dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants The UK Biobank is a large prospective study with phenotypic, genotypic, and neuroimaging data from more than 500,000 participants. We included participants whose combination of cognitive tests allowed for a correlation with the complete g-factor of 0. 70 or higher  (N= 261,701; Williams et al. , 2022). This threshold was used to maximize the robustness of the factor and the number of participants with a g-factor. In a previous paper, we analyzed the Image-Derived Phenotypes from the first Magnetic Imaging Resonance (MRI) visit generated by an image-processing pipeline developed and run by the UK Biobank Imaging team  (Alfaro-Almagro et al. , 2018; Miller et al. , 2016)  and reported 40,028 individuals with sex, age at MRI, and TBV data after excluding outliers  (Williams et al.
, 2021). From here on, age at the first MRI visit will be referred to as age. From the 40,028 individuals with neuroimaging data, there were 39,131 participants with a g-factor of good quality (Table  1 ). Polygenic Score. PGS is scaled before being residualized for birth year and the 1 st 40 principal components of the genotyped data. Childhood Adversity was scaled on the sample (N about 150,000) from which it was created. Max N: maximum number of participants included in the analyses. Corresponds to N for TBV but will be reduced when including other brain measures when data for a region is missing.
Childhood Adversity Score A childhood adversity score was created from questions in the UK Biobank on childhood abuse and social stressors. Childhood abuse was measured with data fields 20488 "When I was growing up. People in my family hit me so hard that it left me with bruises or marks") and 20490 ("When I was growing up. Someone molested me (sexually)") and childhood stressors were measured with data fields 20487 ("When I was growing up. I felt that someone in my family hated me"), 20489 ("When I was growing up. I felt loved"), and 20491 ("When I was growing up. There was someone to take me to the doctor if I needed it"). All questions are rated from 0 (Never True) to 4 (Very Often True). So that all indicators are in the same direction, we subtracted data field 20489 and data field 20491 responses from 4 (reverse coding).
What global measures mediate the g-PGS' effects on the g-factor? We ran two mediation models using the sem function in the lavaan package  (Rosseel, 2012) : one with the significant predictors of the g-factor from equation 1 and one with those CC-BY 4. 0 13 from equation 2. The gPGS was the exposure, global volume(s) the mediator(s), and the gfactor served as the outcome. For all mediation models described in the present study, indirect effects were calculated using the product method. We estimated direct paths (exposure to an outcome) and indirect paths (exposure to mediators to outcomes) and adjusted the mediators and outcome for age (linear and quadratic), sex, and their interactions in the lavaan framework. We set a lenient p-value threshold to 0. 05 and a stricter one to p < 0. 05/N (N: the number of regional and global measures included in the model of interest).
05/N (N: the number of regional measures included in the model of interest). Equation  5 g-factor i = Intercept + Regional Measure 1 i + Regional Measure 2 i CC-BY 4. 0 15 …. + Regional Measure N i + Global Measure N i + Sex i + Age i + Age 2 i + Age*Sex i + Age 2 *Sex i + e 2. 5. 6. Do Regional Measures mediate the g-PGS' effects on the g-factor? For volumes, thicknesses, and surface areas separately, we examined the simultaneous mediation of the global measure and the significant regional measures from equation 5 with the sem function from the lavaan package  (Rosseel, 2012). The gPGS was the exposure, global and regional measures were the mediators, and the g-factor served as the outcome.
5% of Childhood Adversity's effect on the g-factor to be explained by other cerebral measures. Therefore, future research should include additional cerebral measures, such as microstructural properties, white matter measures, dynamic connectivity, or resting-state or task-based functional activation, to better understand the extent to which cerebral measures mediate the environmental and genetic factors on the g-factor. The present study is limited in its ability to generalize to all UK Biobank participants: Individuals with neuroimaging data are different from UK Biobank participants without neuroimaging data  (Lyall et al. , 2022)  and the UK population  (Fry et al. , 2017). Moreover, our analyses were restrained to individuals of British ancestry, suggesting that further research is needed to examine whether genetic factors on intelligence are mediated by the same cerebral regions and to the same degree across ancestries.
This work received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by l' Agence Nationale de la recherche (ANR)  with the references  ANR-17-EURE-0017  and  ANR-10-IDEX-0001-02 PSL. A CC-BY public  , in accordance with the grant's open access conditions. This research has been conducted using the  UK Biobank Resource. cgi?id=200). The UK Biobank received ethical approval from the  Research Ethics Committee  (reference  11/NW/0382 ) and the present study was conducted based on application  46 007.
The participants in the Constant group climbed a training route (TR) 6 to 9 times per session. The participants in the Imposed Variability group climbed the training route 3 times on all the sessions and they climbed 9 variants routes (V1 to V9) across the learning protocol. The Self-controlled Variability group followed a similar protocol as the Imposed Variability group, but the number of variants routes discovered depended on the individuals choice during the practice. The data collected from the trials written in bold characters are those analyzed in the current study. https://doi. org/10. 1371/journal. pone. 0269794. t001 was to efficiently chain movements to reach the top of the climbing route, a climbing skill commonly referred to as route-finding  [35]. Procedure. Each session lasted approximately 1 h and therefore, the entire study comprised a total of 240 hours data collection (testing and practice of all participants).
Data collection Contact time with holds. The climbing walls were equipped with the Luxov Touch 1 system (http://www. luxov-connect. com/en/products/#touch, Arnas, France) as used by  [36]. This system uses a sensor technology to provide a measure of the time of contact and release of the handholds and footholds. The reported accuracy of the system is 1. 57 ms at 99. 7% confidence interval (see patent details: FR3066398-2018-11-23 / WO2018/211062A1-2018-11-22; https://patents. google. com/patent/WO2018211062A1/en). The starting time and ending time of the climb were obtained with this system. The start time was considered when the participant touched another hold other than the starting holds and the end of the climb was considered when the climber touched the last handhold. The time of the first contact with each of the handholds of the routes were also collected.
If the calibration failed or the point of gaze did not overlap with the target, the procedure was repeated until that the gaze location met the center of the target. Synchronization procedure. The data from the mobile eye-tracker and the Luxov Touch system were synchronized by asking the participant to look at one hold while the experimenter tapped the location. Then the time of the first frame in the video of the eye tracker that showed the contact of the experimenter's finger with the hold was used as a reference time to synchronize the two. This synchronization was used to obtain the gaze offset time (see in the subsection Gaze Behavior within the section Data Analysis for more details and reliability measures regarding this dependent variable).
Data analysis Climbing fluency. The coordinates of the hip trajectory were used to compute the geometric index of entropy (GIE). The GIE was designed as a measure of performance that reflects the degree of coherence in information-movement couplings  [37]. The GIE enables assessment of the degree of complexity of the hip trajectory. A complex hip trajectory would reflect a poor sensitivity of the climber to the environmental constraints, whereas a smooth trajectory would reflect fluent climbing movements. GIE is calculated with the following equation: GIE ¼ log 2 ð 2L c Þ L is the length of the hip trajectory and c the perimeter of the convex hull around the hip trajectory. Data analyses to obtain the GIE values were performed with Matlab R2014a 1 software (version 8. 3. 0. 532, The MathWorks Inc.
, Natick, MA, USA). Gaze behavior. Gaze behavior measures focused on the gaze patterns oriented towards the handholds and hand movements of the participants. The analyses of the mobile eye tracker recordings were performed using Tobii Pro Lab© (version 1. 102. 16417, TobiiAB, Sweden). The raw filter was applied to provide data on the location of the gaze position on each frame. A circle with a 20 cm radius around each of the 13 handholds was considered as areas of interest (AOI). Two different aspects of gaze behavior were coded for each ascent: (i) the last period the participant's gaze stayed within an AOI before touching the corresponding handhold for the first time in the trial, and (ii) the temporal series of the AOI locations that the point of gaze passed through. For the first measure, the coder recorded the last period that the participant's point of gaze stayed within an AOI before touching the corresponding handhold for the first time in the trial  [9].
997 and the inter-coder reliability between r =. 991 and r =. 993. For the measure of the gaze offset time (which could be affected by the synchronization procedure), the intra-coder mean difference between the first and second coding was -0. 9ms (Mean 95% CI = [-4. 1ms, 2. 3ms], SD = 14. 6ms) and the inter-coders mean differences was -0. 2ms (Mean 95% CI = [-3. 8ms, 3. 4ms], SD = 16. 3ms). Global observations regarding the gaze sample. The gaze behavior of one participant in the IVG was excluded from the analysis due to the loss of gaze data during the climbs. For the rest of the participants, the offset time and duration of the period of gaze within AOI was obtained for 91. 3% of the visits on the training route and for 86. 6% of the visits on the transfer route. There was no significant difference in the proportion of excluded periods in the three learning groups on the training route [χ2(2, N = 1205) = 0.
Results Two participants only attended the first session and then dropped out and one participant injured herself after the fourth training session and could not continue the protocol. Unfortunately, we did not have the resources (time and availability of the climbing wall) to replace the three participants that dropped out, which equated to 60h of data collection (5 weeks of data collection for one participant represented approximately 20h of time). Thus, these three participants were not included in the statistical analyses and the final sample size was 21 participants (age: M = 20. 6 years, SD = 1. 1; 7 women and 14 men). The data used in the statistical analyses can be found in S1 Data.
Statistical analysis: For pups, we analyzed the evolution of mice proportion over time using Generalized Linear Model (GLM) (Ordinal Logit model, SPSS R IBM R ) with score as dependent variable and genotype and days as covariates. We also used GLM to compare the evolution of weight over time with weight as dependent variable and genotype and days as covariate (Linear Scale Response model, SPSS R IBM R ). For the adults' tests, differences between the three genotypes groups were analyzed using One-way ANOVA or Kruskal-Wallis accordingly to their distribution (Graph Pad Prism 9. 3. 0).
So, we exclude participants over 65 years old in order to avoid any experimental bias related to age. After data cleaning, this study concerned 226 participants (mean age 37. 85 ± 12. 88; 180 female, 45 male, 1 other). All participants were recruited by advertisement on various social networks such as LinkedIn, Facebook, Instagram and Risc (Relay of information on the sciences of cognition). Recruitment was performed via a web link provided in the study announcement. The webpage behind the link contained all the information concerning the nature and objectives of the study, its conduct and its potential risks. It also included the declaration of consent and a shared liability clause. Lastly, before the end of the subscription, participants were submitted to a brief anamnesis to collect socio-demographic information (age, gender, education, meditation expertise).
Data Analysis All statistical analyses were conducted with JASP (0. 11. 1) to test our different hypotheses. We conducted a repeated measure ANOVA on the ratio of MW with two factors: the content of MW (EFT, Planning, EPT and Imagination) and the type of MW (Spontaneous, External, Voluntary). The post-hoc analyses were corrected using the Bonferroni correction. Two multiple linear regressions were performed to predict the PM score with rates of the different components of mind wandering (content and source) and different independent measures collected with the questionnaires (prospective metamemory, mind wandering trait). We also included some covariates that may influence predictions such as Age and Mood (Depression and Anxiety). We reported the effect size for ANOVAs with η 2 p (partial eta squared).
Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual, particle-wave, perspective, integrate time delays due to finite transmission speeds, and derive a normalization of the connectome. When applied to the database of the Human Connectome Project, it explains the emergence of frequency-specific network cores including the visual and default mode networks.
The lack of personalized estimations for the time delays of the connectome is still an unavoidable problem. A possible way forward could be using averaged delays derived from stimulation  (Trebaul et al. , 2018) , combined with the personalized lengths of the tracts, even if these datasets are still incomplete. The lack of directionality in the human connectomes that rely on diffusion tractography is also a confounding factor of those connectomes  (Melozzi et al. , 2019). This has led to introduction of concepts such as effective connectivity  (Friston, 2011; Gilson et al. , 2019) , even though for our framework the derivations are simplified by the symmetry in the coupling strengths. Finally, we have introduced only two metrics for node activity based on the normalized weights, whereas other graph theoretical metrics for wave interactions could be more informative.
Numerical integration is performed using stochastic Heun integration scheme implemented in MATLAB, with an integration time step that is inversely proportional to the natural frequencies and the global coupling, but it is never larger than 0. 005 s. In some of the analyzed scenarios the dynamics also contain white additive Gaussian, with noise intensity D. The critical value of r is different for each connectome and each frequency, and sub-and supercriticality are set such that r is at 0. 2 below or above criticality, respectively.
Indeed, to the extent possible, we sought to design similar justifications between the two conditions, which varied solely by the presence or absence of a conspiratorial dimension in order to maximize experimental control. The second sentence, here written in italics, corresponds to a justification of the environmental position that varies across conditions, being either framed as conspiratorial (treatment condition) or not (control condition). Statements were paired with target photos taken from the Center for Vital Longevity Face Database  [28]. Statements in the control condition were somewhat shorter than statements in the treatment condition. However, even if the additional length leads to more errors, there is no reason to expect a bias towards either more within-category or between-category errors.
Four speakers were white and four were black in order to induce race categorization. Men targets were used because previous studies showed that race categorization for men targets is more resistant to change than race categorization for women targets  [10] [11] [12] , creating a more stringent test for our hypothesis. Target photos were taken from the Center for Vital Longevity Face Database  [28]. Speakers' race was balanced across the environmental dimension, such that both environmental positions were defended by two white and two black targets, removing the correlation between race and present alliances. Within this constraint, the pairing between targets and statements was randomized.
There are limited and controversial data on the possible pathogenetic mechanisms underlying the association between mortality and EDS. LEE at al. [26]  performed a prospective cohort study to assess associations between coronary plaque phenotype, cardiovascular events and EDS. They measured blood lipid parameters and intravascular ultrasound for plaque evaluation in 170 patients undergoing coronary angiography. Patients with an ESS >10 had significantly higher total cholesterol and low-density-lipoprotein cholesterol. The coronary stenoses were longer and the cumulative incidence of adverse cardiovascular events during the 16-month follow-up was higher in the sleepier group. After adjustments for age and smoking habits, the HR for adverse cardiovascular events in the sleepier group was 3. 44 (95% CI 1.
Stimulus materials Participants selected their preferred language for the stimuli to be presented during the experiment. The four available language options were Luxembourgish, French, German, and English. For each participant, the complete stimulus set across all experimental conditions consisted of 96 pictures and 256 visually depicted words in the selected language (see Supplementary Tables  1 2 3 4 5 ). The picture stimuli were naturalistic color photographs depicting every-day, easily nameable objects that were either natural (e. g. , fruits, animals) or synthetic/artificial (e. g. , tools, clothes) (Supplementary Table  5 ). Pictures were selected from normed databases  (Rossion and Pourtois, 2004; Brady et al. , 2008 Brady et al. , , 2013;; Konkle et al. , 2010). Similar to the pictures, the words were nouns that described common natural and artificial objects.
Paradigm and instructions The paradigm consisted of four tasks: (1) Encoding of picture-word pairs, (2) distractor task, (3) Picture recognition, (4) Picture-Word recognition. For clarity, only the English version of the paradigm is described below. All stimuli were displayed using Presentation R Software (Neurobehavioral Systems, Inc. ) on an LCD screen [size: 68. 6cm (diagonal), resolution: 1200 pixels × 800 pixels, frame rate: 60 Hz]. The screen was located behind the scanner and was viewed via a mirror installed on the head coil. The required responses across experimental conditions were button-presses with right hand fingers, which were recorded with an MRIcompatible LUMItouch response pad (Photon Control Inc. , Burnaby, BC, Canada).
Inter-language differences Participants were divided into separate groups depending on the language that they selected for the stimuli. To statistically assess inter-group differences in task performance, a mixed analysis of variance (ANOVA) was used to evaluate the extent to which the behavior (i. e. , accuracy and RT) in each of the experimental conditions (encoding, picture-recognition, and picture-word recognition) could be independently explained by (within-subject) semantic relatedness (Related, Unrelated) and by (between-subject) stimulus language. Other epidemiological factors could potentially drive intergroup differences (if present). Although a large amount of epidemiological data were collected per participant, we only considered parameters of immediate relevance to the experiment itself, namely, age, gender, number of years of education, MMSE score, and the number of practiced languages (self-reported).
05 was considered statistically significant. All statistical analyses on epidemiological and behavioral data were performed with SAS System V9. 4 (SAS Institute, Cary, NC, United States). The structural scan used a T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence (TR: 2250 ms, TE: 3. 03 ms, FA: 9 • , TI: 900 ms, FOV: 256 mm × 245 mm) to obtain a high-resolution image (176 slices, matrix size: 256 × 256, interslice gap: 0. 5 mm, voxel size: 1. 0 mm × 1. 0 mm × 1. 0 mm). An additional structural image was acquired using a T2-weighted fluid-attenuated inversion recovery (FLAIR) sequence, but this image was not used for preprocessing/analysis. Each task was performed on a separate scanning run. Each functional scan began with a 6 TR task-free period to ensure that the MR signal reached a steady state.
Image acquisition and preprocessing Image preprocessing and statistical analysis were performed with the SPM12 software (Wellcome Centre for Human Neuroimaging, London, United Kingdom) implemented for a MATLAB programming environment (MathWorks Inc. , Natick, Massachusetts, United States). For preprocessing and statistical analyses, the acquired images were converted from the Siemens DICOM format to the NIFTI format using the dcm2nii utility  (Li et al. , 2016). Functional images (EPIs) were spatially realigned (to the first volume) and unwarped using the iterative realign/unwarp algorithm implemented in SPM12 to correct for head motion and associated magnetization artifacts  (Andersson et al. , 2001). The EPIs were then slicetime corrected (relative to the middle slice). The mean EPI was co-registered to the structural image.
Using SPM12's unified segmentation/normalization algorithm, the structural image was segmented to distinguish white and gray matter and then deformed to match a standard Montreal Neurological Institute (MNI) template brain image. The deformation fields estimated from this segmentation/normalization procedure were applied to all EPIs to transform them into standard MNI space (normalization) followed by resampling to a voxel size of 3 mm × 3 mm × 3 mm (4 th degree B-spline interpolation). The normalized EPIs were smoothed with an isotropic 8 mm full-width-at-half-maximum (FWHM) Gaussian kernel. Following preprocessing, the fMRI and behavioral datasets of 2 (out of 62) participants were incomplete due to technical scanning difficulties that primarily affected the final picturerecognition task.
Since the fMRI analysis involved inter-task comparisons, the datasets from these 2 participants were excluded from all fMRI analyses. However, the technical problems did not affect the behavioral data acquired from these 2 participants on the remaining tasks. Therefore, to maximize the use of available data, these 2 participants were included in all behavioral analyses except for the picture-word recognition task where a full set of correct trials was unavailable for response time calculations (see Supplementary Table  6  for full details of sample sizes).