dataesr/scientific-paragraphs-categorization huggingface dataset:
These samples had been characterised for their composition (lipid, protein, water, sodium ions), sensory properties (salty, fatty, springy, hard/firm, crumbly, sticky, melting, granular, dry), rheological properties (Young modulus, fracture stress, fracture strain, work at fracture) and chewing activity parameters (total muscular work, number of chewing cycles, chewing duration). The whole data set (2021-01-22_cheese-compo-rheo-senso_Guichard-V3. tab, sheet wholedataset) is available on-line  (Guichard et al. , 2020). The description of the samples, data, associated metadata, experimental, material and analysis methods, together with the scatter-plots of the values in the different projects (Figs. S1eS5), are available in a cosubmitted data paper  (Guichard et al. , 2021). The information concerning the different projects (title, abstract, coordinator, scientists involved, DOI of publications) are available on line in the database website (http://plasticnet.
The final list of composition variables is the following: water, protein, lipid, Na þ. We also added three variables. Lipid/DM is the ratio of lipids per dry matter and represents the impact of lipids in the lipid/protein network and food texture. Na þ /water is the amount of sodium ions in the water phase and should better represent the concentration in sodium ions available for the taste receptors. The ratio moisture-in-non-fat-substances (MNFS) is a useful variable in cheese manufacturing and is used in the classification of cheeses  (Codex Standard 283-1978, FAO). It gives an estimation of the water/protein ratio and influences a wide range of textural characteristics  (Lucey, Johnson, & Horne, 2003). The values for these variables in the different samples were calculated and uploaded in the database.
Multiple linear regression The data extracted from BaGaTel had missing values for several parameters. MLR (XLStat, Addinsoft, Paris, France) was applied to estimate missing values. In addition to Young's modulus (YM) and fracture stress (FS), other rheological parameters such as fracture strain (CS) and work to fracture (WF) were available for several samples. Thus, we estimated the missing values for CS and WF, separately, using multiple linear regression (MLR) with the following variables: Na þ , lipid, protein, water, lipid/DM, YM, FS. Considering sensory profiles, the descriptor hard/firm was estimated with MLR based on the following variables: Na þ , lipid, protein, water, lipid/DM, YM, FS, but crumbliness and stickiness were estimated separately with the same variables, excluding lipid because of strong multicolinearity.
The dataset with the estimated data (2021-01-22_cheese-compo-rheo-senso_Guichard-V3. tab, sheet: dataset þ estimated-data) and the parameters of the MLR used for the estimation (2021-01-22_cheese-compo-rheo-senso_-Guichard-V3. tab, sheet: missing-data-estimation) are available online  (Guichard et al. , 2021). Another alternative would be to use the missMDA (R package), which performs PCA on incomplete data sets  (Josse & Husson, 2016). We performed PCA with the two methods and calculated the RV coefficient, which is of 0. 992, confirming that, in our case, with only few missing data, the mode of estimation has very little impact on the PCA results. We thus used the data estimated using MLR.
Principal component analysis Principal component analyses (PCAs) were performed on different subsets of data to highlight the relationships between food composition, food rheology and sensory perception. PCAs were performed with XLStat (Addinsoft, Paris, France). On the total dataset of 68 samples, a PCA was done on the 7 variables of composition, two sensory descriptors (salty and hard/ firm) and two rheological parameters (YM and FS). We also performed PCA on two subsets, one subset of 32 samples (Adi, Lawrence, PraSel and Tarrega projects) with a sodium ion content below 5 g kg À1 , and a second subset of 36 samples (Adi, Boisard, Lawrence, Phan and Tarrega projects) with a sodium ion content higher than 5 g kg À1. As for some projects, measurements on chewing behaviour were obtained (Total muscular work, Number of chewing cycles, Chewing duration), we also performed two additional PCAs, using only the samples with data on chewing behaviour, to assess (i) the impact of chewing behaviour on sensory perception and assess (ii) the hypothesis formulated by  Lawrence et al.
Data set of 68 samples and 11 variables A PCA was done on the set of 68 samples containing data for the two sensory descriptors hard/firm and salty (estimated data for hard/firm in Phan project), available on-line (2021-01-22_cheese-compo-rheo-senso_Guichard-V3. tab, sheet: dataset_ PCA-68samples). The map of the samples (Fig. 1A  and B ) shows that there is a good overlap of the samples from the different projects. Fig. 1C  and D represents the map of the variables on the three main dimensions, which account for 81. 26% of total information. On axis 1, water content (negative part of axis 1) is opposed to sodium ion and lipid contents (positive part of axis 1). On axis 2, protein content (positive part of axis 2) is opposed to lipid/DM content (negative part of axis 2). The sensory descriptor hard/firm is located on the positive part of axis 2 close to the protein content and opposed to moisture-in-non-fat-substances (Fig.
723) with sensory hardness (graphic representation in  Guichard et al. , 2021: Fig. S6 ). Because of the large differences in sodium ion content in our dataset and since several authors found different conclusions as a function of salt content, the initial set of samples was divided into two subsets according to the sodium ion content (below or above 5 g kg À1 sodium ion). Two other PCA analyses were performed on these subsets of samples. S2 ); the two main components accounted for 70. 06% of the information. Cheese sensory hardness (hard/firm) is located on the positive part of axis 2 (31. 16% of the information), together with protein content, justified by a positive correlation between hard/firm and protein (0. 717) and a negative correlation with MNFS (À0. 641). Salty taste is located on the positive part of axis 1 (38.
tab, sheet: dataset_chewing-activity). Because it was previously observed that the relationships between saltiness and chewing behaviour were highly dependent on salt content  (Lawrence et al. , 2012b) ,   S1. PCAs were performed on two subsets of samples according to their sodium ion contents. The aim of this analysis was to increase the number of samples compared with the Lawrence project  (Lawrence et al. , 2012b). The added samples had a different composition and especially a lower amount of water and a harder texture, so that they required a more intense chewing activity. A first PCA was performed on a subset of 13 samples with a low sodium ion content (projects Lawrence and Tarrega). The results (Fig. 3A ;  Guichard et al. , 2021: Table S4)  showed that salty taste is located on the positive part of axis 1, together with the sodium ion content and, to a lesser extent, the lipid content and the total muscular work (TMW).
Explaining perception of saltiness and fat The individual studies showed different relationships between lipid composition and salty taste. To better understand the influence of lipid content and fat perception on salty taste, a complementary analysis was carried out on a limited number of projects and samples, for which the intensity of fat perception (fatty descriptor) was available. A subset of 22 samples from three projects (Adi, Boisard, Tarrega projects), in which fatty and springy descriptors were assessed, was extracted. We included in this subset other sensory descriptors (crumbly and sticky) and estimations of these descriptors in the case of the Adi project. Thus, this subset contains a total of six sensory descriptors (2021-01-22_cheese-compo-rheo-senso_-Guichard-V3. tab, sheet: dataset_22samples-fatty).
Another important result is that MNFS is a very relevant parameter to predict sensory hardness. In the analyses of the different datasets, the sodium ion content was positively correlated with the Young's modulus and the fracture stress. This supports the knowledge that sodium ions strongly participate in the protein network. In cheese, salt addition improves casein hydration due to a strong binding of sodium ions  (Floury et al. , 2009a,b) , which can influence cheese texture differently according to the type of cheese and its composition in other cations, such as calcium ions. The effect of salt content on cheese texture varies according to the manufacturing process. In rennetinduced lipoprotein gelled matrices, an increase in salt content and pH induced a decrease in hardness and firmness, which was explained by an effect of the substitution of divalent calcium ions by monovalent sodium ions in the casein network  (Lawrence et al.
A preliminary study on nine cheddar cheeses varying in composition and texture  (Jack, Piggott, & Paterson, 1995)  previously suggested that hard cheeses required more chewing which induced a higher rate of sodium release and thus a higher salty taste. This matrix effect was also noticed with camembert cheeses  (Engel, Nicklaus, Septier, Salles, & Le Quere, 2001) , in which saltiness was perceived with a higher intensity in cheeses with a brittle texture and a greater ability to release water and thus water-soluble molecules such as sodium ions. This effect of the chewing process on salty taste was validated by the present study. Indeed, the PCA on 13 samples for which data on chewing behaviour were available showed that, at a low sodium content, an intense chewing activity increased salty taste.
Therefore, the BaGaTel database appears of tremendous support not only to confirm previous hypotheses, but to propose new formulations for well-balanced products in terms of nutritional requirements and sensory acceptability by consumers. However, even though it was possible to estimate some missing data, our study highlights the need to gather data on a great number of relevant parameters, measured in similar conditions, in different projects. Our database allows to gather data from different projects, using a common vocabulary and structuration with their associated metadata (e. g. , materials, methods, experimental conditions). A follow-up of this analysis could be to propose some harmonisation of the methods used and suggest useful parameters to be measured in foods, to efficiently exploit data from different projects, in the objective of a FAIR data management.
Acknowledgements This work was supported by the  Qualiment Carnot Institute  from  French National Research Agency  through the  NutriSensAl project, France  (grant number  16CARN002601 ) and by  French National Research Agency  through the  DataSusFood   project, France  (grant number  ANR-19-DATA-0016 ). We thank  Caroline Peltier  ( ChemoSens platform in CSGA ), for missMDA computing.
Importantly, processing sequences of visuospatial or visuomotor items seem to show right hemisphere dominance  (Janacsek et al. , 2015; Jarret, Stockert, Kotz, & Tillmann, 2019; Roser, Fiser, Aslin, & Gazzaniga, 2011) , and data from brain stimulation experiments have shown that the right dorsolateral prefrontal cortex (DLPFC), but not the left DLPFC is associated with learning and retention of visuomotor sequences  (Janacsek et al. , 2015). Given the bottom-up nature of sequence learning  (Conway, 2020) , and the importance of left IFG in language  (Amunts et al. , 2010; Friederici, 2006) , a difference in laterality between visual and auditory sequences is conceivable. However, as the current study does not compare different modalities, it cannot contribute to this ongoing debate. Rather, potential laterality effects will be reported with tentative explanations only.
| EEG recording and analysis Scalp EEG was recorded with 64 Ag/AgCl electrodes from an elastic cap (EasyCap, Germany), using Synamps amplifier and Neuroscan recording software (Compumedics Neuroscan, USA). For reference and ground, the tip of the nose and AFz electrode were employed. EEG recording was performed with a sampling rate of 1,000 Hz and with an online filter of 70 Hz low-pass, 24 dB/oct. Impedance levels of electrodes were kept below the threshold of 10 kΩ. The recordings were analyzed in BrainVision Analyzer 2. 0 (Brain Products, Germany). The EEG data were band-passed filtered (0. 5-30 Hz, 48 dB/oct), and a notch filter was applied (50 Hz). Then, independent component analysis (ICA, Infomax) was performed to remove only those components that were identified either as eye-movement artifacts or as pulse artifacts based on their temporal properties and spatial distributions  (Delorme, Sejnowski, & Makeig, 2007).
| Residue iteration decomposition The RIDE temporal decomposition method was used for trial-to-trial variability-based analysis of the stimulus-locked ERPs. RIDE aims to distinguish components with variable intercomponent delays based on the single trial latency variability information  (Ouyang et al. , 2015; Ouyang & Zhou, 2020). The single-trial ERPs are decomposed into different components with differential latency variability. Temporal decomposition is achieved in an iterative way. RIDE assesses latency variability in a channel-specific fashion, thus, differences between individual electrodes remain valid in the identified components  (Ouyang et al. , 2015). In the current study, we used the RIDE toolbox in Matlab (Mathworks, Inc. , MA), and followed the protocols of earlier studies  (Mückschel, Chmielewski, et al.
Sources identified with using the sLORETA have been validated in combined MRI/EEG and TMS/EEG studies  (Dippel & Beste, 2015; Ocklenburg et al. , 2018; Sekihara et al. , 2005). Comparisons against zero were used for the sLORETA contrasts. To calculate the statistics on the sLORETA sources, we used voxel-wise randomization tests with 2,500 permutations and statistical nonparametric mapping procedures. Locations of voxels that were significantly different (p <. 05) are shown in the MNI-brain. Significant activations represent critical t-values corrected for multiple comparisons as implemented in the sLORETA software.
ACKNOWLEDGMENTS This research was supported by the  Deutsche Forschungsgemeinschaft   TA 1616/2-1  (to Adám Takács);  National Brain Research Program  (project  2017-1. 2. 1-NKP-2017-00002 );  Hungarian Scientific Research Fund  ( NKFIH-OTKA FK 124412 , PI:  Andrea Kóbor ,  NKFIH-OTKA K 128016 , PI:  Dezso Nemeth ,  NKFIH-OTKA PD 124148 , PI:  Karolina Janacsek );  János Bolyai Research Scholarship of the Hungarian Academy of Sciences  (to  Karolina Janacsek  and  Andrea Kóbor );  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ) (to Dezso Nemeth);  Deutsche Forschungsgemeinschaft FOR 2698  (to Christian Beste). Open Access funding enabled and organized by Projekt DEAL.
Funding information Deutsche Forschungsgemeinschaft , Grant/ Award Numbers:  FOR 2698 ,  TA 1616/2-1 ;  Országos Tudományos Kutatási Alapprogramok , Grant/Award Numbers:  NKFIH-OTKA FK 124412 ,  NKFIH-OTKA K 128016 ,  NKFIH-OTKA PD 124148 ;  IDEXLYON Fellowship of the University of Lyon , Grant/Award Number:  ANR-16-IDEX-0005 ;  János Bolyai Research Scholarship  of the  Hungarian Academy of Sciences.
1g ), suggesting that viral integrations are recurrent, affect diverse eukaryotic lineages, and are generally only transiently retained, but provide an opportunity for the longer-term retention and co-option of individual viral genes given adaptive signi cance and selection for xation. To investigate the functional relevance of these HGTs, we examined the transfer direction and functional enrichments of exchanged protein families. Of the 1,859 families exhibiting HGT with known directionality, the majority (93%) underwent unidirectional transfer (Fig. 2a ). Dividing this dataset by direction, genes involved in viral acquisitions were generally transferred unidirectionally (92%), whereas a larger proportion of families undergoing virus-to-eukaryote transfer participated in bidirectional exchange (29%) (Fig.
2a ), suggesting that some of these exchanges may involve transduction (cell-virus-cell HGT). By moving across the phylogenies of all families exhibiting eukaryotic acquisitions, from viral donors towards the root, we estimated that 30. 5% (n = 259) of viral genes acquired by eukaryotes were originally eukaryotic, whereas fewer (8. 2%, n = 70) originated in prokaryotes (Extended Data Fig. 3 , Supplementary Table  1 ). The remainder had unclear origins (24. 2%, n = 205) or were not attributable to a cellular lineage (37. 1%, n = 315), suggesting that these genes are either viral innovations or ancient viral acquisitions sharing deep cellular homology undetectable in our dataset (Extended Data Fig. 3a ). These data demonstrate that over evolutionary time, viruses have a capacity to mediate intra-eukaryotic and interdomain HGT through transduction.
brucei  43  , altogether indicating that these viralderived glycosyltransferases were co-opted for use in the kinetoplast at the same time as it underwent massive evolutionary change. These data, along with the tendency for viruses to modify cell surfaces, suggest that viral-derived genes may have played various roles in the evolution of cellular morphology across the eukaryotic tree of life, possibly affecting the diversi cation of eukaryotic forms. Horizontal gene transfer between viruses and eukaryotes has been observed and assumed to impact genome evolution in both participants, but until now we lacked the systematic characterization of these gene exchanges necessary to generalize their mode and functional signi cance in both viral and eukaryotic contexts. As with all computational surveys, our dataset is limited by speci city and sensitivity, but nonetheless it provides an extensive resource from which phylogenetic patterns can be observed and their genomic and functional importance may be predicted.
Dataset assembly To systematically identify instances of viral-eukaryotic gene exchange, groups of homologous eukaryotic, viral, and prokaryotic proteins were clustered into protein families and phylogenetic analyses were performed (Extended Data Fig. 1a ). The eukaryotic dataset was generated from 196 genome-predicted eukaryotic proteomes, primarily from UniProt (release 2018_11, see Data and Material Availability)  50  , that represented diverse species from all available major eukaryotic lineages, were individually clustered at 99% percent-identity with CD-Hit v4. 8. 1  51  , and combined. The eukaryotic dataset was further supplemented with ve high quality transcriptomes to ll taxonomic gaps in lineages with poor genomic sampling (four dino agellates and a cercozoan) (Extended Data Fig.
Horizontal gene transfer detection After generating phylogenetic trees for each protein family, we developed an automated pipeline using the python package, ETE 3 64 , to identify HGT-indicative topologies. Speci cally, we aimed to identify eukaryotic species nested within viral clades (viral-to-eukaryote HGT) or viral taxa within eukaryotic clades (eukaryote-to-virus HGT) (Extended Data Fig. 1a ). To this end, phylogenies were initially processed to account for statistical support and directionality (i. e. , rooting), and to assign taxonomic annotations. Firstly, phylogenetic nodes with SH-aLRT values below 0. 8 were collapsed, a threshold with a similar false positive rate to standard bootstrapping that balances speci city and sensitivity 60. Collapsed phylogenies were then rooted using MAD (96.
A eukaryote-to-virus HGT topology was de ned as a viral clade with a eukaryotic sister and cousin whereas a virus-to-eukaryote HGT required a eukaryotic clade with a viral sister and cousin (Extended Data Fig. 2a ). Initially, viral and eukaryotic clades were identi ed and the taxonomy of their sister and cousin groups were assessed. To classify the taxonomy of these groups, the number of viral, eukaryotic, and prokaryotic sequences in each group was counted. Sister and cousin groups were then classi ed as viral, eukaryotic, or prokaryotic if the taxonomies were consistent across the members of the group. If the taxonomies of a group were mixed (e. g. , if both viral and eukaryotic sequences were present), but viral or eukaryotic taxa dominated at least 80% of the sequences, the group was described as 'probably' viral or eukaryotic, or else the group received an ambiguous designation.
Contamination scoring After identifying the HGTs, individual transfer events were assessed for possible alternative sources of phylogenetic incongruence, speci cally contamination. This is important given that eukaryotic and viral genes can be artifactually present in viral and eukaryotic genomes, respectively, which may give the impression that HGT has occurred. To address this, only complete viral genomes and metagenomes with low contamination scores were included in the analysis  10  and individual viral-derived eukaryotic genes were assessed based on a series of criteria and a contamination scoring scheme (Extended Data Fig. 2be ). Contamination was assessed based on two main attributes: 1) the presence of related taxa in the HGT, and 2) the characteristics of the genomic contig upon which the gene was encoded.
Firstly, the taxonomic composition of the HGT-recipients was assessed based on the assumption that the same contamination is unlikely to occur in multiple independently sampled genomic datasets, particularly if the species from which they are derived are closely related. Therefore, points were given if the HGT-recipients included multiple members of the same (+3) or different (+1) phyla as the species encoding the gene of interest (Extended Data Fig. 2b ). Secondly, the characteristics of the genomic contigs encoding each viral-derived gene were inspected based upon the notion that they should share attributes with the host genome, such as consistent GCcontent, reasonable contig size, and that the gene should be anked by eukaryotic regions. To this end, contigs were identi ed by mapping proteins to the genome using tBLASTn (e-value < 10 -5 ) and points were given if the contig was within one standard deviation of the median genomic GC-content (+1) and if the contig was a reasonable size (greater than half of the L50) (+1).
Lastly, the genomic context was inspected by extracting DNA regions (5 kbp) upstream (-2. 5 kbp) and downstream (+2. 5 kbp) of each gene. Extracted regions were then taxonomically classi ed by comparing them to the SWISS-PROT database (release 2019_11) using BLASTx 65 and assessing the taxonomy of the resulting hits. A maximum of 20 hits (e-value < 10 -3 ) were evaluated and normalized scores for viral, eukaryotic, and prokaryotic classi cations were calculated to account for database bias. These scores were calculated for each taxonomic group as: where t prop is the proportion of a taxonomic group in the database, and n t and S t are the number and median bit-score of hits to that taxonomic group, respectively. The taxonomy was assigned to the group with the maximum score and points were given for eukaryotic classi cations (+1 per region) and subtracted for prokaryotic ones (-1 per region), which are more indicative of contamination.
Functional analyses To examine HGT function, eukaryotic and viral proteins were annotated with eggNOG, Pfam and PANTHER (Protein analysis through evolutionary relationships) using a combination of eggNOG-Mapper v2 and InterProScan v. 5. 48 with the default parameters 66-70. For clarity, the resulting gene ontology (GO) terms were simpli ed by mapping the terms to the yeast GO-slim subset using Map2Slim (see https://github. com/owlcollab/owltools/wiki/Map2Slim). Protein families were given functional annotations based on a majority rule (Supplementary Table  1 ) and labeled with gene ontology (GO) terms if a given term was assigned to at least 20% of annotated proteins within a family. To conduct GOenrichment analyses, protein families exhibiting HGT were compared against a eukaryotic background comprising all eukaryotic protein families containing either a virus or at least ten eukaryotic species.