dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analyses Data were first analyzed using a principal component analysis (PCA) where the variables are the sensory descriptors and the observations are the sixteen hydrolysates. The data were also assessed by a two-way ANOVA considering the products and the consumers as factors and the different descriptor scores as the dependent variable. When a significant effect of product was found, a Duncan's multiple range test (p < 0. 05) was performed to compare the products two by two. These statistical analyses were computed with XLStat (XLStat 2020 1. 1, Paris, France).
Mass spectrometry measurements were made in sensitivity and positive mode using a Synapt-G2-Si-IMS (Waters, Manchester, UK) mass spectrometer running with the proprietary MassLynx software (version 4. 1, Waters, Manchester, UK) and previously calibrated using a sodium formate solution. The peptides separated by chromatography were ionized by electrospray using a source voltage of 3 kV and a source desolvation temperature of 300 • C. MS and MS/MS measurements were performed in a data-dependent analysis (DDA) in the measurement range of 50 to 1700 mass-to-charge ratio (m/z). A maximum of 10 precursor ions with an intensity threshold of 10,000 are selected to be fragmented by collision induced dissociation (CID) at a voltage of 8 to 9 V for low molecular weight ions and 40 to 90 V for high molecular weight ions.
Bioinformatics for the Treatment of MS Data The reprocessing of mass spectrometry data and database searches were performed via Peaks Studio version 8. 5 (Bioinformatics Solutions Inc. , Waterloo, ON, Canada) using the UniProt database (10 September 2018) restricted to the complete proteome of Bos taurus organism. The mass tolerance thresholds for precursor ions and fragments were defined at 35 ppm and 0. 2 Da, respectively, and the data searches were performed notifying three missing cleavage sites and without notifying the choice of an enzyme. Variable methionine oxidation was also considered. The relevance of peptide identities was judged according to their identification generated by PEAKS Studio 8. 5 (p < 0. 05) and a false discovery rate (FDR) of strictly less than 1%. Raw data from all HPLC-MS/MS runs were also imported in Progenesis QI for proteomics software (Version 4.
Peptide Characterization by Peptidomics Approach and Bioinformatics Treatment of Raw Data Concentrated and desalted peptides of each hydrolysate were re-solubilized and subjected to an HPLC-MS/MS analysis on a C18-AQ chromatographic column, concomitantly to quality control. MS and MS/MS data were acquired using a high-resolution mass spectrometer. The number of mass spectra scans (MS) ranged from 2949 for hydrolysate H1 to 5118 for hydrolysate H36 with a mean of 4730 MS scans and a standard deviation (SD) of 638. The MS/MS scans ranged from 8721 for hydrolysate H13 to 16,379 for hydrolysate H1 for a mean ± SD of 13,131 ± 1241, respectively. The ANOVA test performed with these values demonstrates that there is no statistically significant differences (p = 0. 62) between the mass spectrometry data collected from each HPLC-MS/MS run.
The HPLC-MS/MS raw data obtained for the 17 hydrolysates and QCs were imported in Progenesis QI for proteomics software (version 4. 1, Waters Corporation, Milford, MA, USA), as described in the Materials and Methods Section. Among the 2929 peak picked mass signals, 2024 mass signals have an intensity threshold > to 1e4 and among them, 208 mass signals were identified as peptides (123 from beta-casein, 35 from alpha-S1 casein, 22 from alpha-S2 casein, 19 from kappa-casein, and nine from beta-lactoglobulin) with certainty. No peptides from alpha-lactalbumin were identified. These 208 peptides represent the global diversity (all hydrolysates combined) of identified peptides where between 169 and 205 peptides were identified per hydrolysate. The peptide length is comprised between five and 30 amino acids with a length mean of 11 amino acids.
Relationship between Sensory and Analytical Data The relationship between the two sets of variables (the first five principal components of the PCA related to mass spectrometry data and the 21 descriptors of sensory analysis) has been evaluated by the RV coefficient calculated to 0. 369 (p-value = 0. 020). This probability lower than 5% indicates that there are more similarities than dissimilarities between mass spectrometry data and sensory data. To better highlight the common features between these two sets of data, Pearson's coefficient was computed between each principal component of mass spectrometry PCA and each of the 21 sensory descriptors. As summarized in Table  2 , the first principal component of PCA related to mass spectrometry data is negatively correlated with the descriptors acid, bitter, acid persistence, bitterness persistence, and vanilla and positively correlated with mild, smelly, milk, milk odor, and milk Figure  3.
Relationship between Sensory and Analytical Data The relationship between the two sets of variables (the first five principal components of the PCA related to mass spectrometry data and the 21 descriptors of sensory analysis) has been evaluated by the RV coefficient calculated to 0. 369 (p-value = 0. 020). This probability lower than 5% indicates that there are more similarities than dissimilarities between mass spectrometry data and sensory data. To better highlight the common features between these two sets of data, Pearson's coefficient was computed between each principal component of mass spectrometry PCA and each of the 21 sensory descriptors. As summarized in Table  2 , the first principal component of PCA related to mass spectrometry data is negatively correlated with the descriptors acid, bitter, acid persistence, bitterness persistence, and vanilla and positively correlated with mild, smelly, milk, milk odor, and milk persistence.
PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. The PCA goal is (i) to extract the important information from the table, (ii) to represent it as a set of new orthogonal variables called principal components, and (iii) to display the pattern of similarity of the observations and of the variables as points in maps  [35]. Note that the RV coefficient (p = 0. 020) demonstrates similarities between the five principal components of the PCA and the sensory data. Three groups can be identified from the PCA. The group composed of the greatest number of hydrolysates among them H1, the bitterest hydrolysate with a strongest bitterness persistence. A group of the four hydrolysates H13, H32, H35, and H36 were identified, and among them, H36 is considered by the sensory panel as one of the least bitter hydrolysates.
Further, three principal components (PC1, PC3 and PC5) of PCA generated from peptides identified by mass spectrometry are correlated with some sensory descriptors, such as bitterness, bitterness persistence, or milk persistence. Finally, the PCA of the more relevant mass spectrometry data appears as a convenient, reliable, fast, and economic intermediate method to evaluating the bitterness of enzymatic hydrolysates as a trained sensory panel can do it. This latter point was confirmed by blind HPLC-MS/MS analysis and blind sensory test of commercially available, unknown milk casein hydrolysate.
Funding: This work has been carried out in the framework of the joint laboratory between the  Charles Viollette institute  and the  Ingredia  project (Allinpep) and in the framework of the  Alibiotech research program , which is financed by the  European Union , the  French State , and the  French Region of Hauts-de-France. The HPLC-MS/MS experiments were performed on the REALCAT platform funded by a  French  governmental subsidy managed by the  French National Research Agency (ANR)  within the frame of the " Future Investments' program  ( ANR-11-EQPX-0037 )". The  Hauts-de-France region , the  FEDER , and the  Ecole Centrale de Lille  and the  Centrale Initiatives Foundation  are also warmly acknowledged for their financial contributions to the acquisition of REALCAT platform equipment.
participants. Data for this study were collected as part of the Tsimane Health and Life History Project (THLHP), a longitudinal study of health and aging that began in 2002  [32]. The sample includes 344 children aged 2 months-8 years (55% female, median age 5 years) from 28 villages (see Table  1  for descriptive statistics). Participants were seen in the stationary THLHP clinic in San Borja (the closest market town to sampled villages), accompanied by a parent between mid-March and early July of 2011, whereupon participants were seen by THLHP physicians and given routine physical exams, which included patient history, symptom investigation, demographic and anthropometric information. A blood sample was taken as a part of this visit, a portion of which was used for assessing immune function.
For the purposes of analysis, length and height were treated interchangeably. Weight was measured using a Tanita digital scale to the closest tenth of a kilogram. If infants were too small to stand on the scale, their weight was calculated by subtracting the weight of the mother from the combined weight of the mother holding her infant. Maternal height and weight measures were collected using the same instruments as used for the child, either at the same visit, or within 30 days of the child's visit. Tsimane-specific z-scores for HAZ, child WHZ and maternal BMI-for-age (BMIZ) were calculated using the open-source R package localGrowth (https:// github. com/adblackwell/localgrowth), which utilizes growth curves from  [39]. These growth references better describe intrapopulation comparison by using locally-relevant growth trajectories  [39].
Statistical analysis Mixed effects models fit with restricted (residual) maximum likelihood (REML), from the R package lme4, were used to evaluate relationships between markers of immune function and child H A Z. M a t e r n a lI Dw a si n c l u d e da sar a n d o me f f e c t ,a sam e a n s to parse contribution of potential genetic clustering or clustering due to immediate environment (e. g. shared household) from individual-level factors that contribute to overall variation in HAZ  [41]. Effects of community-level clustering were also considered, and initial models included community ID as a random effect. However, since the inclusion of community ID did not substantially improve fit for any model, it was not included in final analyses. Height z-s c o r e sf o rm o t h e r sa r ea l s oin c l u de di nm o d e l s ,f o r additional adjustment for potential heritable variation in height.
To adjust for variation in immune function based on current infection and associated immune activation, we include covariates for respiratory illness and residual WBC counts. Additional models were ran using the subset of the sample for whom fecal parasite data were available (72% of sample), which include covariates for current protozoal and helminthic infections assessed from fecal samples. For each model, residual WBC count is WBC count minustheportionofcellsthatwerebeingusedasthemainindependent variable of interest (e. g. in a model testing the association between total T cell count and height, residual WBC ¼ total WBC À T cell count). This adjustment is necessary to minimize collinearity. To measure relative investment in adaptive relative to innate immunity, we calculate a ratio of adaptive (B and T) cells to innate (NK) cells.
, 2000 Spillane et al. , , 1996;; Spillane, McGlinchey, Muircheartaigh, & Benson, 1983; Spillane & Sheahan, 1989; Tuwani, Wadhwa, & Bagler, 2019; Van Der Heijden, Brussel, & Peer, 1979; Vepuri, Tawari, & Degani, 2007; Walters, 2006; Zheng, Chang, Xu, Xu, & Lin, 2019) , the quest to understand the molecular features underlying sweet taste perception is still active. In this study, we present the first online tool able to predict sweet taste perception based on a machine learning protocol. We have updated and curated the previous database of 316 sweet compounds (SweetenersDB) and added new applicability domain metrics to assess the robustness of the predictions. A novel scaffold of natural sweetener, belonging to the lignan chemical family, that have never been annotated as sweet have been identified and experimentally validated.
Data preparation Based on our previous work  (Chéron et al. , 2017) , the database of sugars and sweeteners (Figure  S1 ), named SweetenersDB, was curated and updated with missing compounds (Ruiz-Aceituno, Hernandez-Hernandez,  Kolida, Moreno, & Methven, 2018). Each compound was labelled with a relative sweetness value, corresponding to a measure of the sweet taste intensity relative to sucrose. Relative sweetness is defined as the concentration ratio between a sucrose solution and a solution of sweetener perceived with the same intensity. The relative sweetness of each compound was transformed in logarithmic scale for easier manipulation, and it will be later referred to as logSw. For compounds that were already present in the database, we updated the SMILES (Simplified Molecular Input Line Entry System) to isomeric SMILES in order to differentiate stereoisomers.
When the information on stereocenters was not available, we either regrouped the stereoisomers in a single entry with their average logSw value if the logSw difference was lower than 0. 2, or we discarded both compounds. The resulting dataset consisted of 316 compounds in SweetenersDB (Table  S1 ). The machine learning protocol was applied to two datasets of interest : 4796 natural compounds (Table  S2 ) extracted from the SuperNatural II database and the phyproof catalogue from PhytoLab, already pre-screened by our previous model  (Chéron et al. , 2017). Every compound in the datasets were collected as SMILES strings and sanitized with RDKit  (Landrum et al. , 2018). To assess the importance of predicting protonation states, the major microspecies of each compound was also determined with ChemAxon cxcalc tool (ChemAxon, 2018) at physiological salivary pH (pH=6.
5). Structures were then standardized using the "standardizer" (EMBL-EBI, 2017) Python package: salts are removed from the structure, and a set of around 30 structure-normalization rules are applied to each molecular graph to cover most of tautomerization reactions. 0D, 1D and 2D descriptors were computed using Dragon v6. 0. 38 (Talete srl, 2014), RDKit  (Landrum et al. , 2018) , Mordred  (Moriwaki, Tian, Kawashita, & Takagi, 2018) , and ChemoPy  (Cao, Xu, Hu, & Liang, 2013). Descriptors from the three latter packages were regrouped as "open-source" descriptors. For each of these two descriptors sets, the initial number of features was reduced by removing those that could not be calculated for a molecule, as well as near-constant features (two or less unique values), features with a standard deviation below 0.
001, and features with a correlation greater than 0. 95. The resulting datasets consisted of 635 descriptors for the Dragon dataset, and 506 features for the "open-source" dataset. To avoid any model bias due to overfitting, the number of features used by the model is a hyperparameter that has been optimized. The updated SweetenersDB was split in training and test sets using a Sphere Exclusion clustering algorithm. Dragon descriptors were chosen for this procedure: they were normalized between 0 and 1, and the clustering was initiated from the compound that is closest to the center of the dataset in the descriptor hyperspace. 64 diverse compounds (20. 3%) were selected for the test set, leaving 252 compounds in the training set (Figure  1 , Table  S1 ). The chemical space was mapped using a t-distributed Stochastic Neighbor Embedding (t-SNE) analysis.
Machine-learning model for sweetness prediction Several regression algorithms from the python package scikit-learn were evaluated: Random Forest, Support Vector Machine (SVM), Adaptative Boosting with a Decision Tree base estimator (AdaBoost Tree), and k-Nearest Neighbors. Five-fold cross validation was performed with hyperparameter tuning using a grid search. The workflow for each cross-validation fold was as follow: standardization of descriptors, feature selection, and model training. Selection of descriptors was done by keeping a given percentile of the highest ranked descriptors based on their Mutual Information with our endpoint. The optimal percentile of features was tuned as a parameter of the Grid Search. Once optimal hyperparameters were found for each model, final models were trained using the full training dataset.
unice. fr/predisweet/ Other chemoinformatics solutions are available but none of them has been implemented on a webserver. For instance, the e-Sweet platform  (Zheng et al. , 2019)  is based on a consensus model of various machine learning protocols. The database used to train and test their model is very similar to the database used to setup Predisweet and e-Sweet performs as well as our model (R 2 on the test set is in the same range [0. 75-0. 78] for both solutions). Recently a new functionality to predict sweetness has been implemented on the BitterSweet webserver  (Tuwani et al. , 2019). The performance of BitterSweet is comparable to e-Sweet and Predisweet (R 2 of 0. 72 on our test set) but the protocol is still unpublished, and seven molecules of the test set has not been considered as sweet.
Webserver interface The user is asked for one or several molecules which can either be drawn directly on the chemical structure editor Ketcher or inputted as a simple text query or file in the SMILES format. The workflow (Figure  2 ) followed by query compounds is the same as used during model development. First, a molecule is generated from the SMILES string with RDKit to assess its sanity. The structure is then standardized using the "standardizer" Python module. The 51 molecular descriptors selected during model development are computed and standardized based on the training set transformations. The descriptors are passed to the AdaBoost Tree model in order to predict the logSw. Finally, the quality of each prediction is assessed based on three metrics, namely the applicability, reliability, and decidability domains  (Hanser, Barber, Marchaland, & Werner, 2016).
The applicability domain indicates if the compound is within the descriptor range of the training set and its score is computed using a convex hull approach. The reliability domain highlights the density of information around the compound. The reliability score is calculated by counting the number of molecules from the training set that are inside a sphere centered on the query. The decidability domain shows the confidence in the prediction that was made. The decidability score is based on the weights of each decision tree that compose the AdaBoost model. It is computed by summing the weights of decision trees that made a prediction close to the model prediction and dividing it by the sum of all weights. Each molecule is indexed in the database with its InChIKey, which avoids making predictions for the same molecule twice.
New machine-learning model based on open-source features The performance of the Open-source and Dragon models has been compared. Both models show good predictivity on the test set according to state of the art QSAR rules (Table  1 ). Slightly more than 90% of the test set are predicted with an absolute error lower than a log unit (Figure  S3 ). The models are less accurate for high sweetness values since they have been trained with less information for highly potent sweeteners. Improving the quality of the machine learning model would then requires i) expanding the chemical diversity of sweet compounds and ii) a larger database of in vivo and in vitro experiments. A threshold of LogSw larger than 2 has then been chosen to minimize false positive predictions prior in vitro validation. Since similar performance have been obtained for both models, the open-source version have been implemented on a webserver, freely accessible at the following address: http://chemosimserver.
unice. fr/predisweet/. Another model has been set up with descriptors calculated at salivary pH to assess the effect of the protonation state on the model performance. Even though more than a quarter of the molecules had different descriptor values between the default and the salivary pH dataset, there was no significant difference in terms of performance. The protonation assessment step thus has been skipped in the final protocol. We emphasize that the model has not been trained to predict bitter taste and we envision to include this feature in a future work. Additionally, any QSAR model has a field of application that clearly defines the boundaries within which the model should be used, usually referred to as the applicability domain. We've implemented three different metrics to explicitly inform the user whether the model and its prediction can be trusted for a particular query molecule.
Identification of a new sweet scaffold A large database of natural compounds has been virtually screened to identify new putative sweeteners. The analysis of the resulting sweet chemical space of ~4800 natural compounds shows that it does not fully overlap the chemical space of known sweeteners (Figure  1 ). It suggests that a large part of the natural chemical space remains unexplored. We have finally selected three natural compounds that have been tested for their ability to activate the human sweet taste receptor T1R2/T1R3 expressed in HEK cells, as previously reported  (Poirier et al. , 2012). As a negative control, HEK293T Gα16gust44 cells stably expressing T1R3 were mock-transfected with the empty expression vector to control for T1R2independent non-specific signals. In addition to a LogSw value higher than 2, the price and the commercial availability were two important criteria in the compound choice.
Conclusion In this study we have used machine learning to predict novel agonists of the sweet taste receptor. An AdaBoost Tree model was setup based on open-source chemical features optimized on a curated database of 316 known sweet agents (SweetenersDB) and implemented on a freely available webserver. The virtual screening of a large database of natural compounds identified thousands of putative sweeteners, of which three were selected for in vitro functional assays of the human sweet taste receptor and dose-response analyses. Among them, we identified arctiin as a novel agonist of the T1R2/T1R3 sweet taste receptor with an EC50 value of 2. 5±0. 4mM. It belongs to the lignan chemical family, polyphenols found in plants, of which epi-lyoniresinol has already been annotated as slightly sweet by sensory analyses  (Cretin et al.
Acknowledgements This work was supported by the  French Ministry of Higher Education and Research  [PhD Fellowship], by  GIRACT (Geneva, Switzerland)  [9 th European PhD in  Flavor Research Bursaries  for first year students] and the  Gen Foundation (Registered UK  Charity No. 1071026 ) [a charitable trust which principally provides grants to students/researchers in natural sciences, in particular food sciences/technology]. We also benefited from funding from the  French government , through the  UCAJEDI  " Investments in the Future " project managed by the  ANR  grant No. ANR-15-IDEX-01.
Testing of samples The mixed EU panel samples were finely chopped and divided into 4 aliquots; the Cypriot experimental panel samples were chopped and divided into 3 aliquots. Each aliquot was tested using 1 of the following 3 rapid test kits: IDEXX HerdChek Small Ruminant conjugate SRB-CC (IDEXX SR; IDEXX Laboratories, Westbrook, ME), IDEXX HerdChek Bovine conjugate CC (IDEXX BOV), and Bio-Rad Sheep/ Goat (Bio-Rad SG; Bio-Rad Laboratories, Hercules, CA). In addition, the Bio-Rad TeSeE was applied to all samples except the Cypriot experimental panel, for which data were already available. 28 All assays were carried out according to the manufacturers' instructions. All tests were performed at the Animal and Plant Health Agency (APHA; Weybridge, UK), thereby minimizing operator variability.