dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources, methods and research questions Most studies obtained its information directly from humans (54. 7%), followed by databases (41. 9%). Only 4. 2% of studies used documents as data sources and 1. 2% used inert sources (e. g. basic science studies). Qualitative methods were used by 4. 9% of studies and 3. 1% were basic sciences studies. Almost 92% of studies applied quantitative methods, with the majority of them using cross-sectional (34. 7%), modelling methods (17. 5%) and RCTs (13. 6%). 221 studies were identified as RCTs of which 207 were ongoing studies at the time of the search. Most research questions were related to exposure, prevalence or incidence (66. 4%), followed by benefits and harms of an intervention (21. 5%) and prognosis (6. 7%). See Table  2  for details.
Also, although the review did not search directly in trials registries, the databases that were searched actively search all trial registries, such as the LOVE platform and CENTRAL. To make the search even broader, the included studies of Carracedo et al. 2021  [15] , which searched thoroughly in such registries, were reviewed and included when relevant. Finally, included studies were not critically appraised in this review, and therefore the quality of research cannot be assessed.
Data collection We collected data using a self-administered web-based questionnaire. A secured individualized web link to the questionnaire and information about the study were sent by email to each selected participant who consented to participate in the study. Parents of children aged less than 12 years were asked to complete the questionnaire on behalf of their child, whereas teenagers aged 12-17 years completed their own questionnaire, after parental consent. The same questionnaire was used for all CONNECT phases. The first section of the questionnaire documented key socio-demographic characteristics. The second section was a social contact diary, based on instruments previously used in Polymod and other similar studies  [5] [6] [7]  (an example of the diary is provided in the Additional file 1: Figure  S1 ).
Participants reporting more than 20 professional contacts per day were asked not to report all their professional contacts in the diary. Instead they were asked general questions about these professional contacts: age groups of the majority of contact persons, average durations of contacts and whether physical contacts were generally involved or not. Additional questions about teleworking were included from CON-NECT2 onwards. All CONNECT phases were approved by the ethics committee of the CHU de Québec research center (project 2016-2172) and we commissioned the market company Advanis for recruitment and data collection. All participants gave their consent to participate in the study during the recruitment phone call. Informed consent was taken from a parent and/or legal guardian for study participation in the case of minors.
Analyses We weighted the participants of the CONNECT 1-5 surveys by age, sex, region (Greater Montreal and other Quebec regions), and household composition (households without 0-17-year-olds, households with 0-5-year-olds, if not with 6-14-year-olds, if not with 15-17-year-olds), using the Quebec data of the 2016 Canadian census (Additional file 1: Table  S2 ) and we verified that they were representative of the Quebec population for key socio-demographic characteristics. To obtain daily number of social contacts on a weekly basis, we weighted the number of daily contacts reported during the week (5/7) and the weekend (2/7). We classified the type of employment of workers using the 2016 National occupation classification (NOC)  [8]. We estimated the number of social contacts per person and per day, for all locations combined and for 6 different locations: home, work, school, public transportation, leisure, and other locations.
We used data collected from February 1 st 2018 to March 17 th 2019 as our pre-COVID period. We used data collected from April 21 st to May 25 th 2020 to represent the first wave, data collected from July 3 rd to August 31 st 2020 to represent the summer, and data collected from September 1 st 2020 to February 26 th 2021 to represent the second wave. We further stratified the second wave to represent periods of expected seasonality in social contacts: September with the return to school and at work, fall with gathering restrictions, the Christmas holidays with school and work vacations and closure of non-essential business, January and February 2021 with the gradual return to work and school after Christmas vacations and school/non-essential business closures, and the introduction of a curfew.
We used a Canadian stringency index, adapted from the Oxford COVID-19 Government Response Tracker (OxCGRT)  [11] , to quantify the intensity of public health measures in Quebec over time  [12]. This index is obtained by averaging the intensity score of 12 policy indicators (e. g. , school closures, workplace closures, gathering restrictions, stayat-home requirements, etc. ) and higher values indicate stricter measures. We estimated the mean stringency index for each of the 8 periods described previously by averaging the daily values of the index. We compared the mean number of social contacts over time (total or by location) using weighted generalized linear models with a Poisson distribution and an identity link. Generalized estimating equations with robust variance  [13]  were used to account for the correlation between the two days of diary data collection and overdispersion.
Participants and study design Participants were selected from the Penn Memory Center and the Penn Alzheimer's Disease Research Center (ADRC) Clinical Core Integrated Neurodegenerative Disease Database  [53] , spanning 2009-2019 in accordance with the University of Pennsylvania Institutional Review Board. Data from 758 cohort participants and 690 study partners were included. All participants completed a questionnaire of SMCs at baseline and were classified as having normal cognition (NC; N = 331), mild cognitive impairment (MCI; N = 200), or Alzheimer's disease (AD; N = 227) by expert clinical consensus diagnosis as previously described  [54]. Annual study visits for the cohort consisted of medical history, physical and neurological examinations, and neuropsychological testing. The accompanying study partner was asked to provide their basic demographic data, details about their relationship to the participant, and assessment of the participant's functional status and cognition, described in greater detail below.
Neuropsychological assessment Over the course of the study period (2009-2019), the ADRC Clinical Cohort completed the National Alzheimer's Coordinating Center (NACC) Uniform Dataset (UDS) Version 2  [57]  from 2009-2015, and the UDS Version 3  [58]  after 2015. Both batteries assess global cognition, processing speed, executive function, episodic memory, and verbal and category fluency, all of which were included in the analyses. However, the batteries differ in some of the measures used to assess these cognitive domains (Supplementary Table  1 ). There were strong correlations between all pairs of new and prior tests in the two UDS versions  [59]. Nonetheless, to account for differences in domain-specific measures employed by the two batteries (e. g. , Logical Memory from Wechsler Memory Scale in UDS2 and the Craft Story in UDS3), z-scores were calculated for each test within the UDS Versions 2 and 3, normalized to the NC group.
Study partner data The study partner provided demographic information, including their age, sex, race, ethnicity, and highest level of education achieved. On the FRS questionnaire described above, the study partner is also asked to define the nature of their relationship with the study participant and the frequency of contact, which includes both in-person visits and phone calls. The study partner relationship is categorized as Spouse/Partner, Child, Sibling, Other Relative, or Friend/Neighbor. Frequency of contact is categorized as < 1 day per week, 2 days per week, 3-4 days per week, or 5 or more days per week.
For a subset of patients, neuroimaging data, including amyloid PET status and structural MRI measures, were also collected. Demographic and disease distribution data for these biomarker sub-groups are provided in the Supplementary Table  2. Amyloid status was used to define subgroup analyses in the association of SMCs with cognitive and functional decline in NC and MCI participants only. Participants (N = 121) were stratified into amyloid positive and negative individuals based on expert visual interpretation of amyloid positron emission tomography (PET) scans. PET scans were obtained within five years of clinical testing. Several amyloid PET tracers were used with scanning performed according to manufacturer's recommendations: 18 F-florbetapir (N = 26, 370 MBq ± 20%, 45-min uptake time, 10-min scan), 18 F-florbetaben (N = 106, 300 MBq ± 20%, 90-min uptake time, 20-min scan), and 18 F-flutametamol (N = 2, 185 MBq, 90-min uptake time, 20-min scan).
The SPARE-AD index summarizes the high dimensional data with a single score, wherein higher, more positive scores indicate greater AD-like atrophy and lower, more negative scores indicate greater similarity to normal controls. The SPARE-BA index was calculated using support vector regression to predict brain age from MRI scans, which can then be used to evaluated participants' advanced brain aging based on the patterns of brain atrophy  [43, 45]. A Brain Age Gap was calculated for each subject (predicted age -chronologic age), such that positive scores indicate more advanced brain aging, while negative scores indicate more resilient brain aging.
For analyses of hippocampal volumes, ICV was also included as a covariate. Because we were interested in whether SMCs could predict development of structural brain changes indicative of AD-related neurodegeneration, analyses with neuroimaging data were performed in the NC and MCI group only. To evaluate the effects of baseline SMCs on change in global cognition (MMSE) and function (FRS), mixed linear regression models were performed with the participant as the hierarchical variable to allow for intra-individual correlation. The mixed-effects model accounted for the varying number of visits and time between visits. A maximum follow-up length of four years was chosen to increase statistical power. Separate models were run to predict the effect of self-reported SMCs and partner-reported SMCs on cognitive and functional decline.
While the total study population is derived from a memory center cohort, the NC group is comprised of volunteers from the community. Discrepancies between self-reported SMCs, objective cognitive performance and neuroimaging biomarkers have been shown in community samples  [76, 77] , and perhaps explains the weaker associations observed between self-reported SMCs and these data in our sample. Furthermore, there is no consensus the field regarding the most sensitive SMC questionnaires to detect meaningful cognitive change, and, as such, the varied implementation of SMC questionnaires in research limit comparison across studies and between participants and study partners  [16, 20, 70, 73, 78]. Key advantages of the PRMQ are the analogous self and study partner questionnaires and the large dynamic range compared to shorter questionnaires with binary outcomes  [55, 56].
Participants Forty-one volunteers were recruited at University Bourgogne Franche-Comté. They gave their written consent and were compensated by credit course. Eleven participants were excluded from the eye-tracking data set due to weak eyetracking ratio (<40%). The final data set consisted in 28 participants (22 female), mean age 21 years. They were all native French speakers, right-handed and reported normal or correct-to normal vision. Their task was to visualize 16 scenes in virtual reality and summarize orally the narration. They were naïve to the purpose of the experiment. A power analysis (G*Power 3  (Erdfelder et al. , 1996; Faul et al. , 2009) ) revealed a total sample size of 18 participants would be required to obtain statistical power with (d = 0. 25) and power (1-β) set at 0. 95 (α = 0.
Il n'aime pas les ports, et pas davantage cette ville perchée sur la mer du Nord. (Sadly, he got off the ship. In the middle of the hurrying crowd, he feverishly went down to the docks. He did not like ports, no more than this town perched over the North Sea). From this text material, sixteen video files for VR display were created with typical objects of the narration included in the visual scenes. Twelve video files were used to cross the six experimental conditions of audio and visual change (shift) in perspective as detailed Table  1. The audio versions of the narrations were recorded by the experimenter (Audacity ™ software) and combined with VR scenes created with Unity 2018. 5f1 (see section virtual environment). The experimental design was a within-subject design with two factors: audio perspective shift with two levels: Shift/no Shift; visual perspective shift, with three levels: Shift/no Shift/Grey mask.
In this experimental setup, no interaction with the virtual world was required, thus no VR controllers were needed. The VR-Software set used the SteamVR plugin for viewing in the HTC Vive virtual reality headset. The area calibration was 2,160 × 1,200. The dimensions of the physical stimuli were 2,160 mm (horizontally) and 1,200 mm (vertically). Event detection was at estimated speed. The speed threshold was 40 °per second, the minimum fixation duration was 50 ms, and the minimum high speed detection ratio was 22 ms. The design of the experiment as well as the calibration phase were programmed on SMI Experiment Center software 3. 7. The analysis and the output of the data were carried out on the SMI BeGaze HMD HTC software 3. 7. The computer running this experiment was an XMG PC computer, composed of an Intel Core i7-7700HQ @ 2.
The audio narrations were created with the Audacity software in. avi files according the audio experimental conditions (shift/no shift in perspective). Then video stimuli were associated with audio files in Experiment Center 3. 7 software, used to run the experiment. To be able to counterbalance the order presentation of all the audio-visual stimuli, according the experimental conditions (Table  1 ), we created six experimental lists in Experiment Center. Then the order of presentation of the stimuli within each list, was automatically randomized by Experiment Center 3. 7 software.
Data Preparation Fixations were detected using a velocity-based algorithm (high-speed detection min ratio = 22 ms, peak velocity threshold = 40 °per second, min fixation duration = 50 ms). Three AOIs related to the objects of interest constituting each scene/video were drawn. These AOIs corresponded to the typical objects of the visual scenes that were related to the perspective shift (for example, in the narration cited above, the ship, the crowd and a box on the dock were selected). Automatic Move&Morph TM function (provided in Begaze) for dynamic stimuli in video clips ensured that AOI was on the stimuli targeted across all the duration of the video (corresponding to the typical objects of the visual scenes). However, we controlled frame by frame these dynamic AOIs and it was sometimes necessary to adjust them to the experimental objects of interest.
This index varied from -1 to 1, with a negative value indicating that a horse was more attentive during ADS than PDS, and a positive value indicating the opposite. Heart rate data were extracted from the Polar recordings. A visual correction was applied to eliminate artifactual beats (as recommended in  45  ). The difference in heart rate (beats per minute-bpm) between the last five and first five seconds of each section was calculated. Then, for each horse, the mean of this difference over the two sections of each type (PDS or ADS) was calculated. Statistical analyses. Due to the exclusion of one horse during habituation and three horses during the tests, the statistical analysis considered 28 animals. All statistical analyses were performed using R 4. 0. 3 (R Core Team, 2013). Due to the small sample size (N = 28), nonparametric tests were used.
Acknowledgements We would like to thank the staff from the  UEPAO (Unité Expérimentale de Physiologie Animale de l'Orfrasière)  for technical help; Springer Nature Authors Services for English language and text flow editing;  Estel Blasi  for drawing Fig. 2; the volunteers who were filmed to make up the stimuli of this experiment;  Karine Reynaud  for allowing us to use her pictures for Fig. S1; and  Aline Bertin  for helping to draw Fig. S1 spectrograms. IFCE ( French Horse and Riding Institute ) funded this experiment. This funding source had no role in the study design, data collection and analysis, or preparation and submission of the manuscript.
Profiles could include measures of gene expression, metabolomics, proteomics, or other measures of cell state, with each performed as a synchronized time course. Step III would be to integrate the data generated from each domain across the time course, defining the multidimensional cellular response to challenge and the resolution of that response. Step IV would address how processes with an established role in longevity regulation intersect to influence resilience. This sort of approach is expected to reveal multidimensional mechanistic links between longevity-associated pathways and resilience and bring substantial new insight into the biology of cellular resilience.
ACKNOWLEDGMENTS A special thanks to  Amy Chen  and the people at the  Santa Fe Institute  for hosting the workshop upon which this study is based. We thank  Shripad Tuljapurkar  for discussions and  Ophelie Ronce  for discussions and comments on the manuscript. This work was supported by the  Santa Fe Institute  through the  Complex Time: Adaptation, Aging, Arrow of Time research theme , which is funded by the  James S. McDonnell Foundation  (Grant No. 220020491 ). D. P. was funded in part by support from  NIA  grants  AG049494  and  AG013280  and  NSF  grant  DMS1561814 , the  Glenn Foundation , and a  Longevity Impetus Grant  from the  Norn Group. M. E. H. acknowledges the  James S. McDonnell Foundation  for funding (research award  220020294 ),  ITMO Cancer AVIESAN  ( Program 'HetColi' ) and the  Institut National du Cancer  ( 2014-1-PL-BIO-12-IGR-1 ).
Participants A purposive sampling strategy was employed to gather data from university students from one academic university in the USA and -Belgium as well as one technical university in the Netherlands (c. f. Table  1 ). At Time 1, the majority of the 1056 participants were English speaking (48. 3%), American (46. 8%) 1 females (54. 9%) between the ages of 21 and 25 (58. 80%) who held part-time employment (55. 9%). At Time 2, the majority of the 592 matched participants were Dutch-speaking (71. 3%), and Western European (66. 4%) males (60. 8%) between the ages of 21 and 25 (72. 6%) who held part-time employment (62. 8%).
Confirmatory Factor Analysis: Cross-Sectional and Longitudinal The factorial validity of the GASE at each time point were explored through estimating a single first-order factor model. Here all items were specified to load directly onto a single factor. Observed items were used as indicators for the latent factor. No items were removed. The results summarized in Table  3  showed that the model fitted the data well at both Time 1 (χ 2 Thereafter, a LCFA model was estimated. Here ASE at Time 1 was regressed on ASE at Time 2. Error variances between time-points were permitted to covary. Table  3  indicates that this model also fitted the data well (χ 2 (1056) = 45. 60 p > 0. 001; df = 29; CFI = 0. 99; TLI = 0. 99; RMSEA = 0. 02 [. 008,. 036]; SRMR = 0. 02; AIC = 18,015. 40; BIC = 18, 194. 04 ).
Statement of need Frites (Framework for Information Theoretical analysis of Electrophysiological data and Statistics) is an open-source Python package, designed for researchers and students with little programming knowledge and working on the discovery of cognitive brain networks using neurophysiological data. It combines in a single framework information-based analyses of neurophysiological recordings and group-level statistical inference that can adapt to the inherent variability across participants. The measures of information and the group-level statistics functionalities are wrapped into high-level workflows. In addition, Frites also includes several measures of functional connectivity (FC) for measuring pairwise interactions between brain regions. To the best of our knowledge, Frites is the only Python software combining information-theoretical approaches with flexible group-level analyses for the discovery of cognitive brain networks.
Supported neurophysiological recordings and link with other softwares Frites is suitable for the analysis of continuous and multi-channel neurophysiological data, encompassing non-invasive recordings with uniform spatial sampling (e. g. , M/EEG data) such as invasive and spatially sparse recordings, such as intracranial EEG or Local Field Potentials (LFPs). The package supports standard NumPy array inputs  (Harris et al. , 2020) , neuro-oriented objects from the MNE-Python software  (Gramfort et al. , 2013) , but also multi-dimensional labelled Xarray objects  (Hoyer & Hamman, 2017).
Nevertheless, Frites is not limited to the included estimators as the definition of custom ones is also supported, such as IDTxl kernel methods  (Wollstadt et al. , 2019) , infotheory information decomposition  (Candadai & Izquierdo, 2019)  or scikit-learn cross-validated classifiers  (Pedregosa et al. , 2011). The estimated information can then be combined with the group-level statistics for the identification of feature-specific networks. While Frites is strongly oriented toward neuroscience applications, the estimators of information are agnostic to data types and can therefore be applied to different fields. For statistical inferences, the package integrates a non-parametric permutation-based statistical framework  (Maris & Oostenveld, 2007)  to perform group-level inferences on non-negative measures of information.
Workflows Frites provides a set of workflows that integrate several analysis steps. Those workflows take as inputs the neural data coming from single or multi-participants (or single / multi sessions), estimate the amount of information shared between the brain data and the external variable, at each brain region and time bins, and finally perform network-level statistical inference, corrected for multiple comparisons. Those high-level workflows are particularly useful for users with no or little programming knowledge as all of those steps can be performed in a very few lines of code, with standard inputs like MNE-Python epochs.
Acknowledgements EC and AB were supported by the  PRC  project " CausaL"  ( ANR-18-CE28-0016 ). This project/research has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ). RB acknowledges support through a  PhD Scholarship  awarded by the  Neuroschool. This work has received support from the  French government  under the  Programme Investissements d'Avenir, Initiative d'Excellence d'Aix-Marseille Université  via  A*Midex  ( AMX-19-IET-004 ) and  ANR  ( ANR-17-EURE-0029 ) funding. RAAI was supported by the  Wellcome Trust  [ 214120/Z/18/Z ]. VLC is supported by a scholarship from the European.