dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements All co-authors acknowledge  NASA ,  CNES  and its partner agencies and institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, IC, MPS-MPG ) and the flight operations team at  JPL , CAB, SISMOC, MSDS,  IRIS-DMC  and  PDS  for providing InSight data. The members of the InSight engineering and operations teams made the InSight mission possible and their hard work and dedication is acknowledged here. A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. Additional work was supported by  NASA 's  InSight Participating Scientist Program. French co-authors acknowledge the.
Data Availability The raw to calibrated data sets of InSight are available via the Planetary Data System (PDS). Data are delivered to the PDS according to the InSight Data Management Plan available in the InSight PDS archive. Data from the APSS pressure sensor and the temperature and wind (TWINS) sensor referenced in this paper is available from the PDS Atmospheres node. The direct link to the InSight data archive at the PDS Atmospheres node is: https://atmos. nmsu. edu/data_and_services/ atmospheres_data/INSIGHT/insight. html. Other data used in this paper are available from the imaging node (ICC and IDC images) and the geosciences node (SEIS and HP3) of the PDS. SEIS data is also available from the Data center of Institut de Physique du Globe, Paris http://dx. doi. org/10. 18715/SEIS. INSIGHT.
RESULTS The metabolic network of X. fastidiosa. We built a genome-scale metabolic network of X. fastidiosa (see Data Set S1, Text S1, and Text S2 in the supplemental material) using in-house automatic reconstruction algorithms  (22)  from the genomic sequence of Xylella fastidiosa subsp. multiplex strain CFBP 8418 isolated in 2015 in Corsica  (23). A manual curation of each reaction was then performed using databases, literature, and simulations. Novel reactions were written to take into account the biosynthesis of EPS and lipopolysaccharides (LPS)  (24) (25) (26). To represent the global cost of excreted proteins, which are crucial virulence factors, we included reactions for the biosynthesis and excretion of a proteic virulence factor through the type II secretion system  (27). The reconstruction process is summarized in Data Set S2.
fastidiosa fastidious growth (Table  2 ). Growth is achievable on diverse xylem fluid components, including five amino acids, citric acid, and D-fructose, even though, surprisingly, eight compounds leading to fast growth are not major components. The number of carbon sources identified using Biolog PMs was 25, which is considerably lower than the number of C sources identified in R. solanacearum and E. coli (respectively, 36 and 86)  (22, 44) , confirming our prediction of reduced metabolic diversity. We also used FBA to predict the relative growth rate of X. fastidiosa on several substrates. Relative in silico growth rates were determined by computing the ratio between the growth rate and the highest growth rate obtained (on glycerol). The discrepancies observed between experimental and simulation results (growth in vivo but not predicted in silico) were used to correct the network, by adding the missing metabolic reactions for 12 substrates (as specified in the final metabolic network in Data Set S1).
Alternative enzymes are indicative of functional redundancies in the network and are generally associated with environmen-  a Relative in silico growth rates were determined by computing the ratio between the growth rate and the highest growth rate obtained (on glycerol). If the relative in silico growth rate was Ͼ75%, growth was assessed as relatively fast (ϩϩ), and if the relative in silico growth rate was Յ75%, growth was assessed as relatively slow (ϩ). b Growth assessment was made using Biolog PM respiration rate: ϩϩ for relatively fast growth, referring to a respiration rate above 0. 125 h Ϫ1 , and ϩ for relatively slow growth, referring to a respiration rate under 0. 125 h Ϫ1 (see Materials and Methods and Data Set S3), except for chitin (*), for which growth was previously determined experimentally  (45).
e. , reactions with alternative fluxes able to sustain the biological objective) was significantly lower (32%) in X. fastidiosa than in R. solanacearum (48%) and E. coli (60%). Thus, only a limited proportion of the metabolic reactions can still sustain optimal growth if their fluxes vary. This particular trait confirmed a lack of flexibility in X. fastidiosa metabolism. A higher cost for virulence can compromise X. fastidiosa growth. We finally assessed the efficiency of the network structure for a specific task by determining metabolic yields on proliferation and on virulence factors. Metabolic yields were defined as the maximal proportion of carbon which could be invested in the biosynthesis of a specific macromolecule (virulence factor) or biomass. The obtained efficiency data are presented in Table  4  and compared to another plant pathogen, R.
Growth was considered effective if AUC was ϾAUC negative control ϩ threshold (see Data Set S3). If the respiration rate was above 0. 125 h Ϫ1 , the growth was categorized as fast (ϩϩ); otherwise, it was categorized as slow (ϩ). If the results were nonreproducible, the growth was assessed to be undetermined. For six substrates (out of 190), for which growth was assessed unambiguously as positive for two out of the three replicates, growth was referred to (ϩ). The complete results are available in Data Set S3. Genome-scale metabolic network reconstruction. The genome-scale metabolic network reconstruction was performed from the genomic sequences of the strain CFBP 8418 (GenBank accession no. LUYA00000000. 1) (23). The protocol to generate high-quality genome-scale metabolic network proposed by Thiele and Palsson (66) was followed.
fastidiosa genome (29) and specificities of the studied strain (23) were both used. For missing information, data from Xanthomonas campestris pv. campestris  (74)  and E. coli  (67)  were used (Data Set S2). After curation, the tool CarveMe  (75)  was used to find other potential reactions. The generated reactions were manually curated and added to the network. For metabolic network comparisons between subspecies, draft metabolic networks of Xylella fastidiosa subsp. pauca 9a5c (GenBank accession no. GCA_000006725. 1) (29) and X. fastidiosa subsp. fastidiosa Temecula1 (GenBank accession no. GCA_000007245. 1) (30) were generated with the same reconstruction process. Comparative analysis. The reaction identifiers were given as input of the comparative analysis between E. coli, R. solanacearum, and X.
fastidiosa. The metabolic model considered for E. coli was the model iJO1366  (67). For R. solanacearum, the model iRP1476  (22)  was used, taking into account only the metabolic module. The Venn diagram was generated using the online tool BioVenn  (76). The list of reactions in each part of the diagram is available in Data Set S2. Computational simulations. To test the functionality of pathways and of the global network, simulations were performed on the model. To access intracellular fluxes and growth rate, the flux balance analysis (FBA) methodology was used  (77). The constraints used to model the system are detailed in Text S3. To access the variability of each flux, flux variability analysis (FVA), a methodology based upon the same principles, was then performed  (78). A deviation of 1% from the optimality was allowed.
Each simulation result gave access to the proportion of carbon lost in the form of CO 2 , with the exported CO 2 rate (R_EX_CO2_e). This allowed us to determine the maximal amount of carbon which could be dedicated to a virulence factor or biomass production. Metabolic yields, and consequently cost of virulence factors, were computed as the ratio between maximal production rate and the uptake rate, normalized by the number of carbons. Text S4 provides a detailed description of mathematical modeling of metabolism used in the study. Simulations were performed with the open source tool FlexFlux (79) and are reproducible using the guide provided in Text S4. The linear programming solver CPLEX, developed by IBM, was used to solve the system and get solutions. Scripts and command lines are available online on GitHub: https://github.
com/lgerlin/xfas-metabolic-model. Data availability. All data generated or analyzed during this study are included in figures or tables or in the supplemental material. In particular, raw data from Biolog phenotype microarray plates (for carbon substrate phenotyping) are available in Data Set S3. Model and code availability. The X. fastidiosa subsp. multiplex strain CFBP 8418 metabolic model was named Xfm1158. The metabolic model was deposited in BioModels (80) and assigned the identifier MODEL2003100001. Exploration, omics mapping, and basic flux analyses can be performed on the model in MetExplore (81) (https://metexplore. toulouse. inrae. fr/metexplore2/?idBioSourceϭ5822). The main scripts and command lines used for the study are available on GitHub: https://github. com/lgerlin/xfas -metabolic-model.
(B) Venn diagram depicting the number of metabolic reactions in E. coli, R. solanacearum, and X. fastidiosa. The lists of the reactions found in each part of the Venn diagram are available in Data Set S2. (C) Inputs and outputs of an FBA simulation of X. fastidiosa growth on glutamine under conditions close to experimental conditions. The objective function of FBA was minimization of glutamine import. Growth and production rates were calculated from previous publications (19, 82). Flux values are in mmol•g (DW) Ϫ1 •day Ϫ1 for all inputs/outputs except for biomass (day Ϫ1 ). See Text S3 for the constraints used for the simulation and Data Set S2 for the FBA solution. Scripts are available on https://github. com/lgerlin/xfas-metabolic-model. (D) Distribution of the number of enzymes for each metabolic reaction in X.
ACKNOWLEDGMENTS L. G. was funded by a grant from the  French Ministry of National Education and Research. The study was funded by the  European Union 's  Horizon 2020 research and innovation program  through grant agreement  XF-ACTORS (727987 ) and the  "Laboratoire d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ). The views expressed are purely those of the writers, and the  EU  funding agency is not responsible for any use that may be made of the information it contains. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge  CIRM-CFBP (Beaucouzé, INRAE, France ; http://www6. inrae. fr/cirm_eng/ CFBP-Plant-Associated-Bacteria ) for strain preservation and supply and access to the Omnilog station. The authors declare that they have no competing interests.
1. Ecologists are increasingly interested in plant-pollinator networks that synthesize in a single object the species and the interactions linking them within their ecological context. Numerous indices have been developed to describe the structural properties and resilience of these networks, but currently, these indices are calculated for a network resolved to the species level, thus preventing the full exploitation of numerous datasets with a lower taxonomic resolution. Here, we used datasets from the literature to study whether taxonomic resolution has an impact on the properties of plant-pollinator networks. 2. For a set of 41 plant-pollinator networks from the literature, we calculated nine network index values at three different taxonomic resolutions: species, genus, and family. We used nine common indices assessing the structural properties or resilience of networks: nestedness (estimated using the nestedness index based on overlap and decreasing fill [NODF], weighted NODF, discrepancy [BR], and spectral radius [SR]), connectance, modularity, robustness to species loss, motifs frequencies, and normalized degree.
Other frequently examined structural characteristics of mutualistic networks are connectance, the proportion of realized interactions among all possible ones, and modularity, that is, the extent to which linked interactions between pollinators and plants are organized into delimited modules, as well as motifs, which are subnetworks representing the interactions between a given number of taxa  (Milo et al. , 2002). These properties have been associated with the ecosystem's resilience to perturbations  (Soares, Ferreira, & Lopes, 2017). It has, for example, been shown that high levels of connectance, modularity, and nestedness promote both the structural and dynamic stability of mutualist interaction networks  (Vanbergen, Woodcock, Heard, & Chapman, 2017). A large number of datasets on plants and their pollinators have been collected to date.
However, given the large number of pollinator species potentially present in a community, as well as the relative difficulty in identifying some of these pollinators at the species level, a significant portion of the collected datasets has a taxonomic resolution lower than the species level. For a given research effort, there is therefore a trade-off between the quantity of possible identifications and the taxonomic accuracy of these identifications, which makes it difficult to produce large or numerous sets of data identified down to the species level. An extreme point in this regard is the datasets provided by citizen science programs for pollinators  (Toomey & Domroese, 2013)  such as the Spipoll program in France  (Deguines, Julliard, Flores, & Fontaine, 2012) , which generally allow very large datasets to be collected, although their taxonomic accuracy does not generally extend to the species level  (Dickinson, Zuckerberg, & Bonter, 2010; Kremen, Ullman, & Thorp, 2011).
Currently, network analyses are performed on networks with varying levels of taxonomic precision, which makes comparisons between studies or even sites of the same studies potentially invalid, because we do not know how taxonomic resolution influences the indices of those networks, nor how they should be interpreted. If possible, it would, however, be interesting to use network analyses on such datasets in order to fully exploit the information contained therein and allow comparisons with other studies. Here, we sought to establish whether taxonomic resolution has an influence on the architecture and properties of a mutualistic network estimated using several indices. We used a set of 41 plant-pollinator networks from the literature and compared their index values at three different taxonomic resolutions: species, genus, and family.
| Overview We used plant-pollinator networks from the literature  (Vázquez, Goldberg, & Naik, 2003)  determined to the level of species. For each species-level network, we deduced the equivalent network at the genus and family levels. We then calculated several indices commonly used to estimate mutualistic network properties for each of these networks and then compared their values across taxonomic resolutions. Data manipulation and analysis were conducted with the R language (R version 3. 2. 3, 2015-12-10). The script used for those results is accessible here: https ://gitlab. com/Estel leRen aud/ taxon omic_influ ence_netwo rk_prope rties.
The first uses label propagation to obtain a locally maximized modularity (bottom-up); the second agglomerates the modules found in the first step if it allows for an increased modularity; the third repeats these steps until modularity can no longer be increased. DIRTLPAwb+ then randomizes the initial labeling of nodules multiple times and returns the result with the greatest modularity score. Modularity itself was then calculated as the modularity M proposed by Newman  (Newman, 2006). Following  Burgos et al. (2007) , network robustness was measured as the area under the attack tolerance curve, defined as the speed at which plant taxa disappear as pollinator taxa disappear. Basically, as pollinator taxa disappear, plants that rely exclusively on them (according to the network) also disappear, thus creating a curve in which the percentage of remaining plants depends on the percentage of remaining pollinators.
This method assumes that preferences are static, that is, that plants that rely on one given pollinator taxon will not be able to switch to another pollinator if it disappears. Normalized degree is calculated for each taxon in a network as the sum of the links of that taxon scaled by its number of potential partners. Because the normalized degree index produces one value per taxon, for each matrix, we chose to characterize matrices by their quartile values in order to accommodate differently skewed distributions of the normalized degrees between networks. The R package bipartite  (Dormann, Gruber, & Fründ, 2008)  was used to calculate the normalized degree, robustness, modularity, and connectance of the network. Motifs were compared using the frequency of the 17 different motifs that involve up to five different taxa, which we calculated using the mcount function of the bmotif package  (Simmons et al.
, 2019). The frequency of each motif was calculated as the number of times a given motif occurs in the network, divided by the number of motifs of the same size (involving the same number of taxa) that occurs in the network. (Dormann et al. , 2008). We also generated 500 matrices under that model. Finally, because Z-scores for NODF have been criticized for their sensitivity to connectance and number of taxa  (Song et al. , 2017) , we used the normalization proposed by Song et al. as NODF c = NODF n /(C*log(S)), where C is the connectance, S is the geometric mean of plants and pollinators in the network, and NODF n = NODF/max(NODF), where max(NODF) is the maximal NODF value that could be attained in a network with the same number of rows, columns, and links as the original network. Max(NODF) was calculated using the maxnodf R package  (Hoeppke, 2019).
| Pollination networks We extracted all plant-pollinator interaction networks from the Interaction Web Database  (Vázquez et al. , 2003). All networks were issued from previously published data (Table  S1 ). We only kept matrices for which taxa determination was possible using the taxize package; that is, valid taxonomic names resolved at the genus or species level. In some cases, we replaced old taxonomic names by a current valid synonym. We also only kept matrices that dealt with several families, which left us with a dataset of 41 matrices, 10 of which were binary (presence/absence) matrices. The remaining 31 were weighted according to the frequency of the visitation or a proxy of that frequency. The number of taxa in the matrices varied from seven to 135 for plants, and 12 to 144 for pollinators.
We then used the taxize package  (Chamberlain & Szöcs, 2013 ; version 0. 9. 0) from R to extract from the taxonomic information supplied by the authors the taxonomic affiliation from the superior ranks. Only the identification from the species, genus, family, and order ranks was retained, as these were the ranks most often known for all observations. The database GBIF (GBIF, 2018) was used as a reference. We transformed each of the 41 previously described matrices into interaction matrices determined at the species level by keeping only the observations (within each network) for which both the plant and pollinator were determined to the species level. From these species-level matrices, we deduced the genus-level and then the family-level matrices.
, 2010) , although these are often at the price of a lower taxonomical precision. Here, we showed that datasets with a taxonomic resolution lower than the species level can be used to estimate the properties of networks assembled at the same resolution, even if it is lower than the F I G U R E 3 Correlation strength for all taxonomic levels and all indices. All correlations are significant. BR, discrepancy; NODF, nestedness index based on overlap and decreasing fill; SR_Bin, spectral radius calculated on binary (absence/presence) matrices; SR_Qua, spectral radius calculated on weighted (abundance) matrices; VS, versus; WNODF, NODF calculated on weighted matrices species. However, plant-pollinator interaction data produced by citizen science are probably characterized by relatively low sampling completeness, because detecting all the species interactions is extremely labor-intensive  (Chacoff et al.
Polyglutamine (polyQ) regions are one of the most prevalent homorepeats in eukaryotes. It is however difficult to evaluate their prevalence because various studies claim different results. The reason is the lack of a consensus to define what is indeed a polyQ region. We have tackled this issue by studying how the use of different thresholds (i. e. , minimum number of glutamines required in a protein region of a given size), to detect polyQ regions in the human proteome influences not only their prevalence but also their general features and sequence context. Threshold definition shapes the length distribution of the polyQ dataset, and changes the observed number and position of impurities (amino acids other than glutamine) within polyQ regions. Irrespective of the chosen threshold, leucine and proline residues are enriched both within and around polyQ.
Quantitatively, polyQ is the most prevalent homorepeat in eukaryotes using either thresholds 6/6  [21]  and 4/7  [22] , and the second most prevalent when using threshold 7/7  [23]. Specifically, in metazoans, they are the most prevalent (threshold 6/6  [21] ) and the sixth most prevalent (threshold 8/10 [4]) homorepeat, while in humans polyQs are the seventh (thresholds 5/5  [1]  and 8/10  [4] ) and the eighth (threshold 5/5  [15] ) most prevalent. The different order in the last example, even with the same threshold, may be due to the use of a different human proteome dataset. From these reports, it becomes obvious that there is no consensus in how to define polyQ repeats and that using different thresholds significantly changes their detection and consequently their associated properties.
Data retrieval The complete reference human proteome (73928 proteins) was downloaded from the UniProtKB database release 2019_02 (Uniprot > Proteomes; https://www. uniprot. org), and taken as the default amino acid dataset for most of the calculations. Similarly, we downloaded from the same site the complete reference proteomes of seven model organisms from diverse taxonomic groups: Mus musculus (54425 proteins), Danio rerio (46926 proteins), Drosophila melanogaster (21923 proteins), Caenorhabditis elegans (26893 proteins), Saccharomyces cerevisiae (6049 proteins), Arabidopsis thaliana (39380 proteins) and Dictyostelium discoideum (12746 proteins). All protein coding transcripts of the human genome (GRCh38. p12; 92090 sequences) were retrieved from the Ensembl database (Ensembl > Biomart), by using ''Gene type: protein_coding" and ''Transcript type: protein_coding" as filters.
Threshold usage Several thresholds of a minimum number of glutamines (N) in a window of a given size (L), noted as N/L, were used to consider whether a region is annotated as a polyQ. These were: 4/4, 4/6, 6/6, 6/8 and 8/10. Sequences were scanned with an in-house Perl script using the thresholds independently. This means that a region may be considered to be a polyQ with one or more thresholds (i. e. a pure stretch of six glutamines will be found with thresholds 4/4, 4/6, 6/6 and 6/8). Once the script finds a window matching the threshold, it slides forward until a window does not match; then, the polyQ region is annotated from the first matching window until the last one (Fig. 1 ). A set of polyQ regions was obtained per dataset and per threshold. An additional constraint is taken into consideration to avoid nonspecific polyQ starts and ends.