dataesr/scientific-paragraphs-categorization huggingface dataset:
For each descriptor set, the model's measures of performance were calculated and several models with a coefficient of determination Q 2 CV ≥ 0. 6 for (R MD ) and Q 2 CV ≥ 0. 7 for (ΔG MD ) were selected for consensus modeling. Consensus models were built for each descriptor type separately. In order to assess a propensity to predict data never seen during the training of the model, a nested cross-validation procedure  35  has been implemented. Here the method hyperparameters were found by optimizing the model performance in the fivefold cross-validation inner loop, while prediction was made for the test set from the outer loop, which represent a fold of the outer fivefold cross-validation cycle. To avoid a bias with the compounds numbering in the parent set, this procedure was repeated five times after reshuffling of the compounds.
Each of the selected models was then associated with an Applicability Domain (AD), defined as a boundary box. The pool of selected models extracted from the given dataset can now be used as a consensus predictor, returning for each input solvent candidate a mean value of solubility estimates and its standard deviation, taken over the predictions returned by each model in the pool or, alternatively, over the predictions returned by only those models having the candidate within their AD. Outlying data points were defined as the data points, for which absolute errors (|χexp-χpred | ) from cross-validation were larger than 2×RMSE CV threshold. The absence of chance correlation was checked through the Y-randomization procedure. A Y-randomization test was performed in the following way: -log 10 χ values (y values) were shuffled, models were built using shuffled values and the values from the corresponding cross-validation test set were calculated.
This procedure was repeated 100 times for each fold and the maximum values of the out-of-bag coefficient of determination were reported. A library for virtual screening was performed in the following way. At first, all compounds from ZINC database with molecular weight no larger than 250 g/mol and calculated logP in the range of (-1,1) were retrieved. Structures were standardized and then filtered. All compounds which did not contain tertiary amines, compounds, containing double bonds, aromatic rings, primary or secondary amine groups, ketones and sulfur-containing compounds except for thiols and thioethers were removed. Structures of screened compounds as well as predicted values are available at https://github. com/AxelRolov/ CO2_chemical_solvents. Experimental measurement of CO 2 absorption rates.
The capillary samplers (ROLSI®, Armines' patent) are capable of withdrawing and sending micro samples to a gas chromatograph without perturbing the equilibrium conditions over numerous samplings, thus leading to repeatable and reliable results. Analytical work was carried out using a gas chromatograph (PERICHROM model PR2100, France) equipped with a thermal conductivity detector (TCD) connected to a data software system. Helium is used as the carrier gas in this experiment. The model of the GC column is Porapak R (Porapak R 80/100 mesh, 1 m × 2 mm ID Silcosteel). Each ROLSI® sampler is connected to a TCD. A tube allows either to evacuate or to introduce CO 2 from or into the cell. The kinetics of gas absorption are determined by recording the pressure drop through a calibrated pressure transducer.
A computer equipped with data acquisition system records the pressure as a function of time. The experimental procedure is the following: The desired amount of solvent is introduced into the cell. The density obtained using a low-pressure vibrating tube densitometer (Anton Paar DSA 5000) is used to determine the exact mole number of solvent. At least 5 bar of methane is added. We add methane because with this configuration, it is not possible to sample at pressures lower than GC carrier gas pressure. CO 2 is added from the thermal press. We record pressure and temperature before and after the loading (see Supplementary Fig. 7  as an example). It permits to calculate very accurately the mole number of CO 2 introduced and so, we can estimate very accurately the loadings of CO 2. The experimental method  36  is similar to the one used to calculate the solubility of CO 2 in alkanolamine amine solution at equilibrium.
Data availability All the experimental data are available in Supplementary Materials and at https:// github. com/AxelRolov/CO2_chemical_solvents. Structures of compounds, descriptors and predicted values are also available at https://github. com/AxelRolov/ CO2_chemical_solvents. The data are also deposited into a DOI-minting repository ZENODO: https://doi. org/10. 5281/zenodo. 6010667.
Code availability Jupyter notebooks containing the Python code used for model building, evaluation and virtual screening are available at https://github. com/AxelRolov/CO2_chemical_solvents. The code is also deposited into a DOI-minting repository ZENODO: https://doi. org/ 10. 5281/zenodo. 6010667. Python libraries used for machine-learning and OPERA software are freely available. ISIDA-Fragmentor is available upon reasonable request to Prof. Alexandre Varnek.
67, P = 1. 69e-2) (Figure  S4 ). The most numerous lineages (DcSIRE, DcRetand and DcAthila) contained relatively few single-copy subfamilies (9%, 18% and 19%, respectively) and showed high solo LTR/intact  2. 14 and 4. 57, respectively). Interestingly, the other four lineages with more than 140 copies, DcTekay (Gypsy), DcAle (Copia), DcIvana (Copia) and DcReina (Gypsy), showed higher frequencies of single-copy subfamilies, ranging from 48% to 65%. In particular, DcAle was the most diverse lineage, comprising 174 subfamilies with no more than eight copies per subfamily. Moreover, all but one of those four lineages were characterized by low solo LTR/intact LTR-RT ratio (below 0. 61), whereas for DcTekay the ratio was much higher (3. 82). The abundance of solo LTRs in DcTekay might be explained by the estimated age and fact that these subfamilies carried longer LTRs and had a larger proportion of LTR length to total length of TE (Data S2), as shown for other LTR-RTs in plant genomes  (Vitte et al.
Active LTR retrotransposons in carrot callus cultures Identification of LTR-RT-derived eccDNAs. Illumina sequencing libraries were produced for four carrot eccDNA samples derived from two pairs of callus sublines, derived from different donors (DH1 line and 'Koral' cv. ). Each pair of sublines differed in terms of callus morphology: the 'Koral'-derived sublines, K10w and K10p, produced white and purple callus, respectively, whereas the DH1-derived sublines, DH1py and DH1do, produced pale-yellow and dark-orange callus, respectively (Figure  S8 ; Table  S3 ). The overall alignment rate to the DH1 carrot reference genome was around 70%. Reads mapping to the database of carrot LTR-RTs varied from 9. 82% to 23. 20% of the filtered paired reads for DH1py and K10p, respectively. In order not to downplay the information contained in approximately 30% of the eccDNA reads that did not map to the reference assembly spanning 421.
Plant material and genomic data The carrot DH1 reference genome (GenBank assembly accession no. GCA_001625215. 1; Iorizzo et al. , 2016)  was used to search for LTR-RTs. Each of the two callus lines derived from the DH1 line and 'Koral' cv. (DH1 and K10, respectively) were divided into pairs of sublines differing in callus morphology (Figure  S8 ). The DH1derived sublines were pale yellow (DH1py) and dark orange (DH1do)  (Klimek-Chodacka et al. , 2018) , whereas those derived from 'Koral' were white (K10w) and purple (K10p)  (Oleszkiewicz et al. , 2018). The callus sublines were kindly provided by Prof. Rafał Bara nski (University of Agriculture in Krakow, Poland) and used to investigate the activity of LTR-RTs. Raw reads from 31 resequenced genomes of D. carota (NCBI Sequence Read Archive, accession SRP062070, under umbrella project PRJNA285926; Table  S18 ), comprising 13 wild and 18 cultivated carrot accessions  (Iorizzo et al.
The age of each copy was estimated based on the similarity of its LTRs. Left and right LTRs were aligned using MAFFT 7. 471  (Katoh & Standley, 2013) , with default parameters. DNA distance was calculated in R APE 5. 4-1  (Paradis & Schliep, 2019)  using the 'K80' model. Subsequently, insertion time was estimated using the formula T = K/2r, with the substitution rate of 1. 3 9 10 À8 per site per year, as proposed by  Ma et al. (2004)  and  Wicker & Keller (2007). To define genomic positions of solo LTRs, we used sequences of LTRs extracted from full-length LTR-RTs as a query for a BLASTN  (BLASTALL 2. 2. 26; Altschul et al. , 1997)  against the carrot reference genome disabling sequence filtering with Dust (-F F), with other settings set as default. Using a custom PERL script, based on the BIOPERL module  (Stajich et al.
, 2010; Nov ak et al. , 2013 , Nov ak et al. 2020)  to perform comparative analysis with the use of the curated carrot LTR-RT database. Analysis was run for a random sample of 500 000 reads, equally representing eccDNA reads of the two pairs of callus sublines, using a minimum cluster size for assembly = 3 and default settings for the other parameters. Based on the annotation, we kept clusters meeting the following criteria: automatic annotation as class-I TE; over 50% reads with hits to the carrot LTR-RT database; and cluster size > 500. We also performed pairwise v 2 test for retrieved clusters, separately for DH1 and K10 callus sublines, between the total number of reads used for comparative analysis and the number of reads grouped in the cluster. Additionally, to obtain the complete sequence of the LTR-RT, the de novo assembly of eccDNA raw reads was carried out using the A5-MISEQ pipeline  (Coil et al.
, 2015). BLASTN  (BLASTALL 2. 2. 26, Altschul et al. , 1997)  against the carrot LTR-RT database was used to identify eccDNA contigs representing LTR-RTs. Contigs aligned with 90% sequence identity and 80% length coverage were selected for closer inspection. Subsequently, the mapping of eccDNA reads against the carrot LTR-RT database using BOWTIE2 2. 3. 2  (Langmead & Salzberg, 2012)  was performed, with default parameters. The number of mapping reads was normalized by calculating reads per kilo base per million mapped reads (RPKM) values.
To avoid an increase in false positives, we added a step allowing the identification of soft-clipped reads to the TRACKPOSON pipeline: (i) mapping reads aligned with LTR-RTs and their mate pairs against the reference genome using BOWTIE2 2. 3. 2  (Langmead & Salzberg, 2012) , with parameters --time --very-fast-local; (ii) extracting soft-clipped reads using SE-MEi extractSoftclipped (https://github. com/dpryan79/SE-MEI); and (iii) aligning them back to the reference genome using BLASTN (BLAST+ 2. 6. 0). Finally, those reads were extracted using a script included in the TRACKPOSON pipeline  (Carpentier et al. , 2019). It allowed the fast and efficient identification of specific, non-reference insertion sites in regions comprising up to 200 bp. For defined genomic positions of new insertions, we designed site-specific primers and used them for PCR-based validation.
Statistics and plots All statistical tests were calculated in R 4. 0. 0 (R Core Team, 2020). For data sets containing values below 5, simulated P-values were used for calculation. Charts were plotted in R 4. 0. 0 using the following packages: GGPLOT2 (https://github. com/tidyverse/ggplot2/ issues), SUNBURSTR 2. 1. 3 (https://github. com/timelyportfolio/sunburstR), BIOCIRCOS 0. 3. 4 (https://github. com/lvulliard/BioCircos. R), CORRPLOT 0. 84 (https://github. com/taiyun/corrplot), PERFORMANCEANALYTICS 2. 0. 4 (https:// github. com/braverock/PerformanceAnalytics) and CORRR 0. 4. 2 (https:// github. com/tidymodels/corrr). Table  S1. Superfamilies of LTR-RTs identified in the carrot DH1 reference genome. Table  S2. Characteristics of LTR-RT lineages in the carrot DH1 reference genome. Table  S3.
ACKNOWLEDGEMENTS This research was financed by the  Polish Ministry of Education and Science (MEiN)  and the  French Ministry of 'Enseignement Sup erieur et de la Recherche' (Polish-French bilateral program  POLONIUM), the  Polish National Science Center (NCN)  (project  MINIATURA1 2017/01/X/NZ9/00930  and project  OPUS17 2019/33/ B/NZ9/00757 ) and the  University of Agriculture in Krakow  (funds provided by MEiN). The study was set within the framework of the  'Laboratoires d'Excellences (LABEX) '  TULIP  ( ANR-10-LABX-41 ) and of the  ' Ecole Universitaire de Recherche (EUR) '  TULIP-GS  ( ANR-18-EURE-0019 ). MM and OP are supported by a grant from the  French National Agency for Research  ( ANR-21-CE20-0047  ' CropCircle '). We thank  Prof. Rafał Bara  nski and  Dr Magdalena Klimek-Chodacka  for kindly providing callus images and  Prof.
This can be achieved by mass spectrometry (MS)-based immunopeptidomics, a technology originally co-developed by Donald Hunt and Hans-Georg Rammensee  [49] [50] [51]. While in the early days, technical limitations allowed the detection of only a handful of bacteria-derived peptides  52, 53  , mass spectrometry and analysis algorithms have evolved substantially now allowing to detect dozens of bacterial immunopeptides in a single analysis  [54] [55] [56] [57]. None of these recent immunopeptidomics studies however investigated the MHC class I immunopeptidome of Listeria monocytogenes, and the list of known Listeria antigens is rather limited. Only 206 peptides from 79 Listeria antigens are listed in the Immune Epitope Database (IEDB), mainly derived from LLO (69 peptides) and p60 (41 peptides) as well as from plcB, gap, mpl, prfA, and lmo0209 (three peptides each)  58.
TMT labeling of immunopeptides has previously been shown to extend the comprehensiveness of immunopeptide identification by improving peptide ionizability and fragment ion intensity during LC-MS/MS analysis  60. Following spectral identification with the PEAKS software and filtering for high confident hits with a false discovery rate (FDR) of 1%, in total we detected 15,767 host-and 84 Listeria-derived immunopeptides (Fig. 1b  and Supplementary Data 1). Assessment of the peptide length distribution resulted in the expected predominance of 9mers among all identified peptides, and also a considerable proportion of 8mer, 10mer, 11mer, and 12mer peptides, in line with previous reports (Fig. 1c )  56, 61, 62. Submission of 9mer sequences from both HeLa and fected HeLa or HCT-116 cells were dissolved using a mild lysis buffer and purified as described  61.
One-half of the resulting immunopeptides were analyzed by label-free LC-MS/MS analysis on a Q Exactive HF mass spectrometer, while the other half was differentially labeled by TMT, pooled per cell line and fractionated into 12 fractions prior to LC-MS/MS analysis on a Fusion Lumos. Database searching was carried out using PEAKS Studio X+ ™ (version 10. 5 build 20200219)  123, 126. Created with BioRender. com. b Overview of identified and quantified immunopeptides per cell line and in total (Supplementary Data 1). c Immunopeptide length distribution representing the typical dominance of 9mers in the human MHC I-presented immunopeptidome. d 9mer peptide sequences were submitted to MixMHCp 2. 1 for motif deconvolution using sequence logos for visualization of the modeled position weight matrices (PWMs) after unsupervised clustering  64, 65.
1 ), together with negligible predicted binding affinities for Listeria-derived peptides (Supplementary Data 2) excluding significant contamination of MHC Class II-derived immunopeptides. Taken together, we identified more than 15,000 MHC class I peptides presented on Listeria-infected cells. The peptide length distribution and clustering into expected HLA binding motifs supported bona fide detection of the immunopeptides and high quality of the dataset.
High confident detection of Listeria immunopeptides Next, we compared immunopeptide abundancies between the infected samples and the uninfected controls. We used this quantitative comparison as an additional filtering step to select only highconfidence Listeria peptides. Indeed, bacterial immunopeptides are only expected in the infected samples, however, some of these peptides came with intensity values in the control samples suggesting they are likely false positive identifications (note that with an FDR of 1% we still expect about 160 false positive immunopeptides in the total dataset) or quantifications (i. e. , matching of noise peaks). We therefore only selected Listeria peptides for further analysis that were (i) quantified by the PEAKS software in at least two of the infected samples and (ii) showed a higher average abundance in the infected samples compared to the uninfected control.
Furthermore, Listeria peptide sequences were searched against the human database with all possible leucine/isoleucine permutations since leucine and isoleucine residues are virtually indistinguishable by mass spectrometry due to their identical mass. In addition, also sequences found in the human database were removed from the bacterial immunopeptide list. These filtering steps resulted in 68 high confident Listeria peptides originating from 42 bacterial proteins (Fig. 2a  and Supplementary Data 2). 58 of these peptides were predicted by NetMHCpan EL 4. 1 to bind to at least one of the HLA alleles expressed on HeLa or HCT-116 cells (Fig. 2b ) with most peptides binding to HLA-A alleles (Supplementary Fig. 2 ), further supporting their high confident detection  67. Moreover, we synthesized 24 of the Listeria-derived immunopeptides to compare their experimentally recorded MS2 spectrum with the spectrum of their synthetic counterpart.
Listeria antigens are often derived from the bacterial periphery According to their predicted subcellular localization  78  , the majority of the detected Listeria antigens is located either extracellularly or at the bacterial surface (Fig. 3a ). This makes these antigens more easily accessible to the host antigen processing and presentation machinery, likely explaining their overrepresentation compared to bacterial cytoplasmic antigens, as suggested previously  55, 56, [79] [80] [81]. Similarly, the cluster of orthologous groups (COG) annotation revealed cell wall/ membrane biogenesis as most common COG term for identified Listeria antigens (Fig. 3b ). Mapping physical and functional associations between the identified Listeria antigens in the STRING database  82  yielded nine clusters of associated proteins, with the two largest clusters separating again peripheral (virulence) proteins from cytoplasmic proteins (Supplementary Fig.
12003 Da was used for internal calibration (lock mass). Mass spectrometry raw data were searched with PEAKS Studio X + ™ (version 10. 5 build 20200219, Bioinformatics Solutions Inc, Waterloo, Canada) against a database of the human sequences in UniProt SwissProt (version January 2019, 20,413 entries) merged with Listeria monocytogenes EGD sequences from TrEMBL (version April 2019, 2847 entries)  123. Databases were merged using dbtoolkit 2. 0 (version 4. 2. 5)  124. The peptide length was restricted to 8-30 amino acids, and unspecific digestion was chosen as digest mode. Methionine oxidation and N-terminal acetylation were set as variable modifications, and mass error tolerances were set to 10 ppm and 0. 02 Da for parent and fragment ions, respectively. For indicating potential contaminant peptides, the MaxQuant contaminant database (MQ version 1.
6. 3. 4) was enabled  125. FDR estimation was carried out using the decoyfusion approach. Identified peptide sequences were filtered at the PSM level for an FDR of 1% or better prior to label-free or TMT-10plex quantification in PEAKS Studio. Quantification results were not filtered and TMT-labeled peptide quantifications were not normalized before export as csv files for further processing using the Perseus software platform  126. Exported csv files for label-free and TMT-labeled data were loaded into Perseus separately and intensity values were log 2 transformed. After categorical annotation into uninfected, healthy and Listeria-infected sample groups, the data were filtered for at least two valid values in at least one sample group. Missing values were imputed from a normal distribution around the detection limit and a principal component analysis was performed.
Volcano plots were generated by plotting the results of a two-sided Student's t test of Listeria-infected against uninfected samples employing permutation-based multiparameter correction at an FDR of 5%. Heat maps were constructed by z-scoring Listeria-derived peptide log 2 intensities before hierarchical clustering. Calculation of correlation coefficients between Listeria-derived and synthetic peptides Python 3. 7 was used to calculate spectral correlation including spec-trum_utils version 0. 3. 5 and pyteomics version 4. 5. 2  127, 128. Spectrum processing was performed by annotation of fragment ion peaks for a, b and y ions including singly and doubly charged ions, followed by removal of precursor peaks for up to two isotopes and removal of low intensity (<5% of the maximum) peaks. All steps used a 50 ppm mass error tolerance.
Pearson correlations were calculated on the intensities of all annotated fragment ions per spectrum. The code including an example peptide can be found on GitHub (https://github. com/ RalfG/2022-listeria-spectrum-similarity) and Zenodo (https://doi. org/ 10. 5281/zenodo. 5948475). A runnable version of the script can be found online at Binder (https://mybinder. org/v2/gh/RalfG/2022listeria-spectrum-similarity/HEAD?labpath=2022-listeria-spectrumsimilarity. ipynb).
7) APC (1:100, #100712, Biolegend) (Fig. 5 ) or with antibody CD8a (53-6. 7) FITC (1:100, #100706, Biolegend) and TCRbeta (H57-597) APC (1:100, #17596182, Thermo Fisher Scientific) (Supplementary Fig. 6 ). Cells were then fixed and permeabilized with BDCytoFix/CytoPerm solution (#554714, BD Biosciences), intracellular staining using a IFN-ɣ (XMG1. 2) PE antibody (1:50, #505808, Biolegend) was performed in Cytoperm buffer for 30 min at RT. After additional washing steps, samples were measured by a MACSQuant Analyzer 16 (Miltenyi Biotec) and analyzed with FlowJo® software (BD Biosciences). Three spleen samples from LMON_0149 vaccinated mice (Fig. 5 ) and two spleen samples from PBS vaccinated mice that showed cell viability lower than 35% were excluded from the analysis. The gating strategy is outlined in Supplementary Fig.
Acknowledgements We are grateful to  Prof. Pascale Cossart  for sharing the Listeria monocytogenes EGD strain. We thank  Jarne Pauwels  and  Jonas Erzeel  for technical assistance and the  VIB Proteomics Core  for LC-MS/MS analysis. R. L. M. acknowledges support from the  Flemish Institute for Biotechnology (VIB)  and a faculty interim fellowship from the  UGent Faculty of Medicine and Health Sciences. R. V. is funded by a  UGent BOF  postdoc grant ( BOF20/PDO/041 ) and A. G. is supported by a PhD fellowship from the  Higher Education Commission (HEC) Pakistan. F. I. acknowledges support from  Odysseus  grant  G0F8616N  from the  Research Foundation Flanders (FWO)  and D. E. is funded by an  FWO  PhD fellowship for fundamental research. R. G. receives FWO funding ( 1S50918N ) and F. I. , L.
Code availability The code for the calculation of correlation coefficients between Listeria-derived and synthetic peptides including an example peptide can be found on GitHub (https://github. com/RalfG/2022-listeria-spectrumsimilarity) and Zenodo (https://doi. org/10. 5281/zenodo. 5948475). A runnable version of the script can be found online at Binder (https:// mybinder. org/v2/gh/RalfG/2022-listeria-spectrum-similarity/HEAD? labpath=2022-listeria-spectrum-similarity. ipynb).