dataesr/scientific-paragraphs-categorization huggingface dataset:
The model with the lowest AIC value is considered the best-fitting model (i. e. representing the optimal trade-off between goodness of fit and parsimony, in terms of the number of parameters)  (Burnham et al. , 2011). This strategy has been widely applied in various research fields and with different types of data (e. g. ERPs:  Hall et al. , 2006; Schiano Lomoriello et al. , 2021; behavioral: Novick et al. , 2013; Boldrini et al. , 2020). In the present study, the best-fitting LME models were used for further analyses. All analyses were conducted using the R software (4. 2), specifically the lmer function from the lme4 package  (Bates et al. , 2015). Significance levels for fixed and random effects were computed using the anova function in the lmerTest package, which applies Satterthwaite's approximation for degrees of freedom.
Whereas the early and late ERP components reflected perceptual, cognitive and emotional processing, accuracy and reaction time reflected the entire evaluation process. To conclude, we would like to discuss a few possible limitations of the present study. Indeed, although faces selected from the ELF database have been used in previous studies to investigate participants' empathic responses (e. g. Sessa and Meconi, 2015; Schiano Lomoriello et al. , 2018; Farmer et al. , 2020) , it should be considered that they are static images, which may not be the best to mimic an interaction as real as possible. Although this investigation certainly provides an important indication of the relationship between the PPS and empathy, future studies should consider a more ecological design, in terms of both stimuli and potential interactive scenarios.
Design This study is a randomized double-blind controlled trial comparing two parallel groups. The intervention consists of 4 weeks of use of the Craving-Manager app. The control group will receive a restricted version of the app (EMA only). The study was registered on ClinicalTrials. gov (NCT04732676), and was approved by French Ethics Committee on 31 March 2021 (ID-RCB 2020-A01707-32).
Measurements Data will be collected through different questionnaires: the Addiction Severity Index (ASI), the Mini International Neuropsychiatric Interview (MINI), a Craving Evaluation scale, the Treatment Service Review (TSR), Feasibility and acceptability questionnaires, and some data will be directly collected by the smartphone app (see 2. 7 Craving-Manager app). The Addiction Severity Index (ASI) is a semi-structured interview to assess impairments that commonly occur due to substance-related disorders  (102). The modified and validated French version of the ASI (m-ASI) will be used to take into account tobacco and addictive behaviors  (103). The m-ASI explores six areas that may be affected by addiction: medical status, employment/support status, substance and behavioral addiction, family and social relationships, legal status, and psychological status.
Analysis strategy The primary objective to evaluate the efficacy of the Craving-Manager app over 4 weeks, as compared to a restricted (placebo) version of the app, will be assessed by comparing the percentage of participants for whom a 20% decrease in frequency of use of primary substance/ addictive behavior was observed, between the 1st and 4th week of smartphone app use, in the experimental group versus the control group. This 20% reduction threshold was defined in line with decrease in the "percentage of using days" observed with smartphone apps targeting alcohol  (75)  and substance use disorders  (80). Analyses will be conducted according to the intent to treat principle, in which all randomized participants will be included in the group in which they were first randomized and all their data will be used, regardless of the changes over the study duration.
To control for facial movements in the videos, these three areas moved dynamically to always be centred either in the eyes, the mouth or the face. We used these areas to define the AOIs in the analysis stage. To distinguish between saccades and fixations we used an I-VT fixation filter  40. Raw data were exported and analysed in python 3. 7. We excluded all measures where there was no co-occurring presentation of sound and face stimuli (i. e. , before and after speakers began/ended talking at the very start and at the very end of each video). We used two descriptive statistics to describe gaze patterns: fixation duration and number of fixations. To extract these measures, we computed the percentage of number of fixations within a specific AOI, by computing the ratio between the number of fixations to a specific AOI, to the total number of fixations during the trial.
Similarly, we computed the percentage of fixation duration as the ratio between the amount of time fixating within a specific AOI and the total fixation time in the trial. Pupil size data pre-processing. As for the gaze data pre-processing, we excluded from further analyses all data where there were no co-occurring sound/face signals (at the beginning and at the end of the video sentences). We then used the following preprocessing pipeline to clean pupil data. We measured participants' left and right pupil size with a Tobii Pro X3-120 (sampling rate: 120 Hz) and coded as NaNs pupil measures where pupil validity-as given by Tobii's eye tracker-was above a threshold of 4. When only one of the two measure was valid we kept only the valid one. If the two measures were valid, we averaged left and right measures into a single measure.
As participants had to actively explore stimuli in dynamic faces during the task, eye movement was important during trials, which added noise to the pupil measure. To reduce this noise, we down-sampled pupil measures to 30 Hz and performed a moving-average with a window size of 15 samples. In the following analyses, whenever we collapsed data across time, we computed the median pupil dilation to control for artefacts in the data caused by saccades, blinks and head movements. To further clean the dataset, we removed all the trials where pupil size was not measured correctly for more than 50% of the trial. With this procedure we excluded 2. 8% of the trials. Moreover, we excluded from further analyses all the data (mostly Nans) where pupil size was not computed correctly (e. g. , because left pupil size and right pupil size were not measured correctly), a procedure which rejected ~ 7% of the data points.
05); '. ': marginally significant differences between distributions (p < 0. 1). (χ 2 (3) = 4. 2, p = 0. 23), time × AOI (χ 2 (9) = 3. 3, p = 0. 95) or AOI × congruence (χ 2 (3) = 3. 2, p = 0. 36). These analyses suggest that the effect of congruence on pupil dilation is not localised in a specific temporal cluster-because congruence and time do not interact-but rather that there is a constant effect of congruence on pupil dilation across the trials in the emotion task. Finally, because in our stimuli the audiovisual information mismatch is only present within the mouth area (the only area where our algorithmic transformation manipulates facial pixels), we investigated how the first fixation within the mouth-and other AOIs-influenced pupil dilation. To do this, for each AOI and for each trial, we discarded all the data before participants fixate for the first time that specific AOI.
Acknowledgements Work partially funded by the  Swedish Research Councilto P. J, L. H. and P. A. S. ( 2014-1371 ), by  ERC  to JJA (StG CREAM  335536 ,  ERC   ACTIVATE , and  ANR REFLETS) , and by  UK Research and Innovation  to  P. A. S. ( HORIZON-MSCA-2021-PF-01 ). Experimental data collected at the  Centre Multidisciplinaire des Sciences Comportementales Sorbonne Universités-Institut Européen d' Administration des Affaires (INSEAD). All data reported in the paper are available on request. The authors thank  Florence Brun  for help in the first stages of experimental design and pilot testing.
Recruitment goals for each region were 542 adults, for a total of 4336 adults countrywide. Household participation rate was, on average, 90% (ranging from 86 to 93% depending on the region). Data were collected between January and July 2017. Local staff members received a 5-day training that provided instructions on survey method, cluster identification, a comprehensive and detailed review of questionnaires, and one day in the field testing the questionnaires. In total, the data collection process mobilized 64 teams of two individuals (one male and one female staff ). There was one supervisor for each province to manage the administrative aspects and coordinate the data collection process. Interviews were administered either in Dari or Pashtu according to the language spoken in the selected household.
Statistical analysis Statistical analysis was based on previous studies  (41, 49, 57) ; to facilitate data processing, epochs of five blocks were analyzed instead of single blocks (e. g. Blocks 1-5 corresponded to Epoch 1, Blocks 6-10 to Epoch 2, and so on). The Learning Phase consisted of four epochs, while the Testing Phase consisted of one epoch. Similarly to previous studies, two types of low-probability triplets, repetitions (e. g. 222, 333) and trills (e. g. 212, 343), were eliminated because people often show preexisting response tendencies to them  (56, 58). By eliminating these triplets, we could ensure that any high-versus low-probability differences were due to statistical learning and not to preexisting tendencies. We calculated mean accuracy and median RTs (for correct responses) for each participant and each epoch, separately for high-and low-probability triplets.
For calculating ratio scores, we transformed the data in the following way. We divided each participants' raw RT values of each trial type and each epoch by their own median RT in the first epoch of the task (for a similar approach, see 9,  35, 43, 60). This way, participants' performance was around 1 at the beginning of the task and changed as the task progressed. We then calculated standardized learning and memory scores by subtracting standardized RTs for highprobability triplets from standardized RTs for low-probability triplets. Higher standardized scores indicated better learning/memory. General skill knowledge scores were standardized in an identical way: Each participants' median RTs in Epoch 4 and Epoch 5 were divided by their median RT in the first epoch of the task. For log-transformed RT data, we applied a log N transformation on the trial-based raw RT data.
Then, we computed the mean of log-transformed RTs for each trial type and each epoch, separately for each participant. Log-transformed statistical knowledge scores were calculated by subtracting log-transformed RTs for high-probability triplets from logtransformed RTs for low-probability triplets. Log-transformed general skill knowledge was calculated for each epoch using the mean of trial-based log-transformed RT data. For the sake of brevity, we only refer to the results of these ANOVAs in the main text, where they are relevant in comparison with the results of raw RTs, and we report the exact statistics in the Supplementary Materials. Moreover, to explore consolidation in more detail, we fitted curves to the data of the Learning Phase and used the fitted parameters to predict statistical learning scores and general skill performance in the Testing Phase (see  [61] [62] [63].
Are there age-related differences in the consolidation of statistical knowledge? Before testing the age-related differences in consolidation of statistical knowledge, we tested the potential age-related differences in statistical learning. Analysis on raw RT data in the Learning Phase showed better learning under the age of 13, whereas analysis on ratio scores revealed comparable learning from childhood to young adulthood, followed by decreased learning from the age of 30. We present the exact statistics in the Supplementary Materials. To test 24-h consolidation of the acquired statistical knowledge, we contrasted statistical learning scores computed for the last epoch of the Learning Phase (Epoch 4) with the learning scores computed for the first epoch of the Testing Phase (Epoch 5) and submitted these scores to a mixed-design ANOVA with EPOCH (Epoch 4 vs.
These results suggest that the group differences observed in the raw average RT analyses above were largely driven by some age groups being on average slower in the task than other groups; controlling for this confound eliminated the group differences in the consolidation of general skill knowledge over the delay period. Similarly to the statistical learning scores, we further examined the magnitude of offline gains and possible age-related differences by predicting future general skill performance in the Testing Phase with a power law function (for details, see the Statistical analysis section). RSEs were calculated separately for the age groups, and they were between 4. 32 and 20. 54 (M = 10. 53), suggesting a generally good fit to the data. A difference score was calculated by subtracting the predicted RT data from the observed RT data.
Funding This research was supported by project No. 124148  (PI: K. J. ) and 128016 (PI: D. N. ) that have been implemented with the support provided by the  Ministry of Innovation and Technology of Hungary  from the  National Research, Development and Innovation Fund , financed under the PD and K funding schemes, respectively. The research was also supported by the  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 ); and the  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ; to D. N. ); and  ÚNKP-21-3 New National Excellence Program of the Ministry for Innovation and Technology  from the source of the  National Research, Development and Innovation Fund  (to E. T. -F. ).
To assess the generalizability of the trained model, we employed cross-validation [109]. Cross-validation involves dividing the dataset into multiple subsets, or folds, and training the model on a subset while evaluating it on the remaining folds. By averaging the performance across all folds, we obtained a more robust estimate of the model's performance. Furthermore, we used SHapley Additive exPlanations (SHAP) [110] values to assess the signi cance of each feature in the XGBoost model. By calculating the relative contributions of each feature to the expected result, SHAP values offer a consistent measure of feature relevance. This study gives us insights into the underlying relationships and in uences within the dataset and helps understand the relative impact of features on the model's predictions.
Epigenomic datasets and data analyses The association between aberrant epigenetic modifications leading to dysregulation of gene expression and AD progression has been thoroughly investigated  (Fenoglio et al. , 2018; Stoccoro and Coppede, 2018; Nikolac Perkovic et al. , 2021). We analyzed the panel of candidate genes related to music in connection to epigenomics in AD. We used the datasets from  Nabais et al. (2021)  (Gene Expression Omnibus [GEO]: GSE153712), which contain epigenetic data generated for the Illumina Infinium Human MethylationEPIC Beadchip (Illumina Inc. , San Diego, CA) on 161 AD patients (91 females and 70 males), and 471 healthy individuals (272 females and 199 males). 1 https://doi. org/10. 1093/nar/gkz393 First, the methylation values associated with each of the probes on the MethylationEPIC microarray were converted to Beta values, by calculating the ratio of methylated probe intensity over total intensity (methylated and unmethylated) for each probe.
Samples and probes' quality control was performed with the package minfi, which provides a quality control report on the basis of intrinsic control probes present in the array, in addition to allowing to remove probes and samples according to their signal intensity. Subsequently, to reduce the risk of measurement biases, raw Intensity Data files (*. idat) were filtered using RnBeads package. As usual, cross-reactive probes, probes located within three base pairs of common SNPs, probes with missing values or no variability in methylation, and those located on sex chromosomes, were removed. In the preprocessing step, RnBeads was also used to estimate sample donors' sex based on their DNA methylation status -an important step to identify possible discrepancies between documented gender and biological sex.
Background adjustment of the methylated and unmethylated intensities was performed using the Dasen method  (Pidsley et al. , 2013) , while normalization of Beta values was carried out using the BMIQ normalization method  (Teschendorff et al. , 2013). To identify differentially methylated positions (DMPs), we used the limma package  (Ritchie et al. , 2015) , which performs a linear model, adjusted for sex, to compare DNA methylation patterns between patients with AD and healthy controls. DMRcate package  (Peters et al. , 2015)  was used to identify significantly differentially or variable methylated regions (DMRs). methylGSA package  (Ren and Kuan, 2019)  was employed to carry out the gene set testing and pathways analysis of genes associated with DMPs. This method faces the biggest challenge in performing gene set analysis, that is, assigning differentially methylated features to genes, and adjusting for the number of CpGs instead of gene length.
For Summary of the beneficial effects of music in AD as reported in a total of 107 articles (see Figure  1 ). The inset figure indicates the number of articles that report some benefit vs. those with negative findings for the benefits; while the main figure describes with more detail the different benefits reported. The graphic was carried out considering the total amount of articles (blue), and only those based on the analysis of a minimum of patients (>15). significant DMRs, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, applying the functions enrichGO and enrichKEGG of the ClusterProfiler package  (Wu et al. , 2021). P-values were corrected for multiple testing using the false discovery rate (FDR) method. Only DMPs with an FDR-adjusted p-value <0.
Transcriptomic datasets and data analyses We downloaded gene expression data from four independent microarray datasets located at the GEO database. Overall, these datasets include 972 blood samples from AD patients and healthy controls (HC): GSE140829 (n = 453; cases = 204, controls = 249), GSE63061 (n = 273; cases = 139, controls = 134), GSE63060 (n = 246; cases = 142, controls = 104) and GSE97760 (n = 19; cases = 9, controls = 10). The GSE97760 study was finally excluded from the meta-analysis due to: (i) the low number of samples available, and (ii) the fact that it was the only dataset coming from a different array platform (Agilent), reducing the number of common genes with the other datasets (all of them from Illumina Beadchip arrays v. 3 and v. 4). For raw data processing, we first performed a normal-exponential background correction following a quantile normalization (after a Log 2transformation) of the raw data using limma package  (Ritchie et al.
, 2015). After data normalization, expression data captured by multiple probes belonging to the same gene were averaged. Because of the difference in background measurements for each dataset we used Combat CONormalization Using conTrols (COCONUT)  (Sweeney et al. , 2016)  to correct for batch effects between experiments, and to make the data comparable. COCONUT is an unbiased co-normalization method that assumes that all HC across studies come from the same statistical distribution, estimating first correction factors from each dataset's HC samples, and then applying them to the AD samples in each dataset. This procedure removes technical differences while still retaining within-dataset differences between HC and AD groups. To correct for internal batches in the GSE140829 dataset, we used the function Removebatcheffect from the limma package before COCONUT co-normalization.
Differential expression (DE) analysis between AD samples and HC was carried out with limma and using gender as covariate to correct the model. Volcano plots of differentially expressed genes (DEGs) were built with EnhancedVolcano  (Blighe et al. , 2020)  and Upsetplots from DEGs, and music and dopamine related genes were generated with the ComplexUpset R package  (Lex et al. , 2014). We used the R package Enrichmentbrowser  (Geistlinger et al. , 2016)  to collect genes involved in dopamine-related biological processes from GO database and searching for terms including "dopamine" (Supplementary Table  S3 ). Overrepresentation analysis from music-related genes through GO biological processes was conducted using the Clusterprofiler  (Wu et al. , 2021)  R package. We applied the Benjamini-Hochberg procedure for multiple test correction and thresholds were set to 0.
05. Fold enrichment was calculated as the quotient from gene ratio (number of genes of interest which are annotated to the gene-set/total number of genes of interest) and background ratio (size of the gene-set/size of all the unique genes annotated in the reference database). The flow diagram of Figure  3  provides an overview on the epigenomic, and transcriptomic data used in the present study, together with a schematic representation of the main findings.
Co-expression networks The consensus modules from the global co-expression networks represent biologically robust co-expressed gene groups. The analysis carried out on the AD (vs. HC) and the music transcriptomic data reveal commonalities between both datasets, elucidating common coordinated genetic processes behind musical stimulation and AD. We studied the commonalities in network organization of gene expression between the AD dataset and a dataset including whole blood gene expression data from individuals before and after 20 min of classical music stimulation (GSE48624). For this purpose, we used only AD patients from the AD dataset, and samples collected after stimulation from the music dataset. The consensus weighted gene co-expression network was constructed using the WGCNA R package  (Langfelder and Horvath, 2008).
We used as input normalized expression data (and corrected for differences in gender) from common genes (those represented in both datasets) that showed the most variant expression values between samples (top 75% with the highest variance). Then, independent datasets were integrated into a multi-set format suitable for consensus analysis. We followed the signed network procedure, whereby the similarity between genes reflects the sign of the correlation of their expression profiles. A matrix of correlations between all pairs of selected genes was generated from the expression values, and further converted into an adjacency matrix with a power function. We chose a soft-thresholding power based on the criterion of scale-free topology after testing a set of candidate powers. Considering that the model-fitting index of a perfect scale-free network is 1, we selected a soft-thresholding power of 5 (Supplementary Figure  S3A ) because it resulted in the maximum model fitting index for both datasets (>0.
9). Subsequently, the consensus topological overlap matrix (TOM) from the adjacency matrices and the corresponding dissimilarity (1-TOM) values were computed. Considering the different properties of the datasets, we scaled TOMs to make them comparable (Supplementary Figure  S3B ). The consensus TOM was calculated with component-wise ('parallel') minimum of the TOMs for each set. As co-expression module detection parameters, we chose a minimum module size of 30, a medium sensitivity for cluster splitting, and a 0. 2 as dendrogram cut heigh threshold for module merging. The resulting consensus modules or groups of co-expressed genes were labelled by colors and used to calculate module eigengenes (the first principal component of the module). Module membership (MM) was calculated as a measure of intramodular connectivity.