dataesr/scientific-paragraphs-categorization huggingface dataset:
Electroencephalography Recordings Participants were continuously monitored for EEG recordings with 14 dry active flexible polymer electrodes mounted on finely adjustable straps, so as to minimize discomfort and not influence car-sickness symptoms (Conscious Labs). EEG signals were recorded using opensource acquisition software OpenBCI GUI (OpenBCI). Electrodes were placed according to a modified International 10-20 Electrode Placement System  (Chuang et al. , 2016)  with Fp1, Fp2, F4, Fz, F3, C4, Cz, C3, P4, Pz, P3, O1, Oz, and O2 (Figure  2 ). In addition to these acquisition electrodes, an ear-clip electrode was used for bias and another for reference, each positioned at the earlobes. The sampling rate of the EEG data was 125 Hz. The headsets for the EEG recordings were chosen on the basis of their ready availability and of the high quality of their EEG signals, which enabled impedance and movement artifacts to be reduced.
Electroencephalography Data Analysis All pre-processing and processing stages were performed with MATLAB (MathWorks R , USA, 2016) built-in functions and the Chronux toolbox  (Bokil et al. , 2010). First, raw signals were pre-processed. Local field potentials (LFP) (Figure  3A ) were (i) offline band-pass filtered between 0. 1 and 100 Hz with a zero-phase shift filter function (zero-phase digital filtering filtfilt function) and (ii) detrended using local linear regression (locdetrend function from the Chronux toolbox: window-size 1 s, overlap 0. 5 s) to remove baseline-drifting artifacts, and (iii) notch-filtered (iirnotch function), with the notch located at 50 Hz to remove possible line noise. Then, a band-pass filter with cut-off frequencies at 2-40 Hz was applied to remove muscle artifacts  (Chen et al.
, 2010; Naqvi et al. , 2015). The filtered EEG signals were then visually inspected to remove bad EEG channels based on low-quality acquisition. Signals were visualized using time-frequency representations (Figure  3B ). Multitaper spectrogram method from the Chronux toolbox with time-bandwidth product of 2 and 4 slepian sequences of orthogonal data tapers was used to calculate power spectral density (PSD) of the LFP data with sliding windows of 2 and 0. 2 s overlap. Data analysis was performed using a transverse bipolar montage. Once correct EEG channels were identified, four pairs of electrodes were defined for the different cerebral regions (occipital, parietal, central, frontal). EEG signals were then normalized in order limit the influence of environmental changes, movements, and car vibrations, but also to limit intraand inter-individual variability.
For this, the LFP signal was expressed in z-score units. The z-score normalization used the mean and the standard deviation from each baseline (120 s) of each electrode: (i) baseline parked was used as reference for the static recovery period; (ii) baseline straight road was used as reference for the slalom period (comprising RT start RT ++ RT stop ). Data recorded during baseline straight road were thus only considered for the normalization of slalom data and not analyzed thereafter. Finally, instantaneous amplitude and phase from the LFP were obtained using a continuous Morlet wavelet transform (Figure  3C ), with matcher filter construct parameters: Center frequency = 1 and bandwidth = 2, for the 2-40 Hz range. Due to their high flexibility, robust frequency information was extracted from the sum of these temporal windows to calculate PSD, reflecting the frequency content of each test period considered.
When significant differences were observed (p < 0. 05), post hoc analysis was performed using a Fischer-Snedecor least significant difference test, allowing the results to be refined by comparing the modalities two by two. For each significant effect, the effect size was estimated using the partial eta squared (ηp 2 ). In addition, Spearman rank correlation analysis have been performed at the sample level between carsickness ratings and power density from the four groups of frequencies for each cerebral area, according to the different test periods. All statistical analyses were performed using Statistica software R v. 10 (Statsoft Inc. , France). Data are presented as mean ± SEM for each assessment and significance levels as * p < 0. 05, * * p < 0. 01 and * * * p < 0. 001.
ACKNOWLEDGMENTS We thank to  Julien Bonnet  (from  Stellantis ) and  Aurore Bourrelly  for their help in the preparation of this experiment. We are grateful to  Samir Takillah  for his help with the EEG analysis on Matlab and also to  Julien Dauguet  (from  Conscious labs ) for his technical assistance with EEG headset. Finally, we thank to  Marjorie Sweetko  for the proofreading of the manuscript.
The extracellular solution used in the recording chamber had the same ionic composition for all the experimental conditions. Neurons were visualized on a BX51WI microscope (Olympus, Rungis, France) using a 40Â/0. 80 waterimmersion objective for localizing cells for whole-cell recordings and extracellular electrode positioning. Series resistance was not compensated. Current-clamp recordings were sampled at 50 kHz using the Patchmaster v2x73 program (HEKA Elektronik).
ACKNOWLEDGMENTS We thank  Peter Vanhoutte ,  Nicolas Heck , and  Lisa Huet  for help with the cell cultures, and  France Maloumian  for help with the schematic illustrations of Figs. 2 and3. Research was supported by the  CNRS, INSERM, College de France , the  European Community  ( Human Brain  Project, grants  H2020-785907  and  H2020-945539 ),  Fondation Bettencourt Schueller  and the  ANR  (grant PARADOX). C. P. is a research fellow at  Ecole Normale Sup erieure and LabEx MemoLife (Paris, France ).
Ethics Approval and Consent of Participants The study was performed from May 2015 to July 2021. Approval and ethical clearance were obtained from an institutional review board (CPP Sud Est I 1408189-2015-A00030-49) which was in accordance with the principles embodied in the Declaration of Helsinki. Prior to study initiation, the objectives of the study were clearly explained to subjects in order to obtain written informed consent. The study was a priori registered at ClinicalTrial. gov (NCT02494648).
Statistical Data Analysis Descriptive and inferential analyses were performed using GraphPad Prism 6 (GraphPad, San Diego, United States). Characteristics of the sample and treatment effect between groups were assessed using Student's t-test for independent samples. Pre-versus post-CR differences were examined using paired t-tests, and chi-square test corrected by Fisher's exact test was used for the categorical variables. The results are shown as mean ± standard deviation, number and percentage, and as differences of means and confidence interval (95%). Statistical significance was set at a two-tailed p < 0. 05. The sample size calculation was based on the  Puhan et al. (2006)  study results. For an expected difference of 7 points of AHI between the two groups, with a statistical power of 90%, we calculated a sample size of 44 patients.
This systematic review and meta-analysis aims to identify and quantify the current available evidence of hypnosis efficacy to manage pain in patients with chronic musculoskeletal and neuropathic pain. Randomized Control Trials (RCTs) with hypnosis and/or self-hypnosis treatment used to manage musculoskeletal and/or neuropathic chronic pain in adults and assessing pain intensity were included. Reviews, meta-analyses, nonrandomized clinical trials, case reports and meeting abstracts were excluded. Five databases, up until May 13th 2021, were used to search for RCTs using hypnosis to manage chronic musculoskeletal and/or neuropathic pain. The protocol is registered on PROSPERO register (CRD42020180298) and no specific funding was received for this review. The risk of bias asessement was conducted according to the revised Cochrane risk of bias tool for randomized control trials (RoB 2.
Search strategy Electronic databases MEDLINE, Scopus, PEDro, CINAHL and The Cochrane Library were searched until May 13th 2021. The search strategies, based on text words, their synonyms and index terms (e. g. MeSH), were initially developed for MEDLINE and subsequently adapted for use in the other databases (Appendix A) without any filter. To avoid missing relevant articles, we also searched the grey literature (Google Scholar).
org). GRADE transparent approach which provides guidance on rating the overall quality of research indicating four levels of evidence (high, moderate, low, and very low) based on five factors: risk of bias, inconsistency, indirectness, imprecision and publication bias  (Guyatt et al. , 2011). The GRADE assessment for each meta-analysis was undertaken independently by two reviewers (PL, AO) using the http://www. gradepro. org software. Any disagreements were resolved by a third reviewer (MB).
Data synthesis In the quantitative analysis, mean pain relief following hypnosis compared to control was estimated. Both hypnosis and control arms data were used in the analyses. When available, the mean change between baseline and follow-up and its standard deviation were extracted for hypnosis and control groups. When the standard deviation of the pain intensity score change was not reported, it was calculated using pre-and post-standard deviations according to the formula for imputing standard deviations for changes from baseline  (Higgins et al. , 2011) : SDchange = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ SD 2 baseline + SD 2 follow-up -2 × Corr × SD baseline × SD follow-up √ , The correlation for the within-subject design was calculated using the method described in the Cochrane handbook for systematic reviews of interventions  (section 16.
Study selection The PRISMA flow chart detailing the screening process for the review is presented in Fig. 1. The initial database research indicated 1281 potentially relevant articles. After removing 232 duplicates, 1049 papers were screened. After the title and abstract screening, 23 studies were analyzed as full-text publications, and 14 more studies were excluded. The characteristics of excluded studies are detailed in Appendix B. Nine studies were included in the final review  (Ardigo et al. , 2016; Gay et al. , 2002; Hosseinzadegan et al. , 2017; Jensen et al. , 2020 Jensen et al. , , 2009a Jensen et al. , , 2009b;; Paredes et al. , 2019; Razak et al. , 2019; Tan et al. , 2015).
3 ) showed that the overall estimated Hedges' g was equal to -0. 42 while the study by  Jensen et al. (2020)  had a different Hedges' g: 0. 19 and SE: 0. 19. This study had a large sample size (n = 120, excluding the hypnotic cognitive therapy group, which did not meet the inclusion criteria). Furthermore, the control group (education and cognitive therapy) had a larger effect size than the hypnosis group in this study  (Jensen et al. , 2020). A sensitivity analysis was conducted on the 9 studies included in this meta-analysis using the leave-1-out method to evaluate the robustness of the results when we remove one study at a time from the meta-analysis. Following the sensitivity analysis, the effect sizes of the 9 datasets of 8 studies ranged from -0. 53 CI95%: [-0. 786; -0. 265] to -0. 33 CI95%: [-0.
Acknowledgments We thank  Jeffrey Arsham  for his proofreading of the manuscript and his suggestions regarding medical writing. The authors would like to thank the  CHU of Liège , the  University of Liège , the  Belgian National Fund for Scientific Research  ( FRS-FNRS -Télévie ), the  Belgian Cancer Foundation  ( 2020-118 ), the  Benoit Foundation  and the  Léon Fredericq Foundation.
We have used the Mask R-CNN network  [5]  trained on the MS-COCO data set  [9] , the code and network parameters of which are freely available 5. As can be seen in Fig. 3  the detected objects are surprising: an elephant is detected in the lower left part, and what originally is a sail is mistakenly taken for an umbrella, etc. Notwithstanding these misinterpretations, 5 https://github. com/matterport/Mask_RCNN all detected objects t within the main relative positions that dene the general composition of the painting. These uncontrolled (and uncontrollable) eects of imperfection can be seen as the AI-counterpart of human observers interpreting an artwork with a classical lector in fabula eect  [2, 7] : seeing things and exploring references in a work that were not of the intention of the artist.
Conclusion We have developed and experimented a tool for creating inspirational pseudopaintings based on the visual universe of a painter, highly adaptable to a broad range of uses and congurations. The integration of articial neural networks in the process of creation generates various uncontrolled eects that very likely will spark inspiration and creative questioning The current version of the framework is freely accessible for experimenting at https://lamuse. univ-reims. fr and code is open source on GitHub (LGPL3) 10. The tool is under active development, and further work will consist in extending its scope by introducing other AI resources, like automated caption generation  [8]  and art critique descriptions like, for instance those created by I. Kim 11  , based on the data provided by the artist's universe and the generated images.
Acknowledgment The authors want to thank all the students who contributed to developing and enhancing the software that made all this possible:  M. Fouques ,  F. Abouda ,  E. Dargent ,  J. Levarlet ,  F. Amathieu ,  B. Camus ,  T. Fontenit ,  A. Guyot  and  Y. Petit. Many thanks to the artists who have accepted giving their opinion on Lamuse.
526 [0. 274, 0. 778], z = 4. 090 , p < 0. 001 ). According to the model, the use of Civic Online Reasoning Techniques increased the probability of giving a "definitely valid" ("definitely invalid") correct response by 8. 8% [4. 0%,14. 7%]. This result however was not confirmed by the standard logistic regression on correct guessing, which instead found no significant effect of technique adoption ( β = 0. 219 [-0. 121, 0. 580] , z = 1. 228 , p = 0. 220). Based on these results, we proceeded to test whether pop-up and incentives had some mediated impact on accuracy score through technique adoption. To test mediation we used the R package MarginalMediation  70. Technique adoption was found to mediate the effect of both incentive and pop-up on accuracy score (incentive: unstandardised β = 0. 004 [0.
General discussion In this research, we studied whether presenting fact-checking tips and monetary incentives increases the correct evaluation of science-themed Facebook posts. In two experiments, participants rated the scientific validity of the content of one out of several posts, with some participants receiving a monetary reward when they responded correctly and other participants being shown a pop-up window (superimposed on the Facebook post itself) that contained a list of fact-checking techniques proposed in the literature (Civic Online Reasoning). Results showed that monetary incentives work as an accuracy booster. Moreover, data on search times and extremity of validity ratings corroborated the hypothesis that incentives operate by increasing motivation and, subsequently, attention on the content and other features of the post.
Seeking accuracy in judging news is certainly driven by the intrinsic motivations of individuals. In all likelihood, however, these intrinsic motivations do not conflict with monetary incentives. Seeking accuracy, unlike deliberately adopting ecological behaviour or going on a diet, is a largely automatic process. Another concern was that motivation and attention might not have been sufficient for content that is hardly accessible to non-experts. It was thus unexpected to observe how incentives were effective even when participants evaluated information based on scientific and technical reports, and thus had to rely external knowledge and intuition when claims and data were not immediately available. Compared to work on Civic Online Reasoning 44 , our study finds correlational and causal evidence supporting the importance of lateral reading and click restraint as predictors of accurate information, especially (as initially intended) when the information about the source is scarce.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870883. The information and opinions are those of the authors and do not necessarily reflect the opinion of the  European Commission. We would like to thank  Torbjørn Gundersen ,  Philipp Lorenz-Spreen ,  David J. Grüning , and the members of the  Prosocial Design Network  for their insightful comments and advice.
Statistical Approach 2. 3. 1. Structural Validity The collected data (N = 751) was randomly split into a development and a holdout samples, half (N = 375) for model development, and the other half (N = 376) for validation. Parallel analysis (PA) is available to accurately determine the number of factors in EFA, then PA with promax rotation was conducted to explore a scale structure most appropriate in a development sample. The factorial validity of the model identified from EFA was assessed by CFA in a holdout sample. Fit indices of Comparative Fit Index (CFI), Tucker-Lewis index (TLI), standardized root mean square residual (SRMR), and root mean square error of approximation (RMSEA) and its corresponding 90% confidence interval (CI) were used. Specifically, CFI and TLI ≥ 0. 90, and RMSEA and SRMR ≤ 0.
Analysis Software All data analyses were performed using R (v. 4. 1. 2), RStudio (v. 2021. 09. 1, Build 372), and JASP (v. 0. 16. 1). Missing data was replaced by the mean or median of observed values because the number of missing responses (%) did not exceed 10%  [35]. The multivariate normality tests, CFA and MGCFA, were conducted with package "MVN v. 5. 9"  [36] , "lavaan v. 0. 6-9" [37], and "semTools v. 0. 5-5"  [38]. We ran the figure procedure described below with package "ggplot2 v. 3. 3. 5" [39].
First, the sample was recruited by convenience sampling and from only one site, restricting the generalizability of the results. Our sample data suffered from imbalance in age distribution (minimum 12 to maximum 88 years) such that the average age is young adults. Notably, young adults may have different sleep patterns and insomnia criteria than elderly. This may limit the external validity of the SCI-SC to elderly samples. Ideally, data collection with random sampling and multiple sites would further strengthen the validity of the instrument. Future studies could also establish age-and sex-norms with more representative samples. Second, longitudinal measurement invariance and long-term stability (e. g. , intraclass correlation coefficient) were not evaluated due to time and resource constraints.
Acknowledgments: We would like to thank the voluntarily participants for their contributions, the department head of a community health service (CHS) center for assistance in collecting data and eight undergraduate students ( Bing Zhao ,  Yingying Zhu ,  Xuanwei Shan ,  Qi Jing ,  Yiwen Shen ,  Zizhen Shu ,  Jiajuan Chen ,  Yuhang Bao ) who took part in summer social practice for data collection. Furthermore, we are grateful to reviewers and editors who provided insightful views and constructive comments to facilitate this manuscript.
Here we report the baseline structural, diffusion MRI, and MRS findings. Our goals were (1) to evaluate the feasibility of collecting multimodal magnetic resonance (MR) data using a harmonized advanced protocol in the multisite academic trial setting; (2) to identify the structural, microstructural, and neurochemical MR metrics that are most sensitive to early abnormalities in SCA1 and SCA3 mutation carriers; and (3) to investigate the associations of these MR metrics with early clinical presentation in SCA1 and SCA3 as assessed by a comprehensive battery of outcome measures covering motor, nonmotor, and cognitive function and activities of daily living.
24 The COAs that showed significant group differences in the entire READISCA cohort (n = 200; (2) 3D T 2 -weighted SPACE (sampling perfection with applicationoptimized contrasts using different flip angle evolution; 0. 8mm 3 isotropic resolution, TR/TE = 3200/563 milliseconds, GRAPPA factor = 2); (3) multiband diffusion MRI acquired with opposing phase encoding in the anterior-posterior direction and q space sampling split into 2 sets of 98 and 2 sets of 99 volumes, resulting in 2 sets of 197 volumes including 13 b = 0 per phase encode (184 unique diffusion images). On Prisma scanners, 1. 5mm 3 isotropic resolution, TR/TE = 3,230/89. 2 milliseconds, multiband acceleration = 4, and b values = 1,500, 3,000 s/mm 2 were used. On the Skyra scanner, 1. 7mm 3 isotropic resolution, TR/TE = 3,390/103.
2milliseconds, multiband acceleration = 4, and b values = 1,000, 2,000 s/mm 2 were used. Except b values, q space sampling was the same on Prisma and Skyra. Volumes-of-interest (VOIs) for MRS were pons (16 Â 16 Â 16mm  3  ) and cerebellar WM (CBWM; 17 Â 17 Â 17mm 3 ). VOIs were automatically prescribed with AutoVOI, using predefined voxels on the MNI152 atlas. 25 MRS data were collected using semilocalization by adiabatic selective refocusing (sLASER; TR/TE = 3,000/30 milliseconds, 80 transients),  26  which was recommended by expert consensus for high fields. 27 Unsuppressed water spectra were obtained as an internal quantification reference and for residual eddy current correction. 28 An automated sLASER protocol  29  with integrated B 0 and B 1 calibration minimized operator intervention.
MR Quality Control and Preprocessing Digital Imaging and Communications in Medicine (DICOM) data were deidentified using DicomBrowser,  30  and T 1 and T 2 datasets were defaced using Face Masking 31 on site prior to uploading to a Flywheel (https://flywheel. io/) instance running on a dedicated server at the University of Minnesota for centralized analysis. To ensure uniform data handling across sites, the data handling pipeline was created within a Docker container at the University of Minnesota and shared with other imaging sites. All MR data were analyzed blinded to diagnosis. Image sharpness, ringing, and contrast-to-noise ratio of T 1 and T 2 data were scored 0-2 in subcortical, gray matter (GM), and WM regions, as described previously. 32 Scores for all image features were averaged.
T 1 and T 2 images that passed quality control (QC) thresholds (pass = 0-0. 25, check = 0. 5-1, fail = 1. 25-2) were preprocessed using the HCP minimal preprocessing pipelines  33  and corrected for gradient nonlinearity distortion, bias field, and readout distortions. 33 iffusion data were missing for 2 participants, and another was excluded due to motion artifacts. Diffusion images were corrected for motion artifacts, and susceptibility-induced and eddy current distortions  34  using the HCP pipelines. 33 Three datasets did not include reversed phase-encoding direction due to operator error, and thus were corrected for susceptibilityinduced distortions using Synb0-DISCO  35  and for eddy current and motion artifacts using EDDY from FSL. 34  MRS data were missing for 2 participants. In addition, MRS data were excluded if the VOIs were misplaced when overwriting AutoVOI placement (1 pons spectrum, 3 CBWM spectra) or the linewidth (full width at half maximum) of the associated water reference was broader than 13Hz (11 pons spectra and 3 CBWM spectra).