and the only transcriptomic dataset related to musical stimuli available, aiming at detecting common conserved co-expression modules. After applying a variance gene filtering (see Methodology section), 14,358 and 15,685 genes were retained in AD and music datasets, respectively, from which 11,920 genes were shared between both datasets and therefore available for follow-up consensus analysis. A total of 84 out of the 303 music-related gene-set in the meta-analysis (27. 7%) did not pass the variance filtering; therefore, a reduced subset of 219 (72. 3%) music-related genes was among the 11,920 genes used as input.
The consensus co-expression analysis revealed 20 co-expressed gene modules of very different sizes (ranging from 61 genes of the 'darkred' module, to 3,482 of the turquoise module; Figure  5A ; Supplementary Figure  S4A, B ). The overall preservation of the eigengene networks was moderately high (D = 0. 8; Supplementary Figure  S4C ) and the inter-module relationships in the two data sets has similarities. The highest eigengene preservation measurement was in the salmon and yellow modules (Supplementary Figure  S4C ). The module with more music-related genes relative to the size of the module was the salmon one (18/195; Figure  5B ); interestingly, most of them belong to the core of the module (11 genes in music dataset and 14 in the AD dataset with MM > 0. 8; Figure  5C ) suggesting a major role within it.
, 2016) , attention deficit and hyperactivity  (Castellanos and Tannock, 2002; Gizer et al. , 2009)  and addiction  (Baik, 2013)  has been also pointed out. In line with these neuroscientific observations, our gene expression data suggest that there are connections between music, AD, and dopamine, represented by some common genes participating in these processes. One of the most remarkable genes involved in dopaminergic pathways is SCNA; a gene that also showed up as downregulated in the differential expression analysis of the AD dataset, and which is also included in the music-related gene-set (Supplementary Table  S2 ). SNCA is mainly expressed in the brain, and it plays a role in the synaptic transmission. It has been associated with learning and memory, and neurodegenerative disorders such as Parkinson's disease  (Polymeropoulos et al.
Acknowledgments This study received support from  Instituto de Salud Carlos III (ISCIII) :  GePEM  ( PI16/01478 /Cofinanciado FEDER; AS),  DIAVIR  ( DTS19/00049 /Cofinanciado FEDER, AS),  Resvi-Omics  ( PI19/01039 /Cofinanciado FEDER, AS),  Agencia Gallega de Innovación (GAIN) :  Grupos con Potencial de Crecimiento  ( IN607B 2020/08 , AS);  Agencia Gallega para la Gestión del Conocimiento en Salud (ACIS) :  BI-BACVIR (PRIS-3 , AS), and  CovidPhy  (SA 304 C,.
Methods Participants. Thirty healthy adults took part in the study (17 females; M = 32. 83, SD = 9. 8 years; 26 righthanded). Because this study used a new experimental paradigm, we had no available data to determine the appropriate sample size. They all provided informed consent before participating in the study, which was conducted in accordance with the Declaration of Helsinki. The study was approved by the French ethics committee (Comité de protection des personnes Ouest II, Angers). Participants had normal or corrected-to-normal vision and audition; they did not present any personal history or current neurological diseases, psychiatric disorders or addictions as assessed by the Mini International Neuropsychiatric Interview (MINI)  22. On average, participants had low (M = 40. 9, SD = 6.
9) and moderate (M = 46. 1, SD = 8. 6) scores for state and trait anxiety, respectively, as measured with the State and Trait Anxiety Inventory form Y (STAI-Y)  23. Participants presented normal to moderate depression symptoms (M = 7. 5, SD = 5. 3) as assessed with the Beck Depression Inventory (BDI-II)  24. Participants were recruited by public announcements in the Lille University Hospital, Lille University, and social media and were paid 20 euros for their time. Stimuli. We used forty-five scenarios (15 pleasant, 15 unpleasant, and 15 neutral) ranging from everyday scenes to more arousing and uncommon scenes, involving the same number of animals, landscapes, and interindividual interaction scenes per emotional category. These scenarios correspond to 45 360° videos from either the C2Care database (https:// www.
The mean values for valence and arousal were 2. 56 (SD = 0. 62) and 6. 70 (SD = 1. 28) for the unpleasant stimuli, 5. 27 (SD = 0. 46) and 2. 30 (SD = 0. 37) for the neutral stimuli, and 7. 76 (SD = 0. 36) and 4. 53 (SD = 1. 36) for the pleasant stimuli, respectively. Procedure. Participants were seated comfortably in a chair in front of a computer. They completed the questionnaires, and then, the physiological data collection devices were installed before the start of the experiment. Affective forecasting phase. In the first experimental phase, after the presentation of the instructions, each trial started with a fixation cross (presented randomly between 15 and 20 s) followed by the presentation of a scenario for approximately 5 s (Fig. 1 ). During the following 15 s, participants were asked to predict their emotional responses to the scenario if they were to experience it (see Supplemental information).
Participants responded to the SAM directly in the virtual interface using a virtual reality controller with their dominant hand. More precisely, they had to target the corresponding number with the virtual reality controller and press a button with the index finger to validate their response. The virtual reality program was coded with Unity® Software (2021. 2. 1, Unity Technologies, San Francisco, California, United States), launched with the computer used in the affective forecasting phase, and synchronized with the laptop used for physiological data acquisition. Data recording and processing. Subjective measures. During the two experimental phases, participants had to assess the arousal [from 1 (very calm) to 9 (very excited)] and valence [from 1 (very unpleasant) to 9 (very pleasant)] dimensions of the scenarios with the SAM  25.
Both the affective forecasting and emotional experience phases utilized computerized versions of the scales. During the affective forecasting phase, participants also rated the frequency of the scenarios in daily life on a Likert scale from 1 (never) to 5 (more than once a week). Participants completed an adapted version of the Immersion Propensity Questionnaire (IPQ) to assess their ease of being immersed and the Presence Questionnaire (PQ) [French version translated by the Cyberpsychology Laboratory of the University of Quebec in Outaouais (2002), original version by Witmer & Singer (1998)  26  ] to assess the perceived quality of virtual environments (see Supplementary information). Physiological measures. Physiological signals were acquired with an MP160WSW multichannel integration system coupled with BioNomadix wireless devices to collect electrodermal activity (EDA) and HR data (BIOPAC Systems, Inc.
). Data were initially collected at a rate of 2000 Hz. The laptop dedicated to physiological data acquisition (HP ZBook 15 G2, 1000 Hz) displayed the participants' electrodermal and cardiac activity in real-time with Acknowledge® software (Biopac, System) through a connection to the BIOPAC device. EDA was recorded using 2 disposable Ag/AgCl electrodes with a contact area of 11 mm (Biopac EL507) filled with isotonic gel (0. 5% saline in a neutral base, Biopac GEL101) using the Dermal Conductance technique. The electrodes were placed on the palmar surface of the nondominant hand (middle phalanx of the index and middle fingers). EDA signals were resampled offline at 50 Hz and quantified using the Continuous Decomposition Analysis (CDA) method from the Ledalab Toolbox (Version 3) in MATLAB (R2015a, The MathWorks, Inc.
, Natick, Massachusetts, United States). This method allows the extraction of the phasic (SCRs) and tonic (skin conductance level) components from the electrodermal signal. An analysis of the integrated skin conductance responses (ISCRs) was conducted for each trial and phase for each participant. This indicator, expressed in µS*s, corresponds to the area (i. e. , time integral) of the phasic response relative to the analysis window. Adaptative smoothing using the Gaussian method and a low-pass Butterworth filter (first-order low-pass filter with a 5 Hz cutoff) were applied to the data. Following the current recommendations for EDA analysis, the response window extended from 1 to 10 s after stimulus onset, and the minimum threshold of the response amplitude was 0. 01 µS  27. Finally, the data were square-root transformed to reduce skewness.
Electrocardiograms (ECGs) were recorded using 3 disposable Ag/AgCl electrodes (Biopac EL503) filled with conductive gel (Biopac GEL100). One electrode was placed on each clavicle and the left lower rib. Each individual's HR was computed from the ECG with Acknowledge® software (Biopac, System). For each participant and each trial of each phase, we computed the difference between the instantaneous HR and baseline HR (i. e. , the average instantaneous HR during the 3 s before stimulus onset). We determined the peak HR acceleration Statistical analyses. The final pre-processed dataset is available on OSF (https:// osf. io/ afntg/). Overall, 122 trials (representing, on average, 1. 13%, S. D. = 2. 28, for each participant) were discarded from the analyses due to technical issues. The analyses were conducted with R software (4.
1. 2). We applied linear mixed models (LMM) using the "lmerTest" package  28  on subjective (arousal and valence scores) and autonomic (ISCR and peak cardiac acceleration) data, with Emotion (unpleasant, neutral, and pleasant) and Experimental phase (affective forecasting and emotional experience) as fixed effects and Participants as a random effect (random intercept). Diagnostics of linearity, homoscedasticity, and normality of residuals and random effects indicated that the model assumptions were met. We applied a Type III analysis of variance with Satterthwaite's method to each LMM to assess the global effects of each predictor, using a threshold of significance at p < 0. 05. In case of significant interactions, we performed followed-up contrasts with the "emmeans" package  29  by computing t-ratios with the Kenward-Roger's method based on the estimated marginal means from the LMM.
We were specifically interested in nine comparisons: we intended to compare emotions in each phase and across phases. For these follow-up contrasts, statistical significance was thus accepted at a Bonferroni-adjusted alpha level of 0. 005. For each LMM, we used the "piecewiseSEM" package  30  to calculate the Marginal and Conditional R 2 , which represent the proportion of variance explained by the fixed effects, and by both the fixed and random effects. The associations between subjective and autonomic responses in each experimental phase were assessed by calculating Bravais-Pearson correlational coefficients using mean values computed for each scenario (from the forecasting and experience phases). For each association, we conducted a bootstrap analysis (2000 replications) of the Pearson correlation.
458 Search strategy and study identification. We iden-459 tify eligible studies using the following steps. First, we 460 developed a Boolean search string defining our population 461 (school-aged children), exposure (the COVID-19 pandemic), 462 and outcomes of interest (e. g. , math and reading). The full 463 search string can be found in Section 1 of the Supplementary 464 Information. We used this string to search the following aca-465 demic databases: Coronavirus Research Database, Educa-466 tion Database, ERIC, International Bibliography of the So-467 cial Sciences (IBSS), Politics Collection (PAIS index, policy 468 file index, political science database, and worldwide political 469 science abstracts), Social Science Database, Sociology Col-470 lection (applied social science index [ASSIA] and abstracts, 471 sociological abstracts, and sociology database), CINAHL, 472 and Web of Science.
487 Data extraction. From the studies that meet our inclu-488 sion criteria we extract all estimates of the effect of the pan-489 demic on learning progress, separately for math and reading 490 and for different school grades. We also extract the corre-491 sponding sample size, standard error, date(s) of measure-492 ment, author name(s), and country. Last, we record whether 493 studies differentiate between children's socio-economic back-494 ground, which measure is used to this end, and whether 495 studies find an increase, decrease or no change in learning 496 inequality. We contacted study authors, if any of the above 497 information was missing in the study. Data extraction was 498 performed by the first author and validated independently Pre-registration. We prospectively registered a protocol of our systematic review and meta-analysis in the International Prospective Register of Systematic Reviews (CRD42021249944) on 19 April 2021.
| Methods and measures We used the same stimuli to vary front-of-package flavor-giving ingredient images as for Study 1. A sample of 199 French consumers participated in an online study conducted by a survey institute. Participants were told about a project on a new food product launch. The level of cognitive resources varied by the presence (low) or absence (high) of an audio podcast on which respondents were asked to focus their attention (see Supporting Information: Appendix B for details). Participants then randomly looked at one of the two front of the packages for 25 s and completed a questionnaire. We only retained people likely to consume honey-flavored cereal, with the correct recall of memorized data  (Gilbert & Gregory Hixon, 1991) , and with correct answers to attention trap questions  (Aydınoğlu & Krishna, 2019).
Data collection at birth At birth, parents completed standardized questionnaires that included questions about their child's sex, place of residence, presence of siblings, socioeconomic status (SES), and the level of education of each parent. Family SES was determined according to parents' occupations classified according to three categories (low -unemployed/student/blue-collar workers/low-level white-collar workers; medium -craftsmen/shopkeepers/intermediate-level white-collar workers; high -highlevel white-collar workers), with the highest SES of the two parents taken as the SES for the family.
Data collection during lockdown The online questionnaire included data on weight status, COVID-19 infection, perceptions, and behaviors of adolescents during the first lockdown (between March 17 and May 10, 2020). Possible adolescent cases of COVID-19 were defined according to the European Centre for Disease Prevention and Control as adolescents with at least one of the following symptoms: cough, fever, shortness of breath, anosmia, ageusia, or dysgeusia  [21, 22]. Questionnaires were pilot tested.
Acknowledgements We are grateful to all children and parents of the PARIS (Pollution and  Asthma Risk : an Infant Study) birth cohort who participated in this study, and to the administrative staff ( Dominique Viguier ,  Marianne Bijou , and  Chimène Yimier ) for their involvement in the PARIS cohort follow-up. We thank  Agnès Lefranc  and  Claude Beaubestre  for reviewing the questionnaires.
The concordance for expert clinicians' assessment of obsessivecompulsive symptom severity was excellent (Swerdlow et al, 1999). Brain volume estimations based on magnetic resonance imaging MRI data were collected for a subsample of the subjects included in the study (n = 152), gathering 42 individuals with ASD, 89 rst-degree relatives and 13 subjects from the general population. Acquisitions were performed using the following parameters: spoiled gradient recalled echo (SPGR), 1mm isotropic, repetition time (TR) = 25ms, echo time (TE) = 6ms, ip angle = 30º. For all participants, MRI data were collected using previously described parameters, with a 1. 5 Tesla scanner using a T1-Weighted acquisition  (Maruani et al. , 2015). Raw DICOM images were converted to NIFTI format with dcm2niix (https://github.
com/rordenlab/dcm2niix) and defaced with MRIdeface  (Bischoff et al. , 2007). Cortical reconstruction and volumetric segmentation were performed with Freesurfer software version 6. 0. 0 (http://surfer. nmr. mgh. harvard. edu/) and visual control of the segmentation quality was done using the QCAPP (https://github. com/neuroanatomy/QCApp).
To identify the items belonging to a speci c factor, we observed which items had high loadings (> 0. 20 ) for a speci c factor but low loadings (< 0. 10 ) for the others. PCA was performed using JMP Pro 16. 0 (SAS Inc. , Cary, NC). To explore the interactions between RRBI-derived factors and brain volumes, we ran a similar analysis on brain volumes extracted from automatic segmentation of the grey and white matter structures. Each brain volume was standardized. We also used general linear models adjusted for age, sex, and age*sex interactions to generate structural brain volume residuals. The relationship between the RRBI-derived factors and standardized structural brain volume residuals was explored using linear regression methods. All tests were two-tailed. Type I error rate was controlled using the false discovery rate (FDR) method.
FUNDING This study was part of a large genetic study sponsored by  INSERM , and funded by the  Institut Pasteur ,  INSERM ,  Fondation FondaMental ,  APHP ,  Fondation de France ,  Fondation à la Recherche Médicale ,  Roche Institute for Research and Translational Medicine , and  Investissements d'Avenir program  ( ANR-11-IDEX-0004-02 ,  ANR-10-COHO-10-01 ,  ANR-12-SAMA-0014 ).
In wave 2, information about the experience of potentially traumatic events before the attacks was collected using the following questions (yes/no): "Did you personally experience other traumatic moments before the November 2015 attacks where you felt brutally threatened or that your life was in danger? The events: assault, sexual assault, rape, military combat or war zone experience, prison, and childhood sexual contact with an older person, were classified as intentional traumatic events. The events: natural disaster, serious accident, explosion, fire, and life threatening illness, were classified as unintentional. The responses "other" were reclassified into one of the two previous categories. These same data were collected for traumatic events that occurred after the attacks. For the second wave, preparedness to face potentially traumatic interventions related to terrorist attacks, which is usually not measured in epidemiological studies after terror attacks, was assessed through two questions asked on preparedness before and after the attacks (never/once/several times): awareness of psychological consequences (overwhelmed stress, PTSD, burnout, etc.
These data were used to create a continuous variable ranging from 0 to 14 co-occurring somatic problems. As the data were collected during the COVID-19 pandemics, participants' concerns about the COVID-19 pandemic were also rated on a scale of 0 "Not at all" to 10 "A lot". This variable was recoded into 3 modalities: 0-3, 3-7, 7-10 [25].
Regarding the other independent variables, in order to keep the model as parsimonious as possible and because they evaluated phenomena that are relatively close, it was chosen to keep only one variable for each of the following dimensions: mental health history, previous traumatic or difficult events, training/awareness, social support. The choice of variables retained was based on the significance of the association in the multivariate model and/or on expert opinion when multiple variables were of interest. The analyses were performed using SAS Enterprise Guide version 7. 11 software.
, 2018). However, building a comprehensive group-scale network requires the engagement of a large proportion of the participants involved in the self-report activity in order to accurately represent the social network, calling for specific survey instruments that allow to collect the social ties of a participant while minimizing survey burden. The recent availability of such survey instruments has rendered collaborative network data collection easier and scalable, allowing researchers to capture temporal organizational networks within groups of various sizes  (Tackx et al. , 2021). In this study, we tackle the question of how participatory processes shape project performance within early citizen science projects addressing the SDGs. To do so, we focused on Crowd4SDG, a European project that guides young individuals from pitching an idea on a social platform to the design of prototypes of citizen science projects via a one-year cycle of innovation.