dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis For data distribution, Shapiro-Wilk normality test was performed. For the within group comparison of behaviour with follow-up, one-way ANOVA (non-parametric, two tailed, repeated measures) was performed with Bonferroni's correction for multiple comparison. For comparison between two groups t-test (nonparametric, Mann-Whitney, two tailed) was performed. For cell count, area of the viable cell and subcellular localization, we performed one-way ANOVA (non-parametric, two tailed, ordinary), in GraphPad Prism 8. 0 (GraphPad Software Inc, San Diego, CA). Distribution of swim-time in the four quadrants were analyzed using a continuous multivariate probability distribution named Dirichlet distribution using a python pipeline. The ethograms in EPM test were analyzed and plotted using a specific algorithm (Kinoscope).
Identification of TPS Genes in R. chinensis The reference rose genome sequence used in this study was downloaded from the R. chinensis 'Old Blush' genome website (https://lipmbrowsers. toulouse. inra. fr//pub/ RchiOBHm-V2/, accessed on 24 February 2022)  [31]. A. thaliana TPS (AtTPS) protein sequences were collected from the Arabidopsis Information Resource (https://www. arabidopsis. org/, accessed on 24 February 2022)  [44]  (Table  S1 ). AtTPS were used as query sequences by BLASTP searches  [45]  (e-value 1 × 10 -6 ) against the Rose's protein sequences to predict the TPS family genes in R. chinensis. In parallel, we downloaded two specific TPS domains (PF03936 and PF01397) in the Pfam database (http://pfam. xfam. org/, accessed on 24 February 2022) and used HMM search tool  [46]  to identify the rose RcTPS genes as well.
Chromosomal Distribution, Duplication Event of RcTPS Genes Duplication event of TPS family genes in rose genome was analyzed using MCScanX program with default parameters  [47]. The chromosome position of each RcTPS gene was mapped on the assembled R. chinensis 'Old Blush' genome (https://lipmbrowsers. toulouse. inra. fr//pub/RchiOBHm-V2/, accessed on 24 February 2022)  [31] , using TBtools software  [48].
Gene Structure, Conserved Motif and Phylogenetic Analysis Multiple sequence alignments between the TPS protein sequences of R. chinensis, A. thaliana, S. lycopersicum and M. domestica were performed using MEGA7. 0 software  [49]. Neighbor-joining (NJ) method was used to construct the phylogenetic tree with bootstrap replications set to 1000 and other parameters set as default values. The information of exon-intron structures was downloaded from the R. chinensis 'Old Blush' genome website (https://lipmbrowsers. toulouse. inra. fr//pub/RchiOBHm-V2/, accessed on 24 February 2022)  [31]. Conserved motifs of TPS proteins were analyzed using MEME program (http:// www. meme-suite. org, accessed on 24 February 2022). The maximum number of predicted motifs in the parameters was set to 10, and the other parameters were set as default.
Analysis of Cis-Acting Elements in the Promoter Region of RcTPS Genes For each identified RcTPS gene, 2000-bp sequence located upstream ATG translation initiation codon, was extracted from the R. chinensis 'Old Blush' genome sequence  [31] , and was then submitted to the PlantCARE database (http://bioinformatics. psb. ugent. be/ webtools/plantcare/html/, accessed on 24 February 2022) to predict cis-acting elements.
Gene Expression Analysis Based on Transcriptome Data Raw transcriptome data from nine different tissues (roots, stems, leaves, stamens, pistils, green petals in the flower buds (FB_GP), color-changing petals in the flower buds (FB_CP) and pink petals in the flower buds (FB_PP), and pink petals of the open flowers (OF_PP)), were downloaded from NCBI under BioProject PRJNA546486  [50]  and BioProject PRJNA351281  [51]. The SRA files were converted to FASTQ files using the SRA toolkit (http://www. ncbi. nlm. nih. gov/Traces/sra/, accessed on 24 February 2022). Then the FastQC software  [52]  was used to assess read quality, and Trimmomatic  [53]  was used to discard low quality portions of reads (QUALITY: 15, LEADING: 20, TRAILING: 20, MINLEN: 50, SLIDINGWINDOW: 5: 20). The reads were subsequently mapped to R.
chinensis 'Old Blush' genome with HISTA2  [54]  and the bam files were processed to analyze gene expression quantitatively using StringTie  [55]  with default parameters. Fragments per kilobase per million mapped reads (FPKM) values were used to evaluate transcript abundance of RcTPS genes. The heat map of expression profiles of RcTPS genes in different tissues was drawn using the TBtools software. The expression levels of RcTPS genes are expressed by log2-based FPKM with row standardization, other parameters were set as default parameters.
The glyceraldehyde-3-phosphate dehydrogenase (RcGAPDH) was used as housekeeping gene to normalize samples  [56]. The relative expression values and error values were calculated using the comparative CT(2 -∆∆CT ) method  [57]. Gene expression values were log2 transformed and the TBtools software was used to generate the heatmaps. Statistical analysis was performed using one-way ANOVA along with Duncan's multiple range test using SAS version 9. 4 (SAS Institute), and P <0. 05 were considered significant.
Identification, Classification and Properties of RcTPS Genes To identify TPS genes in the R. chinensis genome, we performed a BLASTP search using known A. thaliana TPS protein sequences as queries. To further verify the identity of the detected rose TPS sequences, the hidden Markov model (HMM) profile of the conserved C-terminal domain (PF03936) and N-terminal (PF01397) domain of TPSs was downloaded from the Pfam database  [58]  and used as a query in the HMM search. All putative TPS genes were further confirmed by SMART  [59]  and NCBI-CDD searches for the presence of the complete domain. We identified 49 TPS gene candidates in the R. chinensis 'Old Blush' genome (named RcTPS01 to RcTPS49; Table  1 ). The lengths of the proteins encoded by these RcTPS genes varied from 455 (RcTPS07) to 857 (RcTPS49) amino acids.
Analysis of Cis-acting elements in the Promoter Region of RcTPS Genes Most of the functional cis-acting elements is concentrated within proximal promoters, usually in the region from -1,000-bp to + 200-bp relative to the transcription start site (TSS)  [63]. In this study, we analyzed the 2000-bp nucleotide sequence located upstream of the ATG initiation codon for all RcTPS genes using the PlantCARE database  [64]. We identified three categories of cis-acting regulatory elements that share similarities with previously reported motifs associated with plant growth and development, phytohormone response and stress responses (Figure  6 ). Cis-acting regulatory elements involved in plant growth and development mainly include flowering regulation elements (CCAAT-box, AT-rich element, and MRE), endosperm-specific expression elements (GCN4-motif and AAGAA-motif), a shoot and root meristem expression element (CATbox), a seed-specific regulation element (RY-element), and a shoot-specific expression element (As-1 element).
Analysis of Cis-Acting Elements in the Promoter Region of RcTPS Genes Most of the functional cis-acting elements is concentrated within proximal promoters, usually in the region from -1,000-bp to + 200-bp relative to the transcription start site (TSS)  [63]. In this study, we analyzed the 2000-bp nucleotide sequence located upstream of the ATG initiation codon for all RcTPS genes using the PlantCARE database  [64]. We identified three categories of cis-acting regulatory elements that share similarities with previously reported motifs associated with plant growth and development, phytohormone response and stress responses (Figure  6 ). Cis-acting regulatory elements involved in plant growth and development mainly include flowering regulation elements (CCAATbox, AT-rich element, and MRE), endosperm-specific expression elements (GCN4-motif and AAGAA-motif), a shoot and root meristem expression element (CAT-box), a seedspecific regulation element (RY-element), and a shoot-specific expression element (As-1 element).
Phytohormone-responsive elements included a salicylic acid-responsive element (TCA-element), gibberellin-responsive elements (P-boxes, GARE motifs, and TATC boxes), an auxin-responsive element (TGA-element), an ethylene-responsive element (ERE), an abscisic acid-responsive element (ABRE) and jasmonic acid-responsive elements (TGACG motifs and MYCs). The stress response category included defense and stress responsiveness (TC-rich repeats), wounding responses (WRE3 and WUN-motif), anaerobic induction (ARE), drought response (MBS), dehydration response (DRE), low temperature response (LTR) and stress response (STRE) (Figure  6 ). These data indicate that RcTPS genes are likely regulated by multiple phytohormones and abiotic stresses. low temperature response (LTR) and stress response (STRE) (Figure  6 ).
Expression Patterns of RcTPS Genes in Different Tissues of R. chinensis The transcriptome data of nine rose tissues, including roots, stems, leaves, stamens, pistils and flowers, at different developmental stages were downloaded from the NCBI to analyze the tissue-specific expression patterns of RcTPS genes. RcTPS17, RcTPS29 and RcTPS45 were not expressed in any of the nine analyzed tissues. Sixteen percent (8 of 49), 6% (3 of 49) and 18% (9 of 49) of the RcTPS genes showed the highest transcript abundance in roots, stems and leaves, respectively. Notably, 53% (26 of 49) of the RcTPS genes showed the highest transcript abundance in floral organs. Among them, RcTPS23 showed the highest transcript abundance in the stamen; RcTPS02, RcTPS08, RcTPS11, RcTPS13, RcTPS22, RcTPS27, RcTPS31, RcTPS33, RcTPS40 and RcTPS41 in the pistil; RcTPS12 in the green petals of the flower bud; RcTPS20 and RcTPS37 in the color-changing petals of the flower bud; RcTPS14 and RcTPS19 in the pink petals of the flower bud; and RcTPS01, RcTPS06, RcTPS09, RcTPS16, RcTPS26, RcTPS34, RcTPS35, RcTPS36, RcTPS44 and RcTPS46 in the pink petals of the open flower (Figure  7 ).
In A. thaliana, four TPS-a proteins are cytosolic sesquiterpene synthases, one is a mitochondrial sesquiterpene synthase, and the remaining TPS-a proteins are plastidial or mitochondrial diterpene synthases. Six A. thaliana TPS-b proteins are monoterpene synthases that produce myrcene or ocimene  [18]. In S. lycopersicum, all 15 TPS-a proteins have been reported to be sesquiterpene synthases  [20]. In our study, 34 TPS proteins that all belong to the TPS-a subfamily in R. chinensis were predicted to be sesquiterpene synthases, 10 RcTPS proteins were predicted to be monoterpene synthases, and 5 RcTPS proteins were predicted to be diterpene synthases by the Terzyme database  [81]. The 10 putative monoterpene synthases contained 6 TPS-b and 4 TPS-g proteins, and the 5 putative diterpene synthases harbored 1 TPS-c and 4 TPS-e/f proteins.
These findings indicate that TPS proteins within the same subfamily may share similar functions. However, further functional analyses are required to confirm this hypothesis. It was previously reported that plant TPS genes generally exhibit conserved intronexon structures  [18, 19, 82]. In R. chinensis, we found that 39 out of the 44 RcTPS genes of the subfamilies TPS-a, TPS-b and TPS-g contained 7 exons. Five of the remaining RcTPS genes contained fewer exons, probably because of intron loss. In contrast, RcTPS genes in the subfamilies TPS-c and TPS-e/f contained more exons (between 10 and 14), likely because of the presence of an additional exon encoding the ancestral 200 amino acids of unknown function at the N-terminus  [7, 18, 83]. Previously, the features of RcTPS proteins were found to be related to two domains, one each at the N-terminus (PF01397) and Cterminus (PF03936)  [1, 84].
[31]acterization of TPS family identified in R. chinensis. Accession Number, it is annotated in R. chinensis genome (https://lipmbrowsers. toulouse. inra. fr//pub/RchiOBHm-V2/,accessed on 24 February 2022)[31]; Chr, Chromosome; AA, amino acids; MW, molecular weight; pI, theoretical isoelectric point; Subcellular localization is predicted by Plant-mPLoc (http:// www. csbio. sjtu. edu. cn/bioinf/plantmulti/, accessed on 24 February 2022) and pLoc-mPlant (http://www. jcibioinfo. cn/pLoc-mPlant/, accessed on 24 February 2022) tools. a, b indicates the result of Plant-mPLoc and pLoc-mPlant, respectively.
Acknowledgments We appreciate the technical support of  Laura Jaraba Soto. We also thank  Craig MacLean ,  José Penadés ,  José Antonio Escudero  and  Daniel Padfield  for constructive comments. This work was supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  ( ERC  grant agreement no. 757440-PLASREVOLUTION ) and by the  Instituto de Salud Carlos III  ( PI19/00749 ) co-funded by  European Development Regional Fund  'a way to achieve Europe'. The  R-GNOSIS  project received financial support from the  European Commission  (grant no. R-GNOSIS-FP7-HEALTH-F3-2011-282512 ). A. S. -L. is supported by the  European Commission  ( H2020-MSCA-IF-2019 ,  895671-REPLAY ) and by the  European Society of Clinical Microbiology and Infectious Diseases (ESCMID , Research Grant 2022).
J. R. -B. acknowledges financial support by a  Miguel Servet  contract from  Instituto de Salud Carlos III (ISCIII)  (grant no. CP20/00154 ), co-funded by  ESF , ' Investing  in your future',  CIBERINFEC , co-funded with  FEDER  funds, and project  PI21/01363 , funded by  Instituto de Salud Carlos III (ISCIII)  and co-funded by the  European Union.
8. 1. 3 ( 813 Medaka v1. 4. https://github. com/nanoporetech/medaka) was used to obtain consensus sequences. Several rounds of Pilon 50 v1. 24 were performed mapping the trimmed Illumina reads. Contigs were rotated with Circlator fixstart 51 v1. 5. 5. Longread assembly quality was controlled in IGV 52 v2. 11. 1. PVs assemblies were confirmed by mapping short-and long-reads and by aligning the assemblies to the reference pOXA-48_K8 (MT441554) with BWA-MEM 53 v0. 7. 17 and minimap2 54 v2. 21. Alignments were visualized in IGV. Closed assemblies were annotated with PGAP 55 v2021-07-01. build5508. Breseq v0. 36. 0 was used to identify SNPs and structural variants. To discard false-positive calls different Supp. Fig. 3 Plasmid copy number (PCN) and conjugation rates do not correlate with plasmid fitness costs.
38 v3. 15. 2. Assembly quality was assessed with Quast 39 v5. 0. 2. All assemblies reached a size of 4. 6-4. 8 Mb and contigs >500 bp count was under 110. Prokka 40 v1. 14. 6 was used to annotate genomes. Snippy v4. 6. 0 (https://github. com/tseemann/snippy) was used to identify variants in the E. coli J53 genome by mapping Illumina reads back to its assembly. Variants in the J53 strains carrying PVs were called with Snippy and breseq 41 v0. 35. 6 using the annotated E. coli J53 genome as reference. Variants matching in J53 were discarded as assembly errors. From breseq output only.
Results and discussion Design of the methodology for CO 2 absorbents screening. The workflow of the methodology is presented in Fig. 1. Chowdhury et al. 20  published a consistent experimental dataset of the absorption rates of CO 2 for 24 aqueous tertiary amines (313 K, 30 wt% amine). In the absence of a clear relationship between the structure or the chemical properties (e. g. , the basicity) of the amines and the CO 2 absorption rates, we developed a molecular dynamics (MD) based model that can accurately predict those experimental CO 2 absorption rates  19. It was found that, while the basicity of the amine (quantified by the pK a ) is important, the key to the precision of molecular simulations is the inclusion of subtle but important solvation effects in the calculation of the activation Gibbs free energy of the reaction with an accuracy better than 1 kJ mol -1.
One of the important features of the MD model  19  is the robustness to reasonable changes in the concentration of amine and in temperature, enabling to apply it to a rather wide range of experimental setups. Hence, the model was applied to predict the rates at 13 mol% of amines and at 323 K, because these conditions are more representative of industrial absorption  5. Being much less resource-and cost-demanding, molecular simulations can thus be used instead of the experiments to get enough data for building a reliable QSPR model with a wide applicability domain. Molecular simulations of CO 2 absorption process. A dataset containing 100 structurally diverse tertiary amines was composed based on the in-house TotalEnergies's dataset of amines with known experimental properties, complemented with tertiary amines extracted from literature and public databases (PubChem  21, 22  , ZINC  23, 24  ).
This is in line with the data from Chowdhury et al. 20  , who showed that 3-piperidino-1,2-propanediol (3PP-1,2-PD) and 1-methyl-2-piperidineethanol were significantly faster than the industrially used methyldiethanolamine (MDEA). Figure  2c  illustrates that the computed CO 2 absorption Gibbs free energies ΔG MD are almost perfectly correlated with the CO 2 absorption rates, R MD (Spearman ρ -0. 98): the slower the CO 2 absorption, the higher the absorption Gibbs free energy. The correlation is not linear, and the decrease of ΔG MD slows down significantly at higher CO 2 absorption rates. Virtual screening of tertiary amines and experimental validation. Machine-learning algorithms were applied to establish quantitative structure-property relationships and screen a set of tertiary amines from a public dataset.
a A high precision molecular simulation-based model for absorption rate prediction is developed  19  and validated with experimental data on CO 2 absorption rates for 24 tertiary amines  20. The accuracy of the Gibbs free energies of absorption is better than 1 kJ mol -1 in comparison to experimental values  19. b This model is applied to a diverse dataset containing 100 tertiary amine structures to calculate the CO 2 absorption rate (R MD ) and free energy of absorption (ΔG MD ) (see "Methods"). c QSPR models were built for R MD and ΔG MD. d QSPR models were used to select perspective commercially available amines from public datasets. e Experimental measurement of CO 2 absorption rates for selected amines. f The most selective ones can be further studied and eventually tested in a pilot unit.
eXtreme Gradient Boosting (XGBoost))  29  machine-learning algorithms on a merged subset of ISIDA fragments and descriptors generated with the OPERA tool. Although the predictive accuracy in terms of RMSE is of the same order of magnitude as in Kuenemann et al. 13  for absorption rates (Supplementary Table  2  and Supplementary Fig. 1 ), the applicability domain of our models is much larger, since the training set contained three times more compounds. It is worth noting that a QSPR model which did not allow one to achieve an excellent accuracy can still be useful for ranking the amines from the large compounds databases  13, 30. Therefore, we retrieved from the public database ZINC  23  the tertiary amines which were not too large (Mw ≤ 250 gmol -1 ), not too lipophilic (-1 ≤ clogP ≤1), and readily available from suppliers.
It is a measure of the rate of CO 2 absorption. Eighteen amines comprising 7 amines from the initial set of 24 amines from Chowdhury et al. 20  , 3 amines from the diverse dataset of 100 amines, and 8 novel amines that were never present in the training set were purchased and an assessment of their absorption rate was performed (Fig. 3b, c , e and Supplementary Tables  3  and 4 ). Both ΔG QSPR and absorption rates R QSPR were highly correlated with r(CO 2 ) for eight novel amines (Spearman ρ 0. 93) as well as the predicted pK a values. Five out of eight purchased amines absorbed CO 2 faster than MDEA. Two amines: 1methyl-and 1-ethyl-3-pyrrolidinol (EPOL) were especially effective. These compounds represent an interesting class of the tertiary amines, which to our knowledge have not been explored yet.
The methodology is based on the combination of state-ofthe-art molecular dynamics simulations that generate a sufficiently large dataset that are used as an input for machine-learning modelling followed by large-scale virtual screening. In parallel, the approach is experimentally validated. It allowed the identification of amines that absorb CO 2 faster than those currently used in the industry. Since the development of an optimal solvent is a multiobjective task, we believe that the proposed methodology can be provisionally repurposed to application for modeling of other industrially important properties of alkanolamine-based solvents.
19. For the calculation of the regeneration energy, the following three reactions are considered: OH À þ CO 2 ¼ HCO 3 À ð3Þ Amine þ H 2 O þ CO 2 ¼ ammonium þ HCO 3 À ð4Þ Amine þ H 2 O ¼ ammonium þ OH À ð5Þ The free energy of absorption is ΔG 4 (=ΔG MD in Fig. 2 ) = ΔG 3 + ΔG 5 with ΔG 3 calculated from the molecular simulations (ΔG(T) in Eq. (  2 )) in every aqueous amine and ΔG 5 calculated from the amine pK a. Quantitative structure-property relationship modeling. All compound structures were standardized using RDKit  31  nodes in KNIME  32. The standardization procedure included aromatization, stereochemistry depletion, removal of salts/ solvents, neutralization, removal of explicit hydrogens. Standardized structures for 124 amines are given in Supplementary Table  1  and at https://github.
com/ AxelRolov/CO2_chemical_solvents. In all, 193 different ISIDA fragment descriptors were generated using the Fragmentor17 software  26, 27. These fragments represent either sequences (the shortest topological paths with an explicit presentation of all atoms and bonds), atom pairs, or triplets (all the possible combinations of three atoms in a graph with the topological distance between each pair indicated). Various physicochemical properties (pK a , logP, melting and boiling points, vapor pressure, water solubility, etc. ) and several substructural fragments counts (ring count, heavy atom count, etc. ) used as descriptors, were calculated using OPERA v. 2. 6  25. All descriptors used in this work are available at https://github. com/AxelRolov/ CO2_chemical_solvents. Prior to the application of machine-learning algorithms R MD and ΔG MD values were transformed to a logarithmic scale, i.
e. , the negative value of decimal logarithm was taken (-log 10 R MD , -log 10 (-ΔG MD )). Random forest (RF): RF algorithm  28  implemented in sci-kit learn library (v. 0. 22. 1)  33 , was used. The following hyperparameters were optimized (grid search): number of trees (100, 300, 1000), number of features (all features, one-third of all features, log 2 of the number of features), the maximum depth of the tree (10, 30, full tree), bootstrapping (with and without the usage of bootstrap samples for building the tree). XGBoost (XGB): XGBoost algorithm  29  as implemented in XGBoost python module (v. 1. 2. 0; https://xgboost. readthedocs. io/en/latest/python/python_intro. html) was used. The following hyperparameters were tuned during optimization (grid search): number of trees (50, 100, 300, 500), number of features (all features, 70% of all features), number of samples (all samples, 70% of all samples), the maximum depth of the tree (5, 20, full tree), learning rate (0.
3, 0. 1, 0. 5, 0. 05). All other parameters were left as default. Support vector regression (SVR): SVR algorithm  34  implemented in sci-kit learn library (v. 0. 22. 1), was used. The descriptors were scaled to the [0,1] range before applying the algorithm. The following hyperparameters were tuned during optimization (grid search): kernel (linear, rbf, poly, sigmoid), kernel coefficient (1, 0. 1, 0. 01, 0. 001, 0. 0001), regularization parameter (0. 1, 1, 10, 100, 1000). The modeling workflow was implemented using the sci-kit learn library (v. 0. 22. 1) in Python 3. 7 scripting language (Supplementary Fig. 2 ). Identical modeling workflows were used for modeling absorption rates (R MD ) and energies of absorption (ΔG MD ). The values were expressed as negative logarithms of base 10. At the first stage of the modeling, a machine-learning algorithm: RF, SVR, and XGB were tested in fivefold cross-validation, which was repeated five times.