dataesr/scientific-paragraphs-categorization huggingface dataset:
To facilitate a deeper understanding of the metrics, we provide an example hyperscanning dataset to test hyper-connectivities metrics. With regard to statistical tests, we include a traditional t-test with possibility of multiple comparisons correction through cluster-based permutation test. Future implementations will include correlation with behavioral coding, meta-analyses across different metrics and analyses of variance combining groups and conditions. On top of these analyses, we offer 2D and 3D visualizations of hyper-connections between brains and we will implement an equivalent for connectivity within brains. Correlation and coherence are traditional linear methods to estimate brain connectivity. Correlation coefficients of bloodoxygen-level dependent signal (BOLD) signals in hyperscanning fMRI studies have been found to characterize joint attention  (Koike et al.
, 2004; Dikker et al. , 2019). Non-linear brain connectivity metrics are also implemented in HyPyP, including versions of phase synchrony, power correlation and causality measures. The most commonly used phase synchrony measure is PLV, which was employed to estimate synchronization in joint action  (Dumas et al. , 2010 2011 ), verbal interaction (Perez Repetto et al. , 2017) , decision-making  (Tang et al. , 2016)  and other tasks. PLI is similar to PLV but designed for events based experimental designs. It has been adopted in a series of musical coordination studies  (Lindenberger et al. , 2009; Sänger et al. , 2012). CCorr measures the covariance of phase variance between two data streams and is more robust to coincidental synchrony  (Burgess, 2013)  compared to PLV or PLI. CCorr has seen an increasing popularity and has been successfully implemented in studies investigating touch  (Goldstein et al.
, 2018) , learning  (Bevilacqua et al. , 2019)  and language  (Perez Repetto et al. , 2017). Correlation between PSD and envelope in EEG data has long been used in single-brain studies  (Shaw, 1984; Guevara and Corsi-Cabrera, 1996)  and also adopted in hyperscanning  (Zamm et al. , 2018). PPC, a version of power correlation between orthogonalized time series to discount spurious synchronization  (Hipp et al. , 2012) , has been found to be correlated with personality traits in naturalistic interaction  (Dikker et al. , 2019). Recent studies have advocated for establishing causal interpretations of synchrony values  (Dean and Dunsmuir, 2016). For inferring causality in a multivariate dataset, there are autoregressive modeling and information-theoretic approaches, represented by Granger causality (GC) and transfer entropy (TE), respectively.
Roadmap To our knowledge, HyPyP is the first comprehensive toolbox dedicated to quantifying brain connectivity across multiple participants. Tables 2-4 list the roadmap for HyPyP iterations. These roadmaps are subject to change and are actively maintained on the HyPyP project page (pypi. org/project/HyPyP). We aim to integrate the study of behavioral variables from inter to intra-brain level and extend statistical and visualization functions to group analyses. Also, taking advantage of MNE source level functionality, we aim to integrate sourcelevel hyperscanning to complement our channel-level visualization. In addition to streamlining and implementing these functionalities (listed in Tables  2 3 4 ), we will continue releasing documentation around the pipeline, including documented code, tutorial materials and guidelines for linking hypotheses between connectivity metrics and psychological processes.
Conclusion The HyPyP is an analysis toolbox designed to support (social) neuroscientists interested in comparing brain data across two or more participants. It already integrates the core tools to run inter-brain connectivity measures from pre-processing to visualization and will continue to be improved upon in a community-driven fashion. The specific tools provided in HyPyP will facilitate standardized inter-individual neurophysiological analyses to support scientific progress and replicability in social neuroscience research.
Acknowledgements The authors wish to thank:  Denis Engemann  for his early help regarding the MNE and the Autoreject libraries;  Alexandra P. Key  and  Sahana Nagabhushan Kalburgi  ( Vanderbilt Kennedy Center ) as well as  Nigel Janssens  and  Lena Adel  ( Free University Amsterdam ) for working with us to test pipeline functionalities;  Yafeng Pan  ( Karolinska Institutet ),  Ihshan Gumilar , and  Yufang Yaang  for their precious feedback and interactions at the early stage of HyPyP development.
Funding This project is supported by the  Institut Pasteur, INSERM, Fon-daMental Foundation ,  Ecole Normale Supérieure from Lyon, APHP, DHU Protect, Bettencourt-Schueller Foundation ,  Cognacq Jay Foundation ,  Conny-Maeva Foundation ,  Fondation de France ,  Labex BioPsy , a grant from  Pierre Deniker Foundation , a grant from  Congrès Français de Psychiatrie   CFP-BM-2019-03 ,  National Science Foundation  Award # 1661016 ,  Netherlands Organisation for Scientific Research  Award # 406. 18. GO. 024. GD is funded by the  Institute for Data Valorization (IVADO) ,  Montreal , and the  Fonds de recherche du Québec (FRQ).
, 2018 Non-linear Yes Causality measures plv (phase-locking value) Lachaux et al. , 1999; Dumas et al. , 2010 Non-linear No pli (phase lag index) Stam et al. , 2007; Stam et al. , 2007Fig. 2. Analysis pipeline for EEG datasets in HyPyP. Steps shown in blue are module-specific, extending functionalities offered by the Python-MNE environment, in coherence with the workflow. artifact rejection and applies ICA on epoch. AR_local applies local Autoreject in the second step: # high-pass filtering filt_raws = prep. filt(raws=[raw1, raw2] # computing global AR and ICA on epochs icas = prep. ICA_fit(epochs=[epochs1, epochs2], n_components=15, method='infomax', fit_params=dict (extended=True),random_state=42) # selecting components semi-automatically and remove them cleaned_epochs_ICA = prep. ICA_choice_comp(icas, epochs= [epochs1, epochs2]).
2% Triton X-100) antirabbit (Jackson ImmunoResearch). To covisualize c-fos expression with cell nuclei, sections were counterstained with 4,6-diamidino-2-phenylindole (DAPI, 1 μg/ml for 3 min), mounted on polysined P glasses, and coverslipped with fluoromount. Images of c-fos immunostaining in the prelimbic (PL) region of the medial prefrontal cortex and in the basolateral nucleus of the amygdala (BLA) were acquired with a confocal microscope (Zeiss LSM700) at a ×20/zoom 1 magnification. In each region of interest (ROI), four sub-regions were randomly selected for automatic detection of c-fos immunoreactive spots by IMARIS software (version 7. 6. 5). For each ROI, the counts were obtained from the average of three representative coronal sections taken from the same rat. Counting of c-fos-labeled cells was made in the prelimbic (PL) region and the basolateral nucleus of the amygdala (BLA), by an observer blind to the experimental condition.
Measurements were carried out using a microscope (DMLB, Leica) equipped with a motorized stage and a camera connected to a software for morphological analyses allowing quantitative 3D analysis of complete dendritic arborization (Neurolucida 7. 5; MicroBrightField, Inc. ). The length of the dendritic trees was quantified tracing the entire apical and basal dendrite arbor, and then performing Sholl analyses. Briefly, using the center of the soma as reference point, dendritic length, diameter, and branch points were measured as a function of their radial distance from the soma by adding up all values in each successive concentric segment (segment radius: 25 μm). Morphological measurements were made by an experimenter blind to the experimental conditions.
Statistical analyses ANOVAs were carried out using VAR3 software allowing post hoc planned comparisons  34. Before treatment, between-group differences in behavior were assessed by means of one-way ANOVAs with group as the main factor. After treatments, between-group differences in behavior, c-fos-labeled cells, and dendrite length were assessed by means of two-way ANOVAs with group ReacƟvaƟon with: • D29 = WSB (1 min) • D31 = Tone (1 min) B) Groups CTRL (n=14) SPS-R (n=13) SPS-V (n=23) CTRL (n=7) SPS-R (n=7) SPS-V (n=11) CTRL (n=14) SPS (n=36) CTRL (n=7) SPS-R (n=6) SPS-V (n=12) Saline (SAL) Oxytocin (OXY).
Acknowledgements This work was supported by  "les Gueules Cassées" Foundation  (grant number. Claire Le Dorze was supported by a  DGA-MRIS  ( Direction Générale de l'Armement ) scholarship. We thank  Mareva Bleuzé  for her contribution to the experimental phase of this work. We would like to thank  Professor D. C. Riccio  for his thorough re-reading of this article.
Background and aims: Shift work is common worldwide and linked to deleterious cardiovascular effects that might be underlined by dyslipidemia. The aim of this systematic review and meta-analysis is to determine the impact of shiftwork on dyslipidemia. Methods: Searching in PubMed, Cochrane Library, Science Direct and Embase databases without language restriction on 15 February 2020, included studies that describe blood lipids levels or a risk measure in shift workers compared with fixed-day workers (controls). Differences by study-level characteristics were estimated using stratified meta-analysis by type of shift work, and meta-regression to examine relations between dyslipidemia and demographic, lifestyle and work characteristics. Estimates were pooled using random-effect meta-analysis. Results: We included a total of 66 articles, representing 197,063 workers.
Literature search A systematic search of all observational epidemiological studies about shift work and lipid metabolism was conducted. The following databases were searched on 15 February 2020: PubMed, Embase, Cochrane Library, and ScienceDirect. The following relevant mesh terms/keywords were used: "night work" OR "nightwork" OR "shift work" OR "shiftwork" AND "lipids" OR "metabolism" OR "blood" OR "LDL" OR "HDL" OR "cholesterol" OR "triglycerides" (details of search strategy is presented in Supplementary Data 1). The search was not limited to specific years and no language restrictions applied. The studies included in the meta-analysis had to describe blood lipids levels or a risk measure i. e. odds ratio (OR), hazard ratio (HR) or relative risk (RR) in shift workers compared with fixed-day workers (controls).
There is also heterogeneity in reporting blood lipid levels between studies, and different cut-off for dyslipidemia for studies reporting odds ratio or hazard ratio. Moreover, odds ratio and hazard ratio were adjusted on different variables between studies, precluding robust conclusions. Most studies have a cross-sectional design. Therefore, the associations between shift work and dyslipidemia risk might be underestimated by the potential healthy worker survival effect in the cross-sectional studies  [133]. Even if methodological quality of the included studies was evaluated by subjective assessments (STROBE and NOS), overall methodological quality was reasonably good without major comparability bias between exposed and non-exposed groups, with a large volume of data available and consistency for the main outcomes of interest.
Statistical analysis We first analyzed the independent effects of psychiatric symptoms on entrepreneurial intention and BAS and the independent effect of BAS on entrepreneurial intention using the ordinary least square (OLS) regression. We then examined the joint effect of all psychiatric symptoms on entrepreneurial intention and the joint effect of all psychiatric symptoms on BAS using the OLS regression. The standardized coefficients of the above relationships are summarized in Table  2. We then tested whether the psychiatric symptoms are indirectly linked to entrepreneurial intention via BAS by estimating the bootstrap indirect effect with 5000 bootstrap samples using the SPSS macro PROCESS  (Hayes and Preacher, 2013; Zhao et al. , 2010a,b). The bootstrap approach is considered suitable for research on psychiatric symptoms and entrepreneurship  (Wiklund et al.
Acknowledgements The authors acknowledge useful comments from the reviewers and participants of the 2016 Workshop of Entrepreneurship and  Mental Health at Syracuse University. They also want to thank  Indy Bernoster  for her invaluable contribution to the early stages of the present investigation. Lastly, they acknowledge the support of  Jeffrey Pollack  and two anonymous referees of JBVI. Roy Thurik  is a member of the Entrepreneurship and Innovation chair, which is part of  Labex Entrepreneurship (University of Montpellier, France ) and is funded by the  French government (Labex Entreprendre ,  ANR-10-Labex-11-01 ).
Pairing was automatically generated by our institution's software after selection of the five patients having delivered immediately after the case emanating from the nonspontaneous cephalic presentation group and meeting all three paring criteria. Data are expressed as the means ± standard deviation (SD) for continuous variables and as numbers (percentages) for qualitative variables. Continuous variables were compared using the Mann-Whitney test, whereas categorical variables were compared using the χ2 test or Fisher's exact test. Statistical analyses were performed using p-value® freeware (https://www. pvalue. io, Paris, France). The threshold for statistical significance was p < 0. 05 (two-sided). Data were obtained from our institution's centralized electronic patient records. The study was declared and registered on the internal register of the Clinical Research and Innovation Department (DRCI) of the Amiens-Picardie University Hospital.
emotion components (autonomic and expressive) were conducted in accordance to the methodological standards and the conditions to design and implement psychophysiological studies  (Lajante et al. , 2012 (Lajante et al. , , 2017;; Lajante and Ladhari, 2019; Lajante and Lux, 2020;  see Figure  1 ). Five respondents were eliminated because of a lot of missing values across items or identical responses to all items (self-reported measures) or because of technical problems during data recording (psychophysiological measures). The results of our statistical analysis agree with our expectations (see Table  1 ). First, we tested the contribution of aesthetic emotion components (autonomic, expressive, and subjective) on Aad formation. The level of arousal measured by both the self-reported measure (SAM; 1-5 scale, M = 1.
During the listen condition, they listened to the audio recording of the other text read by the reader of their gender. Texts were assigned to conditions in a counterbalanced manner. During the playback condition, participants listened to their own voice recorded (see section 2. 3 for data acquisition details) earlier during the read condition. Obviously, playback condition was performed in all subjects after the read condition. This playback condition was used (i) to assess the impact of possible sensory prediction about upcoming speech (as subjects had some hints about speech content and production from the prior read condition) on the cortical tracking of speech and on tracking directionality, and (ii) to control for potential differences in speech rhythm between listen and read. For both listen and playback conditions, sounds were played with VLC running on a MacBook pro and delivered at 60 dB (measured at earlevel in every participant) through a MEG-compatible front-facing flatpanel loudspeaker (Panphonics Oy, Espoo, Finland) placed ~2 m from the participants.
Data acquisition Neuromagnetic signals were recorded at the Basque Centre on Cognition, Brain and Language (BCBL) with a whole-scalp-covering neuromagnetometer installed in a MSR (Vectorview & Maxshield™; MEGIN Elekta Oy, Helsinki, Finland). The 306-channel MEG sensor layout consisted in 102 sensor triplets, each comprising one magnetometer and two orthogonal planar gradiometers characterized by different patterns of spatial sensitivity to nearby or right beneath cortical sources. The recording pass-band was 0. 1-330 Hz and the signals were sampled at 1 kHz. The head position inside the MEG helmet was continuously monitored by feeding current to five head-tracking coils located on the scalp and observing the corresponding coil-induced magnetic field patterns by the MEG sensors. Head position indicator coils, three anatomical fiducials, and at least 150 head-surface points (covering the whole scalp and the nose surface) were localized in a common coordinate system using an electromagnetic tracker (Fastrak, Polhemus, Colchester, VT, USA).
, 2001; Vigario et al. , 2000). Independent components displaying a correlation exceeding 0. 15 with any EOG or ECG signals were subtracted from the full band and full rank MEG data. The mean AE SD of rejected components was 7. 2 AE 1. 4 (read), 5. 1 AE 1. 8 (listen), 4. 9 AE 2. 0 (rest), and 5 AE 2. 0 (playback). MEG data were then low-pass filtered at 145 Hz and notched at 50 and 100 Hz for landline artifact removal. Finally, when the maximum MEG amplitude exceeded 5 pT (magnetometers) or 1 pT/cm (gradiometers), data within 1 s before and after the excessive amplitude were discarded from further analyses to avoid inclusion of MEG data compromised by any other high-amplitude artifacts (e. g. , head movements or excessive muscle artifacts; Muthukumaraswamy, 2013) not removed by tSSS or ICA.
Note that we did not attempt to remove muscle artifacts because these are typically not well removed by classical linear regression techniques (for a review, see  Muthukumaraswamy, 2013) , and most importantly, because their power mainly lies at frequencies above 20 Hz  (Muthukumaraswamy, 2013 ) on which we did not focus. That is, we here focused on brain activity at frequencies below 8 Hz. To estimate the efficacy of the preprocessing steps described in the paragraph above, MEG data were also minimally preprocessed, with signal space separation (SSS) only. Power spectral densities were then estimated for minimally and fully preprocessed gradiometer data, based on the same time segments (identified based on fully preprocessed data). Speech temporal envelopes were obtained from all sound recordings as the rectified sound signals low-pass filtered at 50 Hz.
Coherence was previously used to assess the coupling between voice and brain signals at the frequencies corresponding to phrasal/sentential (<1 Hz), word (2-4 Hz) and syllable (4-8 Hz) rates  (Bourguignon et al. , 2013b; Keitel et al. , 2018; Luo and Poeppel, 2007; Molinaro and Lizarazu, 2018; Peelle et al. , 2013b; Poeppel, 2003; Vander Ghinst et al. , 2016b). Based on these studies, we focused on these predefined frequency ranges. Identifying relevant frequency ranges in a data-driven way might have proven challenging for the read condition since we expected remaining artifacts to give rise to significant values of coherence with speech temporal envelope. Coherence was first estimated at the sensor level. Data from gradiometer pairs were combined in the orientation of maximum coherence as done in  Bourguignon et al.
, 2018; H€ am€ al€ ainen and Ilmoniemi, 1994; Wens et al. , 2015). We also disregarded some local maxima that, based on their location, were likely to arise out of artifacts. The directionality of the coupling between the temporal envelope of voice signals and the activity within brain areas displaying a significant local maximum of coherence (see section 2. 8), was assessed with renormalized partial directed coherence (rPDC;  Schelter et al. , 2009 Schelter et al. , , 2006)). To this aim, the time-course of brain electrical activity within these brain areas was estimated with the beamformer described in section 2. 5, in the orientation maximizing the coherence with speech temporal envelope. Source and voice signals were low-pass filtered at 10 Hz and down-sampled at 20 Hz. Then, for each source separately, a vector autoregressive (VAR) model of order 40 was fitted to the source and the voice data using the ARfit package  (Schneider and Neumaier, 2001).
Acknowledgments Mathieu Bourguignon  has been supported by the  program Attract of Innoviris  (grant  2015-BB2B-10 ), by the  Spanish Ministry of Economy and Competitiveness  (grant  PSI2016-77175-P ), and by the  Marie Skłodowska-Curie Action  of the  European Commission  (grant  743562 ). Nicola Molinaro  has been supported by the  Spanish Ministry of Economy and Competitiveness  (grant  PSI2015-65694-P ), the  Agencia Estatal de Investigaci on (AEI) , the  Fondo Europeo de Desarrollo Regional (FEDER)  and by the  Basque government  (grant  PI_2016_1_0014 ). Mikel Lizarazu  has been supported by the  Agence Nationale de la Recherche  (grants  ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL ). Xavier De Ti ege is Post-doctorate Clinical Master Specialist at the  Fonds de la Recherche Scientifique (F.
Materials. The material consisted of a set of 180 word stimuli, drawn from a pool used in a previous study (see  Kowialiewski & Majerus, 2018). All stimuli were monosyllable words with a mean frequency of Mlog = 1. 536 and SDlog =. 695 (Lexique 3 database;  New, 2006). They formed 60 different triplets of semantically related words. The semantic relationships included taxonomic (e. g. , gold -iron -lead) or thematic (e. g. , tree -branch -trunk) categories. Semantic relatedness had been determined via an online survey in which participants were invited to judge the semantic relatedness of words by resampling them in pairs of items, on a scale ranging from 0 (not related at all) to 5 (strongly related) (for more details, see  Kowialiewski & Majerus, 2018). In average, each pair had been judged 75.
The BF value for each term is then assessed by directly comparing the full model against the same model, but by dropping the term under investigation. In order to minimize error of model estimation, the number of Monte Carlo simulations generated was set to Niterations = 1,000,000. In each graph, we furthermore computed Bayesian credible intervals using the 95% highest density interval on the sampled posterior distribution of the model under investigation (Niterations = 100,000). All the analyses were performed using the BayesFactor package  (Morey & Rouder, 2014)  implemented in R (R Development Core Team, 2008) using the default wide Cauchy prior distribution of r = √.
Method Participants. Twenty-nine undergraduate students (21 females and 8 males) aged between 18 and 27 years (M = 21. 9, SD = 2. 91), recruited from the university of Liège, took part in the experiment. All participants were native French speakers with no history of neurological disorders or learning difficulties. All participants gave their written informed consent prior to their inclusion in the study. The study had received approval from the local ethics committees. Materials. We used a set of 192 words (one to three syllables long) with a mean frequency of Mlog = 1. 164 and SDlog =. 732 (Lexique 3 database;  New, 2006). The stimuli were drawn from thirty-two different semantic categories, with six words per category. All the stimuli were recorded by a French native male speaker in a neutral voice.
Method Participants. Thirty undergraduate students (20 females and 10 males) aged between 18 and 25 years (M = 21. 34, SD = 2), recruited from the university community, took part in the experiment. All participants were native French speakers with no history of neurological disorders or learning difficulties. All participants gave their written informed consent prior to their inclusion in the study. The study had received approval from the local ethics committee. Materials. The stimuli were taken from a previous study  (Kowialiewski & Majerus, 2018)  in which they produced reliable imageability effects in standard immediate serial recall conditions. The stimuli were two sets composed of ninety unisyllabic words, selected from the Lexique 3 database  (New, 2006). One of the sets scored high on the imageability dimension (M = 6.