dataesr/scientific-paragraphs-categorization huggingface dataset:
Currently, only one observational study has suggested a possible negative outcome regarding allergenicity with a partial hydrolysate  [23]. This study analyzed data from a French longitudinal birth cohort study. All children who received a HA formula based on a partially hydrolyzed protein at 2 months were consolidated into one single HA group (N = 251) and compared with children who received a cow's milk formula at 2 months (N = 7149). Apart from the extreme imbalance of the groups, the study does not allow for any conclusions concerning the effect of a specific HA formula, as partially hydrolyzed protein HA formulas from different manufactures were used. As previously mentioned, the properties of a hydrolysate are not only determined by the degree of hydrolysis or protein source, but, crucially, are dependent on the hydrolysis process itself, which varies between manufacturers  [14].
Thermal analysis by DSC Thermal properties of pecan kernel oils were carried out on a diferential scanning calorimetry (DSC) using a Q2000 V23. 5 scanner (TA instruments, New Castle, DE, USA). Dry nitrogen was used as the purge gas at a rate of 20 mL/min. Oil samples (6-8 mg) were weighed directly into aluminium pans. An empty sealed pan was employed as control. Samples were subjected to the following temperature program: 90 °C isotherm for 5 min, and then cooled to -80 °C at a rate of 10 °C/min, held for 20 min. Oil samples were then heated from -80 °C to 90 °C at a rate of 5 °C/min to acquire the data of the melting thermogram. The crystallization was achieved by heating the oils at 90 °C for 5 min and then decreasing the temperature to -80 °C at a rate of 10 °C/min. Crystallization and melting parameters: the melting enthalpy (rH m ), the temperature of the major peak of melting (P m ), the initial and end temperature of the melting phase (T onset m and T end m ), the crystallization enthalpy (rH c ), the temperature of the major peak of crystallization (P c ), the initial temperature of the crystallization phase (T onset c ), the range of the transition phase (R: the temperature difference between T onset and T end) were determined.
Methods study population and data collection This study was based on the prospective NutriNet-Santé e-cohort, launched in France in May 2009, with an open ongoing enrolment of volunteers. The main objective was to investigate the relations between nutrition and health. 37 Participants are French adults, aged 18 years or older, with internet access, recruited from the general population by means of multimedia campaigns. They are followed through their personal account created at inclusion on the study website (https://etude-nutrinet-sante. fr/). Immediately after enrolment, each person completes five online questionnaires about diet (24 h dietary records, detailed below), health (eg, personal and familial history, prescription drug use), anthropometric data (height and weight  38 39  ), lifestyle and sociodemographic data (eg, date of birth, sex, education level, professional occupation, smoking status, number of children  40  ), and physical activity.
Nutritional contributions of mixed dishes were estimated by standard French recipes defined by nutrition professionals. Dietary assessment through these 24 h dietary records were validated against interviews by a trained dietitian  45  and against blood and urinary biomarkers. 46 47 The basal metabolic rate and the Goldberg cut-off method enabled any under reporting to be identified  [48] [49] [50]  ; participants who under reported were excluded from the analyses. Briefly, food additive intakes, including artificial sweeteners, were assessed through the interactive online 24 h dietary record tool, in which commercial names and brands of industrial products consumed could be recorded. The presence of food additives was first determined for each food and beverage using ingredients lists available from three large scale food composition databases: Open Food Facts (https://world.
openfoodfacts. org/)  6 ; the French food safety agency database Oqali (https://www. oqali. fr/oqali_eng/)  52  ; and Mintel's Global New Products Database. 53 Doses of additives were determined by around 2700 assays performed by accredited laboratories, requested by the Nutritional Epidemiology Research Team or by a consumer association (UFC Que Choisir). These quantitative data were completed by average doses per food group provided by EFSA and the Joint FAO/WHO Expert Committee on Food Additives. 54 ood additive composition data were matched by date to account for possible industrial reformulations and changes in additive composition (date of consumption was considered to match the product to the closest consumption data). Supplementary method 2 gives additional information on food additive and artificial sweetener intake assessment.
cardiovascular disease determination Throughout follow-up, biannual health questionnaires and a permanently open personal health interface on the study account allowed participants to report any new health events, medical treatments, and examinations. For each incident CVD event declared, participants were contacted by a physician of the team and asked to provide any relevant medical records (eg, radiological reports, electrocardiogram, angioplasty). When necessary, the study physicians contacted the patient's physician or any hospitals providing treatment to collect additional information. These medical data were reviewed by physician experts. An investigation was also conducted by the physicians of the NutriNet-Santé study by contacting the participant's family or their physician when no connection to the study website was made for more than a year.
Beyond this proactive health follow-up, data were paired with the medico-administrative databases of the national health insurance system database (SNIIRAM) and the national mortality registry (CépiDC), thereby limiting potential bias due to people with CVD not reporting their disease to the study investigators (further information available in supplementary method 3). International classification of diseases clinical modification, 10th revision, was used to classify CVD. 55 For this study, first incidence of CVD, coronary heart disease (myocardial infarction, code I21; acute coronary syndrome, code I21. 4; angioplasty, code Z95. 8; angina pectoris, code I20. 0), or cerebrovascular disease (stroke, code I64; transient ischaemic attack, codes G45. 8 and G45. 9) diagnosed between inclusion and 5 October 2021 were considered as events and investigated in the analyses.
Associations between artificial sweeteners, overall and the most represented (aspartame, acesulfame potassium, and sucralose, consumed by more than 5% of participants), and CVD (overall, coronary We first tested dose-response analyses using the restricted cubic spline (RCS) functions with the SAS macro developed by Desquilbet and Mariotti. 56 Given the logarithmic profile of the associations suggested by the RCS curves (supplementary fig  2 ) and to account for the large proportion of non-consumers (especially for each specific artificial sweetener), artificial sweetener intakes were log transformed (log10 of sweetener consumption in mg/g+1) to compute continuous models (+1 was uniformly added to all consumptions because log(0) is not allowed). The continuous model was used as the primary analyses to obtain hazard ratios and 95% confidence intervals.
We added a table showing the rationale for selection of each covariate and information on how they were collected and measured (supplementary method 4). Analyses by specific artificial sweeteners (aspartame, acesulfame potassium, and sucralose) were additionally adjusted for other artificial sweetener intakes. Multiple imputation by chained equations  66  was applied to handle any missing values for covariates (15 imputed datasets; supplementary method 5). Cox proportional hazard assumption was verified using the rescaled Schoenfeld type residual method (supplementary fig  3 ). Competing risks were accounted for in all analyses using cause specific Cox models,  67  with death considered a competing risk for CVDs, coronary heart diseases, and cerebrovascular diseases. Additionally, cerebrovascular events were considered competing risks for coronary heart diseases and vice versa.
All observed associations between sweetener intakes and CVD events went in the same (positive) direction, which is not in favour of random findings observed by chance. The two complementary methods (self-reporting and medico-administrative databases) ensured good identification of CVD outcomes. However, the possibility of missing some events cannot be entirely ruled out. Additionally, despite efforts to identify transient ischaemic attacks as objectively as possible (based on medical or hospital reports, if possible a specialised neurological diagnosis, computed tomography or magnetic resonance imaging scan, or symptoms precisely described by the participant or a person close to them), these CVD events could not be diagnosed with the same certainty as for strokes or myocardial infarctions because they generally do not reveal sequelae on brain imaging.
11. 19) and the IdEx Université Paris Cité (ANR-18-IDEX-0001). This project was awarded the NACRe (French network for Nutrition and Cancer Research) Partnership Label and was awarded the Bettencourt Schueller foundation prize Coup d'élan pour la recherche française 2021. Researchers were independent from funders. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.
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).
Centrale. Protected by copyright. on 17 April 2023 at Conservatoire National des Arts et Metiers Bibliotheque http://www. bmj. com/ BMJ: first published as 10. 1136/bmj-2022-071204 7 September 2022. Downloaded from doi: 10. 1136/bmj-2022-071204 | BMJ 2022;378:e071204 | the bmj Contributors: CD, EC, MDT, BS, MT: designed the study; EC, NDP, YE, FSdE, CA, ADS, RL, SH: developed the additives composition database and matched consumption/composition data; CA: coordinated dietitian work; FSdE: data management work; NDP, YE: global technical work; NDP, YE, EC, MT: supervised this technical work; CD:.
Sequence data analysis Sequencing was performed on a HIseq 4000 sequencer (Illumina, 2 × 125nt) using the v4 chemistry/HBS Hiseq kit. One line was used for each sample to produce up to 300 × 10 6 reads per sample. Image analysis and base calling were performed using Illumina Real-Time Analysis software version 2. 7. 3 with default parameters. Raw sequence data produced by the Genotoul platform and Integragen were sent to the laboratory for processing and analysis. The two sequence extremities were trimmed to remove the remaining primer sequences and any bases with poor quality base calling often present at the extremities of the reads with Cutadapt  [25]. Trimmed sequences were then aligned using STAR. v2 through Galaxy (Sigenae)  [25]. The resulting BAM files were analyzed with Samtool, Bedtool, Cufflink and Stringtie  [26]  using the Toulouse Genocluster and with the Geneious suite  [27].
Acknowledgements We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing resources and to  Clemence Genthon  and  Olivier Bouchez  from the  Genomic Plateform INRA Auzeville. We also like to thank  William Ollier  for his careful and professional correction of the English lenguage.
Proton transfer reaction-time-of-flight-mass spectrometry (PTR-ToF-MS or PTR-MS) is an analytical chemistry technique that can be used for measuring the concentration of volatile organic compounds directly in the subjects' noses (nosespace, in vivo analysis) during a tasting and over time. It can be combined with temporal sensory methods such as temporal dominance of sensations (TDS) or temporal check all that apply (TCATA) in order to obtain simultaneous sensory and physico-chemical signals. This paper aims to provide a methodology to analyze in vivo PTR-MS and temporal sensory data and illustrate it on a real dataset. First, relevant pretreatments of PTR-MS data were established, including breathing correction, blank periods removal, and standardization. Then, a statistical multiblock analysis was presented: the regularized generalized canonical correlation analysis (RGCCA).
The versality of the approach was demonstrated, as it can be used to answer most of problematics (exploratory or supervised). Finally, this methodology is illustrated on a dataset of PTR-MS and TDS or TCATA data collected simultaneously. In this study, 16 semitrained subjects evaluated three chocolates in TDS and TCATA on six flavor attributes (Spicy, Cocoa, Woody, Fruity, Roasty, and Dry Fruits) with two replicates for each sensory method. Results showed that TCATA and TDS gave similar results, but TDS was shown to slightly better preserve the PTR-MS observed product configuration than TCATA. All computing tools developed in this work are freely available.
| INTRODUCTION Proton transfer reaction-time-of-flight-mass spectrometry (PTR-ToF-MS) is a powerful tool for fast, direct, and highly sensitive online monitoring of volatile organic compounds (VOCs) that was extensively applied in food science. 1  PTR-ToF-MS uses a soft chemical ionization based on proton transfer from a protonated reagent, most commonly H 3 O +. Sensitive online monitoring capabilities allow performing in vivo aroma release measurements such as nosespace analysis. 1 The addition of a time-of-flight (ToF) mass analyzer to the PTR-MS adds the advantage of providing high mass range, very fast measurement, and high mass resolution. 2  After first pretreatments (such as mass recalibration, deconvolution of the peaks …), resulting data can be considered as ion trajectories along time.
Recently, Esmerino et al. 12  stated in their study that the major part of the variation is common between the two methods but that there were subtle differences showing better discrimination for TCATA than TDS. The datasets obtained by pairing sensory and physico-chemical measurements are multiblock data: They are constituted of data from the same individual (as rows) scoring (or being scored on) different sets of variables called "blocks" (whose columns are the variables). A natural way to analyze such data could consist to compute a Principal Component Analysis (PCA) of the concatenated blocks, ignoring the block structure. However, this approach would favor the block with the highest variance. For example, if all the variables were scaled (as ions in usual PTR-MS analysis), the block with more variables would have more importance in the analysis than the other ones.
Multiblock analyses were proposed to overcome this limitation. The regularized generalized canonical correlation analysis (RGCCA) is a multiblock method that was introduced by Tenenhaus and Tenenhaus  13  as a general framework for a large panel of multiblock methods. It was mainly used for the research of multimodal biomarkers as presented in Xicota et al. 14  or Lejeune et al. 15  This paper aims to propose a methodology to relate in vivo PTR-MS data and temporal sensory data obtained by TDS or TCATA including pretreatment taking breathing into account and statistical analyses based on RGCCA. After a presentation of the example dataset, relevant pretreatments of PTR-MS data were established, including breathing correction, blank periods removal, and standardization. Second, a flexible statistical multiblock analysis was presented: RGCCA.
| Overview The experiment took place in CSGA, Dijon, France. Three chocolates were tasted by 16 subjects with TDS and TCATA, while connected to a PTR-ToF-MS device, with two replicates. Consequently, each subject tasted the same products four times in two separated (TDS or TCATA) sessions. The order of sessions was counter-balanced. We used PTR-TDS (resp. PTR-TCATA) for referring the PTR-ToF-MS experimentation related to TDS (resp. TCATA). TDS and TCATA data were collected via the TimeSens software, developed in the laboratory of the authors.
| Overview This paper presents a pipeline of pretreatments including several steps that are detailed in the next subsections and summarized in Figure  1. For each PTR-ToF-MS acquisition file, the breathing is detected on a breathing reference, then corrected for each ion (Section 2. 2. 2). Then, the averaged intensity during the blank period is subtracted for each ion intensity (Section 2. 2. 3). Then, the ions evolving during the tasting are selected (Section 2. 2. 4. ). At this stage, the results of whole acquisition files are aggregated in order to obtain a list of relevant ions common to all files (final ion list). Finally, the AUCs of each ion of the final ion list are calculated for each file, and the data are standardized (Section 2. 2. 5), then aggregated in a single dataset (final dataset).
| Pretreatment of sensory data In TDS/TCATA data, the durations of dominance/applicability of each descriptor are calculated for each evaluation. These durations are divided by the total duration of the evaluation (giving relative durations) to be aligned to PTR-MS data. Consequently, the total sum of durations in TDS is 1, while the total sum of durations in TCATA is higher than 1 when several descriptors are clicked simultaneously.
Deflation steps are also included in order to obtain more than one component. The obtained deflated components are orthogonal by construction. With its flexibility, the RGCCA framework can be used for answering most of research questions, supervised, or exploratory, with two blocks or more. RGCCA calculations were computed with RGCCA package available on github (https://github. com/rgcca-factory/RGCCA). 2. 5 | Application of pretreatment and multiblock analysis on the example dataset.
| Pretreatment of the dataset Both PTR datasets (paired with TDS or TCATA) were pretreated according to the method presented in Section 2. 2. The parameters chosen for breathing correction were as follows: isoprene, C 8 H 9 + , m/z 69. 069 for the breathing reference, a half window size of 5 for moving average filter, n TimesHigherThanBlank = 3, d min = 2 s and i min = i max /5 for the expiration peak where i max is the maximal intensity during the evaluation. Two examples of pretreatments were computed for illustration (Figure  2 ). The first example (E1) corresponds in the subject "S002" for TDS, product C and replicate 1, while the second one (E2) corresponds to the subject "S001," product C replicate 1 in TDS. For a better visualization, only some ions (m/z 71. 085 and m/z 115. 113 found relevant in the rest of the paper) were represented.
The pretreatment visualization consisted in drawing the raw intensity then the corrected intensity along time. The breathing reference and detected breathing cycles were also drawn. F I G U R E 2 Thirty-five ions (excluding isoprene) were selected with the method presented in Section 2. 2. 3 and described in the supporting information Table  S1. In the next sections, the pretreated chemical data tables were noted X PD for PTR-TDS and X PC for PTR-TCATA, while pretreated sensory data tables were noted X D for TDS and X C for TCATA. These tables have N lines (with N = number of products* number of subjects * number of replicates) sorted in the same order. X PD and X PC have P columns (with P = 35 is the number of relevant ions). X D and X C have D columns (with D = 6, the number of descriptors).
| Pairing sensory data with chemical data To find out which sensory method fits the chemical results best, RGCCA was conducted for two components by maximizing the following criterion: cov X PD a PD , X D a D ð Þ þ cov X PC a PC , X C a C ð Þ under the following constraints: 8j PD, PC, C, D f g , a j 0 a j ¼ 1 With this criterion, the TDS/TCATA durations of sensory attributes were explained by the intensities of relevant ions (independently of products) for all evaluations. This approach is strictly equivalent to run two separated PLS, but they are computed in one single calculation. Maximizing this criterion results in getting two components per block that are comparable with principal components in PCA. As in PCA, these components can be plotted on a map. For each j block, the two components are noted, y 1 j and y 2 j.
Further components could also be calculated, but we focused here on the first two components, allowing maps to be plotted and the three products to be discriminated (if the interproduct variability was higher than the intraproduct variability). For each combination of two blocks ( j and k), univariate Pearson correlations between y 1 j and y 1 k (respectively y 2 j and y 2 k ) were calculated and noted R_1 (resp. R_2). Bivariate RV coefficients (noted RV) between the two individual configurations on 1-2 map were also calculated. They indicated the similarities between PTR-TCATA and PTR-TDS and their related sensory spaces. It should be noted that RGCCA did not use the product information in its calculations: It only maximized the covariance of sensory data with chemical data. It was expected that the two first axes (maximizing the covariance between chemical and sensory data) should reflect the product variability rather than intersubject or intrasubject variability.
| Software for statistical analysis All calculations were done with R (4. 0. 2). All pretreatments were computed with a specific home-made R package (PTRMSR) available on github (www. github. com/ChemoSens/PTRMR). The multiblock analysis was conducted with RGCCA package available online (https://github. com/rgcca-factory/RGCCA). The code required for pretreatment and multiblock analysis is available online (https://github. com/ChemoSens/ExternalCode/ /TCATAvsTDS_paper. r). In the same repository, data for pretreatment are available online (. /dataForPretreatementExample) and data for RGCCA analysis are also available online (. /dataForMultiblockAnalysis). Figure  2B ,E shows raw data for the ions m/z 71. 085 and m/z 115. 113 that present oscillations due to breathing cycles (as expected). Furthermore, the intensity during the blank period (left of the vertical black line) is clearly not null and different between these two evaluations and thus should be properly removed from the signals.
| Pretreatment of the dataset Figure  2C ,F shows the result of the calculation of the mean by the breathing cycles obtained with isoprene curves, then corrected by the blank period. Therefore, in Figure  2C ,F, points-whose abscissa are the centers of the breathing cycles and whose ordinates are the intensities averaged by breathing cycles minus the average intensity during the blank period-are plotted then connected by segments. The signal is still not completely smoothed but represents the averaged intensity measured during each breathing cycle corrected from the blank period.