dataesr/scientific-paragraphs-categorization huggingface dataset:
Neurosynth decoding of covert experiential and overt task spatial maps. We used Neurosynth's online meta-analytical decoder  51  to identify terms most commonly associated with our (unthresholded) group-level neural maps in the available literature (5 maps: vigilance, target detection, off-task thought, deliberate thought, and verbal, self-relevant thought). When provided with an unthresholded whole-brain map, the Neurosynth decoder performs a reverse inference analysis to identify the terms describing cognitive and psychological functions that are most strongly associated with the patterns of neural activation shown in the map. Specifically, the decoder compares the patterns of activation in the input map to patterns of activation in the studies in the Neurosynth database, resulting in a list of terms that are most likely to be positively or negatively associated with the patterns of activation in the input map.
Linear Mixed Models. Linear Mixed Models (LMMs) were fitted by restricted maximum-likelihood estimation in R [4. 1. 1 53 ] using the lme4 package  [1. 1. 31 54  ]. We used the lmerTest package  [3. 1. 3 55  ] to obtain P values for the F-tests returned by the lme4 package. For each set of models, the alpha level for each Fstatistic was set based on 0. 05 divided by the number of models (i. e. , Bonferroni-corrected alpha level). Degrees of freedom were calculated using Satterthwaite approximation and for F-tests, type 3 sum of squares was used. Contrasts were set to "contr. sum," meaning that the intercept of each model corresponds to the grand mean of all conditions and that when a factor has two levels, the parameter estimate is equal to half of the difference between the two levels  56.
Data Availability Ethical approval conditions and European Research Council (ERC) grant stipulations do not permit the public sharing of raw data. However, anonymized Multidimensional Experience Sampling data and Gradient coordinates are publicly available via Mendeley: https://doi. org/10. 17632/mx76fvdm3v. 1. In addition, all group-level unthresholded brain maps presented in the figures are available via NeuroVault: https://identifiers. org/neurovault. collection:13520. The code for the task paradigm is publicly available at: https://vcs. ynic. york. ac. uk/hw1012/go_nogo_experience_sampling/tree/master/. All code used in the current analysis and preparation of figures is publicly available via GitHub at: https://github. com/Bronte-Mckeown/mDES_States.
24  Along this line, a novel study linked suicidal ideation with depletion of the oral microbe Alloprevotella rava spp. , further emphasizing the role of microbiota in neuropathology. 25  Indeed, previous studies have associated SPI with increased gastrointestinal (GI) tract permeability measured by the levels of microbial translocation markers. 26, 27  In this regard, earlier data linked SPI to the translocation of Escherichia coli spp. (E. coli) into the host systemic circulation. 28, 29  In addition, neuropsychiatric symptoms, including psychosis, were documented in 2011 during an E. coli outbreak in northern Germany, further connecting this bacterium to neuropathology. 30, 31  Moreover, E. coli antigens, including lipopolysaccharide (LPS) and flagellin, were shown to aberrantly activate microglia, potentially leading to pathological phagocytosis of viable neurons and/or synapses.
Walking test Participants were familiarized with walking on an instrumented gait analysis treadmill (Zebris; FDM-T) for 5 minutes at their pre-calculated preferred walking speed previously measured, followed by a 2-minute seated rest period. If participants experienced any difficulty in walking on the treadmill, they were given an additional 2 minutes of training. Subsequently, participants performed three 1-minute trials, with each trial separated by a 5-minute seated rest period. Data were collected for a period of 10 seconds, starting at the 10th second of each trial. The treadmill software (Zebris, Suite FDM-T) sampled the gait parameters at 100 Hz from the treadmill force plates. These parameters included spatiotemporal parameters, walking cycle phases, GRFv, CoP displacements. Spatiotemporal parameters such as step length, step width, and stride length were recorded.
The software divided the walking cycle into support phase (SU) and swing phase (SW), consisting of the 1st double support (1st DS), a single support (SS), and the 2nd double support (2nd DS). The absolute GRFv peaks were used to calculate the relative GRFv peaks (P1, P2, N/kg, respectively) by dividing the absolute value of the GRFv peak by the body mass. The CoP data were used to extract the CoP length during the SU and SS, the CoP anteroposterior position, and the CoP velocity during walking (cm/s).
Electromyography test Electromyographic (EMG) data from ankle joint muscles were collected during MVC of PF and DF, and during a treadmill walking test using Trigno® Wireless Biofeedback System (Delsys Inc. , Natick, MA). The EMG recording was synchronized with the treadmill data using a control device operating on the principle of a synchronization switch system (ON/OFF). The sensors composed of two pairs of silver bar contacts with 10 mm interelectrode spacing electrodes were placed on the gastrocnemius medialis (GM), soleus (SOL), and tibialis anterior (TA) of the dominant leg, conforming to the recommendations of SENIAM. The raw EMG signals were post-processed using Matlab software (Matlab R2013a, MathWorks, Natick, USA). The data from 10 strides were collected for a period of 10 seconds, in the 10th second of each trial (i.
Statistical analysis The statistical analyses were conducted using Statistica Software 13. 0 (Software, Inc. , Tulsa, OK). The normality of the data distribution was checked using Kolmogorov-Smirnov tests. When the data distribution followed a normal distribution, paired t-tests were applied to compare the results of the same group before and after the PSM program. The independent samples t-test was also used to compare between the TG and CG before and after the PSM program. The relationships between the changes in gait parameters and the relative maximal force of the DF and PF, anthropometric parameters and muscle activity of GM, SOL and TA were evaluated using the Pearson's correlation analysis. The data were expressed as means and standard deviations. The significance threshold was set at p < 0.
Analysis was by intention-to-treat. The trial was prospectively registered (ClinicalTrials. gov, NCT03576586). Of the 2068 women assessed for eligibility, 166 were eligible and 146 were randomly assigned to the intervention (n = 74) or attention-placebo control group (n = 72). For the PCL-5, at six weeks, a marginally significant intervention effect was found on the total PCL-5 PTSD symptom count (β = -0. 43, S. E. = 0. 23, z = -1. 88, p < 0. 06), and on the intrusions (β = -0. 73, S. E. = 0. 38, z = -1. 94, p < 0. 0525) and arousal (β = -0. 55, S. E. = 0. 29, z = -1. 92, p < 0. 0552) clusters. At six months, a significant intervention effect on the total PCL-5 PTSD symptom count (β = -0. 65, S. E. = 0. 32, z = -2. 04, p = 0. 041, 95%CI[-1. 27, -0. 03]), on alterations in cognition and mood (β = -0.
Study design The Swiss TrAumatic biRth Trial (START) was a two-arm (intervention vs attention-placebo), parallel-group, multicentre, double-blind, explanatory superiority RCT conducted in two Swiss University Hospitals. The study was preregistered on ClinicalTrials. gov before recruitment began (NCT03576586). The published study protocol was approved by the local ethics committee (2017-02142)  [29]. A steering committee, including international perinatal mental health researchers and clinicians, advised on the conduct of the START trial from the study funding application to data analysis.
Other outcomes included a self-report questionnaire on the acceptability and expectancy of the intervention completed at T1. Sociodemographic and psychological information, such as maternal age, nationality, education, marital status, and prior psychological trauma were collected via a self-report questionnaire at T2. Obstetric data (gravidity, parity, blood loss >1 litre during childbirth) and infant data (Apgar score at 1 and 5 min, birth weight) were retrieved from hospital medical records. Based on previous clinical research on the same single-session behavioural intervention  [19] , no substantial risk was assumed to be associated with the intervention. Therefore, in agreement with the local ethics committees, no routine safety assessments took place.
Statistical analysis A sample size calculation, based on a previous proof-of-principle RCT  [19] , identified a sample size of n = 120 participants was necessary to have 80% power (α = 0. 05) to detect a between-group difference of d = 0. 30 on each of the primary outcomes, i. e. , on the presence and severity of maternal CB-PTSD symptoms at six weeks postpartum. Predicting a 20% drop-out rate, we expected to recruit 144 women. Parametric or non-parametric tests were chosen depending on whether the appropriate statistical assumptions were met. The sample was described with means (M) and standard deviations (SD), or median (Mdn) and interquartile ranges (IQR) when the data were not normally distributed according to Shapiro-Wilk tests. Group differences in the perception of the simple activities were examined with Mann-Whitney U tests.
They were carried out on n = 136, corresponding to the number of participants who performed a simple activity and completed the acceptability-expectancy questionnaire, regardless of whether they completed other assessments. PCL-5 and CAPS-5 symptom counts were taken as the main dependent measure in the follow-up analyses. Besides being clinically relevant, they have the advantage of including both disorder presence and its severity assessment. The distributions of symptom counts were compared between groups, first on the total symptom count and then within each DSM-5 cluster of symptoms. A well-known aspect of such data is the zeroinflation phenomenon: a mass of zero counts is often apparent, exceeding what would be expected from standard count models, resulting in a bimodal distribution. Ignoring this structural aspect of data may lead to bias in regression coefficient estimation  [35].
Another inherent aspect of such counts is that, as within-subject counts, they are dependent, and under-or overdispersion is to be expected. Both issues were addressed in the analyses by using zero-inflated count models, with various degrees of flexibility to model under-or overdispersion: The Poisson, Negative Binomial I (linear parameterisation), Negative Binomial II (quadratic parameterisation), and Generalised Poisson  [36]  distributions were systematically compared for each count variable, using the R software package version 4. 2. 1  [37] , and the glmmTMB R package  [38]. A log link was used in all models. A model comparison approach was adopted, where all three components (distribution choice, variable inclusion in the conditional model, and variable inclusion in the zero-inflation part) were systematically varied.
The numbers of eligible participants for these analyses were n = 128 at T3, and n = 113 at T4. Given there were missing responses for six participants at T3 and five at T4 on at least one PCL-5 sub-score, the analyses included n = 122 participants at T3 and n = 108 participants at T4. Concerning the CAPS-5, the exclusion of participants with missing responses resulted in a total of n = 116 participants (n = 54 control, n = 62 intervention) at T3, and n = 100 (n = 49 control, n = 51 intervention) at T4. A similar approach was adopted for analysing the diary data (n = 96), consisting of daily counts of trauma-related IMs, during the first week postpartum. All four candidate distributions were fitted within a generalised linear mixed model regression, with time (i. e. , days 1-7) and the group as the main explanatory variable, and subjects as a random intercept variable.
RESULTS Recruitment occurred between August 2nd 2018 and Oct 10th 2021 in the main study centre, and between March 3rd 2019 to July 29th 2020 in the secondary study centre. In both centres, recruitment was paused between March 16th 2020 and June 8th 2020 due to restrictions related to the COVID-19 pandemic. Final follow-up data were collected on Feb 8th 2022 for the main study centre and on Feb 7th 2021 for the secondary study centre. A total of 2068 women gave birth in the two study centres over the study period: 1902 of them were finally ineligible and 166 were enrolled in START, with 74 allocated to the intervention group, 72 to the attention-placebo control group, and 20 excluded between the study enrolment and randomisation for various reasons (Fig. 1 ). Among the intervention group, three participants did not receive the intervention, three dropped out of the study between T1 and T2, and eight between T3 and T4.
ACKNOWLEDGEMENTS The authors would like to acknowledge: -Contribution to the study design and data monitoring:  Dr Suzannah Stuijfzand. -Coordination of the study, recruitment, and data collection:  Ms Dominique Delecraz ,  Ms Véronique Othenin-Girard ,  Ms Margot Gafsou ,  Ms Daniela Barros Rodrigues ,  Ms Annabelle Reuse ,  Ms Mathilde Duroux ,  Ms Yasmina Lotfi Mahmoud ,  Ms Cassie Pernet ,  Ms Thais Sajin Consuela ,  Ms Marion Décaillet ,  Ms Céline Piffaretti , and  Ms Maria Jaramillo. -Help with data handling:  Ms Maria Jaramillo  and  Ms Esther Pallandini. -Recruitment and hierarchical support:  Ms Isabelle Eragne ,  Ms Danièle Losguardi. -Hierarchical and logistic support:  Pr David Baud ,  Dr Hélène Legardeur. -Members of the study steering committee :  Pr Susan Ayers ,  Dr Myriam Bickle Graz ,  Dr Camille Deforges ,  Pr Ulrike Ehlert ,  Pr Manuella Epiney ,  Pr Emily A Holmes ,  Pr Antje Horsch ,  Pr Nadine Messerli-Bürgy ,  Dr Mathilde Morisod Harari ,  Dr Kate Porcheret ,  Dr Vania Sandoz ,  Pr Daniel Schechter ,  Pr Nicole Sekarski ,  Dr Suzannah Stuijfzand ,  Pr Yvan Vial.
MATERIALS AND METHODS Design and patients We prospectively included all COVID-19 inpatients treated at the COVID-19 treatment unit (CTU) of Mulago Hospital in Uganda from December 2021 to February 2022. We collected data on clinical characteristics, comorbidities, coprescribed treatments, disease severity, and treatment outcomes (i. e. , all-cause mortality, duration of hospitalization and complete symptom resolution). The details of the Mulago Hospital CTU have been previously described  [48]. Briefly, to be admitted or treated for COVID-19, patients had to have either a positive SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) test or a positive COVID-19 rapid antigen SARS-CoV-2 test (COVID-19 RDT) or clinical signs and radiological tests consistent with a COVID-19 diagnosis.
ACKNOWLEDGEMENTS We acknowledge the leadership of the  Mulago National Referral Hospital  for the administrative support offered during this study. We appreciate the contribution of health workers at the  CTU , medical officers, nurses and research assistants who collected data. We also acknowledge  Julien Potet  of the  Médecins Sans Frontières Access Campaign, Paris, France  for the input to the manuscript. We acknowledge the funding support from the  Government of the Republic of Uganda  through the  Makerere University Research and Innovations Fund (Mak-RIF ).
| Data collection During observation sessions, we used "instantaneous scan sampling"  (Altmann, 1974)  every 15 min to record the activity of five individuals. To avoid bias on activity records, we chose the first individual randomly and observed the others from a right to left direction from the first one. We recorded scans on all individual categories excluding infants (<1-year-old). We divided activities into five mutually exclusive behavioral categories: foraging (i. e. , searching for food items, including turning over stones, digging into the ground, searching beneath litter, hunting insects, and cleaning plant items), feeding (i. e. , actually ingesting food items), moving (i. e. , any type of locomotion that was not associated with another activity), resting (i. e. , all inactive postures not associated with another activity), and socializing (i.
In each model, in addition to the type of site, we included the month and type of site * month interaction as fixed effects, and the site and group as random effects. To control for a possible effect of group size on diets and activity-budgets, we included group size (excluding infants) as a control predictor. Whenever a model included a significant interaction of type of site and month, we ran post hoc Tukey's tests to determine in which months the response variable (i. e. , food category or activity) differed between types of site. We built the GLMMs using the "glmmTMB" R package  (Brooks et al. , 2017) , and performed the Tukey's tests using the "lsmeans" R package  (Lenth, 2016).
| Influence of anthropogenic disturbances and diets on activity-budgets and ranging behavior We performed a Multiple Co-inertia Analysis (MCOA;  Bady et al. , 2004)  to assess whether the activity-budgets and diets of the groups were influenced by human and domestic dog disturbances. We used three sets of multivariate data for all sites: the monthly activity-budgets of the groups, their monthly diets, and the hourly rates of disturbances by humans or domestic dogs (cattle were not included in this analysis because they were never aggressive towards macaques). The activity-budget data set contains the mean monthly percentages of time spent on the different activities at each site. The diet data set contains the mean monthly percentages of feeding time spent on 10 categories of food items, including 5 types of cultivated items, and 5 types of noncultivated items.
e. , the synthetic plane which maximizes covariance between all data sets), as well as between each pair of tables. We used Mantel permutation tests with 1000 permutations to assess the significance of those correlations. Graphically, the angle between the arrows from the origin to the variables indicates how they are correlated: an acute angle indicates a positive correlation, an obtuse angle a negative correlation, and a 90°angle indicates no correlation. The distance between the origin and the variables represents the strength of the correlation: the longer the distance, the stronger the correlation. All co-inertia analyses were performed using the "ade4" R package  (Thioulouse et al. , 2018).
ACKNOWLEDGMENTS Financial support for this study came from the  NGO AAP (Animal Advocacy and Protection, Morocco and Netherlands) , the  French ANR  project  COHUMAG , the  CNRS  ( Centre National de la Recherche Scientifique, France ). Our study was conducted in close partnership with the " Haut Commissariat aux Eaux et Forêts et à la Lutte contre la Désertification " and the Director of the  Ifrane National Park, Morocco. We thank  L. Oukannou  who helped us in the choice of the study site in Morocco, the team of the  Ifrane National Park  who assisted us in the field, and the local farmers who let us work on their lands. We also thank  B. Gaci  and  M. Muller  for permission to work in the  Djurdjura National Park (Algeria) , and the  Ministry of Hydraulics, Environment and Forests of Algeria  which permitted our field studies in the Djurdjura Park and Akfadou forest.
Measuring student intentions to pursue math Our analyses are based on data from PISA 2012, an international assessment of the knowledge and skills of 15-year-old students in mathematics, reading, and science conducted every 3 years. PISA 2012 was conducted in 2012 in the 34 (mostly) developed countries belonging to the Organisation for Economic Cooperation and Development (OECD) and an additional 30 developing countries, accounting for a total of 80% of the world economy. PISA 2012 focuses primarily on math and includes several measures of students' attitudes towards math (see Supplementary Methods for details). PISA 2012 includes questions related to the intentions to pursue studies and careers related to math rather than to other school subjects. These intentions are measured through a series of five questions that ask students if they intend to (i) take additional math versus English/reading courses after school finishes, (ii) take a major requiring math skills versus a major requiring science skills in college, (iii) study harder than required in math versus English/reading courses, (iv) take a maximum number of math versus science classes and (v) pursue a career that involves a lot of math versus science.
This percentage is slightly higher among the non-OECD countries than among the OECD countries (24. 5% versus 22. 0%), but the gender gap is smaller (5. 2% versus 9%). Our main variable captures student intentions to pursue mathrelated studies rather than actual enrolment. In addition, we use a dataset containing information on both student intentions and actual choices in France to show that in this country in particular, intentions and enrolment are correlated and that our conclusions hold when using actual enrolment instead of intentions. We also use data from the High School Longitudinal Study (HSLS:09) in the United States as well as previous work relying on longitudinal datasets in Canada  15  and Australia  21  to suggest that our conclusions extend to actual enrolment. Finally, we use data on adult math skills and occupations to further suggest that our conclusions may also extend to math-related or STEM occupations.
Actual enrollment in math-related studies/careers vs math ability We now present evidence to assess the extent to which the patterns observed for educational and career intentions in high school may extend (i) to actual enrolment in math-related education and, later on, (ii) to careers in math-related jobs. Regarding enrollment in math-related studies, we start by using a data source for France that contains information on ~12,000 10thgrade students and 5500 12th-grade students enrolled in French high schools in the Paris region and observed during the 2016-2017 and 2017-2018 academic years. The data were collected for another research project, and detailed information on them can be found in ref. 24. The dataset includes information about sex and math ability measures in the form of national exam scores, with the exams taking place at the end of 9th grade (Diplôme National du Brevet des Collèges) for 10th graders and at the end of 12th grade for 12th graders (Baccalauréat scientifique).
For the 10th graders, the dataset also includes information on intentions to specialise in a scientific track (Première S, emphasising math, physics and natural science) in 11th grade, which is declared in 10th grade between January and March, as well as actual enrolment in the scientific track in grade 11. For 12th graders, it includes intentions to enrol in a math-related programme after high school and detailed information on actual enrolment after high school. From the observed higher education enrolments, we construct indicator variables for enrolment in a STEM programme, in a math-related programme (math, physics, engineering or computer science), and in a selective (prep school) math-related programme. These variables are similar to those used in ref. 24, and more details can be found there.
Together, these results show that in France, our conclusions regarding the intention to study math also extend to actual enrolment in math-related studies (and even STEM studies), at least at college entry. We also use a dataset from the High School Longitudinal Study (HSLS:09), a nationally representative US study conducted by the National Center for Education Statistics (NCES), designed with a focus on STEM. We show that our results hold when we consider the actual enrolment of high school students in a given set of courses in engineering, computer science and math as an outcome depending on their sex (see Supplementary Note for more details). These outcomes are particularly important since the leaky pipeline starts in high school level and high school choices impact college choices (see ref.