dataesr/scientific-paragraphs-categorization huggingface dataset:
In our experimental data set, in contrast to all these patterns of breakage to long bones and crania, small square and compact bones, i. e. , calcanei and astragali, are almost always unbroken, except in two instances out of 9 astragali (Figure  11 ). The broken astragali were compressed in dry conditions and broke only when placed in a dorsal position on any type of substrate (sands or silts), that possibly represents a more irregular or weaker surface. Figure  11  shows two astragali compressed in the same compression attempt, using identical force, one close to the other, one broken, the other complete. Calcanei never broke in any instance (Figure  11C ). Table  5. Frequency of long bones (femora and humeri) obtained following compression on current specimens. Several long bones bear cracks or fissures that may result in detachment of parts during sample processing, conforming to what we have called the "compressed + processed sample".
Acknowledgments: We are especially grateful to the editors of this volume,  Emmanuelle Stoetzel ,  Janine Ochoa  and  Juan Rofes  for inviting us to participate in this special issue on Taphonomy, and to the reviewers whose comments have greatly improved this paper. All field work and research on the old collections of Wonderwerk Cave is undertaken under permit from the  South African Heritage Resources Agency (SAHRA). Thanks also are given to IZIKO museum and curators  Thalassa Mathews  and  Sarena Govender. We offer our warm thanks to  David Morris  (Head,  Department of Archaeology, The McGregor Museum ) for facilitating access to the Wonderwerk Cave small mammal collections in his care. Wonderwerk Cave fossil collections are currently hosted at the  McGregor Museum of Kimberly (South Africa ).
Significance Statement Model-free and model-based computations that guide instrumental, decision-making processes may also be recruited in Pavlovian based behavioral procedures. Here, we used a within-subject design to test the hypothesis that both Pavlovian and instrumental learning processes were driven by common reinforcement-learning mechanisms. Signtracking and goal-tracking behaviors were assessed in rats using a Pavlovian conditioned approach task, and, then instrumental behavior characterized using a multistage decision-making (MSDM) task. We report that sign-tracking behavior was associated with greater model-free, but not model-based, learning in the MSDM task. These data suggest that Pavlovian and instrumental behaviors are driven by conserved reinforcement-learning mechanisms.
Trial-by-trial data were collected for individual rats and the probability that rats would select the first stage option leading to the highest reinforced second stage option (p(correct|stage1)) was calculated, as well as the probability that rats would select the highest reinforced second stage option (p(correct|stage2)). Training on the deterministic MSDM task occurred for three primary reasons: (1) to reduce spatial biases that are common in rats, (2) to ensure rats understood the alternating probabilities of reinforcement at the second stage options, and (3) to verify that rats understood the structure of the task and how first stage choices led to different second-stage options. If rats appreciated the reinforcement probabilities assigned to the second state options and how choices in the first stage influence the availability of second-stage options, then the probability that rats choose the first-stage option leading to the second-stage option with the maximum reward probability (e.
The parameter estimates that yielded the largest log-likelihood were retained and are reported in Table  1. Logistic regression of choice data in the MSDM tasks We have previously shown that the choice behavior of rats in the MSDM task is guided by previous trial events, such as previous trial outcome, choice, and -in the probabilistic MSDM task -state transitions. Trial-by-trial choice data in the deterministic and probabilistic MSDM was analyzed with a logistic regression model using the glmfit function in MATLAB (MathWorks, Inc. v 2017a). These logistic regression models predicted the likelihood that rats would select the same first-stage choice on the current trial (trial t) that they had on the previous trial (trial t-1), namely the probability of staying or p(stay). The model used to analyze choice data in the deterministic MSDM contained the following predictors: Intercept: +1 for all trials, which quantifies the tendency for rats to repeat the same first stage option regardless of any other trial events.
This quantifies the tendency for rats to repeat the same first-stage option regardless of any other trial events. Rewarded: +1 if the previous trial was rewarded and the rat chose the same lever (first -stage choice) that was selected on the subsequent trial -1 if the previous trial was rewarded and the rat chose a different lever Pavlovian score x rewarded) were included in the model and significant interactions tested using lower order analyses. All other analyses were performed in SPSS. Repeated measures data were entered into a generalized estimating equation (GEE) model using a probability distribution based on the known properties of these data. Specifically, event data (e. g. , number of trials in which rats chose the highest reinforced first stage option) were analyzed using a binary logistic distribution.
Acknowledgments This work was funded by public health service grants  NIDA   DA041480  (JRT),  NIDA   DA043443  (JRT),  NIDA   DA051598  (SMG),  NIDA   DA043533  (DJC) and  McKnight Memory and Cognitive Disorders Award  (DJC). Additional support was provided by the  State of Connecticut  through its support of the  Ribicoff Laboratories. The views and opinions expressed in this manuscript are those of the authors and not shared by the  State of Connecticut. The authors would like to acknowledge the useful discussions led by  Matthew Roesch  that made this collaborative work a possibility.
Moodle has various module activities (forums, chat, etc. ) and types of course materials, both static (text pages, images, web pages) and interactive (lessons, surveys, quizzes, assignments, etc. ). Logs storage from Moodle uses databases (MySQL, PostgreSQL, Oracle, etc. ) that are flexible and more powerful than text log files. The utilization of databases in Moodle can gather and access information from high-level usage data collected in the LMS. The Moodle application can show statistical information through an assessment scale in grade or rate used by the teacher or instructor to evaluate student learning performance  [25]. From the online learning evaluation, LMS can support personalized learning by finding the fit learning style. Several other systems and applications such as WELSA (Web-based Educational system with Learning Style Adaptation) and TSAL (Two-Source Adaptive Learning system) are used to observe students' academic performance through materials and assignments according to their learning styles  [26].
Klašnja-Milićević et al. [27]  also used the online course on Protus (Programming Tutoring System) module to support student learning activities in online learning. The Apriori algorithm was applied to the Protus module for analyzing learning/cognitive personality styles based on the Felder-Silverman Learning/Teaching Style model. The effectiveness of online learning approaches has been reported by the Association of Higher Education (ASHE) to be better compared to traditional learning (requiring faceto-face and classrooms)  [28]. In the ASHE report, it was mentioned that the K-12 education system in America shows an increase in reflective learning, integrative thinking, and order thinking skills in online learning compared to conventional learning (classroom-based).
F. METHOD The online learning portal used in this study was built using the Moodle framework version 3. 9. 3. This portal was subsequently installed on a server with specifications using Intel Xeon E5-2620 processor, NVIDIA Tesla K40 graphical processing unit, 32 GB of RAM, and 4 TB of storage media with RAID 5 configuration. Installation was performed using a cloud computing-based system based on Docker virtualization technology. The process began with the creation of a container containing the latest version of the Moodle framework. The additional software modules were then installed into the    2 ) the AI modeling. In the online learning session, the students were given six learning materials to learn about the concept of numbers. The learning materials were tailored and validated by experts to be suitable for the visual, auditory, and kinesthetic learning styles, based on the dePorter et al.
's variant of the VARK model  [48]. After the learning session, the students were asked to rate the six learning materials from 1 to 5 according to their preference. These ratings were compiled into a ranking matrix to train the AI. In the next stage, the process of AI modeling was started by cleaning the data by removing incomplete records. Afterward, the cleaned data were split into two sets: training and test. The proportion was 80% and 20% of the total data, respectively. To search for the most optimal hyperparameters value, we conducted five-fold crossvalidation using the training set. The hyperparameters to be searched were α, η, and λ. The search space for α was {0. 1, 0. 15, 0. 2, 0. 25, 0. 3}. Meanwhile, both the η and λ search space was {0. 01, 0. 005, 0. 001, 0. 0005, 0. 0001}. The optimal configuration was afterward used to train the main model with the whole training set.
Instrument development To test the proposed hypotheses, we collected self-report data based on participants' subjective perceptions. We prepared the questionnaire to collect data by modifying existing pre-validated scales so that they better aligned with the conceptualisation of our study. We followed all procedural steps discussed in recent studies to ensure the face and content validity of the developed questionnaire (e. g. Dhir, Talwar, Kaur et al. , 2021). First, we consulted three experts from the areas of sustainability and VRT to evaluate whether the proposed items correctly captured the intended constructs. Based on their feedback, we modified some of the items to make them more representative of the underlying constructs. Thereafter, we pilot-tested the instrument by inviting 14 respondents who represented our target group.
Data collection and study of respondents We invited the survey participants via the online crowd-sourcing platform, Prolific Academic. The survey was open to all adults who were between the ages of 21 and 50, residing in the US and aware of VRT, preferably with a recent experience of it. We identified the US as the geography of interest for the following reasons: (a) VR use is projected to rise considerably in the country, with usage estimates for North America as a whole at nearly 20% by 2025  (Alsop, 2021)  We received 350 valid responses and compensated those respondents per the Prolific Academic policy. Because the data included no outliers or missing responses, we proceeded with all 350 responses for our analysis. Table  2  presents the respondents' socio-demographic profile.
Data analysis We analysed the collected data using SPSS and AMOS (Version 27). Following the standard approach for covariance-based structural equation modelling (CB-SEM), a popular data analysis method employed by recent studies (e. g. Luqman et al. , 2021) , we first assessed the fit of the measurement model and evaluated the reliability and validity of the measurement scale. Thereafter, we analysed the structural model and confirmed support for the proposed hypotheses on the basis of the statistical significance of the generated path coefficients  (Byrne, 1994).
Measurement model, validity and reliability As the first step of data analysis, we conducted a confirmatory factor analysis (CFA) to test the quality of the measures used to collect the data. To this end, we assessed the measurement model by examining the model fit indices and the reliability and validity statistics. The goodnessof-fit indices were consistent with the recommended values and thus indicated an acceptable model fit  (Hair et al. , 2020). Specifically, the values were as follows: chi-square/degree of freedom (χ 2 /df = 1. 86), comparative fit index (CFI = 0. 96), Tucker-Lewis index (TLI = 0. 96) and root mean square error of approximation (RMSEA = 0. 05). Although we had adapted the survey items from pre-validated scales, we nevertheless confirmed the instrument's validity and reliability with the following measures.
Limitations and future research areas Despite its notable contributions to research and practice, the present study suffers from certain limitations related to its method and scope. First, from a methodological perspective, the study relied on data collected through a self-report, one-wave cross-sectional survey from only one geographical location, i. e. the US. Consequently, the generalisability of our research outcomes may be limited, and issues associated with cross-sectional studies may have impacted our results. We did, however, employ several procedural remedies to mitigate potential data-related issues, as discussed in the preceding parts. With regard to a single geography, we contend that our choice of the US context is relevant because the country's developed economy is acknowledged as a pioneer in technological innovations and a leader for sustainabilityoriented behaviours across the world.
Justification of sample size A power analysis was conducted using G*Power version 3. 1. 9. 7  37  to determine the minimum sample size required to test our hypotheses. Results indicated the required sample size to achieve 80% power for detecting at the lowest expected effect size of η p 2 = 0. 12 as in SPSS  38  (derived from previous ASRT studies that detected group differences using mixed-design analysis of variance, ANOVA  19, [39] [40] [41]  ), at a significance criterion of α = 0. 05, was N = 30 and N = 38 for the between-within interaction and a between-subjects main effect of a 2 × 4 ANOVA, respectively. Thus, the obtained sample size of N = 48 is adequate to test the study hypotheses.
ACKNOWLEDGEMENTS We are grateful to  Hanna Marossy  for her help in data acquisition. 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. );  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 );  János Bolyai Research Scholarship of the Hungarian Academy of Sciences  (to K. J. ). 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.
The Portrait items are created specifically for populations of children and teenagers to reflect a less abstract form of values 2 This gender disparity may be because the study took place in a general high school institution (as opposed to vocational) where, according to recent data (DEPP, 2018), the gender ratio is skewed in favor of girls. This will be taken into account when planning the main study, in which we will sample schools based on a review of their gender distribution in order to ensure a roughly 50/50 gender ratio. endorsement. For instance, instead of presenting value-items as desirable end-states in noun form (e. g. , competiveness, dominance) or desirable ways of acting in adjective form (e. g. , being competitive, dominant), this method presents operationalized value-items more closely related to the way in which individuals express these values rather than to simple endorsement.
Participants. Based on an a priori power analysis (see below), we aimed to recruit around 480 students, nested in 20 classrooms. To increase the generalizability of our findings, we sampled classrooms from different schools. In order to maintain a design with only one nesting level, we planned to recruit only one classroom per school; thus, we needed 20 schools. During the 16-month data collection period (from September 2020 to December 2021), we recruited 684 students, nested in 41 classrooms from 12 different schools. To remain consistent with the pilot study, we sampled the same grade level, i. e. , the tenth grade in each school. Power analysis. To conduct the power analysis, we used the Simr package  (Green & Macleod, 2016)  in R, based on the data from the pilot study. This package calculates power for generalized linear mixed models, using simulation.
The schools were randomly selected using contact information presented on the website of the Ministry of education. Some schools were specifically contacted based on the knowledge that they were situated in working-and middle-class neighborhoods. To avoid introducing a confounding variable and remain consistent with the pilot study, we targeted only general (not vocational) high-school institutions. We anticipated an imbalance in the ratio of students with lower and higher parental education. We therefore recruited around 200 extra students (more than three-way interaction term 5. Given the complexity of our model, we employed the procedure detailed by  Bates et al. (2015)  to select the random effects in the final model (using the R package RePsychLing). Thus, our final model included intercepts for classrooms and byclassrooms slopes for the interaction effect between gender and parental education level as random effects.
Detecting Influential Observations. To detect influential observations, we used Cook's distance with the R package influence. ME  (Nieuwenhuis et al. , 2012) , which provides a summary measure of the change in all parameter estimates from when a particular case is included to when it is not included. Observations were considered as too influential if the associated value for Cook's distance exceeded the cut-off value of 4/N, where N is the total number of students and if the deletion of the observation affected the level of significance of the variables in the model. For both the primary and secondary analyses, we looked for influential data in the final model (i. e. , after we have proceeded with the selection of the random effects). Several observations exceeded the cut-off value for Cook's distance, but as their deletion did not affect the significance level of the predictors, they were retained in the data set.
| Anthropometric measurements Height and weight at diagnosis were measured in upright position during the hospitalization and those at symptom onset were collected from the carnet de santé (record of all anthropometric, vaccinal, and health-related data about a child from birth by a pediatrician). Anthropometric data at a time-point close to symptom onset were considered for analysis when there were no available data at symptom onset. Symptom onset was defined as the occurrence of sleepiness because it appears before other symptoms, such as cataplexy in the population studied herein. The time of PSG evaluation corresponded to the diagnostic time. The BMI was computed at symptom onset and disease diagnosis. Overweight and obesity were defined as BMI was > +1 standard deviation (SD) and > +2 SD for sex and age in children >5 years old, respectively, and as BMI was > +2 SD and > +3 SD for sex and age in children ≤5 years old, respectively, in accordance with the criteria recommended by the World Health Organization (WHO).
Acknowledgments: We would like to thank  B. Richmond ,  M. Eldrige  and  W. Lerchner , both for providing material and for their numerous advices during the initial steps of this work. We are also grateful to  T. Minamimoto ,  C. Wilson  and  E. Procyk  for their continuous advices and support. We would like to thank  M. Weissenburger  and  L. Aubree  for their assistance during surgery and MRI scanning sessions. The  ICM Histomics Core facility  provided a critical expertise for histology and immuno-histochemistry, and we are particularly grateful to  D. Tande  and  A. Prigent. We also thank  C. Guigon  for her feedback on early versions of the manuscript.
The chamber with the visitor mouse is referred to as "social chamber", while the empty chamber is referred to as "non-social chamber". Neutral chamber is the one in between (Fig  1 ). Localisation of the visitor mouse was counterbalanced between each experiment. The behavior of the host mouse was recorded during 8 min by a webcam connected at the ANY-maze software. The behavioral parameters analysed off-line were the following: total distance traveled, total immobility duration, maximum speed, time spent, immobility duration and distance traveled in each chamber, time spent in contact with the congener. The time spent in contact with the congener was evaluated when the host mouse pokes with its nose into holes and touches the visitor mouse. Social interaction task. The Social Interaction Task (SIT, see Fig 1 ) was previously described in detail  [64, 65].
Briefly, the isolated mouse (i. e. , "host mouse") was placed in a transparent Plexiglas cage (L 50 cm � l 25 cm � h 30 cm) in an unfamiliar quiet room for 30 min of habituation. The diffuse light in the room was set at 100 Lux. After the habituation session, a "visitor mouse" was introduced into the cage. The two animals can establish spontaneous, free and reciprocal social interactions that were recorded for 8 min by a numeric video camera (webcam) connected to a computer. The mouse behavior was analysed off-line with the Solomon Coder software (version beta 19. 08. 02) developed by A. Peter. We distinguished multiples categories within social behaviors: • Affiliative behaviors: number and duration of contacts initiated by host or visitor mice, i. e. when animals touched each other with various parts of the body.
• Dominance: number and duration of host or visitor follow episodes. Other parameters of dominance were scored, such as "paw control" that refers to the number of times the host mouse placed its forepaws on the head or the back of the visitor. • Escaping: the number and duration of escape behaviors were scored every time the host or visitor mouse interrupted a contact. • The number of rearing was scored as a marker of exploratory behavior. • Aggressive behavior was reflected by the number of bites and latency to attack. USV recordings. USVs were recorded during the 8 min of the social interaction task. A condenser ultrasound microphone Polaroid/CMPA was placed above the experimental chamber, high enough so that the receiving angle of the microphone covered the whole area of the test. It was connected to an ultrasound recording interface Ultrasound Gate 416H, which was itself plugged into a personal computer equipped with the recording software Avisoft Recorder USG (Sampling frequency: 250 kHz; FFT-length: 1024 points; 16-bits).
Statistical analysis Non-parametric analyses were performed using R software (version 3. 5. 0 of the R foundation for Statistical computing with Rcmdr-package) as part of behavioral data that were not following a normal distribution. We used the Kruskal-Wallis chi squared test to assess for global difference between groups (WT young; ChAT-Cre Young, WT M. A and ChAT-Cre M. A. ). This statistic is refered to as group effect (noted H in the Result section). We use Wilcoxon rank sum test (noted W in the Result session) when comparing two independent groups and paired Wilcoxon rank sum test (noted V in the Result session) when comparing two behavioral measures of the same animals. Gene expression data were analysed using analysis of variance (ANOVA) with genotype and age as factors (Graphpad Prism 8 software, San Diego, CA).