dataesr/scientific-paragraphs-categorization huggingface dataset:
We then compared the values (in weeks) obtained for the provisioning data to those of the neurodevelopmental trajectories to test our prediction that interindividual negotiations (such as those occurring when infants beg caregivers for food) follow a similar developmental pattern to that of social brain regions. P4 -do regions with similar GM developmental profiles and fMRI response to stimuli of social interactions have stronger functional connectivity? First, we matched the corticocortical connectivity data with the GM neurodevelopmental data, which resulted in a dataset of 1400 connections encompassing 28 target and 51 source regions (Data S2). We also matched the connectivity data with the fMRI data and included only the connections for which fMRI data is present for both the target and the source.
a control sentence made with most of the same words, but not evoking the event context. We thus create a linking hypothesis, such that the value that we calculate, based on cosine similarity, varies inversely with the reaction time in the original McKoon task. In our study, the score for evaluating performance on this task is calculated as follows: (4) McKoon Inference = CosSim(Context,Target) -CosSim(Control, Target) To evaluate a model, the model is run on the context sentence and the control sentence, and the resulting vectors are compared to the target word vector, for the 32 experimental trials provided in the original article. We performed a paired t-test on these semantic relatedness values. The t-value of the comparison is the measure of performance. Analysis code and data for these experiments is openly available at this repository: https://github.
DISCUSSION The current research explores how event knowledge that is not explicitly stated in the narrative may be coded in language models, and how this can be used to better understand the computational neurophysiology of human inference processing. This research takes place in the context of an exciting new development in human language neurophysiology, that exploits a form of "big science" based on massive availability of datasets and multiple models (e. g. (Schrimpf et al. , 2021) ). Within this context, the current research takes a complimentary approach, that focuses on specific tasks within the same domain (inference) and the differences between them. We focused on two well-characterized inference tasks from the human experimental literature. The Metusalem task examines how the addition of contextual discourse allows inferences about events to be made.
They observed that brain score is correlated with the ability to predict the next word in text, but not necessarily with the ability to perform other language tasks, including inferencing (evaluated as a subset of language tasks in  (Wang et al. , 2018) ). Interestingly, the brain activity used in these analyses was recorded while subjects were reading, but not directly performing other language tasks. This leads us to propose the hypothesis that if neural activity is recorded while subjects are performing tasks that require inference processing, then brain scores on such a dataset will correlate with the performance on the corresponding inference tasks. Indeed, it is highly likely that behaviorally dissociable forms of language processing will be associated with distinct neural computations, and distinct spatiotemporal patterns of brain activation.
Animals HS rats (Rat Genome Database NMcwiWFsm:HS #13673907, sometimes referred to as N/NIH:HS) were created to encompass as much genetic diversity as possible by outbreeding eight inbred rat strains (ACI/N, BN/SsN, BUF/N, F344/N, M520/N, MR/N, WKY/N and WN/N) in 1984  (Hansen and Spuhler, 1984 ; Solberg Woods and Palmer, 2019 ). HS rats were bred at Wake Forest University School of Medicine by Dr. Leah Solberg Woods (n = 600). To minimize inbreeding and control genetic drift, each generation of the HS rat colony consists of at least 64 breeder pairs and is maintained using a breeding strategy that minimizes inbreeding, with each breeder pair contributing one male and one female to subsequent generations. Each rat received a chip with an RFID code that was used to track animals throughout the experiment.
1 mL of the drug per lever press over 6 s followed by a 20-s timeout period that was signaled by the illumination of a cue light above the active lever, during which active lever presses did not result in additional infusions. Responses on the left inactive lever were recorded but had no scheduled consequences. Fluid delivery and behavioral data recording was controlled by a computer with the MED-PC IV software installed. Initially, rats were trained to self-administer cocaine in 10 short access (ShA) sessions (2 h/day, 5 days/week). At the end of the ShA phase the animals then underwent 14 extended access (LgA) sessions (6 h/day, 5 days/week) in order to measure escalation of drug intake.
Statistical Analyses Data were analyzed using Prism 9. 0 software (GraphPad, San Diego, CA, USA) and R Studio. Selfadministration data were analyzed using repeated-measures analysis of variance (ANOVA) followed by Bonferroni post-hoc tests when appropriate. For pairwise comparisons, data were analyzed using the Student's t-test. Correlations were calculated using Pearson r analysis. Missing data due to session failure was imputed as the average from the sessions before and after (<2% of data). The data are expressed as mean ± SEM unless otherwise specified. Values of p < 0. 05 were considered statistically significant.
79; p = 0. 0069 vs naïve after t-test), compared to their naïve counterparts. Finally, to identify patterns in the multidimensional dataset of addiction-like behaviors, a principal component analysis (PCA) was used (Fig. 1H  ). This method allows for the identification of latent variables -principal components -that can account for most of the variance within the data, thus simplifying complex data structures. Three significant findings emerged from this analysis. First, the majority of the variance was accounted by the first two principal components, with PC1 and PC2 explaining 48. 6% and 15. 7% of the variance respectively. Second, measures related to short-term cocaine intake were orthogonal to those collected during longer access periods, showing a consistent daily shift from primarily loading onto PC1 to gradually loading onto PC2.
Future studies will combine these differences with genetic data to perform a genome wide association studies (GWAS) aimed at identifying specific alleles and gene that contribute to vulnerability and resilience to cocaine addiction-like behavior  (Chitre et al. , 2020 ). Moreover, biological samples from animals that are resilient or have mild, moderate, or severe addiction-like behaviors are currently available through the Cocaine Biobank (www. cocainebiobank. org (http:// www. cocainebiobank. org/) ) to facilitate collaboration and investigation on the biological mechanisms of addiction-like behaviors  (Carrette et al. , 2021 ). In summary, this study examined addiction-like behaviors in a large population of rats. We found that ∼80% of the rats escalated their cocaine intake, while ∼20% maintained low stable levels.
Likewise, the data are presented strictly in the number of cocaine infusions, which does not allow for consideration of body weight. In response to the concern about maintaining the 0. 5 mg/kg/infusion cocaine dose throughout the study, we will explain our dosing procedures and any adjustments made to account for changes in body weight. Additionally, we will consider presenting data in terms of total cocaine intake (mg/kg) to account for potential differences in body weight between animals and sexes. In the Introduction, the authors make a number of arguments in the second paragraph that have no citations and, therefore, are unsupported. We will ensure that all statements in the Introduction are supported by appropriate citations, providing a solid foundation for our research question and the significance of our study.
Epidemiological studies suggest about 29%-34% of adults report sleeping below and ∼8% above the recommended sleep duration range for adults. 16, 17  Importantly, these studies only assess self-reported average sleep duration (often via phone interview or single-timepoint surveys) and do not capture how often individuals sleep outside the recommended sleep duration range. In studies that used objectively derived estimates of sleep duration with devicebased measures, most assessment periods were limited to 1-2 weeks. This window may be insufficient to capture real-world variations in sleep duration over time. 18, 19  Sleep irregularity itself, with frequent deviations from the recommended sleep duration range, may also contribute to adverse health outcomes. 20  Given the prolific and growing use of consumer sleep trackers in the global community, data from these devices present an opportunity to observe sleep over extended recording periods on a much greater scale than has previously been possible.
Such datasets have already been used to evaluate intra-individual changes in sleep during significant historical events (eg, the COVID-19 pandemic and Britain's exit from the European Union  21, 22  ), as well as interindividual differences in sleep and their associations with health markers (eg, heart rate). [23] [24] [25]  The current study presents objective, device-based derived sleep durations collected over approximately 9 months from an under-mattress sensor with almost 68,000 adults from around the world. The aim was to determine average sleep duration and sleep duration variability in a large global community sample, and how often, and how many people within, this sample meet the recommended sleep duration range. This was examined across age groups and sex, given that sleep duration varies by these factors.
Participants In total, 67,254 participants (52,523 males and 14,731 females) aged between 18 and 90 years old who registered to use an undermattress sleep sensor device (Withings Sleep Analyzer [WSA]) between July 2020 and March 2021 were included in this analysis. Inclusion criteria for the study were availability of ≥28 sleep recordings and an average of ≥4 sleep recordings per week. Participant characteristics such as age, sex, height, and weight were self-reported by participants on the first use of their device. All participants provided consent for their de-identified data to be used for research by agreeing to the WSA terms and conditions. Withings provided the dataset for this work but had no role in study design, data analysis, interpretation, decision to publish, or manuscript preparation.
Statistical analysis The association between sleep duration (average, SD, and proportion of nights within and outside the recommended 7-9 hours range) and age and genders was investigated using linear and logistic regressions. Age was categorized (18-25, 26-40, 41-50, 51-65, 65+ years, determined by considering the age distribution) as required to visualize the association between sleep duration, age, and sex. Data are presented as means and SD or 95% confidence intervals (CI) for continuous data, and frequencies and percentages for categorical data. A p-value of <. 05 was considered statistically significant. All analyses were conducted in R (v 4. 2. 2) using the ggplot2 modeling package. 35.
Indeed, sleep durations were 30 minutes longer on the weekend compared to weekdays. These findings from a large sleep tracker consumer dataset contribute to the growing body of evidence regarding suboptimal sleep duration habits in the global community. Prior estimates have suggested that about 30%-35% of US adults do not obtain 7-9 hours of sleep per night,  16  which is consistent with the findings from the current study. These prior estimates in the community were largely derived from self-report assessments. While this under-mattress sleep sensor has not been directly compared to self-report assessments, similar overestimation of about 30-40 minutes with self-report versus polysomnography would suggest that they yield similar overall estimates of sleep duration. 19, 36  Notably, in this study, even among participants who slept between 7 and 9 hours per night, average sleep duration was outside the recommended sleep duration range on ∼40% of nights.
Given that the under-mattress sleep sensor used in this study overestimates sleep by about 30 minutes on average, even more adults than the rate reported here may be regularly sleeping less than 7 hours. Experimental studies have demonstrated acute impairments from short sleep on health and function. 10  Thus, it is concerning that such a large proportion of people are not sleeping within the recommended sleep duration range. Recent evidence from another consumer sleep device dataset found that irregular sleep patterns differ across countries, but nonetheless, was a common global phenomenon. 37  Irregular sleep itself is emerging as an important factor likely contributing to adverse health,  [38] [39] [40]  including a previously-reported association with hypertension from participants within this dataset.
Secondly, the presence of sleep disorders, comorbid conditions, and potential sleep treatments (medications, CPAP, etc. ) within this sample is unknown. Thirdly, socio-economic status, work status (shift workers, nonshift workers, unemployed, etc. ), and other factors known to influence sleep duration were also unknown in this dataset. Fourthly, while the underbed sensor has been designed to measure sleep in only 1 bedpartner or one half of a larger bed, it is possible that data from a co-sleeper or even a pet may also be detected should they get sufficiently close to the sensor. This may confound the estimates in some cases but is unlikely to be a common occurrence when appropriately positioned in a user's bed. Fifthly, the COVID-19 pandemic has been shown to have influenced sleep duration globally to some extent,  22, 47  and the impact of the pandemic on the findings of this study is unclear.
ACKNOWLEDGEMENTS The study was supported by research grants from the  Grenoble University Hospital , the  French University Institute , the  Grenoble Cognition Center , and the  Health and Society Research Network of the Pierre Mendès-France University (Grenoble). The  Grenoble MRI facility IRMaGe/ Neurophysiology  was partly funded by the  French program "Investissement d'Avenir " run by the " Agence Nationale pour la Recherche ". SK was partially funded by a grant " Société Française de Radiologie -Alain Rahmouni ". The Paris site was funded by a grant " Infrastructure d'avenir en Biologie Santé "- ANR-11-INBS-0006 " and the  Agence Nationale pour la Recherche  ( ANR-11-IDEX-0004   Labex BioPsy ,  ANR-10-COHO-10-01 psyCOH ),  Fondation pour la Recherche Médicale (Bioinformatique pour la biologie  2014) and the  Fondation de l'Avenir (Recherche Médicale Appliquée  2014).
Data collection and preprocessing Since 2001, Save The Elephant (STE) organization has been providing free news services that disseminates English speaking daily news about elephants from around the world. The Asian Elephant mailing list is dedicated to Asian elephant with an emphasis on wild ones. Captive elephant related articles are not included unless they show relationships with wilds. STE news service administrator claims that "listserv […] try to represent all elephant stories, whether we agree or disagree with the opinions" (Elephants in the Media: A Conversation with Melissa Groo, National Geographic, January 17, 2014). STE news service articles are sent on a daily basis by email to a list of subscribers. First author registered to the Asian Elephant news list on the 6th of April 2006.
Articles included in this study are ranging from this date up to May 2019. Articles were imported as independent text files in program R (R Core Team, 2017) using the stm package  (Roberts et al. , 2019) , keeping track of the publishing date and country. We removed the list service administrator signature, disclaimers, English stop words and punctuation. After inspecting the list of words, we combined most frequent compound names such as Sri Lanka, Viet Nam, Dak Lak, Tamil Nadu or Hong Kong into one word respectively  (srilanka, vietnam, etc. ). We applied a stemming process reducing words to a common root and removed all terms occurring in <55 articles-0. 5 % of the corpus  (Roberts et al. , 2019).
Natural language processing Natural Language processing allows to automatically organize and summarize large collections of textual information from unlabeled documents in an unsupervised way. Among various quantitative methodologies, topic modelling aims to identify semantic structures or patterns typically represented as sets of important words. Topic modelling is based on a statistical approach where topics constitute clusters of specific words or stems that co-occurred with unusual frequency  (Blei et al. , 2010). Articles usually comprise several topics with variable proportions, with topics being distributions of words. We used the stm R package  (Roberts et al. , 2019)  to investigate the prevalence of topics within the corpus or an article, and explained their variations using covariates-in our case the date of publication and the country.
In other words, the social network approach aims at drawing a global portray of all topics, their interconnections, their aggregation in wider communities of topics or their relative isolation within the corpus of articles. Here topics represent the nodes of the network, while the edges are given by the positive correlation between two topics. The size of nodes and thickness of the edges are defined respectively by the expected topic proportion-expected proportion of the corpus that belongs to each topic, and the pairwise topic correlation given by the structural topic model. The network graph is built using Gephi software  (Bastian et al. , 2009). We used modularity partition and eigenvector centrality to identify the network structure  (Newman, 2006). Modularity detects communities of topics, based solely on the network topology and structure.
Factor analyses We further aimed to determine the potential latent structure of our sets of EF measures in a data-driven manner. Therefore, we investigated the factor structure of the EF measures in both of our datasets separately, using maximum likelihood exploratory factor analysis (ML EFA) with varimax rotation, as implemented in the psych package in R (Revelle, 2022), with default settings. To aid interpretation, the scores that reflect error percentages were subtracted from 100, so that higher values represent better performance in all variables. To assess the factorability of the data, we utilised 3 complementary approaches  (Dziuban & Shirkey, 1974). Firstly, we inspected the off-diagonal elements of the anti-image covariance matrix. If the dataset is appropriate for factor analysis, these elements should be all above.
Meta-analysis In addition to testing our hypotheses in each of the two samples separately, following the recommendation of  Braver et al. (2014) , we also carried out a continuously cumulating metaanalysis (CCMA) of the two studies. A meta-analytic approach allows us to pool individual effects sizes into a single estimate, while quantifying their heterogeneity. In order to make the two studies comparable, in the two datasets separately, we extracted factor scores from a single factor ML EFA applied to the set of EF tasks that were shared in both studies. These were Lexical fluency, Semantic fluency and CSPAN. We then calculated the Pearson's correlation between these EF factor scores and the implicit SL learning scores and ran our meta-analytic model on these effects. We fit a fixed-effect meta-analysis model implemented in the 'meta' R package  (Harrer et al.
Factor analyses of executive function measures Besides investigating the relationship between performance on individual EF tasks and implicit SL, we also aimed to investigate the relationship between implicit SL and general EF ability that might be captured by shared variance on all tasks. To extract such a common EF measure from the individual tasks, we used Maximum Likelihood Exploratory Factor Analysis (ML EFA). We opted for EFA in order to find a set of latent constructs that capture our specific set of EF measures, while remaining a priori agnostic about the exact structure of these components. While the ML approach allows the computation of various goodness of fit indices, that are unavailable to principal factor approaches. Study 1 The dataset we considered for factor analysis consisted of the set of EF measures presented in Figure  3a.
Study 2 We proceeded in the same manner for Study 2. The dataset we considered for factor analysis consisted of the set of EF measures presented in Figure  3b. We followed the same approach to establishing the factorability of data, as in the analysis of Study 1. The diagonals of the anti-image correlation matrix of the data were all over. 5. The overall KMO measure of sampling adequacy was. 60, meeting both more conservative and more liberal cut-off criteria. Finally, Bartlett's test of sphericity was significant (χ2 (10) = 54. 591, p <. 001). Based on this, the data were deemed appropriate for factor analysis. To determine the number of factors to extract, we again relied on parallel analysis. Parallel analysis suggested that 2 factors are extractable, based on the comparison of their eigenvalues with random data (Figure  4b ).
Acknowledgements The authors are grateful to  Romane Phelipon  and  Imola Mihalecz  for their help in data acquisition and  Jeoffrey Maillard  for managing participant enrollment. This research was supported by the  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) (to D. N. );  János Bolyai Research Scholarship  of the  Hungarian Academy of Sciences  (to K. J. ). National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 ); Project no. 128016  has been implemented with the support provided by the  Ministry of Innovation and Technology of Hungary  from the  National Research, Development, and Innovation Fund , financed under the  NKFI/OTKA K  funding scheme (to D. N). Project no. 124148  has been implemented with the support provided by the  Ministry of Innovation and Technology of Hungary  from the  National Research, Development, and Innovation Fund , financed under the  NKFI/OTKA PD  funding scheme (to K.
Statistical analysis Statistical analyses were made using the R software. Comparison was made within the same genotypes and between different stress conditions. Results are presented as the mean ± SEM. The comparison of the mean of two groups was done by using the Student t-test when the variance was equal and with a Welch correction when it was not. Comparison of three groups were made with a one-way ANOVA. The two parameters, genotype and stress condition, were compared with a two-way ANOVA, using ANOVA function of the package "car" and corrected for the type III error for unbalanced datasets. Statistical details can be found in figure legends and in supplemental materials (Supplemental table  S1 ). To study the effect on anxiety levels of the interaction between stress and genotype, mean ranking was computed as follows.
Participants Thirty-four right-handed aphantasic (n=17) and phantasic (n=17) participants were included in the study. Four participants with extreme values were excluded from the analysis (see Data and statistical analysis for details). We performed the statistical analyses with 15 aphantasics (9 women, mean age: 20; range: 18-26) and 15 phantasics (6 women, mean age: 23; range: 19-26). We ensured right laterality with the Edinburgh inventory  (Oldfield, 1971). All participants were French native speaker and completed the questionnaire by  Lefaucheur et al. , 2011  prior to participation to determine whether they were eligible for TMS. All procedures (excluding pre-registration) were approved by an ethics committee (CPP SOOM III, ClinicalTrials. gov Identifier: NCT03334526) and were in accordance with the Declaration of Helsinki.