dataesr/scientific-paragraphs-categorization huggingface dataset:
A novel cgMLST as a tool for identification of Y. pseudotuberculosis clusters From our 1,346 Yersinia reference genomes dataset, constituted for the phylogenetic analysis of the Yersinia genus  (14) , 485 genomes representative of the Yptb species diversity were selected. Selection of the genes was performed as described previously  (14)  and led to the selection of 1,421 Yptb core genes to which the 500 genes of the Yersinia spp cgMLST scheme  (14)  were added, resulting in 1,921 core genes deemed suitable for cgMLST analysis and molecular investigation (Table  S1 ). A database was created for Yptb in the Institut Pasteur's MLST and whole-genome MLST resource (https://bigsdb. pasteur. fr), which uses the BIGSdb software tool  (15, 16). All de novo assembled genomes were uploaded into the isolates database, and the reference alleles of the 1,921 cgMLST loci were defined in the linked database of reference sequences and profiles ('seqdef').
Within BIGSdb, a scan of the genome sequence was performed for each isolate using parameters (min 80 % identity, min 80 % alignment, blastn word size of 20 nt) to check for the presence of each core gene and to determine its allele number. The BIGSdb - Yptb database of cgMLST profiles is accessible at https://bigsdb. pasteur. fr/yersinia/. A comparison of the allelic profiles can be performed either with the 'Genome comparator' plugin or by the construction of a minimum spanning tree (MST) with GrapeTree (17) using the corresponding BIGSdb plugin. ( Since 2018 at the YNRL, each isolate identified as Yptb is also submitted routinely to this new cgMLST to evaluate its genetic distance with other isolates from the database. When a cluster of isolates (≤ 5 allelic difference) is identified, the YNRL alerts SpF who determines whether an epidemiological investigation is required.
Core-genome SNP analysis Genome sequences of all the isolates and paired-end quality-filtered FASTQ files were obtained as described by Savin et al. (14). Variant calling was performed using the IP32953 reference strain (accession number: NC_006155) with Snippy version 4. 6. 0 and core-SNPs were extracted using snp-sites (https://github. com/tseemann/snippy). A comparison of the isolates using the core-SNPs was performed by the construction of a MST with GrapeTree  (17).
Historical diversity of Y. pseudotuberculosis isolates in France According to the French YNRL database, among the 324 Yptb isolates received between 1991 and 2019, seventeen lineages currently circulate in France: isolates from lineages 15 and 10 are the most frequent (76 and 70 isolates respectively), followed by lineages 17, 5, 7, 2 and 16 (Figure  S1 ). Even if the number of isolates has been quite stable over time (11. 2 ±5. 2 per year since 1991), very few strains were reported in 1997 and 2002 (2 isolates each year) while a peak was observed in 2005 (28 isolates) (Figure  S1 ).
Cases were defined as any patient with identification of a Yptb lineage 16 isolate in the YNRL national database, from any type of specimen sampled from July 1 st in metropolitan France. In total, 8 cases were identified with sampling dates between July 23 rd and September Food investigation established that the suspected tomatoes originated from a local production unit, based in the same geographical area. On-site inspection did not identify any non-conformity potentially leading to contamination of the tomatoes during production, harvest, storage, packaging, or transport, nor any problem with traceability. Three other companies commercialized tomatoes from the suspected batch, but no trace back could be performed as no sales records were kept. The tomatoes were not rinsed before distribution and the water used for irrigation came from the public distribution network for agricultural use.
Performance comparison between the novel Y. pseudotuberculosis cgMLST and classical cgSNP analysis Taking advantage of our novel, easy-to-use Yptb cgMLST scheme, we compared the performance of both methods on 39 lineage-16 clinical isolates from our French database  (1969 -2020). Minimum spanning trees (MST) were reconstructed with both cgMLST data and cgSNP analyses (Figure  3 ). cgMLST-based MST allows to determine the genetic distance in terms of allelic distance. Among the 39 studied isolates, 37 cgMLST profiles were identified. Only 3 isolates belonged to the same cgMLST profile, and they corresponded to specimens from the 2020 Corsica outbreak (Figure  3. A). Pairwise allelic distances between the 39 isolates (table  S3 )   S3 ). ( The cgSNP-based MST (Figure  3. B) allows to determine the genetic distance in terms of point mutations (SNPs).
Acknowledgements This project received funding from  Santé publique France (Saint-Maurice, France)  and the  LabEX Integrative Biology of Emerging Infectious Diseases  ( ANR LBX-62 IBEID ). This work used the computational and storage services (MAESTRO cluster) provided by the  IT department at the Institut Pasteur, Paris. We acknowledge the continuous support of  Keith Jolley  ( Oxford University ) for the development of the BIGSdb web application.
The complete source code for this analysis and raw sequencing data is available at https://gitlab. com/morganlab/Simpson2021 and http://www. ncbi. nlm. nih. gov/bioproject/781964. Detailed methodology is in Supplemental Methods. In the healthy gut, resident AIEC can utilize a variety of mucus-derived substrates, including fucose and EA, for growth, with use of fucose/rhamnose linked to symbiosis. In the inflamed ileum, the mucosal niche of AIEC is characterized by loss of mucolytic Bacteroides and symbiont Firmicutes and culture of Enterococcus, oxidative stress, and an increase in metabolites associated with hypoxia and loss of barrier integrity. AIEC are better able to utilize regionally available EA than non-AIEC, and they cometabolize ileitis-associated amino acids and GSH to support metabolism of EA and resist stress in the inflamed and hypoxic mucosa.
Acknowledgments Grant support was provided by  CCFA/Helmsley Trust (KWS, RBS , and EJS: 1625 G RH551 CCFA-Helmsley Trust, 3142  Crohn's & Colitis Foundation of America) ;  Jill Roberts Center for Inflammatory Bowel Disease (KWS, SZ , and BD); and  NIH  (RL and KWS: 1  R01 DK114252 :  RBS ,  P01DK094779 ,  P40OD010995 ). We thank  Nestle Research  for 1H-NMR metabolomic analysis and Enterome for providing gene sequences of AIEC. This paper is dedicated to the memory of the late  Jill Roberts. We thank  Elaine Nimmo ,  Marian Aldhous ,  Bryan Bosworth ,  Laeticia Da Silva , and the late  Francis Davis  for technical support. Address correspondence to:  Kenneth W. Simpson ,  VMC2011 ,  College of Veterinary Medicine, Cornell University, 602 Tower Road, Ithaca, New York 14853, USA. Phone: 607.
Lipid matrix models The lipid composition used as our model comprised CER EOS, CER NS, CHOL and FFA C24 in a 0. 4:0. 6:1:1 molar ratio and mimics several aspects of the lipid organization in the SC, including the formation of the LPP and a primarily orthorhombic lateral packing. This composition mimics the lamellar organization and lateral behaviour of more complex models while remaining simple enough for clear data interpretation  [18]. The CER EOS concentration was increased from native concentrations (around 12%) to 40 mol% of the CER content, to ensure the LPP would form exclusively  [19, 20]. This elevation does not affect the LPP's structure  [19]. The lipid composition was then changed by substituting a single CER NS with a FFA C24 and either free sphingosine (x% Sphingo-sub) or FFA C18 (x% FFA sub), with x representing the percentage of CER NS substituted.
FTIR measurements (Varian) Samples were prepared with a method similar to that described in Section 2. 3, but with 1 mg of sample sprayed onto a AgBr window, over an area of 10 × 10 mm. Samples were hydrated in a 100% humidity  environment in deuterated acetate buffer (pH = 5) at 37 • C for >15 h. FTIR spectra were measured with a Varian Bio-Rad Excalibur FTIR spectrometer (Cambridge, MA), combined with a liquid nitrogen cooled broad-band mercury cadmium telluride detector. During measurements, air moisture was purged with dry air. Data were collected at a resolution of 1 cm -1 by the coaddition of 256 spectra over 4 min. Samples were measured over a wavenumber range of 500-4000 cm -1. The thermotropic behaviour was measured in the range of 10-100 • C at a 1 • C interval, resulting in a heating rate of 0.
Measurements at LARMOR The lipid arrangement when 25% of the CER NS in the model was substituted with sphingosine and FFA C24 (25% Sphingo-sub), was measured with the time-of-flight SANS Larmor instrument at the ISIS neutron and muon source (Rutherford Appleton Laboratory, UK). Samples were measured using neutrons with wavelengths of 1 ≤ λ ≤ 13 Å at a fixed incident angle of 2. 5 •. The incoming beam size was 1 × 30 mm, while the 3 He tube detector was set at a 2θ angle of 5 • at a distance of 4. 4 m from the sample. The detector covers an area of 664 × 600 mm with a pixel size of 4 × 8 mm. The SANS patterns were normalized for the profile of the incident wavelength spectrum using a measurement of the direct beam on the detector and an upstream monitor. After normalisation the data were reduced to normalized intensity vs.
q by diffraction focusing  [22] , using the Mantid framework  [23]. This culminated in a q range of 0. 032 to 0. 991 nm -1. A custom made hydration chamber was utilized to ensure the samples remained fully hydrated throughout the measurement. Samples were measured for 160-200 μAmp h of accelerator proton charge (~4-5 h) at 25 • C at each hydration depending on the signal to noise ratio. Scattered data were background subtracted using the Mantid software and the peaks fitted using the Fityk software, a software that offers more control for overlapping peak fitting  [21].
Measurement at D16 The lipid arrangement measurements when 25% of the CER NS was substituted with FFA C24 and C18 (25% FFA sub) were performed on the D16 neutron diffractometer at the Institut Laue-Langevin (Grenoble, France). The measurement and data analysis procedure have been described previously  [11, 18, 24]. In brief, the incoming slit-collimated beam (wavelength 4. 49 Å) was set to 30. 2 mm vertically and 3. 0 mm horizontally. The diffraction patterns were measured in reflection mode, with the sample positioned 0. 955 m from the 320 × 320 mm 3 He detector with a spatial resolution of 1 × 1 mm. Each samples were mounted in an aluminium humidity chamber  [25] , maintained at 25 • C and measured for a total of 5 to 6 h depending on the signal to noise ratio. Samples were rotated (Ω) between 0.
05 and 10. 2 • at detector angle (γ) 11 • (Ω = 0. 05-2. 25 • ) and 12 • (Ω =1. 8-10. 2 • ), at 0. 05 • steps to cover the first 9 diffraction orders. For each diffraction order, the scans measured at the specular angle and ± 0. 1 • (culminating to a total of 5 Ω steps), were averaged together and fitted. The scattering data were reduced, background subtracted, and the peaks fitted using the data processing software LAMP  [26] , peaks were fitted using Fityk. While converting the data from scattering angle (2θ) into q-spacing, a rearranged Bragg equation was used; q = (4π sinθ)/λ (1) When in the lamellar phase, a series of peaks at equal q-distances to one another are detected, and the repeat distance (d) of the lamellar phase can be calculated from the positions of the peaks as. d = 2nπ/q n (2) with n as the order number of the diffraction peak located at position q n.
No changes in the lateral packing and conformational ordering after the substitutional loss of CER NS SAXD data showed phase separation occurring at ≥50% CER NS substitution. To further investigate the apparent indifference when 25% of CER NS was substituted with FFA C24 and either sphingosine or FFA C18, the conformational ordering and lateral lipid organization were examined with FTIR spectroscopy. The conformational ordering is determined by the position of the CH 2 symmetric stretching mode (νCH 2 , 2845-2855 nm -1 ), and the thermotropic response for the 0% sub model is presented in Fig. 4A. The thermotropic response curves for the 0% sub, 25% Sphingo-sub and the 25% FFA-sub samples were similar as shown in Fig. S3 , and at 24 • C the νCH 2 were similar with values of 2848. 9, 2848. 7 and 2848.
Acknowledgements We are grateful to  Evonik  for the donation of CERs used in this study. We wish to acknowledge the  ILL, France  for awarding us time on the D16 beamline (DOI: https://doi. org/10. 5291/ILL-DATA. 9-13-826 ),and the  ISIS neutron facility, UK  for awarding us time on the Larmor beamline (RB1869022 DOI: https://doi. org/10. 5286/ISIS. E. RB1869022 ), and  ALBA  synchrotron.
RESULTS AND DISCUSSION Our study combines structure-based modeling and machine learning to build models for predicting UGT1A1 inhibition. The workflow is shown in Figure  2. For the dataset preparation, we collected known ligands of UGT1A1, inhibitors and substrates, from the ChEMBL, DrugBank, and PubChem databases. We performed curation of the collected compounds (see method details) and finally, 89 actives (listed in the Supplemental Information (SI) and 450 decoys were retained for docking and ML datasets. Approximately 5 times more decoys than actives were used because of the lack of experimentally validated inactive molecules in the dataset. The training and external test sets were constructed by randomly dividing both the final actives and decoys, according to a ratio of 70 and 30%, respectively.
The position and orientation of these catalytic residues are very similar to what is found in the plant flavonoid 3-O-glycosyltransferase VvGT1 (PDB: 2C9Z)  (Offen et al. , 2006) , where the corresponding residues are H20, S18, and F121, respectively. Other regions, especially loop segments at the entrance and the edge of the substrate-binding pocket, show considerable fluctuation among the 6 conformations, including residues 34-39, 99-109, 175-210, and 307-316 (Figure  5 ). The flexibility of these loops also promotes the admission and the accommodation of the diverse ligands and further emphasizes the importance of considering enzyme dynamics in docking experiments, and therefore, in enzyme inhibition prediction studies. Highly correlated descriptors with an absolute value of the Pearson correlation coefficient greater than or equal to 0.
85 and descriptors with near null variance were removed. This selection resulted in a total of 162 descriptors. The IE scores of the compounds of the training set calculated for the six selected UGT1A1 conformations were added as structure-based descriptors accounting for the protein-ligand interactions. To avoid overfitting and decrease the calculation time, we selected the best descriptors based on their relative importance in predicting the interaction with UGT1A1. The selection comprised of building a number of Random Forest (RF) models on the training dataset and selecting the subset of descriptors with the highest Gini importance  (Kantardzic, 2019). The Gini impurity index is a measure of the probability of incorrectly classifying a randomly selected element in a dataset. Thus, we performed 1000 RF runs with the 162 MOE and 6 IE descriptors with the default values of ntree, mtry, and sampsize (Table  S1 ) to calculate the mean importance of the 168 descriptors, according to the diminution of the Gini criterion (see method details and Figure  S2 ).
The first 25 descriptors (including 4 IEs) were most important for the model performance (see Table  S2 ). Then, the importance decreased slowly, and we decided to consider all the descriptors showing an importance greater than 0. 5, including thus a total of 56 MOE and the 6 IEs. The most important descriptors are related to polarity, lipophilicity, and charges. Principal component analysis (PCA) was performed on the 56 best MOE descriptors, and the training and the external test sets are shown in the subspace spanned by the first two PCs in Figure  6. Overall, the training and test sets' compounds covered similar chemical space. Thus, our models are applicable within a domain given by the ''soft'' drug-like filter thresholds (see method details). Of interest, even though our negative dataset contains decoys instead of real non-inhibitors, Figure  6C  shows that an important part of the actives is in different chemical space.
Performance of the ML models in predicting binders of UGT1A1 Firstly, we created initial RF and Support Vector Machine (SVM) models using only MOE descriptors without parameter optimization. These preliminary RF and SVM models showed unsatisfactory performance (shown in Tables  S3  and S4 ), in particular in terms of sensitivity (between 50 and 67% on the cross-validation (CV). Taking into account that our non-inhibitor molecules are decoys, the sensitivity performance is the most reliable assessment evaluation. Because of the strong imbalance in our dataset between the number of active molecules and decoys, the optimization of hyper parameters (see Figure  3S  and method details), including sample size and weight for RF and SVM, respectively, is critical to build robust predictive models.
Binding positions of UGT1A1 ligands Various small and bulky compounds are known to be metabolized by UGT1A1. The predicted binding positions of three different substrates of UGT1A1 as docked into the MD47 structure are shown in Figure  7. Bilirubin was present in our training set. Quercetin and raloxifene, being in the external test sets, were successfully predicted to be binders of UGT1A1 by the two RF and the two SVM models, '56 MOE +6 IE' and '162 MOE +6 IE'. The poses were selected based on the best predicted IEs among the six different UGT1A1 MD conformations. The top scored poses of quercetin (IE = À8. 0 kcal/mol), raloxifene (IE = À10. 2 kcal/mol), and bilirubin (IE = À10. 0 kcal/mol) are shown in Figure  7. Of interest, the binding pose of quercetin corresponds to that of the crystal structure of quercetin bound to the plant flavonoid 3-O-glycosyltransferase VvGT1 (PDB: 2C9Z)  (Offen et al.
g. see in thestructures PDB: 4I3D, 2VUE). V109 stabilizes the pyrrole group, and the two pyrroline cycles are stabilized by L175 and F181, and by P194 and F217, respectively. The docking pose of bilirubin suggests that it adopts a conformation similar to its structure in solution  (Nogales and Lightner, 1995). To further investigate the predicted binding modes of raloxifene, bilirubin, and quercetin, additional 50-ns long MD simulations were performed starting from the docking complexes shown in Figure  7. In the case of raloxifene, its 6-O-glucuronidation site remained in close contact with the catalytic S38, its benzothiophene part maintained a stable contact with F153 whereas its piperidine tail displayed more flexibility. At the beginning of the dynamics the piperidine tail of raloxifene tightly covered the cofactor whereas with time, it lifted slightly in direction of a sub-pocket of Q107, P194, F181 and F217 (see Figure  S4A ).
Dataset preparation We collected 113 known ligands of UGT1A1, inhibitors and substrates, from the ChEMBL (ebi. ac. uk/ chembl), DrugBank (go. drugbank. com), and PubChem (pubchem. ncbi. nlm. nih. gov) databases. Substrates were also included as they could cause concentration-dependent enzyme inhibition, a commonly observed phenomenon for metabolic enzymes  (Wu, 2011). Among the collected 113 actives, 10 compounds had activity between 20 mM and 50 mM, and only 4 compounds had activity above 50 mM. To increase the applicability domain of our models, we thus decided to retain the compounds with activity below 50 mM (IC50). The four compounds showing very low activity (>50 mM) were not included into the dataset. Decoys (putatively inactive molecules) for docking and ML model validation were taken from the diverse chemical compound collection Maybridgeâ HitFinderä (maybridge.
Ensemble docking We performed docking simulations of the final dataset compounds into the centroid protein conformations of the 57 most populated clusters using the software AutoDock Vina 1. 1. 2 (Trott and Olson, 2010) , which employs gradient-based conformational docking and an empirical scoring function predicting proteinligand interaction energy (IE, in kcal/mol). The protein conformations and the ligands were pre-processed with AutoDockTools  (Morris et al. , 2009) , the solvent molecules were removed, non-polar hydrogens were merged, and Gasteiger charges were assigned. A grid box of 24 A ˚3 20 A ˚x 22 A ˚was used with 1 A ˚spacing in the substrate-binding cavity. The maximum number of binding modes was set to 10, and the exhaustiveness of the global search to 8. The protein was kept rigid while the ligands were handled flexibly.
Machine learning classification modeling Random forest (RF) classification  (Breiman, 2001)  was performed using the Random Forest R library  (Liaw and Wiener, 2002)  of the statistical software package R. Multiple decision trees were built with bootstrap samples from the training data. A small subset of descriptors was randomly selected to make decisions at each tree node to introduce diversity between the trees of the RF. The classification was obtained by taking the results of all the trees through a majority vote. To find the optimal size of the forest, 'ntree' (number of trees), and the optimal number of descriptors, 'mtry' (number of selected descriptors), for each model, we ran RF calculations performing an exhaustive nested-loop search of the ntree (128-1024) and mtry (5-50) parameters.
As the dataset is imbalanced, the parameter 'sampsize' (numbers of actives and decoys) was also optimized. We selected the combinations of ntree, mtry, and sampsize parameters for each model that yielded the best internal balanced accuracy (BA) while retaining the lowest acceptable ntree (see Table  S1  and Figure  S3 ). Five-fold cross-validation (CV) procedure was repeated ten times. Support vector machine (SVM) approaches are based on the minimization principle from statistical learning theory and place data into hyperspaces through different kernel functions for its separation into datasets for classification or regression modeling  (Cortes and Vapnik, 1995). For the nonlinearly separable cases, the kernel function allows SVM to transfer the data points into a higher-dimensional space where linear separation is possible.
Assessment of the quality of the models Different statistical quantities were evaluated to assess the predictive ability of the models. Sensitivity, or the true positive rate, is the fraction of true positives among all positively classified instances (Equation  1 ), specificity is the true negative rate (Equation  2 ), and balanced accuracy (BA) is an overall performance estimator used in the case of imbalanced datasets (Equation  3 ). The area under the receiver operating characteristic curve (AUC) was also calculated. The AUC ranges from 0 to 1. Values of 0. 8 or greater generally indicate good to excellent performance of a predictive model. The Matthew's correlation coefficient (MCC, Equation  4 ) was calculated to measure the quality of binary classifications according to the following formulas: Sensitivity = TP TP + FN (Equation  1 )  2 ) Specificity = TN TN + FP (Equation Balanced Accuracy = Sensitivity + Specificity 2 (Equation  3 ) MCC = TP 3 TN À FP 3 FN ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ðTP + FPÞ 3 ðTP + FNÞ 3 ðTN + FPÞ 3 ðTN + FNÞ p (Equation  4 ) where TP and TN are the true positive and true negative, and FP and FN the false positive and false negative instances, respectively.