dataesr/scientific-paragraphs-categorization huggingface dataset:
HRG subjects were better discriminated from LRG ones by a higher chewing activity (number of chewing cycles, chewing duration, maximal amplitude and total muscle work), confirming previous results (e. g. [15] ), higher mouth coating, and higher velum opening frequency  [168]. A multiblock partial least square regression conducted on the different physiological and behavioral oral processing datasets (masticatory behavior, bolus rheology, saliva composition and flux, mouth coating and bolus moistening) confirmed the importance of masticatory behavior on aroma release  [162]. Although breath flow was not measured, and despite some contradictory results [160], the influence of breathing flow on aroma release could also account for the difference between the two groups. Thus, during wine drinking, higher releasers were characterized by higher volume of air breathed out [170], indicating that breathing capacity could also be important in the aroma release process [160], confirming earlier results (e.
Aims: To synthesize and evaluate the available scientific evidence on the efficacy of antihypertensive drugs on arterial stiffness in patients with hypertension by using a network meta-analysis approach. Methods: A systematic search of the MEDLINE (via PubMed), Scopus, and Web of Science databases was conducted to identify experimental studies addressing the effect of different antihypertensive drugs on arterial stiffness parameters (pulse wave velocity [PWV] and augmentation index [AIx]) in adults with hypertension. Comparative evaluation of the effect of antihypertensive drugs was performed by conducting a standard pairwise meta-analysis and a network meta-analysis for direct and indirect comparisons between antihypertensive drugs and placebo/ other antihypertensive drugs. Analyses were performed including studies of any duration and only studies longer than 6 months length.
Search methods for study identification electronic search The literature search was conducted through the MEDLINE, Scopus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science databases. The above searches were supplemented by manual searches of published or ongoing randomized controlled trials (RCTs) in international trial registries (ClinicalTrials. gov) and on drug approval agency websites. Prior to the final analyses, the searches were repeated only to include all current and potential studies. To perform the literature search, search strategies were performed by antihypertensive drug groups in combination with the following search terms applying Boolean operators (Supplementary Table  S1 ).
However, studies combining antihypertensive drugs with nutritional or lifestyle interventions were excluded when data regarding the effect of antihypertensive drug interventions on arterial stiffness could not be extracted separately. Reductions in different arterial stiffness parameters were analysed as primary outcomes: pulse wave velocity (PWV), augmentation index (AIx), and cardio-ankle vascular index (CAVI). Indeed, since PWV (and similar approaches, such as CAVI) is a measure of large artery stiffness, whereas AIx is an integrated measure of arterial stiffness and wave reflection, these two measures are analysed separately.
Synthesis of data We qualitatively summarize the included RCTs in an ad hoc table describing direct and indirect comparisons. The present NMA was conducted as follows: we assessed the strength of the available evidence using a network geometry graph to display the evidence in the network for arterial stiffness (PWV and AIx). In addition, the network geometry graph to show the evidence in the network for arterial stiffness (PWV and AIx) was performed including only studies longer than 6 months length. In this graph, the size of the nodes was proportional to the number of participants in trials who received the intervention specified in the node, and the thickness of the continuous line connecting nodes was proportional to the number of trials directly comparing the two treatments. (Salanti et al.
• An odor blending, which results from a configural processing of the mixture and occurs when the perceived odor is a new odor that differs from those of the odorants in the mixture; • A complete overshadowing (or masking) when the odor of only one of the components of the mixture is recognized. Our aim was to improve knowledge about blending and masking mixture perceptions by highlighting links between the odors and the structure of the odorants. Establishing the relationships between odors and molecular structures remains challenging  [10]. The recent studies aiming to link the odors to the structure of the odorants involve large databases and the use of computational methods applying machine learning approaches  [11] [12] [13] [14] [15] [16] [17] [18]. These approaches are applied to perform classifications of the odorants considering both their odor notes and structural features.
Another way is the pharmacophore approach, which is a ligand-based approach focusing on the necessary characteristics of the odorants to activate a potential OR target. Indeed, a pharmacophore is defined as "a set of structural features in a molecule that is recognized at a receptor site and is responsible for that molecule's biological activity"  [19, 20]. The pharmacophore approach has been demonstrated to be a very effective approach  [21, 22] , especially relevant when the receptor and consequently the structure of ligand-receptor complex are unknown, as is up to now the case with ORs  [23]. With the purpose to reveal links between the odors and the structure of the odorants in the context of blending and masking mixture perceptions, we developed a classification approach similar to those previously used  [14]  to group the odorants of the large dataset into clusters, and then used subsets of molecules belonging to these clusters to generate pharmacophores models.
Overview of the Dataset In a previous study, we built a large dataset that lists 5665 odorant molecules with their associated odors; most of these odorants are described by 2-5 odor notes  [14]. Inside this dataset, we focused on seven molecules that are components of two mixtures of odorants that were previously studied at the CSGA (Table  1 ). Tonka; coumarinic; coconut; toasted; nutty; celery; burnt; woody; lactonic; maple  2  ; lovage 2  The first mixture constitutes a blending mixture called red cordial (RC mixture) and is made up of isoamyl acetate (IA), vanillin (V), frambinone (F), ethyl acetate (EA), betadamascenone (bD) and beta-ionone (bI)  [25, 27]. The RC mixture is perceived as having a grenadine syrup odor. The experimental sensory tests have shown that each molecule has a distinct odor quality.
Whiskey lactone 39212-23-2 Tonka; coumarinic; coconut; toasted; nutty; celery; burnt; woody; lactonic; maple 2 ; lovage 2  1 Isoamyl acetate is common to the two mixtures. 2 The odor notes having less than 5 occurrences in the database are not considered in the analysis. The first mixture constitutes a blending mixture called red cordial (RC mixture) and is made up of isoamyl acetate (IA), vanillin (V), frambinone (F), ethyl acetate (EA), betadamascenone (bD) and beta-ionone (bI)  [25, 27]. The RC mixture is perceived as having a grenadine syrup odor. The experimental sensory tests have shown that each molecule has a distinct odor quality. Nevertheless, the odor of the mixture of these six components in specific proportions, i. e. , 41. 8, 41. 8, 5. 0, 4. 3, 4. 3 and 2. 8% for V, F, IA, B, EA and D, respectively, is judged to be similar to those of the grenadine syrup  [25].
Distribution of Odor Notes within Clusters The number of occurrences of the odor notes varies in a large range from 5 (tallow) to 1790 (fruity). Consequently, the direct comparison of the number of occurrences of odor notes in each cluster is irrelevant. Hence, we considered the following relative amounts related to each cluster  [14] : • the relative frequency of every odor notes compared to its frequency in the database (Equation (  1 )); • the relative frequency of odorants carrying each odor note compared to the number of molecules in the considered cluster (Equation (  2 )). % odor note = %ON = Number of occurrences of an odor note in the cluster Total number of occurrences of this odor % odorant molecule = %OM = Number of occurrences of an odor in the cluster Number of elements (molecules) in this cluster ( The values of the number of occurrences of the odor notes in the whole database and per cluster are reported in Table  S1.
% 𝑜𝑑𝑜𝑟 𝑛𝑜𝑡𝑒 = %𝑂𝑁 = Total number of occurrences of this odor %   = % = Number of occurrences of an odor in the cluster Number of elements (molecules) in this cluster The values of the number of occurrences of the odor notes in the whole database and per cluster are reported in Table  S1. We examined the occurrences of the 25 most frequent odor notes across the 6 clusters SOM16. The radar plots of the %ON odor profiles are displayed in Figure  3  (Additionally, the %OM odor profiles across the clusters are displayed in Figure  S2 ). Furthermore, the odor description of the components of mixtures implicates 39 odor notes that have at least 5 occurrences (Table  1 ). Some of these odor notes are common to the list of the 25 frequent odor notes, but several other less frequent notes are characteristic of the odorant profile for the molecules related to a specific cluster.
Co-Occurrences of Odor Notes across the Clusters SOM16 In addition to the odorant profile examinations, we look at the co-occurrences of the odor notes (Table  S2 ). We focused on 55 odor notes (the 25 most frequent odor notes in addition to the 39 odor notes of the mixtures, 10 common to both). We used the nonsymmetrical square matrix of odor notes obtained from the co-occurrence symmetric square matrix by weighting the number of associations by the frequency of occurrences to generate the heatmaps displayed in Figure  5. Vanillin belongs to the cluster SOM16-Cl-2, where the note vanilla is associated with the notes sweet, chocolate and creamy. For the entire database, vanilla is mainly associated with a sweet note (63% of its occurrences) and with creamy, spicy (both 25%), floral (18%), woody and balsamic (both 11%) odor notes.
In the odorant descriptions of the compounds that belong to SOM16-Cl-2, the frequencies of associations are quite similar but occur slightly more frequently with creamy (27%), spicy (31%) and balsamic (19%) notes. The occurrences of vanilla to chocolate is approximately 10% in the entire base as well as in SOM-Cl-2. Nevertheless, the occurrences of chocolate notes are noticeably more frequently associated with vanilla, creamy and sweet notes in SOM-Cl-2 than in the entire base. Indeed, 70% of the occurrences of chocolate notes are associated with sweet and 60% Vanillin belongs to the cluster SOM16-Cl-2, where the note vanilla is associated with the notes sweet, chocolate and creamy. For the entire database, vanilla is mainly associated with a sweet note (63% of its occurrences) and with creamy, spicy (both 25%), floral (18%), woody and balsamic (both 11%) odor notes.
There are several co-occurrences between these odor notes, especially between coconut and coumarinic, lactonic, tonka and toasted (42%, 31%, 42% 21% of the occurrences of these four odor notes in the entire database and 59%, 33% and 60% in SOM16-Cl4, respectively). Conversely, there are few associations between these six odor notes and the celery, caramellic, woody, nutty, burnt, and toasted notes, which themselves have few co-occurrences in the database except for toasted and nutty (43% of occurrences of toasted notes). However, only 20% of the occurrences of the toasted note are associated with the nutty note in SOM16-Cl-4. In contrast, caramellic and burnt notes are associated more frequently in SOM16-Cl-4 than in the entire base (50% against 19% of the occurrences of the burnt note). In SOM16-Cl-5, we considered the following odor notes: fruity, sweet, floral, rose, balsamic, berry, plum, raspberry and ripe.
The fruity, sweet and floral notes are strongly associated among themselves, as well in the database as in SOM16-Cl-5. The rose, balsamic, berry, plum, raspberry and ripe notes are also strongly associated with fruity, sweet and floral notes. Although only 15% of occurrences of ripe notes are associated with floral notes in the base, the unique occurrence of this odor note in SOM16-Cl-5 is associated with fruity, sweet and floral. Conversely, there are relatively few co-occurrences between rose, balsamic, berry, plum and raspberry notes, as well as fruity, sweet, and floral notes, among themselves. Balsamic, berry and raspberry notes are more frequently associated with the rose note in SOM16-Cl-5 than in the entire base (25% against less than 15%); plum and rose notes have no co-occurrence in SOM16-Cl-5.
Discussion The present work aimed to qualitatively highlight the links between the structural properties of odorants and their perceived odor to improve the understanding of the homogeneous perception of aroma mixtures. For that purpose, we focused on seven molecules belonging to a large database containing 5665 odorants. Previously, we used this database to highlight the links between the molecular structures and the odor notes using several dimension reduction methods associated with clustering approaches  [14]. Because we have pointed out the advantages of the UMAP dimensional reduction method, we chose this method and extended the distribution of the elements in a 3D space. We evaluated two clustering methods, k-means and SOM, using the 3D coordinates in the UMAP space. We selected the SOM method using a grid of 4 by 4 for its effectiveness in discriminating between the odor profiles of the 13 clusters thus obtained (Figures  3  and 4 ).
At the same dividing levels L9 and L16, the number of SOM clusters decreased, respectively, to 7 and 13 (Figure  2 ). This could be due to the flexibility of the SOM algorithm, which can better adapt the number of clusters to the topology of the data than k-means. That does not mean that SOM decisively outperforms k-means; several groups of odorants defined by k-means or overlaps between k-means and SOM clusters could be useful to other analyses in future studies. The analysis of the co-occurrences especially highlights the specificity of the association between odor notes according to the clusters (Figure  5 ). Moreover, the examination of several associations between odor notes suggests some ways to refine the understanding of structure-odor relationships. For example, the raspberry note is common to F, which belongs to cluster SOM16-Cl-5; bI and bD are gathered in cluster SOM16-Cl-8.
Consequently, the key components of the RC mixture do not share a common binding site or binding mode. Examination of the hypothesis models generated from the subsets based on odorant profiles presents some suitable alignments of the molecules with the features of the models (Figure  8 ). There are also satisfactory pharmacophore mappings by pairwise comparison between pharmacophore generated from the single molecules and from the subsets based on odorant profiles (Figure  11 ). That means that each component of the mixture shares common features with numerous molecules belonging to the same cluster. That is true for V, IA and WL, except for F. Indeed, neither molecule F nor the model hyp-F-c can map the hypothesis model hyp-F-s. That could suggest less efficient binding to some ORs and be related to the weak "raspberry" odor note described in several databases  [30, 31].
Description of the Dataset and Molecules of Interest For this study, a dataset of 5665 smell compounds and 162 odor notes that occurred at least 5 times  [42]  were extracted and compiled from the databases "The Good Scents Company" [43] and "Flavor Base"  [31]. All data are available upon request. Within the database, we focused on the components of two mixtures: a blending mixture (red cordial mixture, RC mixture) and a masking mixture (noted WL/IA). The RC mixture was made of six odorants: vanillin (V), isoamyl acetate (IA), frambinone (F), ethyl acetate (EA), beta-damascenone (bD) and beta-ionone (bI)  [25, 27]. The WL/IA masking mixture was composed of whiskey lactone (WL) and isoamyl acetate (IA)  [24]. In addition, we focused on several subsets that were defined by the classification methods and the odorant profile of the molecules.
Fingerprint Generation, Dimension Reduction and Clustering The structure of each molecule was converted with KNIME software (v 3. 6. 2) to extended-connectivity fingerprints (ECFP) of 1024 bits in the same way as performed in a previous study  [14]. The uniform manifold approximation and projection (UMAP) dimensional reduction technique was performed using the R package umapr (version 4. 1. 1) [44]  from the fingerprint values  [28]. The k-means and SOM (Kohonen Self-Organizing Map)  [45]  classifications were performed and based on the coordinates in the UMAP 3D space using R software (version 4. 1. 1) [44]. The Kohonen package was used for the SOM calculation  [46]. Both k-means and SOM are nonhierarchical clustering methods for which the desired number of clusters k must be predefined. Nevertheless, there are several differences and specificities between these two clustering algorithms  [47] [48] [49].
SOM maps high-dimensional data in an orderly fashion on a two-dimensional grid and can change its internal structure under the influence of elements from the same system by preserving the original topology of the dataset with the aim of optimally describing the domain of observations. SOM allows to represent the similarity of data and cluster data by placing the most similar nodes in the same class or neighboring classes closer, and the dissimilar nodes farther apart in the grid. The optimal number of clusters was determined using both the elbow curve and the Kelley penalty score  [52] ; the calculations were performed respectively using the R packages map tree (kgs function) and factoextra  [44]. The visualization of the distribution of the molecules and clusters in the 3D-UMAP space was performed using the package XLSTAT-3Dplot  [53].
Construction of Pharmacophores The pharmacophores were generated using the PHASE Schrödinger software release 2021-4  [29]. All input molecules were defined as active using default settings. The values of the feature tolerance were 2 Å. All the default hypothesis settings were used except for the minimum number of features in the pharmacophore hypothesis, which was set at 3. The generation of conformers was performed by setting the target number of conformers to 50 within an energy range of 21 kJ/mol. The maximum number of generated hypotheses was set to 10. The minimum number of features in the pharmacophore hypothesis was fixed at 3, and at least 50% of the active molecules had to match the hypothesis. For our study, we used hydrogen bond acceptors (A features), hydrophobic (H features), and aromatic rings (R features).
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/molecules28104028/s1, Figure  S1 : (a) Elbow curve (representation of intra-cluster variability as a function of the number of clusters). (b) Progression of the penalty score (Kelley score) according to the number of clusters. The optimal number of clusters is around the bend of the curve. Figure  S2 : Radar charts of the distribution of the %OM values for the 25 most frequent odor notes across the 6 clusters SOM16 that contain the molecules of interest. (a) SOM16-Cl-2, (b) SOM16-Cl-4, (c) SOM16-Cl-5, (d) SOM16-Cl-8, (e) SOM16-Cl-13, (f) SOM16-Cl-14. Table  S1 : The number of occurrences of the odor notes in the database and in the clusters obtained using k-means and SOM methods at the three levels of clustering.
26 In addition, it appeared that the hydroxy group is stabilized in its position by the hydrogen bond donating effect of the nearby Ser380 residue, not conserved in abTYR. As observed for natural substrates L-tyrosine and L-DOPA, the approach of inhibitors may be facilitated by the face-to-face π-stacking effect involving copper-coordinating His367. Similar effects were seen upon examination of reported crystal structures of substrate-Zn-soaked-TYR complexes, 27 and therefore superimposing L-tyrosine and L-DOPA from these structures (PDB id 4P6R and 4P6S, respectively) with the best docking pose obtained with 1l resulted in a nearly perfect alignment of the copper-interacting phenyl rings and hydroxy groups, as depicted in Figures  2D  and 2E , respectively. Nevertheless, the rest of the indanone structure seems to fully participate to the binding geometry by establishing interactions with remote residues.
3 mL of lysis solution (NaOH (2 M) plus 10% DMSO) and repeated sonication/vortexing were done until complete solubilization of melanin. Cell-associated melanin contents were measured at 600 nm using pure melanin (purchased from Sigma-Aldrich) diluted in lysis solution as standard. Protein contents were measured using the Lowry's procedure, bovine serum albumin (BSA) being used as standard to allow the normalization of the melanin contents. Molecular Modeling. A homology model of hsTYR built from the crystal structure of hsTYRP1 was retrieved from Swiss Model Expasy. 24 The docked compound and the protein were prepared using LePro tool. Compounds 1l, 1m and 1n were then docked using LeDock software with standard settings, and for each ligand a total of 20 poses were generated. Only poses with low energy conformations and plausible geometries were considered.
UCSF Chimera 1. 14 was used to display the small molecule ligand-protein docking results. The crystal structures of tyrosinase from Bacillus megaterium with bound L-tyrosine and L-DOPA were retrieved from the Protein DataBank (PDB is 4P6R and 4P6S, respectively) and were superimposed with the best docking poses using UCSF Chimera 1. 14. https://doi. org/10. 26434/chemrxiv-2023-n05tm ORCID: https://orcid. org/0000-0002-6271-4717 Content not peer-reviewed by ChemRxiv. License: CC BY-NC-ND 4. 0.
ACKNOWLEDGEMENTS This work was supported by the  ANR ( " Agence Nationale de la Recherche ") through  Labex ARCANE  and  CBH-EUR-GS  ( ANR-17-EURE-0003 ) and through  ANR  project  EPIDERMIS  ( ANR-19-CE44-0002 ). The authors thank  Labex ARCANE  for the fellowship grant of L. M. L in the frame of project  SHAPESHIFT , the  ANR  for the fellowship grant of M. B. in the frame of project  EPIDERMIS , and the  French MESR ("Ministère de lʼEnseignement supérieur, de la Recherche et de lʼInnovation")  for the fellowship grant of B. R.
Introduction Watermelon (Citrullus lanatus) is a popular fruit with great economic and nutritional value. It is adaptable to most tropical and subtropical regions, such as Iran. According to the statistical database of the Food and Agriculture Organization, China is the largest producer of watermelon, with a production of 60. 25 million tons, followed by Turkey (3. 47 million tons), India (3. 25 million tons) and Iran (1. 25 million tons)  [1]. The flesh (68%), seeds (2%) and rinds (30%) are three major components of watermelon biomass  [2]. Watermelon rind as agro-waste is usually discarded, leading to the generation of environmental waste and loss of plant materials. Nevertheless, it can be used as a source of citrulline as an antioxidant, dietary fibers and phenolic compounds  [3] , and/or it can be considered a valuable source of pectin because it contains a significant amount of this polysaccharide.
Morphology of Watermelon Rind Powder The morphology of watermelon rind powder before and after ultrasonication was examined via SEM (XI30 model-Philips). The micrographs of the samples, coated with gold, were taken at an accelerating voltage of 10 kV. The obtained micrographs (scale of 50 µm) were analysed by the ImageJ software (National Institutes of Health, Bethesda, MD, USA). The thresholding tools (Otsu and the manual) were used for image segmentation  [15] , then the porosity and pore size were measured.