dataesr/scientific-paragraphs-categorization huggingface dataset:
emulsifier intakes We quantified the intakes of food additives on the basis of data provided in the participants' dietary records, in which the commercial brand or name of the industrial products consumed were recorded. The method for quantification of food additive intakes has been described previously. 7  Briefly, for qualitative assessment we matched each food item consumed and reported in a specific dietary record against Superieur (ABES). Protected by copyright. on 12 September 2023 at Agence Bibliographique de l Enseignement http://www. bmj. com/ BMJ: first published as 10. 1136/bmj-2023-076058 on 6 September 2023. Downloaded from three databases to identify the presence of any food additive: OQALI,  30  a national database managed by the Institut National de la Recherche pour l'Agriculture, l'alimentation et l'Environnement and the French food safety authority (Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail) to characterise the quality of the food supply, Open Food Facts, an open collaborative database of food products marketed worldwide, 8 and the Mintel Global New Products Database, 31 an online database of innovative food products worldwide.
Among the available food additives quantified from the participants' dietary records, we identified 61 food additives classified as emulsifiers or emulsifying salts from the 261 additives under the functional class "emulsifier" or "emulsifying salt" of Codex General Standard for Food Additives database, 32 or according to US or UK regulations when not included in Codex (eg, E404, E418, E468)  6  and considered the sum of intakes as intake of total emulsifiers (see table  2 ). In addition, we summed individual emulsifiers with similar chemical structures into eight groups: total phosphates (E339, E340, E341, E343, E450, E451, E452), total lactylates (E481, E482), total polyglycerol esters of fatty acids (E475, E476), total monoglycerides and diglycerides of fatty acids (E471, E472, E472a-bc-e), total celluloses (E460, E461, E464, E466, E468), total carrageenans (E407, E407a), total alginates (E400, E401, E402, E404, E405), and total modified starches (generic European Union code for this category E14xx).
To ensure acceptable statistical power, we restricted analyses on individual emulsifiers to those consumed by at least 5% of the included participants. The proportional hazard assumption was tested using the Schoenfeld residual method implemented in the survival R package (see supplementary eFigure2),  33 34  and the log linearity between emulsifier intakes and hazard ratios was assessed using restricted cubic splines (see supplementary eFigure3). 35  Participants contributed person time to the models until the date of CVD diagnosis, date of death, date of last completed questionnaire, or 5 October 2021, whichever occurred first. Cause specific hazard ratios were computed so that death and CVD events other than the one studied (for coronary heart disease and cerebrovascular disease specific analyses) occurring during follow-up were handled as competing risks.
When values for covariates were missing, we used multiple imputation by additive regression, followed by bootstrapping, and predictive mean matching (n=20 imputed dataset) as implemented in the Hmisc R package (see supplementary eMethod3). 36  The adjustment strategy was defined according to a directed acyclic graph (see supplementary eFigure4). The main model was adjusted for age (timescale); sex; body mass index (BMI, continuous); physical activity (categorical International Physical Activity Questionnaire variable: high, moderate, low); smoking status, (never smoker, former smoker, occasional smoker, regular smoker); number of smoked cigarettes in pack years (continuous); educational level (less than high school degree, <2 years after high school degree, ≥2 years after high school degree); family history of CVD (yes/no); daily alcohol intake (continuous, g/ day); consumption of fruit and vegetables (continuous, g/day), red and processed meats (continuous, g/day), and wholegrain foods (continuous, g/day); proportion of ultra-processed food consumed in the diet, in weight (continuous, %), as defined by the NOVA classification  37  ; and the number of dietary records (continuous).
39  Finally, we conducted analyses for all emulsifiers with at least one statistically significant association with the specific outcomes of stroke, myocardial infarction, angioplasty, acute coronary syndrome, transient ischaemic attack, angina pectoris, and severe CVD (ie, total CVD except for transient ischaemic attack and angina pectoris). We found no evidence in the mechanistic literature on specific interactions (for example, with BMI and age), or sexual dimorphism, for the association between intake of emulsifiers and CVD risk. All statistical tests were two sided, and we considered P<0. 05 to be significant. All statistical analyses were conducted in R version 4. 1. 2,  40  except for the restricted cubic spline method, which was implemented in SAS version 9. 4. Other emulsifiers included triphosphates (european code e451), gum arabic (e414), polyphosphates (e452), carob bean gum (e410), cellulose (e460), tricalcium phosphate (e341), monoacetyl and diacetyl tartaric acid esters of monoglycerides and diglycerides of Fas (e472e), hydroxypropyl methylcellulose (e464), polyglycerol esters of Fas (e475), lactic acid esters of monoglycerides and diglycerides of Fas (e472b), polydextrose (e1200), sodium stearoyl-2-lactylate (e481), sodium alginate (e401), ammonium salts of phosphatidic acid (e442), esters of monoglycerides and diglycerides of Fas (e472), polyglycerol esters of interesterified ricinoleic acid (e476), citric acid esters of monoglycerides and diglycerides of Fas (e472c), silicon dioxide (e551), tripotassium phosphate (e340), methylcellulose (e461), carboxymethylcellulose (e466), trisodium phosphate (e339), acetic acid esters of monoglycerides and diglycerides of Fas (e472a), agar (e406), sucrose esters of Fas (e473), propylene glycol esters of Fas (e477), gellan gum (e418), sorbitan tristearate (e492), processed Euchema seaweed (e407a), beeswax (e901), potassium alginate (e402), maltitol (e965), triethyl citrate (e1505), xylitol (e967), glycerol esters of rosin (e445), polyoxyethylene sorbitan monooleate (e433), potassium dihydrogen citrate (e332), calcium alginate (e404), calcium stearoyl-2lactylate (e482), konjac flour (e425), cross linked sodium carboxymethylcellulose (e468), sucrose acetate isobutyrate (e444), sodium tartarate (e335), polyoxyethylene sorbitan monostearate (e435), sorbitan monostearate (e491), alginic acid (e400), propylene glycol (e1520), Quillaia extract (e999), sodium aluminium phosphate (e541), magnesium hydrogen phosphate (e343), propylene glycol alginate (e405), and dimethyl polysiloxane (e900).
strengths and limitations of this study The strengths of this study included its prospective design and large sample size. The NutriNet-Santé study was able to assess the intakes of food additives qualitatively and quantitatively with accuracy using detailed and repeated 24 hour dietary records, links to multiple food composition databases (OQALI,  30  Open Food Facts, 8 Global New Products Database,  31  European Food Safety Authority, and Codex General Standard for Food Additives 32 ), ad hoc laboratory assays, and dynamic matching to account for reformulations of industrial food items over time. 7  Although more limited than in long term historical cohorts such as the Framingham study (20 years), the duration of follow-up (median 7. 0 years, maximum 12. 4 years) was similar to that of other cohort studies such as the UK Biobank,  56  and to the duration of nutritional intervention studies on cardiovascular diseases prevention such as the Prevención con Dieta Mediterránea trial.
Besides, intakes might have been underestimated in food items exempt from food labelling (eg, bakery pastries), and non-additive originated emulsifiers occurring naturally in food products, such as lecithins in eggs, were not captured, because to our knowledge food composition databases that estimate their presence in foods are not available. Although these potential measurement errors may have biased the associations towards an unclear direction, this was more likely towards the null (non-differential errors due to the prospective design). In addition, some individual emulsifiers were consumed by an insufficient number of participants to be investigated individually. However, all available intakes of emulsifiers consumed were included in the calculation of exposure to total and groups of emulsifiers.
Contributors: LS and BS contributed equally and are co-first authors. EC, NDP, YE, FSE, CA, ADS, RL, and MT developed the additives composition database and matched consumption and composition data. CA coordinated dietitian work. FSE coordinated data management work. NDP, YE, EC, and MT supervised the technical work. LS, BS, and MT designed the research. LS and GJ performed the statistical analysis. BS and MT supervised the statistical analysis. LS drafted first version of the manuscript. BS revised later versions of the manuscript. MT supervised the writing. All authors contributed to the data interpretation, revised each draft of the manuscript for important intellectual content, and approved the final version. BS and MT had primary responsibility for the final content and are the guarantors.
Funding: The NutriNet-Santé study was supported by the Ministère de la Santé, Santé Publique France, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche pour l'agriculture, l'alimentation et l'environnement, Conservatoire National des Arts et Métiers, and University Sorbonne Paris Nord. EC was supported by doctoral funding from University Sorbonne Paris Nord -Galilée Doctoral School. CD was supported by a doctoral grant from the French National Cancer Institute (INCa). This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 864219), the French National Cancer Institute (INCa_14059), the French Ministry of Health (arrêté 29. 11. 19), and the IdEx Université de Paris (ANR-18-IDEX-0001), and a Bettencourt-Schueller Foundation research prize 2021.
Ethical approval: Electronic informed consent is provided by each person included in the NutriNet-Santé cohort. The study is registered at https://clinicaltrials. gov/ct2/show/NCT03335644, conducted according to the Declaration of Helsinki guidelines and approved by the institutional review board of the French Institute for Health and Medical Research (IRB-Inserm) and the Commission Nationale de l'Informatique et des Libertés (CNIL No 908450/909216).
Data Collection Data collection occurred between March and May 2021 through visits to the referred HC. The collection procedure occurred in two ways: I-spontaneous demand in the HC, where participants were approached during routine care in the units and invited to participate in the study; II-active search in residences, where visits were performed by community health agents (CHA) who indicated the habitations to be visited. Individual interviews were conducted using a structured questionnaire, and the instrument contained questions related to the population's knowledge, attitudes and practices regarding schistosomiasis, as well as questions aimed at identifying the participants' sociodemographic profile and health service-seeking behavior. This instrument was developed by the authors of this study through bibliographic research and a survey of studies that investigated the aspects of KAP related to schistosomiasis, which were the basis for the questionnaire's construction  [10, 18, 22, 28, 29].
Statistical Analysis Data were stored in a Microsoft Excel 2016 database (Microsoft Corporation; Redmond, WA, EUA) where scores of each component were calculated. Later, statistical analyses were performed in R 4. 0. 0 software (RStudio 1. 4. 1717 (GNU Affero General Public License). Descriptive analyses of the independent variables were performed, calculating absolute and relative (%) frequencies. For the dependent variables in the descriptive analysis, median, interquartile interval, and minimum and maximum values were calculated. Adherence to the normal distribution of the scores was checked using the Kolmogorov-Smirnov test  [30]. Associations of the scores with independent variables were performed through the nonparametric Mann-Whitney and Kruskal-Wallis tests  [30] , with the respective measures of effect size: r statistic  [31] , in which values between 0.
Data collection Web-assisted telephone interviews, lasting 60-minutes on average, were conducted by moderators who spoke the local languages (German, English or Spanish) and guided by a brief, semi-structured discussion guide, and a short set of slides of hypothetical scenarios. The discussion guide included questions on 1) patient burden of AML and its treatment including symptoms and daily life impacts, 2) patients' overall treatment experiences and preferences regarding location and MOA of treatment; 3) patients' perspectives on a hypothetical oral treatment that were presented as "Product X"; 4) patients' objective views on treatment scenarios a hypothetical patient had to choose from; and 5) drivers of treatment preference. The moderator used the interview guide to tailor the discussion but also asked follow-up questions as appropriate.
(Please see Supplementary Material 1 for the interview guide). The interviews were audio-recorded with interviewee permissions and transcripts were created verbatim from the recordings. Spanish and German interviews were transcribed to local language before being translated to English. Transcripts were analyzed using MAXQDA, a qualitative analysis software  (31) , with a predeveloped coding framework, which was used to identify key themes. During analysis, this framework was expanded with spontaneously mentioned as well as probed themes emerging from the data.
* Recruitment difficulties in the UK and Spain resulted in lower-than-expected sample size in these countries as described in limitations. 1 Self-reported using the ECOG rating scale ranging from 0-4, with a score of 0: Normal with no limitations, and a score of 4: Pretty much bed ridden, rarely out of bed. 2 Includes current treatment and prior treatment of those who were treated in the past but did not receive treatment at the time of screening. 3 Oral treatments received in combination were not HMA. 4 Includes treatment naïve patients and patients that did not provide. MOA data of their treatment.
Landform Calculation of topographic shielding was included considering the fieldwork observations. Age calculations consider an erosion rate of 0 mm/ka, as the percentage of variation per mm of erosion is only 1%, significantly less than the error associated with the 10 Be measurement. Additionally, the 10 Be exposure ages obtained by  Glasser et al. (2006)  in the Tranquilo River valley were recalculated using the same calculator. Aniya et al. (2007)  radiocarbon ages for wood, plants, and organic matters were calibrated and converted to calendar years BP using the OxCal 4. 4 program with the SHCal20 terrestrial calibration curve for the southern hemisphere  (Hogg et al. , 2020)  and are presented with a 2σ confidence interval (95. 4% of the distribution probability). Ages >300 years are presented with the median values resulting from this calibration (Fig.
Acknowledgments G. Aguilar  and  M. Gallardo  were supported by the  Advanced Mining Technology Center (AMTC)  financed by  ANID  Project  AFB180004. J. Martinod  and  C. Sue  acknowledge financial support by the  INSU-Syster program. We thank  V. Flores-Aqueveque  and  E. Sagredo  for contributing with the undergraduate thesis of  M. Gallardo. This text was improved thanks to the comments and revision of  Dr. V. Thorndycraft  and the editor  Dr. W. Vivallo. Particular thanks are due in this paper to  D. L. Bourlès †  of the  ASTER  team, who in his lifetime was involved in numerous contributions to various applications of cosmogenic nuclides in Andean geology (Braucher et al. , 2021). The  ASTER AMS national facility  (CEREGE,  Aix-en-Provence, France ) is supported by the  INSU/CNRS , the  ANR  through the " Projets thématiques d'excellence" program  for the " Equipements d'excellence "  ASTER-CEREGE  action and  IRD.
Sequence analysis QIIME2 (Quantitative Insights into Microbial Ecology), version 2019. 04  (22)  were used to process sequences. Analyze according to the guidance of the official website (https://docs. qiime2. org/2019. 4/ tutorials/). Removal of primers, quality control, denoising, splicing of double-terminal sequences, removal of chimera and identification of amplicon sequence variants (ASVs) were performed using DADA (23). For taxonomic classification, we selected the Greengenes database (version 13. 8) using the Naïve Bayes classifier in QIIME2  (24, 25). ASVs that were identified in only a single sample or classified as non-bacterial were discarded. The sequence of each horse was randomly selected to achieve a uniform sequencing depth for fair comparison  (26).
Statistical analysis of data Rarefaction curves were plotted for each sample to determine the abundance of communities and sequencing data of each sample  (27, 28). The QIIME2 software has been used to calculate alpha diversity index and evaluated by Observed_species index and Shannon index  (29). The Observed_species index is used to evaluate species of abundance, and the higher the value of the index, the more species are included in the sample. The Shannon index measure species diversity, affect the species richness and evenness in the sample microbial community. Differences in alpha diversity indices between groups were analyzed by Kruskal-Wallis Rank sum test, and P < 0. 05 was considered significant  (30). Beta diversity measurements, including gut microbiota trees, were calculated as described to compare species diversity between different samples  (31).
Acknowledgments The authors would like to thank the  Guanzhong Horse Conservation Farm in Mei County ,  Shaanxi Province ,  Debao Dwarf Horse Breeding Farm in Debao County ,  Guangxi , and  National Ningqiang Horse Conservation Farm in Ningqiang County ,  Shaanxi Province  for allowing sample collection from their hospitalized horses. Then, authors would also like to thank the horse veterinarian for assisting in providing animal care and instruments during sample collection.
Confocal image and data analysis Spinal cord sections were immunostained under identical conditions and then imaged with a confocal microscope (FluoView® FV10i-W, Olympus Ltd. ). To quantify the immunohistochemical staining pattern, 1 µm-thick serial optical sections were created with the 10x objective by using 1. 5x zoom. Intensity measurements were made (ImageJ, NIH) from every fifth section from four different regions within the L4 and L5 segments: the lateral ventral horn (lvh), the ventral funiculus (vf), the medial ventral horn (mvh) and the region comprising laminae I-II of the dorsal horn (dh). Mean pixel intensities at different survival times were normalized to the uninjured contralateral side on the same section.
However, the actual size of the cropped area changed from sample to sample depending on the shape and size of the selected cells. Typically, 10,000 frames were captured with an exposure time of 30 ms. Reference images with a full field of view were captured at low intensity when the majority of fluorescent molecules were still active, and the overall structure of the labelled sample could be visualized. Because of the reduced volume, the switching buffer could provide ideal photochemical conditions only for a limited period of time (~2 h). Whenever the imaging process required more time, the switching buffer was replaced. The captured and stored image stacks were evaluated and analysed by the rainSTORM localization MATLAB software  (Sinkó et al. , 2014). Localizations were filtered via their intensity, ellipticity and standard deviation values.
Cluster analysis with density-based spatial clustering of applications with noise A density-based spatial clustering of applications with noise-based (DBSCAN) cluster analysis module implemented into the rainSTORM program was used for further quantitative evaluation. This algorithm requires two input parameters: a minimum number of points that form a cluster (N core ) and the maximum distance between two adjacent points (ε). After the reconstruction of the high-resolution image, the user can select a region using the box tracking tool, and set the two cluster analysis parameters (N core , ε). The program plots and saves data for further evaluation and visualization. Larger areas (entire cells etc. ) can also be selected, but the code automatically segments them into smaller regions to avoid computation failure.
Membrane analysis with Image J The 180 cells having KCC2 expression also in their membranes were analyzed by using the ImageJ program (NIH). The number of localizations in the selected membrane section was obtained as described below. Identically sized areas taken from both the membrane and cytoplams were used for analysis. As the next step, the difference between the mean intensity obtained in the case of the area containing the membrane and that of retrieved in the cytoplasm divided by two was calculated. This division was necessary since in the case of the membrane only half of analyzed area was inside the cell. Afterwards the mean intensity value retrieved this way was multiplied by the area of the selection, which gave a good estimate for the number of localizations in the analyzed rectangle.
The relative volume of the EDDs representing the calcium precipitates which was determined by point counting methods  (Mayhew, 1992) , which was modified for these unique structures and photographic conditions  (Obál et al. , 2006). The pictures were analyzed with Image-Pro Plus image analysis software. First, the tessellation of sampling points was superimposed on each electron microscopic image, then sampling points hitting the perikaryal profile of individual motor neurons in each image served as reference areas and were counted. Sampling points hitting EDDs within the reference areas were counted, and expressed as percentages compared to the total number of sampling points superimposed on the reference area. The corresponding counts obtained in the individual fields were summed up throughout the series of the identified motor neurons in each animal, and the appropriate ratios expressing the relative amount of EDDs within these neurons were calculated for each animal.
Calculations and statistical analysis All statistical analyses were done using R version 3. 6. 2 (R Core Team, 2019). The pig was the experimental unit. The average postprandial plasma concentrations of AA, glucose, urea, and insulin were analysed using a generalised additive mixed model (GAMM function) of the gam package of R  (Hastie, 2019). The model included time of blood sampling and diet as fixed effects and the pig as the random effect. Time was declared as a repeated measure and a smoothing variable. A difference was considered to be significant at P < 0. 05 for all statistical analyses done. Plasma AA concentrations were further analysed using nonlinear regression with a modified compartmental Erlang function  (van Milgen et al. , 2022). The model assumes the existence of a target AA concentration that the animal tries to maintain.