dataesr/scientific-paragraphs-categorization huggingface dataset:
The two Na intake estimations (from dietary recalls and from INTERSALT equation) were compared by t tests and the difference was quantified by the ratio of the two values and expressed as a percentage. Finally, all predicted values (24-hUNa excretion, predicted Na intake and salt equivalent) were recalculated by applying the calibration coefficients obtained in the validation study. All analyses were carried out with Stata® software version 14 (StataCorp). All tests were two-tailed, and P values <0•05 were considered statistically significant. Survey data analyses (in the application study) were realised using the 'svyset' function (for the declaration of the complex sampling scheme) and 'svy:' option.
However, the morning sample in the validation study was done in a fasting state and may not differ that much from an overnight sample. Second, only healthy volunteers were included in the validation study. Previous work has studied the non-representative nature of the overall sample of NutriNet-Santé study where women and well-educated individuals are over-represented compared with national data  (48). This volunteer bias was partly limited by the sampling strategy of the Dietary Validation Study. In addition, the ENNS showed that salt intake of French adults differed according to sex but not according to age or education level  (49). Third, the INTERSALT equation has been developed and validated in a relatively young population of adults (20-59 years)  (16)  and recent work showed that this equation was not consistently valid for older adults  (23).
Acknowledgements The authors thank all the staff involved in this study: the dietitians, the nurses and physicians from the health examination centres of the  French National Health Insurance System  and the  Cetaf (Centre Technique d'Appui et de Formation des Centres d'Examens de Santé). The  NutriNet-Santé Study  was supported by the  French Ministry of Health, Santé publique France  (former  Institut de Veille Sanitaire  and  Institut National de la Prévention et de l'Education pour la Santé) , the  Fondation pour la Recherche Médicale (FRM) , the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Institut National de la Recherche Agronomique (INRA) , the  Conservatoire National des Arts et Métiers (CNAM)  and the  University of Paris 13. The Dietary Validation Study was funded by  Santé publique France  (former  Institut de Veille Sanitaire ) and supported by grants from the  Région Ile de France (CORDDIM).
Comparison of Hydroethanolic Extraction of Individual Phenolics Compounds from Horchata by-Products with Those of Folchand Supercritical-CO 2 Extraction The data on individual phenolics obtained in the present study were compared to those obtained after conventional extraction method (Folch) and an innovative extraction method (supercritical CO 2 extraction). The results for the comparison were obtained from a previously published article about the extraction of phenolic compounds in the oil fraction of "horchata" by-products  [31]. It can be noticed that there was a great difference in the compounds obtained with the three different extraction techniques. As for conventional extraction with Folch method, the major compound obtained by far was 4-vinylphenol (216. 9 µg/kg DW), followed by p-coumaric acid (25.
MS data were obtained between 80 and 1200 m/z on negative mode, and the IDA acquisition method was carried out in the survey scan type (TOF-MS) using the dependent scan type (product ion). Ion spray voltage (-4500 V); declustering potential (90 V); collision energy (-50 V); temperature with 25 psi curtain gas (400 • C); 50 psi for both ion source gas 1 (GC1) and ion source gas 2 (GS2) were used as the main parameters for the MS analysis. The IDA MS/MS analysis was carried out with ion tolerance of 50 mDa, 25 V collision energy and activated dynamic background subtract. The software analyst PeakView1. 1 (AB SCIEX, Foster City, CA, USA) and its applications (XIC Manager and Formula Finder) were used for data acquisition and processing. Finally, an external calibration curve using resveratrol as standard was prepared for the quantification of phenolic compounds.
Acknowledgments:  Paulo E. Munekata  acknowledges the postdoctoral fellowship support from the  Ministry of Economy and Competitiveness (MINECO, Spain ) " Juan de la Cierva" program  ( FJCI-2016-29486 ). The authors would like to thank the  Regulatory Council D. O. Tiger nut of Valencia (Valencia, Spain)  for providing "horchata" by-products. Moreover, we are also grateful for the technical support from the  "Central Service for Experimental Research (SCSIE) " at the  Universitat de València  for the help provided in the analysis using the Triple TOF-LC-MS-MS.
Finally, the PN bag label of the pharmacy/CU will not reflect which lipid is used, but only the amount of lipid. Thus, it is guaranteed also that the patient is kept blinded. Based on the randomization, list sets of emergency envelopes have been prepared for each participating site and the sponsor in case unblinding becomes necessary. Except for emergency reasons, the study will only be unblinded after closure of the database and determination of the analysis populations in a blinded data review meeting.
Statistics All programming of tables, figures, listings and statistical analyses will be performed using a statistical software package (SAS® version 9. 4). Statistics will be performed in accordance with the principles outlined by the guideline E9 of the International Conference on Harmonization (ICH) and will be outlined in detail in the statistical analysis plan that will be finalized before close of database. The primary analysis will be performed for the VCAS (FAS for sensitivity). No imputation will be performed. The t test will compare the (one-sided) null hypothesis (H 0 : Δ T ≥ Δ S + δ) against the alternative hypothesis (H 1 : Δ T < Δ S + δ) on the level of 0. 025, where Δ T and Δ S represent the increase of the primary endpoint upon T = test and S = standard, respectively. Non-inferiority (with non-inferiority margin σ = 1.
Acknowledgements The authors would like to thank  Simone Keller ,  B. Braun Melsungen AG , for medical writing services. We also thank all members of the  HOME Study Group  for their participation in the trial. HOME Study Group: Alison Culkin (Lennard Jones Intestinal Rehabilitation Unit, St Mark's Hospital ,  Watford Road ,  Harrow HA1 3UJ, UK );  Karolina Dabrowska  ( Samodzielny Publiczny Szpital Kliniczny im. Prof. dr W. Orlowskiego ,  Oddzial Kliniczny Zywienia i Chirurgii , ul. Czerniakowska 231, 00-416 Warszawa, Poland);  Jane Fletcher  ( University Hospitals Birmingham NHS Foundation Trust ,  Queen Elizabeth Hospital ,  Mindelsohn Way , Birmingham  B15 2TH, UK );  Mariacristina Guidetti  ( Chronic Intestinal Failure Center, Digestive Disease Department, St. Orsola-Malpighi Hospital, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40,138, Italy );  Angelique Huijbers  ( Department of Gastroenterology and Hepatology, Geert Grooteplein 8, 6500 HB Nijmegen, Netherlands );  Krystyna Majewska  ( Samodzielny Publiczny Szpital Kliniczny im.
Fluorescence was determined on an ABI PRISM 7500-sequence detection system (Applied Biosystems, California,USA). Data were analyzed using the comparative threshold cycle (Ct) method, results are expressed as relative fold change  (Mingam et al. , 2008 , Madore et al. , 2013 , Madore et al. , 2014 , Delpech et al. , 2015a)  to control target mRNA expression.
Principal component analysis (PCA) PCA was used to assess the oxylipin pattern from both dietary groups under inflammatory conditions. The PCA is a dimension-reduction technique that summarizes data into principal components (PC) that maximize the variance of the data considered. These PCs are uncorrelated linear combinations of the initial variables which can be interpreted as pattern. PCA generates factor loadings that reflect the correlation of each variable with the PC and a principal component score for each individual. We selected the number of components using the Cattell criterion. All data were expressed as means ±SEM. Statistical analyses were performed using R version 3. 3. 0 (FactoMineR package).
Multivariate data analysishighlightsthe separation of oxylipin pattern according to diet only in mice challenged by inflammation We first considered the data for multivariate analysis to evaluate if diet groups or LPS groups could be discriminated by a pattern of oxylipins. In the multivariate data analysis, 23 variables corresponding to the 23 identified oxylipins in the hippocampus were analyzed to compare spectra from n-3 LC-PUFA deficient and n-3 LC-PUFA supplemented groups. The correlation matrix highlighted the most correlated variables. Only significant correlations were reported (p<0. 05) (Figure  4. A). Strong correlations were observed between oxylipins: AAmetabolites (PG, Tx, lipoxin, 9-HODE, 15-HETE and 8-HETE) were positively correlated together (in blue) and negatively correlated with 18-HEPE (in red) (Figure  2.
Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction.
The complexity of available databases, they include both thousands of chemical properties and a large heterogeneity in perceptual descriptions,  [18] [19] [20] [21]  means that the manual generation of multiple rules is not feasible. In other words, to better understand the stimulus-percept issue in olfaction, there is a clear need to extract knowledge automatically and in an intelligible manner. Such an approach is positioned upstream of predictive modeling since it will enable modeling that extracts descriptive rules from the data that link subgroups belonging to both chemical and perceptual spaces. The main aim of our study was to develop such a computational framework to discover new descriptive structure-odor relationships. To achieve this, we first set up a large database containing more than 1600 odorant molecules described by both physicochemical properties and olfactory qualities.
We then developed an original methodology based on the discovery of physicochemical descriptions distinguishing between a group of objects given a target or class label, namely odor qualities. This approach has been widely studied in Artificial Intelligence (AI), data mining and machine learning. Specifically, supervised descriptive rules were formalized through subgroup discovery, emerging pattern/contrast-sets mining  [22]. In all cases, we face a set of objects associated with descriptions and these objects are related to one or several class labels. This new pattern mining method, a variant of redescription mining  [23] , allows the discovery of pairs consisting of a description (of physicochemical properties) and a label (or sub-set of labels, olfactory qualities). The strength of the rule (SOR in our application) is evaluated through a new qualitycontrol measure detailed in the Methods section.
Olfaction database We designed and set up a database describing odorant molecules by both their perceptual and physicochemical properties. Here, data from different sources were extracted and grouped: (i) for odorant identification and olfactory qualities, we referred respectively to the PubChem website (https://pubchem. ncbi. nlm. nih. gov/) and the textbook by Arctander  [24] ; (ii) for physicochemical properties, we referred to the Dragon software package (http://www. talete. mi. it/ index. htm). Olfactory qualities were thus gathered from the book "Perfume and Flavor Chemicals", published in 1969 by Steffen Arctander. In this book, Arctander gives a complete description, including olfactory and trigeminal qualities as well as flavors, of 3102 odorants (detailed physicochemical properties of 1689 odorants among these 3102 odorants were retrieved, see below).
g. Arctander and Flavornet), others contain only a limited number of odorants (e. g. Boelens and Dravnieks). Moreover, there is great heterogeneity between these different sources with regards to the number and the degree of expertise of the evaluators. Some sources involve a large number of evaluators but with heterogeneous profiles (e. g. Dravnieks) and others involve a limited number of experts (e. g. Boelens and Arctander). Finally, whereas some sources have, on average, between 10 and 30 qualities per odorant (e. g. Boelens and Dravnieks), the average number is around three for others (e. g. Arctander and Flavornet). In view of these parameters, and because the descriptive approach used in this study requires a large database, we used the Arctander book because it contained the highest number of odorant molecules (1689) and a reasonable number of qualities per odorant (2.
88 on average). Odorant physicochemical properties were then obtained using Dragon, a software application that enables the calculation of 4885 molecular descriptors (Talete). Descriptors included in our dataset ranged from the simplest atom types, functional groups and fragment counts, to topological and geometrical descriptors. As Dragon requires 3D structure files, these were collected from the PubChem website (https://pubchem. ncbi. nlm. nih. gov) by using the compound identifier number of each odorant (CID). Individual odorant CIDs were obtained by using the CAS Registry Number and/or the chemical name of the odorant as an entry in the PubChem website. In total, 1689 CIDs were found for the 3102 odorants. In the following section, we study the set M of odorant molecules that are described by n physicochemical properties denoted F.
The developed algorithm Here, we developed an original subgroup discovery approach to mine descriptive rules that specifically characterize subsets of olfactory qualities (O). The specificity of this approach is intended to be able to extract rules with several olfactory qualities as targets, and also to treat unbalanced classes robustly, i. e. , the fact that some olfactory qualities are very rare (e. g. "musty") compared to others (e. g. "fruity"). Subgroup discovery is a generic data mining method aimed at discovering regions in the data that stand out with respect to a given target. We instantiated this framework in order to identify the conditions on some odorant physicochemical properties that are strongly associated with olfactory qualities. The molecules whose values on physicochemical descriptors belong to the intervals of the description D are members of the coverage of D: coverageðDÞ ¼ fm 2 M 8i ¼ 1.
Those parameters make it possible to identify rules that are supported by sufficient odorant molecules, and also that are specific to a small set of olfactory qualities. The maxQual parameter enforces that the right-hand side of the rule contains a limited number of olfactory qualities to be interpretable by the analyst. Similarly, a max-Prop parameter allows to limit the number of (physicochemical) conditions in the left-hand side of the rules. To illustrate the previous definitions, let us consider the toy olfactory dataset given in Table  1. This dataset contains 6 molecules identified by their IDs M = {1,2,3,4,5,6}. Each molecule is described by its molecular weight MW, its number of atoms nAt and its number of carbon atoms nC, that is, F = {MW, nAt, nC}. Besides, the molecules are also associated with their olfactory qualities among O = {fruity, vanillin, woody}.
Let us consider the description D ¼ h½128; 151�; ½23; 29�; ½½9; 12�i Its coverage is coverage(D) = {2, 3, 5, 6}. If we consider the odorant quality Q = {vanillin}, as there is 2 molecules of coverage(D) with this quality, the precision of the rule is equal to: P D ! Q ð Þ ¼ 2 4 As there are 3 molecules in the whole dataset with that quality, the recall of the rule is: R D ! Q ð Þ ¼ 2 3 Its F 1 measure is thus equal to: F 1 D ! Q ð Þ ¼ 2 2 7 Detailed information regarding the principle of the algorithm are provided as S1 Text.
Olfactory dataset: 1689 odorant molecules described by both olfactory qualities and physicochemical properties Our olfactory dataset includes 1689 molecules described by 74 olfactory qualities. The dataset is multi-labeled, each molecule being associated with one or several olfactory qualities. On average, each molecule refers to 2. 88 olfactory qualities among the 74 possible labels. Moreover, the frequency of olfactory qualities across odorants is unbalanced: on average a quality is used in 65. 79 molecules (standard deviation: 105. 28), the maximum is reached for the "fruity" quality (used in 570 molecules), the minimum for musty (used in only 2 molecules).
Physicochemical properties: Selection and interpretation With regard to the physicochemical properties, our original database contained more than 4000 physicochemical features. For the purpose of a rational approach where features can be interpreted on a chemical basis, we selected attributes that were relevant, but more importantly easily interpretable. This approach is strongly inspired by the so-called 3D-olfactophore, where such easily interpretable features computed on odorants sharing the same olfactory percept are gathered in the 3 dimensions of space. Such features are typically Hydrogen bond donor/acceptor, Aromatic cycle, Charged atom, etc. This methodology is typically useful for molecular scientists to learn about structure- property relationships and design new molecules which fulfill the properties of these olfactophores  [28].
Here the features we used were a series of physico-chemical properties. Thus, we selected constitutional, topological and chemical descriptors that represent molecular features which can be easily interpreted and extrapolated for further predictive models. They include the following categories: constitutional indices (n = 29; ex. "Molecular weight"), ring descriptors (n = 7; ex. "Number of rings"), functional group counts (n = 40; ex. "Number of esters"), molecular properties (n = 6; ex. "Topological polar surface area"). To select these descriptors, we screened the whole set of descriptors proposed by Dragon. We carefully selected descriptors able to provide information interpretable by any molecular scientist. The cost of selecting interpretable descriptors is a reduction in the description of the dataset.
To evaluate the loss of information on the variance of a given molecular dataset, descriptors were computed on a set of 2620 odorants provided by Saito and colleagues  [29]. Finally, 347 descriptors remained after filtering the following: correlated (above 0. 85), constant for the whole dataset (no variation across parameters), not available for the whole dataset. After the dimensionality reduction, our selected 82 descriptors accounted for 37. 2% of the original variance. When choosing randomly 82 descriptors within this set of 347, the variance always falls below 25%, suggesting that our descriptors performed quite well at describing a molecular set with a certain degree of variability. Finally, when projecting the entire set of molecules on to the two first components of a PCA, the dataset remains well split and molecules were still distinguishable.
To sum up, we provide here a computational framework that enables the automatic extraction, from a complex and heterogeneous dataset, descriptive rules linking subgroups in a chemical space onto subgroups in a perceptual space. As can be seen in Fig  3a , only 3 qualities could be best described by a single physicochemical rule whereas more than two thirds of the qualities needed between 3 and 5 rules to be described. When dealing with the confidence of the rules, a gradient was observed whereby some rules were associated with a good rate of recall and minimum rate of error, whereas other rules exhibited a lower confidence in describing olfactory qualities. Note that all the generated rules are available to the reader in S1 Table. The computational approach that we developed is available at the following address: https:// projet.
Validation of the physicochemical rules in novel odorants To evaluate the validity of the generated physicochemical rules, we applied them to novel sets of odorants. For a given quality, we checked whether novel odorants that fulfill physicochemical criteria according to our descriptive model indeed evoked significantly more of the studied quality than novel odorants than do not fulfill these physicochemical rules. To this end, we isolated from 4 different databases, 4 sets of odorants not present in the Arctander database and therefore not used to build the descriptive rules. These databases were from the Dravnieks study  [26]  (n = 45; i. e. 45 odorants not present in our original dataset could be used), the Boelens & Harding study  [30]  (n = 56), one set from the Keller et al. study  [15]  (n = 118), and one set from the Licon et al.
study  [31]  (n = 19). Within each of these four novel sets, olfactory quality was coded using a continuous variable Our descriptive model was tested in qualities that were common between the Arctander database and these four different databases. Moreover, for statistical purposes and for a given quality, only when the rules were filled for at least five odorants, comparisons were performed between odorants that filled the criteria for the rules and those that did not filled the rules. The qualities that satisfy these criteria were: 1/ for the Dravnieks study: Woody (n = 5), Camphor To sum up, the present validation involved four sets of stimuli for a total of 238 odorants. It allowed us to test the descriptive model on seven perceptual qualities and for six of them (woody, earthy, camphor, balsamic, sulfuraceous, fruity), the rules generated by our model have been consistent with the ratings provided in these independent datasets.
Discussion The interaction between the odorant molecule and the olfactory receptor(s) induces a percept called "odor". Chemists have previously attempted to characterize this phenomenon by working to obtain descriptive and/or predictive rules connecting physicochemical properties to odors  [32]. Such is the case with olfactophores or the exploitation of more specific molecular features for predicting intensity or pleasantness  [8, 11, 33, 34]. Recently, a large database of compounds as well as a large number of human panelists were used in order to predict percepts, intensity and pleasantness  [15]. In our study, we also considered that, to a certain extent, the odor quality of a molecule is encoded in its chemical structure. Our aim was to provide a descriptive model of the relationship between molecules and their perceived odors.
This will allow us to better understand how the interaction between the chemical features of odorants and olfactory receptors is mediated and processed in the brain to build olfactory percepts. One question that may be raised from the current finding is how our descriptive approach is different from other machine learning methods and how it may help chemists and neuroscientists interested in olfaction solve scientific issues? In contrast to classical predictive machine learning tasks where the goal is to turn the data into an as-accurate-as-possible prediction machine, exploratory data analysis such as ours aims to automatically discover new insights about the domain in which the data was measured (e. g. , olfaction). To this end, the notion of interpretability is fundamental as it is the premise of descriptive rules.