dataesr/scientific-paragraphs-categorization huggingface dataset:
The results were manually curated to invalidate peptides belonging to deleted or mutated genes that were erroneously detected in the dataset. Intensity-based absolute quantification (iBAQ)  (Schwanhausser et al. , 2011) , values were calculated from MS1 intensities of razor and specific peptides. The iBAQ values were normalized by the sum of iBAQ values in each sample, before averaging the values of the three replicates to generate the final iBAQ value of each sample type. MS data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (Perez-Riverol et al. , 2019)  with the dataset identifier PXD029397.
ACKNOWLEDGMENTS We thank  Isabelle Vallet-Gely  and  Bruno Lemaitre  for providing the P. entomophila mutants and plasmids, and  Teresa de Kievit  for P. chlororaphis PA23 strain. P. aeruginosa IHMA87 was obtained from the  International Health Management Association, USA. We would like to thank  Emma Chastaing  for her technical assistance for the experiments with amoebae and fungi during her master internship (MAP, Lyon). We thank  Emmanuel Tailleboug  for the technical assistance in Drosophila melanogaster experiments. The work was supported by grants from  Agence Nationale de la Recherche  ( ANR-15-CE11-0018-01 ), the  Laboratory of Excellence GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ) and the  Fondation pour la Recherche Medicale (Team FRM   2017 ;  DEQ20170336705 ) to I.
The solution was adjusted to pH 7 with 50 mM phosphate buffer in D 2 O. The samples were not degassed. All poly-SLIC pulses had a common RF amplitude  1 SLIC = 27 Hz and a common duration  SLIC = 319 ms. Monoexponential fitting was used to extract T LLS relaxation times. In a few cases, a weak oscillatory behavior attributed to zeroquantum coherences was observed. The fitting was restricted to data points where such oscillations had decayed. The MRI images were obtained without temperature stabilization at approximately 295 K in a 5-mm Micro2. 5/MicWB40 probe with a Bruker 800-MHz WB system (B 0 = 18. 8 T) using the pulse sequence shown in Fig. 4B. The proton linewidth was typically between 15 and 20 Hz, leading to a reduction of the yield during the highly selective SLIC pulses. A concentric NE-5-CIC tube (Newera-spectro) with an outer diameter of 2 mm and an inner diameter of 1 mm was inserted inside a normal 5-mm NMR tube with an inner diameter of 4 mm.
2C ). The Rickettsia taxon was present in 100% of the R. helvetica-positive samples, in agreement with the positive microfluidic results of R. helvetica in our samples. Rickettsia was also present in 73% of the pathogen-free samples (8 out of 11), but the presence of the pathogens R. helvetica, R. conorii, R. slovaca, R. massiliae, R. aeschlimannii and R. felis was ruled out based on microfluidic PCR analysis. This taxon was also more abundant in R. helvetica-infected samples (Ave 36. 25 ± 22. 40 centered log ratio (clr)) than in pathogen-free samples (Ave 3. 91 ± 4. 13 clr). Differential relative abundance (expressed as clr) analysis showed that 20 taxa changed significantly between the two datasets. Some taxa were significantly more abundant in R. helvetica-positive samples (i. e. , Hyphomicrobium, Enhydrobacter, Pantoea, Kineococcus, Sphingomonas, Methylobacterium-Methylorubrum, Phyllobacterium, Bradyrhizobium, Acinetobacter, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Ca.
The keystone taxon in R. helvetica-positive samples was Sphingopyxis (Fig. 4B ). To have a better idea of changes in the hierarchical organization of the microbiota, and their impact on the bacterial environment, we identified the direct neighbors of keystone taxa in pathogen-free and the R. 4. Ticks were either infected with R. helvetica, or uninfected by major tick-borne pathogens (referred hereafter as 'pathogen-free'). In this study, the R. helvetica-infected and pathogen-free DNA samples were used for 16S rRNA amplicon sequencing and microbiome analysis. Bacterial composition and abundance were analyzed. The alpha-, beta-diversity and betadispersion of the two datasets were compared, and co-occurrence networks were inferred to assess the structure of the microbial communities. Functional gene prediction in R.
Impact of R. helvetica infection on the inferred functional profiles of I. ricinus microbiota. We checked if the changes observed in the composition and the structure of the microbial communities had an impact on the inferred functional profile of tick microbiome. We analyzed and compared the composition, diversity and relative abundance of predicted enzymes and metabolic pathways in the microbiome of the pathogen-free and the R. helvetica-positive ticks. The two datasets shared 2175 (94. 9%) of the 2293 total of predicted enzymes (Fig. 5A ). The pathogen-free and R. helvetica-positive ticks also had 412 (97. 4%) of the 423 total functional pathways in common (Fig. 5B ). Bray-Curtis index and a PERMANOVA test demonstrated that the beta-diversity of enzymes (Fig. 5C ) and pathways (Fig. 5D ) was not significantly different between the two datasets.
However, an ANOVA test demonstrated that the number of predicted enzymes of pathogen-free samples were more variable than the R. helvetica infected one (Fig. 5C ). Differential analysis showed that the relative abundance of predicted genes (KO) encoding 13 enzymes (Fig. 5E ) and 2 pathways (Fig. 5F ) changed significantly between the two datasets. These 13 enzymes were involved in various metabolic functions including energy metabolism (methane and nitrogen metabolism), amino acid metabolism (arginine and proline metabolism as well as glycine, serine and threonine metabolism), glycan metabolism (glycan biosynthesis), nucleotide metabolism (purine metabolism), pyruvate metabolism, carbohydrate metabolism (glyoxylate and dicarboxylate metabolism) and steroid degradation (Fig. 6 ). In the methane metabolism pathways, the methane monooxygenase (EC:1.
Two extraction reagent controls were included, in which the different DNA extraction steps were performed using the same conditions as for the samples but with water as the template. DNA amplification was then performed on the extraction controls under the same conditions as the other samples. The raw 16S rRNA sequences obtained from tick samples were deposited at the SRA repository (Bioproject No. PRJNA803003). Processing of raw sequences. The analysis of 16S rRNA sequences was performed using QIIME2 pipeline (v. 2019. 7)  62. The raw sequences (demultiplexed in fatsq files), were denoised, quality trimmed and merged using the DADA2 software  63  implemented in QIIME2  62. The ASVs were aligned with q2-alignment of MAFFT  64  and used to construct a phylogeny with q2-phylogeny of FastTree 2  65.
Taxonomy was assigned to ASVs using a classify-sklearn naïve Bayes taxonomic classifier  66  based on SILVA database (release 138)  67. Only the target sequence fragments were used in the classifier (i. e. , classifier trained with the 515F/806R primers)  68, 69. Taxa that persisted across serial fractions of the samples using QIIME 2 plugin feature-table (core-features) were considered ubiquitous  62.
Diversity indexes and taxa abundance. The alpha diversity provides a summary of the microbial community in individual samples on the richness (number of species) and evenness (how well each species is represented). Alpha diversity metric was estimated using the q2-diversity plugin in Qiime2 environment. The alpha diversity richness was explored using observed features  70  , and the evenness with the Pielou evenness index  71. Differences in alpha-diversity metric between groups were assessed using Kruskal-Wallis test (alpha = 0. 05) using QIIME 2  62  and GraphPad Prism version 8. 0. 1 (GraphPad Software, San Diego, California USA). Beta diversity is a measure of diversity between individual samples that assesses similarity of communities compared with the other samples analyzed. Bacterial beta-diversity was assessed using the Bray Curtis dissimilarity index  72  , and compared between groups using the PERMANOVA test (p < 0.
05) realized with QIIME 2  62  and Past 4 version 4. 08 (2001). Beta dispersion was calculated using betadisper function and Vegan script implemented in Rstudio  73. Beta dispersion statistical analyses were performed using ANOVA test (p < 0. 05). The taxa abundance differences were tested using a t-test with a realized with ANOVA-Like Differential Expression (ALDEx2) package  74  in R-studio  73. Microbial diversity analyses were carried on rarefied ASV tables, calculated using the q2-diversity plugins. Only taxa with a significant uncorrected p-value (p < 0. 05) were used for representation of the taxa abundance. Relative abundance was measured as centered log ratio (clr), calculated with ALDEx2 package  74  implemented in R-studio  73. The resulting data were used to construct the heatmap with the heatmap.
2 function implemented in R-studio environment  73. Inference of bacterial co-occurrence networks and statistical network estimation. Co-occurrence networks were built for the two sets of samples using the taxonomic profiles at genera level. The networks allow the graphic visualization of the microbial community assemblies. Bacterial taxa are represented by nodes and the significant correlations between taxa are represented by edges. Analyses of significant positive (weight > 0. 75) or negative (weight < -0. 75) correlations were performed using the SparCC method  59  implemented in R studio environment  73. The visualization and assessment of the networks were performed using the software Gephi 0. 9. 2  75. Network topological features were calculated: number of nodes and edges, network diameter (the shortest path between the two most separated nodes), modularity (the strength of the division of a network into modules), average degree (the average number of links per nodes), weighted degree (the sum of the weight of all the edges connected to a node) and clustering coefficient (the degree to which nodes in a network tend to form clusters).
75, and (iii) high mean relative abundance (i. e. , higher than that of the mean relative abundance of all taxa in an experimental group). The eigenvector centrality measures the influence of a node in a network, a high eigenvector score means that a node is connected to many nodes which themselves have high scores  78. Eigenvector centrality values were exported from Gephi 0. 9. 2 software  75. For each sample in both data sets, the mean clr value was calculated and plotted together with eigenvector centrality values using GraphPad Prism version 8. 0. 0 (GraphPad Software, San Diego, California USA). Prediction of functional traits in the tick microbiome. The 16S rRNA amplicon sequences from each data set were used to predict the metabolic profiling of each sample. PICRUSt2 61 implemented in QIIME2 was used to predict the metagenomes from 16S rRNA amplicon sequences.
The ASVs were placed into a reference tree (NSTI cut-off value of 3) containing more than 20,000 full 16S rRNA sequences from prokaryotic genomes, which is then used to predict individual gene family copy numbers for each ASV. The predictions are based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO)  79. The output of these analyses included pathways and EC (Enzyme Commission number) profiling. The pathways were constructed based on the Meta-Cyc database  80.
ANOVA test was performed and showed that beta dispersion of the two sets of samples is significantly different (p = 4. 775e-05). (D) Dendrogram heatmap resulting from the heatmap. 2 function implemented on R studio 73. The taxa were clustered based on relative abundance (calculated as clr transformed values). Each column represents the clr values for bacterial taxa per sample and per group. Each line represents bacterial taxa with significant changes between the two datasets. Color represent the clr value.
These EoS were selected mainly for two reasons: (i) they are among the most efficient predictive EoS currently available in the open literature, and (ii) they enable to model simple to complex mixtures including the ones were chemical association (e. g. , hydrogen bonding) takes place. To compare these models on a fair basis, the extensive COMPSOL databank  [18]  was used as reference data for the computation of the solvation quantities of interest (Gibbs energy, entropy and enthalpy). In order to provide an instructive comparison of the different models, a systematic classification of the binary mixtures was carried out based on the associative character of their compounds. It is important to make it clear that this benchmarking study focused only on neutral molecules.
Reference dataset (COMPSOL databank) The benchmark of the different models was based on experimental data extracted from the COMPSOL databank  [18]  (where COMPSOL stands for Comprehensive solvation). The advantage of using this database is the large variety of chemical species available, as well as the wide range of pressure and temperature conditions. It is certainly the widest database of solvation properties currently available. Indeed, the original COMPSOL dataset comprises 14,202 binary mixtures involving 865 different solvents and 775 different solutes. Altogether, 70,062 experimental values for solvation energy and 29,344 experimental values for solvation entropy and enthalpy data, at infinite dilution, are reported in this database  [18]. It was not possible to make use of the entire COMPSOL data.
In fact, due to some limitations related to the models considered in this study, certain filters had to be applied in order to ensure that the various approaches were being used within their respective scopes of application, or even that their application was actually possible. Thus, we considered only experimental data for which the following conditions were satisfied:  The solvent is liquid. Therefore, the experimental pressure and temperature must be lower than the critical coordinates of the solvent ( and ).  The solvent remains incompressible, which is a starting assumption for the use of COSMO-based models. This assumption is considered valid provided.  The solute and solvent must have COSMO files (i. e. , the files containing input information for use of COSMO based models) present in both COSMOTherm and Simulis databases (see section 2.
Binary association codes (BAC) As stated in the introduction section, one of the aims of this work is to perform a systematic analysis of the results obtained for the different binary systems available in the extensive COMPSOL database. To discuss the results, binary mixtures will be sorted according to their binary association code, a descriptor revealing the presence and strength of molecular association, through hydrogen bonding mainly. Note that the degree of association in a mixture has a significant impact on the solvation properties. For a detailed discussion on this topic see  [35]. In general terms, the binary association code typifies the hydrogen-bonding capability of both compounds in the binary mixture. At first, this capability is evaluated for each pure compound based on their σ-profiles obtained from DFT and COSMO calculation, as described in  [36].
Technical details The predictions of solvation Gibbs energy using COSMO-RS were performed in the commercially available software COSMOTherm. The quantum chemical level considered was BP/TZVPD-FINE and the COSMO-RS parametrization was BP_TZVPD_FINE_C30_1401. All the COSMO files were already available in the COSMOTherm database. In COSMO-RS, the solvation Gibbs energy is directly calculated from the solute chemical potential in liquid and gas phase, as showed in Eq. (  5 ). In this equation, the chemical potential of the solute molecule infinitely diluted in the solvent ( ) is obtained from statistical thermodynamic calculation that considers the σ-profiles and the σ-potential, whereas the solute's chemical potential in the gas phase ( ) is estimated from the DFT and COSMO calculation outputs, as denoted in Eq.
(  6 ). solv i i,liq i,gas g (T) (T) (T)      (5) i,gas i,gas i,cosmo ring i,ring gas (T) E E n (T)        (6) In the equation above, and are the quantum chemical total energies of the solute in the gas phase and in the COSMO conductor, respectively. The adjustable parameter is a correction for ring compounds, and is the number of ring atoms in the molecule. Finally, provides the link between the reference states of the system in the gas and in the liquid phase. Meanwhile, the calculation with COSMO-SAC-dsp were carried out using Simulis Thermodynamics v. 2. 0. 38, with the original parameterization of COSMO-SAC-dsp published in  [27]. In this case, the σ-profile of the different molecules was generated by GGA-WVNBP/DNP ab initio method. Again, only the COSMO files already available in the software database were taken in account.
CONCLUSIONS In this paper, we have compared the accuracy of different methods (COSMO-based approaches and EoS) in the prediction of solvation energies of binary mixtures at infinite dilution. For this purpose, around 65,000 experimental points from the COMPSOL database were used as reference data. To analyze the results in more detail, the binary mixtures were sorted according to their degree of association (through hydrogen bonding). As expected, all models (COSMO-RS, COSMO-SAC, PSRK and UMR-PRU) provided satisfactory predictions for non-associating binary mixtures. In such cases, the average absolute deviation did not exceed 0. 30 kcal/mol for the calculation of the solvation Gibbs energy. For binary mixture with more complex association schemes, COSMO-RS and PSRK showed better performance than COSMO-SAC and UMR-PRU.
Data sources The Global Dietary Database (GDD) is an international collaborative effort to produce comprehensive and comparable estimates of dietary intakes of major foods and nutrients in 185 countries and territories. 1] [32] [33] [34]  Briefly, we performed systematic searches for individual-level dietary surveys in countries and territories globally, as well as extensive personal communications with researchers and government authorities throughout the world, inviting them to be corresponding members of the GDD. The results of our search strategy by dietary factor, time, and region have been detailed. 30 Surveys were prioritised if nationally or subnationally representative and using individual-level dietary assessments with standardised 24-h recalls, food frequency.
Research in context Evidence before this study We systematically searched PubMed, Embase, Web of Science, LILACS, African Index Medicus, and the Southeast Asia Index Medicus to identify studies reporting nationally or subnationally representative estimates of individual-level consumption of seven animal-source foods (ASF). We included 1248 studies conducted between 1980 and 2016 using 24-h recalls, food frequency questionnaires, or short standardised questionnaires. When national or subnational individual-level surveys were not identified for a country, we searched for individual-level surveys from large cohorts, the WHO Global Infobase, and the WHO Stepwise Approach to Surveillance database. Household budget surveys were used on the rare occasion when individual-level dietary surveys were not identified for a populous country.
Implications of all the available evidence This study highlights regions, countries, and population strata with both lower and higher than optimal ASF intakes. The findings can inform the targeting of intervention, surveillance, and policy priorities relevant to both human and planetary health questionnaires, or short standardised questionnaires (eg, Demographic Health Survey questionnaires). When national or subnational individual-level surveys were not identified for a country, we searched for individual-level surveys from large cohorts, the WHO Global Infobase, and the WHO Stepwise Approach to Surveillance database. Household budget surveys were used on the rare occasion when individual-level dietary surveys were not identified for a populous country. We excluded surveys focused on special populations (eg, pregnant or nursing women, or individuals with a specific disease) or cohorts (eg, a specific occupation or dietary pattern).
The final GDD model incorporated 1248 dietary surveys representing 188 countries and 99•0% of the global population in 2018. Of these surveys, 499 (40%) reported data for ASF including unprocessed red meat, total processed meat (ie, any meat, including poultry, that was cured, smoked, dried, or chemically preserved, excluding seafood and eggs), seafood, egg, milk, cheese, and yoghurt consumption (appendix pp 9-11). Based on the original focus on factors with potential causal associations with NCDs, the data collection did not include poultry, which we hope to update in future iterations. The 499 surveys included 3.
Data extraction For each survey, we extracted data using standardised methods on survey characteristics and dietary metrics, units, mean, and standard deviation of consumption, by age, sex, education level, and urban or rural residence. 31 Data were assessed for extraction errors and for plausibility using standardised algorithms, and survey quality by evaluating evidence for selection bias, sample representativeness, response rate, and validity of diet assessment method (appendix pp 3-5). Measurement comparability across surveys was maximised by using a standardised data analysis approach that leveraged the average of all days of dietary assessment to quantify mean individual-level intakes; used harmonised dietary factor definitions and units of measure across surveys; and adjusted for total energy using age-specific energy intakes to reduce measurement error and account for differences in body size, metabolic efficiency, and physical activity (appendix pp 3-8).
Statistical analysis Global, regional, national, and within-country population subgroup intakes and their uncertainty were calculated as population-weighted averages using all 4000 posterior predictions for each of the 264 demographic strata in each country-year. Population weights for each year were derived from the United Nations Population Division,  39  supplemented with data for education and urban versus rural status from Barro and Lee  40  and the United Nations. 41 ntakes were calculated as both grams per day (g/day) See Online for appendix and servings per day or week, using standardised portion sizes. Spearman correlations between national mean intakes of different ASF were assessed. Changes in consumption between 1990 and 2018 were calculated at the stratum-specific prediction level to account for the full spectrum of uncertainty and standardised to the proportion of individuals within each stratum in 2018 to account for changes in demographics over time.
Our study has several strengths. Global ASF intakes were estimated using 499 dietary surveys, mostly nationally representative and assessing individual-level dietary intakes and including 3•8 million individuals from 134 countries representing 95•1% of the world's population. Data were harmonised using standardised protocols, with Bayesian hierarchical modelling to incorporate survey and country covariates and address heterogeneity, missingness, and sampling and modelling uncertainty. Findings were assessed over time and by important demographic characteristics including age, sex, education, and urban or rural residence. Potential limitations warrant mention. Survey availability was limited for some (particularly lowincome) countries, demographic groups, time periods and ASF subtype (eg, <200 surveys on cheese and yoghurt vs >400 on milk and unprocessed red meat), increasing uncertainty in these estimates.