dataesr/scientific-paragraphs-categorization huggingface dataset:
The mean values and standard deviations of each of the parameters studied (μ d , μ r , μ rms , μ sf , and μ sa ) were calculated over the six repetitions performed for any given set of conditions. Results were represented in the form of histograms or plots of the mean values of these repetitions, for which the error bars correspond to the standard deviations. Data analysis and graphical visualizations were done with MATLAB (The MathWorks, Natick, Massachusetts, USA).
The duration of these two phases varies greatly across the different experimental conditions. Fig. 2  (b) shows two of the most distinct scenarios that can be observed on the entire dataset, both obtained with the roughest TMS (T 140 ). On these examples, lubrication with thin G 0 led to a high level of friction reached at the end of a long static phase. Contrarily, lubrication with thick G 100 displayed a low level of friction after a short static phase. Hereafter, friction analysis was focused on the plateau values of |F T /F N | reached at the end of the motion and rest steps of the tests (μ d and μ r ).
9 (a) and (b): the smoother the TMS, the lower the levels of μ d and μ r. Fig. 9 (c) illustrates the possible relations between μ d and μ r by plotting them against each other for each test performed. The points of the same color correspond to all the tests carried out with the same TMS (all lubrication conditions combined). The general scatter plot obtained does not seem to reveal a simple relationship between the two parameters. As the values of μ d increase, the range of observed values for μ r also increases. The residual level of friction at the end of the rest steps cannot therefore be explained solely by the amplitude of the level reached at the end of the motion steps. Looking color by color (meaning roughness by roughness), we see that the measurement points corresponding to the two smoothest TMSs (T 20 and T 30 ) group together in a limited space corresponding to the lowest values of μ r and μ d observed in the entire dataset.
Therefore, for the subsequent analyses, we tested decoding in the right piriform cortex using voxel correlations (decoding method #2) in the limited sample wherein clear discriminable patterns for real odors were observed. The results remain largely unchanged when including the full sample (Supplementary Table  1 ). To determine if the decoding of imagined odor quality was associated with our perceptual measure of odor imagery ability (Fig. 1a ), we correlated it against the interference effect. A strong positive association was identified (Fig. 2g ). By contrast, there were no significant correlations between odor imagery and the decoding of fMRI patterns evoked during actual odor presentations or in the cross-modal datasets (Fig. 2h-i ). Importantly, when we ran similar analyses for the left and right primary visual cortices as control regions, no significant effects were observed (Extended Data Fig.
Thus, in contrast to our prediction, no significant associations were observed between current adiposity and odor imagery ability, though we cannot exclude the possibility of a nonlinear relationship with the self-report measure. Lastly, we tested for associations between olfactory perception or FCR and current adiposity. The only significant effect we observed was a negative correlation between the cookie odor detection thresholds and BMI (Supplementary Table  5 ). These data demonstrate that neither odor imagery ability nor FCR is related to current adiposity in our sample. With respect to changes in adiposity (BMI or body fat percentage), no relationships were observed for any measure of odor imagery or perception (Extended Data Fig. 4 ; Supplementary Table  5 ). However, there were significant associations with FCR.
Participants The current study was classified as basic observational research in humans and did not meet the National Institutes of Health definition of a clinical trial. A flow diagram depicting the number of individuals at each stage of the study (e. g. , eligibility, recruitment, completion, analysis) is provided in Extended Data Fig. 5. Participants were recruited from the local New Haven, CT, USA community and university population via flyer and social media advertisements. Individuals interested in this study or other previous studies in our lab filled out an online form using Qualtrics software versions October 2020-June 2022 (Qualtrics, Provo, UT, USA) to indicate through self-report initial information such as their sex assigned at birth, age, estimated BMI, drug use, etc. We pre-screened subjects in this database to identify individuals aged 18-45 and free from known taste or smell dysfunction, dieting behaviors, food restrictions, nicotine or drug use, serious medical conditions including metabolic, neurologic, and psychiatric disorders or medications used to treat these, cognitive deficits or memory loss that could impact mental imagery, and any MRI-contraindications (e.
80 power (alpha = 0. 05, two-tailed test, two dependent means). Twelve additional participants were then recruited to complete only the odor imagery condition and all other study measures (with one excluded from scanning due to extreme claustrophobia). This was sufficient to achieve 0. 80 power (n = 42, alpha = 0. 05, twotailed test, bivariate normal model) for the effect observed between self-reported odor imagery ability and obesity risk (r = 0. 42) in previous work  8  and for the ability of FCR measures to predict longer-term changes in eating and weight (r = 0. 42) from a prior meta-analysis 2. Data from three participants were removed due to an inability to obtain proper odor thresholds such that their detection accuracies fell below chance level (less than 50% correct responses). Participant characteristics of the final sample (N = 45) by BMI group are provided in Supplementary Table  7.
Stimuli Odors included "phenylethyl alcohol white extra" (rose, #001059147) and "cookie dough" (cookie, #10610208) from International Flavors and Fragrances (IFF; New York, NY, USA) diluted in food-grade propylene glycol. Rose and cookie were selected for us by IFF after we requested odors that were highly volatile, discernable, and equally pleasant. We also wanted both odors to have a "sweet note," with one being edible and one inedible. Although rose flavor is used in some food cultures, ratings of odor edibility were not significantly associated with any measure of odor imagery ability in the current study (Supplementary Table  4 ). Ratings of odor liking also did not significantly differ for the rose and cookie odors in our sample (Extended Data Fig. 6d ). The bogus taste test consisted of eight "Grandma's Homestyle Chocolate Chip Cookies" broken into bite-sized pieces across two plates (for a total of ~280g or ~1360 kcal) presented alongside a 16 fl oz water bottle.
Experimental Procedures The study consisted of three behavioral sessions and one fMRI scan at baseline, along with a follow-up session one year later. Full data collection from the first (baseline) to last (follow-up) sessions spanned 10/6/2020-6/3/2022. The fMRI scan was scheduled between 8:00am-1:00pm, and all other sessions took place between 8:00am-8:00pm. We ensured that food craving and intake were assessed between the hours of 11:30am-7:00pm. Individuals were instructed to arrive to all sessions neither hungry nor full, but at least one-hour fasted. Data collection and analysis were not performed blind to the conditions of the experiments.
Follow-Up Session The primary goal of the follow-up session was to assess changes in adiposity. All but one participant returned to the lab approximately one year later (days elapsed from first to last session: M = 363. 17, SD = 7. 33, range = 340 -378) and repeated the adiposity, questionnaire, and FCR measures, but not the odor imagery measures. Follow-up data from one participant was excluded after they began a strict diet and lost more than 3 SD above the group mean in weight change from the baseline to follow-up sessions.
Behavioral Analyses Pearson correlations, linear regressions, linear mixed effects models, ANOVAs, and Student's t-tests were performed in MATLAB 2020a (Mathworks, Natick, Massachusetts, USA). For ANOVAs assessing the interaction of two variables, we included in the model each of the two variables independently, the interaction of the two, and any control variables indicated in the text. Variables of interest with outliers > 3 SD above or below the group mean were removed if they changed the nature of the results. Data distribution was assumed to be normal, but this was not formally tested. All statistical tests were two-sided. Corrections for multiple comparisons were made by adjusting the p-value for the number of tests at each step using the Bonferroni method. The only exception was in determining variables that should be included as covariates.
In these limited cases (e. g. , the associations between food intake and sex or food liking), correction for multiple comparisons was not performed to err on the side of caution. Data were plotted in Prism version 9. 4. 1 (GraphPad Software, San Diego, CA, USA). For test-retest reliability, intraclass correlation coefficient estimates and 95% CIs were calculated in SPSS based on single measure, absolute agreement, 2-way mixed models. All measures showed moderate to good reliability (Supplementary Table  8 ). For details of the sniff analyses (reported in Extended Data Fig. 7  and Supplementary Table  9 ), see the Supplementary Methods. Mediation and moderated mediation models were tested with bootstrapping (10000 samples, 95% CIs) using the "PROCESS" macro version 4. 1 46 models 4 and 7 implemented in SPSS Statistics version 28 (IBM, Chicago, IL, USA).
fMRI Analyses Preprocessing. The fMRI data were preprocessed and analyzed using FSL version 5. 0. 10 (FMRIB Software Library, Oxford, UK  47  and SPM12 (Statistical Parametric Mapping, Wellcome Centre for Human Neuroimaging, London, UK) implemented in MATLAB R2019b. Functional EPIs were realigned to the mean and unwarped using fieldmaps, slice-time corrected, and motion-corrected with the FSL tool MCFLIRT  48. The anatomical T1 image was coregistered to the mean EPI and spatially normalized to the standard MNI-152 reference with unified segmentation in SPM12. Prior to the univariate analyses, the resulting nonlinear deformation fields were applied to the EPI images, which were then smoothed with a 3mm fullwidth-half-maximum Gaussian kernel. First Level Models. General linear models (GLMs) were estimated for each participant and run, separately for the normalized and smoothed EPI data (for univariate analyses) and the non-normalized and non-smoothed EPI data (for decoding analyses).
ACKNOWLEDGEMENTS This work was supported by the  National Science Foundation Graduate Research Fellowship  under Grant No. 2139841  (E. E. P. ), the  National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health  under Award No. F31DK130556  (E. E. P. ), and the  Modern Diet and Physiology Research Center  (D. M. S. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or the  National Institutes of Health. We would like to thank  Jason Avery  for advice on the fMRI decoding methods;  Bojana Kuzmanovic  for guidance on the fMRI preprocessing pipeline;  James Howard  for example code to perform the sniffing analyses;  Thomas Hummel ,.
DATA AVAILABILITY The raw MRI data and sniff airflow traces can be downloaded from the OpenNEURO repository under accession #ds004327 at: doi:10. 18112/openneuro. ds004327. v1. 0. 1. Statistical maps of the human brain data are available on the NeuroVault repository at: https://neurovault. org/collections/14751/. All other data analyzed in this study are included in the source data files provided with this paper.
Study variables and data collection (2019) Clinical examinations allowed to evaluate oral health status comprehensively but this manuscript only relates to carious status. The ICDAS classification (International Caries Detection and Assessment System) was used and the threshold for identifying dentinal lesions (D 3 T/d 3 t) was set at ICDAS grade 4 and more  [15]. The presence of dental sealants on permanent molars was also recorded with the visual and tactile evaluation of the presence of sealant material (no sign vs some part vs all the occlusal fissures with sealant material)  [13]. Dental examinations were performed at school. The investigators used disposable examination equipment with adequate lightening and drying devices. Children's health determinants were assessed using an interviewer-administered "child" questionnaire, with the addition of a "parent/ adult responsible for the child " questionnaire.
Statistical analyses • Descriptive analysis (2019) A double entry was carried out and discrepancies were checked. Statistical analyses were performed using Stata software (version 16, Stat/IC, StataCorp, College Station, US) and RStudio (RStudio, PBC Version 1. 4. 1106). p-values < 0�05 were considered to be statistically significant. A descriptive analysis was conducted to describe the prevalence (% of children with disease) and cumulative experience of carious disease (mean caries indexes) and to present the characteristics of the study population. Results were expressed with means and standard deviations for caries indexes and percentages with 95% Confidence Intervals (CI) for other variables. For some explanatory variables, answering categories were grouped to facilitate the analyses depending on the number of answers and pertinence.
• Comparison of dental status between 2012 and 2019 The comparison of dental status between 2012 and 2019 was performed using both initial raw data sets. Indeed, in 2012, a representative sample of 789 9-years-old children had also been randomly selected. Dental status had been clinically recorded, families and children had answered questionnaires about oral health determinants. The methodology (sampling, study variables. ) was similar to the one used in the 2019 study. Namely, the threshold for dentinal lesions (d 3 /D 3 ) used in the 2012 study was considered to be equivalent to the ICDAS grade 4 and more in 2019  [11]. For comparing prevalence (dichotomous variable) and caries indexes (continuous variable), logistic regression (for prevalence) and Mann-Whitney tests as well as zero-inflated negative binomial (ZINB) models (for caries experience) were used.
Conventional XAS measurements were performed in uorescence mode at liquid nitrogen temperature using a multi-element silicon drift detector with Xspress3 electronics. High energy resolution Hg L 3 -edge XANES (HERFD) spectra were collected using the CLEAR spectrometer available at the beamline based on Johansson-like dynamical-bent diced-analyser Si crystals for scanning-free energy dispersive acquisition  (Simonelli et al. 2023 ). The Hg Lα 1 emission was collected using the Si(444) re ection of the analysers working in backscattering geometry. The energy resolution estimated from the FWHM of the quasi-elastic line was around 1. 4 eV. The X-ray absorption spectra were processed using Athena software of the Demeter package (Ravel and Newville 2005) following standard procedures. The linear combination tting (LCF) analysis was also performed using the Athena software of the Demeter package  (Ravel and Newville 2005).
72 m, Acti-Wheel Software; Intellibio, Seichamps, France). The running wheel activity with wheel revolution and time spent running was recorded every 10 min. Running wheel activity was divided into night-time activity (06:00 PM to 06:00 AM; the usual active phase for mice), food anticipatory activity (FAA, 1:30 PM to 5:00 PM) described as an active phase in anticipation to food delivery in FR mice, and total activity during 24 h. Data from running wheel activity were extracted with an Excel® macro to obtain cumulative activity data for these three time-periods. Running wheel activity data were extracted for each home cage and therefore is presented for each pair of mice (n = 3 pairs per group in experiment 1). Body weight was monitored every weekday during the protocol and blood glycaemia was measured three times per week to ensure tolerance to FR (FreeStyle Optium Neo, Abbott, Netherlands).
Behavioral analysis The three chambers box was placed on an infrared floor and a camera was secured to the ceiling in order to track the animal during the test (Viewpoint Video Track software). Seven areas were discriminated to explore the animal's behavior: the three chambers (right, central, left), a close area of exploration of the objects (a circle of 17 cm diameter around the object being either wheel of novel object) and a narrow area containing only the object to assess the animal activity in the wheels. Video recording of the entire session was made and recorded for each area: time spent, distance travelled and animal speed. Three levels of activity were characterized depending on speed: inactivity, low activity (i. e. exploratory behavior) and high activity (i. e. running). Exploratory behavior included investigation of the walls, exploration of the object or the wheel, touching the object, while running concerns only the wheel running activity.
01; DAG, p = 0. 039), but no significant difference  on the AG/DAG ratio (p = 0. 06). Both AG and DAG levels were negatively correlated with body weight (AG r 2 =0. 647 p < 0. 001, DAG r 2 =0. 810 p < 0. 001, data not shown) and body weight loss expressed as the percentage of ad libitum body weight on day 0 (AG r 2 =0. 667 p < 0. 001, DAG r 2 =0. 836 p < 0. 001, data not shown). Food-restricted mice display higher preference for the running wheel over a novel object exploration in a modified 3-chambers paradigm. To investigate behavioral preference toward running wheel activity in foodrestricted mice, we used a modified version of the 3-chamber paradigm using a running wheel in a chamber and novel object in the opposite chamber. We collected data only for 5 mice in the FR group (for technical reasons) and all other groups count 6 mice.
Acknowledgments We are grateful to the staff in the animal experimentation platform at the  Institute of Psychiatry and Neurosciences of Paris (IPNP)  for the care of the animals and to the PhenoBrain phenotyping for providing a platform for animal experimentation. We thank  Juliette Morel  for her participation to the experiments. We also warmly thank  Dr Cécile Viollet  for her valuable help and advices in the use of the video tracking for the behavioral analysis. Finally, we are extremely grateful to the  Professor Suzanne Dickson (University Goteborg ) for proofreading and editing the English.
Funding This work was supported by  Fondation de France -Maladies Psychiatriques  (to P. G. ),  Université Paris Cité  and  Institut National de la Santé et de la Recherche Médicale (INSERM). C. T. is the recipient of a PhD fellowship (N• FDM202006011161 ) funded by "  Fondation pour la Recherche Médicale " (FRM).
This survey is a part of the wider Asian Food Barometer initiative and supplementary to the national Food Barometers, currently, the Malaysian  (10)  and the Indonesian databases. While the national surveys are including data on the food content and quantities, thus enabling analysis of the nutrient composition  (11) , "Eating Out" is focusing on the food day patterns. This article briefly reviews the available data on eating out-specifically in Asia, proposes a framework, and details the methods regarding the organization of the initial data collection (2019-2020). Expected uses and limitations of the data as well as their possible contributions conclude the article. /fnut.
Paradoxically, empirical social scientific studies at the national or cross-national levels are scarce, as mentioned earlier. The development of important economic factors in the food service sector in the 1970s in Europe  (38, 39)  has contributed to the production of data that have made this phenomenon visible. When existing, studies are mainly framed by the nutritional perspective [for example  (42) (43) (44) (45) (46) ] which, while being a matter of controversies in the West (47-49), is further applied without much consideration paid to the particularities of the Asian contexts and histories. Thus, it has been undertaken by the Asian Food Barometer initiative  (6, 10, 50). "Eating Out" database contributes with a focus on the movements of food intake between home and out of home in relation to compressed modernization and provides an empirical basis to the debate of the social and public health implications of eating out in the Asian contexts.
To the extend where, based on her study of the foodscape from poor Jakarta (Indonesia) kampungs, Arciniegas ( ) posits the need to revise the binary distinction between "home" and "out-of-home" eating behaviors. Frontiers in Nutrition frontiersin. org A combination of stratified random sampling-selection from panel respondent sources-and quota sampling was applied to optimize the benefits of both sampling methods. The data collection aimed at achieving national representative samples. For China, global representativity is practically very difficult. The sample focuses on a range of medium urbanized areas. Minimum sample sizes were set for each location. Respondents were selected out of the national populations aged 18 years and older, across all income groups and rural and urban areas. Quotas were implemented in each location, so to minimize challenges in representativeness for the samples on age, gender, urbanization (except for metropolitan areas of Hong Kong and Singapore), and ethnicity where relevant (Singapore and Malaysia).
"Out of the Home" and source of the food from "Food prepared at home" vs. "Food Purchased outside or delivery"; • Sociality, between "Alone" vs. "With company". Invitations to participate in the survey were staged in the week and sent out on Tuesdays with the aim to spread out 3-day recall across 2 weeks to include at least 1 day from the weekend (defined as Saturday and Sunday). The country of the collection was automatically detected when the respondent started the questionnaire. First, questions were to filter the respondents based on quotas and collect data on age, race/ethnicity, religion, gender, number of children, number of family members living under the same roof, urbanization, education attainment, monthly household income, or Wealth Index for Indonesia. Given the potential interest of the database for public health, height and weight were also included to allow the computation of the body mass index (BMI) as an indicator of nutritional status.
Data collection and procedures The data for the initial survey were collected in partnership with a company-Toluna, specializing in conducting large multilocation surveys and holding large panels and affiliated networks in each of the locations of the "Eating Out" data collection. Invitations to participate were sent using email, mobile text, or on the partner company's mobile application platforms. The data were collected in two windows--6 January to 24 January 2020 and 31 January to 9 February 2020. The research team has developed along with Toluna, a web interface, to collect data in diverse languages and unit systems. The initial questionnaire is compared with the one obtained after backtranslation. Where di erences are observed between the two versions, revisions along with the translators are engaged.