dataesr/scientific-paragraphs-categorization huggingface dataset:
Training dataset We gathered the assembled genomes, provided as contigs, of 1,665 strains to develop MIC prediction models for K. pneumoniae  [8]. This set of genomes defines our training dataset. We focused on the 10 clinically most relevant antibiotics (listed in Table  1 ), which belong to 7 different antibiotic classes. The reference MICs were cast into resistant, susceptible, and intermediate according to the Clinical and Laboratory Standards Institute breakpoints. The intermediate and resistant strains were finally merged into a common category to define a binary classification problem aiming to distinguish susceptible (S) from nonsusceptible (NS) strains.
k-merization of the training dataset The k-merization was computed from the contigs of all training genomes, using the DBGWAS software  [25] , with a k-mer size of 31 and filtering patterns with a minor allele frequency (MAF) <1%. As discussed in previous studies  [22, 25] , k = 31 is a safe default choice, offering good predictive performance while preserving the specificity of the k-mers to particular genomic loci, which is key to annotating them properly. DBGWAS allows for the deduplication of the strictly equivalent k-mers by compacting overlapping non-branching paths of k-mers into unitigs, thanks to the use of a compacted de Bruijn graph (cDBG) (Fig. 1A ). DBGWAS stores the profiles of presence/absence of each unitig in the training genomes in a matrix V such that V i, j = 1 if the jth unitig is present in the ith input genome and V i, j = 0 otherwise (Fig.
Test dataset To validate the predictive performance of the models, we built an independent test dataset involving 634 strains, including 114 strains from our bioM érieux collection (NCBI Bioproject PR-JNA449293 and PRJNA597427) and 520 strains from the PATRIC database (https://www. patricbrc. org/). Such strains were mostly from the USA, the UK, Serbia, Greece, and other European countries, and the MICs were obtained with either agar dilution, broth microdilution, or VITEK 2 (bioM érieux, Marcy l' Étoile, France) (see Supplementary Section S1). Table  1  provides the number of S/NS phenotypes available in the test dataset.
Learning step Finally, we summarize each identified cluster as a new composite variable, defined as the average of the original variables defining the cluster, and carry out a standard lasso at the cluster level. Because in our case the original variables encode the presence/absence of a given DBGWAS pattern in the genomes, these composite variables correspond to the proportion of patterns involved in a cluster that are present/absent in the genomes. Fig. 2  summarizes this 3-step method.
Model selection Our approach involves 3 hyperparameters that must be optimized for each antibiotic: the screening and clustering thresholds s 1 and s 2 used to build the clusters of correlated variables, and the regularization parameter λ involved in the final clusterlevel lasso model. We relied on the glmnet software  [34]  to fit the lasso models involved in both the screening and learning steps. We used the default heuristic proposed by the software to define the grids of candidate values for the regularization parameters. The screening and clustering thresholds were both systematically set to 0. 95 on the basis of preliminary experiments (see Supplementary Section S2), and we relied on a 10-fold crossvalidation procedure to optimize the regularization parameter involved in the final cluster-level lasso model.
For this purpose, we first split the training dataset into 10 folds, stratified by se-Downloaded from https://academic. oup. com/gigascience/article/9/10/giaa110/5928222 by guest on 26 June 2022 quence type and phenotype. This cross-validation strategy allows us to assess the impact of regularization on the predictive performance and feature selection ability of the model, in the absence of genetic drift between the training and test folds. We emphasize, however, that it may lead to an optimistic estimation of the predictive performance, which must not be considered as an estimate of the generalization of the model. The actual generalization of the model is subsequently estimated from the independent test set, using a model trained from the entire training set and the value of the regularization parameter optimized according to the following procedure.
For each of the 10 folds, nine-tenths of the training dataset were used to screen variables and identify clusters. The final cluster-level lasso model was then fit and applied to the held out strains (the test fold), for each candidate value of the regularization parameter. Our model selection strategy aimed to simultaneously maximize its sensitivity and specificity, respectively defined as the fractions of correctly classified non-susceptible and susceptible strains. For this purpose, a receiver operating characteristic (ROC) curve was built for each candidate regularization parameter after completion of the cross-validation procedure, and the point closest to the optimal one (defined by a true-positive rate of 1 and a false-positive rate of 0) was used to define the optimal sensitivity/specificity trade-off.
Interpretation of the predictive signature We use the DBGWAS software to interpret the genomic signatures, based on the cDBG built during the k-merization step. The unitigs defining the patterns involved in the final model are visualized within their neighborhood in the cDBG, which repre-sents their genomic environment and hence provides insight on the type of variant involved, typically a plasmid-based acquired gene vs a local mutation (single-nucleotide polymorphism [SNP] or indel) in a chromosomal region.
Second, it assesses the overall contribution of the gene presence in the decision rule, while, in the lasso signature, this contribution is shared by several distinct yet correlated features. Likewise, Fig. 5  presents the DBGWAS analysis of the lasso and cluster-lasso signatures obtained for cefoxitin. We focused on the 2 first subgraphs provided by the software, which represent the 2 genomic neighborhoods of the most important patterns or clusters of patterns involved in the models. The subgraphs are indeed ordered according to the maximal absolute value of model coefficients among all patterns or clusters involved in the subgraph. While DBGWAS identifies the same resistance genes in both methods (the efflux pump ompK36 and bla KPC ), the nature of the underlying resistance determinants cannot be deduced from the lasso signature.
By its ability to leverage correlations between patterns, the cluster-lasso approach allowed us to identify that the second causal determinant involved in the cefoxitin model is the bla KPC gene, which was brought by a plasmid. As was the case for meropenem, it offers a far better interpretability than the lasso, which did not even explicitly identify bla KPC in its features, but only a specific sequence of its direct plasmidic environment. We emphasize however that this improved interpretability may have a price in terms of predictive performance. Indeed, correlations between genomic features may be overestimated if the training dataset is not diverse enough, which may lead to the reconstruction of oversized clusters. This may for instance be the case here of the second cluster-lasso cluster, which identified bla KPC within a specific plasmid, while bla KPC is known to jump frequently between plasmids  [36] , many of which may not have been observed in the training set.
Performance on the test set Table  3  shows the predictive performance obtained on the test set by the lasso and cluster-lasso signatures, in terms of sensitivity, specificity, bACC, and AUC. We first noted that the lasso and cluster-lasso strategies reached a similar level of bACC for most drugs, although they did not always achieve the same trade-off in terms of sensitivity and specificity. We noted however that the confidence intervals This table summarizes the results obtained on the test dataset by the lasso and cluster-lasso models for the 10 antibiotics, in terms of sensitivity, specificity, balanced accuracy (bACC), and AUC. of the corresponding sensitivities and specificities largely overlapped for all drugs but ceftazidime (Fig. 6  and Supplementary Fig. S8 ), indicating that they were not significantly different between lasso and cluster-lasso, except for 1 drug.
We often observed a serious drop between the predictive performance estimated by cross-validation and that observed for the test set: >5 points of bACC for 6 of 10 drugs, and up to 10 points or more for amikacin, cefoxitin, imipenem, and meropenem (13. 4, 10. 2, 10. 9, and 9. 9 points, respectively). This suggested that the training dataset taken from Nguyen et al. [8]  could not account for the entire diversity displayed by K. pneumoniae. A simple analysis of the strain's resistomes and sequence types (ST) using the kleborate software  [37]  revealed that the prevalence of several STs and well-known resistance genes was sometimes very different in the 2 panels. This latter point is illustrated in Fig. 7  for amikacin and imipenem, which had the largest decrease in performance. Supplementary Section S1 (Fig.
S3 ) shows the difference in the ST prevalence, highlighting that the training set involves 2 main STs (ST307 and ST258), which have a much lesser prevalence in the test dataset. Redesigning the training and test datasets by shuffling the original ones to obtain a homogeneous split fixed this generalization issue (Supplementary Section S7). This illustrates that while ML models can indeed succeed in learning accurate prediction rules, they fail to generalize when the dataset on which they are trained does not account for the overall diversity of the bacterial species. Finally, Table  3  and Supplementary Fig. S9  show an uneven level of prediction performance among the 10 antibiotics considered. The best performances were obtained for ciprofloxacin and ceftazidime, with an AUC ∼95% using either the original or the redesigned datasets (Supplementary Fig.
g. ,  [10, 17, 26, 45] ). The underlying model is moreover easy to interpret because it combines several genomic determinants in a global probabilistic model with weights modulating their respective effects, hence reflecting the fact that they can be associated with different levels of resistance. Last but not least, the R package glmnet offers a very ef-ficient implementation, scaling gracefully to large datasets like the one involved in this study, as shown in Fig. 8. The principle of our method is nevertheless generic and could readily be transposed to other sparse ML algorithms, such as xgboost  [4, 8]  or set cover machines  [26]. Likewise, it could straightforwardly be extended to handle MICs or other phenotypic traits, as well as other types of genomic features (e. g. , relying on SNPs instead of k-mers).
The group-lasso penalty leverages pre-defined groups of features, ensuring that all features of a given group are either active or inactive simultaneously  [48]. This strategy was for instance considered in human GWAS, using groups of SNPs defined spatially to account for their LD  [49]. Transposing this idea to bacterial genomes is challenging because no such prior information is available to define groups, as LD can be genome-wide  [29]. A solution could be to identify clusters of correlated k-mers using agglomerating strategies  [31]  but is hard to carry out in practice from the high-dimensional datasets involving 10 5 -10 6 features encountered in our application. Our approach can therefore be seen as a simple and efficient strategy to approximate such a group-lasso process in very high-dimensional settings.
Indeed, AST is notoriously subject to a high level of technical variability  [52] , which intrinsically brings noise to the reference labels used to train and validate supervised ML models. A natural question therefore arises whether an ML model learned from data provided by a given AST method will generalize to data provided by a different AST method. A dedicated study described in Supplementary Section S11 suggests that this issue was not critical on this dataset, which therefore suggests that the lack of generalization observed on the test set is mainly driven by its genomic heterogeneity with respect to the training set. Finally, the ML methods developed in this study are available in a generic R package that can be easily transposed to other bacterial species, as shown in Supplementary Section S12, and even other applications, not necessarily involving k-mers or AMR phenotypes.
Availability of Source Code and Requirements r Project name: Cluster Lasso r Project home page: https://gitlab. com/biomerieux-data-sci ence/clustlasso r Operating system: Unix r License: GNU GPL-v2 r RRID:SCR 018820 r bioTools ID: clustlasso r Step-by-step procedure for using clustlasso package avail- able at: https://gitlab. com/biomerieux-data-science/clustla sso-dbgwas-integration.
Data collection In each trap, seeds were collected daily for eight days and then grown in a greenhouse at Université Paris-Saclay (Orsay, Essonne, France). If the trap contained less than 50 successfully germinated seeds, all the plants were used for genetic analysis by taking one leaf per plant. Otherwise, only 50 randomly selected plants were used. We genotyped 3104 leaf samples taken from the seed-traps. Several seeds failed to germinate. Every trap with less than 50 plants (except one) was genotyped. Nearly every trap with more than 50 plants (except 7 traps) were genotyped. OSR field plants were sampled by taking one leaf per plant. Ten plants were sampled per field. We were able to genotype a total of 1015 leaf samples from 97 of the 118 fields. For cultivar assignment, we previously had genotype information about 58 cultivars  [54]  that were potentially sown in the area of Selommes from 2002 to 2005: 45 pure-line cultivars (homogeneous homozygous), 11 hybrid cultivars (homogeneous heterozygous), and 2 cultivar associations (heterogeneous genotypes).
Data collection genotypes The two new markers (Na12C08 and Na12E01-A/Na12E01-B) returned 53% of missing data for field and seed-trap genotypes (against 15. 1% for the other markers). We thus focused only on the former eight SSR loci for all the field and seed-trap genotypes. On this basis, for seed-trap genotypes, we only considered genotypes with a maximum of 6 missing alleles (for a total of 16 alleles). We finally obtained 2923 proper genotypes. For field genotypes, as they constituted our references, we only considered entire genotypes with no missing data. We obtained 865 full genotypes from 98 fields (only 1 field was discarded). We then constituted a field database reference of 444 genotypes with unique genotypes for each field.
001 au, but this diagnosis was performed only at Hartree-Fock level and simply transferred to the DFT calculations. In order to assess if further contamination could be present in the DFT results of Hait and Head-Gordon,  7  we performed analytical polarizability calculations using the ORCA program package, version 4. 1. 2,  41 ] [10] [11]  All species were treated with the spinunrestricted formalism, and the integrals were computed over an angular Lebedev grid consisting of 770 points and a radial grid consisting of 50, 55, and 60 points for first, second, and third row elements, respectively ("grid7"). Self-consistent field convergence was accelerated by the direct inversion of the iterative subspace (DIIS) method. 42, 43 The total energy change was converged to within 1 × 10 -9 E h , and the one-electron energy change to within 1 × 10 -6 E h (as defined by the "VeryTightSCF" ORCA keyword).
RESULTS AND DISCUSSION A challenge in computational benchmark studies is the precision of the basis set: one has to assume that the computed reference property is at the CBS limit. The large GTO basis set aug-pc4  7, 48  has been assumed to be close to the CBS limit for electrical properties. Here, we attempt to evaluate if aug-pc4 indeed is at the CBS limit for static polarizability predictions, by quantifying the BSIE associated with this basis set. We do this by comparing our reference MW polarizabilities to a recent aug-pc4 benchmark on DFT static polarizabilities. 7 All data presented herein are available via the Supporting Information accompanying this Article, or as a separate document at the Dataverse open-data repository. 49 n order to isolate BSIEs, we need a detailed understanding of other potential errors, and in particular the error associated with using a finite field approach.
Participants and procedure Data were drawn from an ongoing online survey among first-year university students in the French portion of the World Mental Health International College Student surveys. All incoming students received an e-mail inviting them to take part in the survey during the 2019 and 2020 academic year. Participants were provided with a description of the study and informed consent was obtained prior to starting the survey. Only respondents who were 18 years old or older at the time they completed the consent form were eligible. Recruitment and consent procedures were approved by an ethics committee and by the national data protection authority. A brief section that was specific to Covid-19 was added to the survey while French residents were under mandatory confinement. The present study focuses on respondents who completed the survey during that time frame (n = 291).
Data analysis For the present study, respondents who endorsed "not applicable" were removed from analyses for anxiety and alcohol questions, and each stress variable was recoded to reflect 'moderate' or 'very severe' stress (coded as 1) versus 'no stress' or 'mild stress' (coded as 0). Between-group comparisons of those who relocated during the confinement period and those who did not were conducted using chi-squared tests in SPSS.
Background: In the UK certain groups with pre-disposing conditions are eligible for vaccination with the pneumococcal polysaccharide vaccine (PPV23). Uptake of the vaccine in these individuals has not been reported for 10 years. Hence this study investigated the rates of pneumococcal vaccination, the time to vaccination since diagnosis, and factors associated with vaccination in individuals aged 18-64 years with certain underlying medical conditions. Methods: A retrospective database analysis was conducted using the Clinical Practice Research Datalink (CPRD). Individuals aged 18 to 64 years who had a diagnosis for underlying medical conditions of interest at the index date (January 1, 2011 to December 31, 2015) were included in this study. Both underlying conditions and pneumococcal vaccination were identified using Read codes.
Study aim, design and data source The objective of this study was to examine the pneumococcal VCR, potential factors associated with pneumococcal vaccination uptake, and time since diagnosis to pneumococcal vaccination in individuals in the UK with underlying medical conditions. A retrospective cohort study was conducted using anonymised Electronic Health Record (EHR) data from the CPRD, a database jointly sponsored by the Medicines and Healthcare products Regulatory Agency (MHRA) and the National Institute for Health Research (NIHR), as part of the DoH in the UK. The CPRD contains over 13 million records, of which 5 million are active, and is drawn from approximately 650 primary care practices in the UK. This database contains clinical and prescription data, which is a valuable source of information to inform policy makers by supporting real world public health research.
Study variables Underlying medical conditions of interest within this study included the following: asplenia, chronic heart disease, chronic liver disease, chronic kidney disease, chronic respiratory disease, diabetes, and immunosuppression. These variables were chosen based on the clinical risk groups recommended for pneumococcal vaccination within the Green Book  [9] , and data availability within the CPRD. Underlying medical conditions were identified solely by Read code (a coded thesaurus of clinical terms used by primary care within the National Health Service in the UK). Persons without evidence of these underlying medical conditions were classified as healthy. Pneumococcal vaccination was also identified by Read codes. The pneumococcal VCR was calculated for 1 year following the index date (defined as the date of the first diagnosis of any of the underlying medical conditions of interest between January 1, 2011 to December 31, 2015) and for the average follow-up period to examine whether pneumococcal vaccination had been given.
Study sample and follow-up period Individuals who were at least 18 years of age and before their 65th birthday with a clinical diagnosis for one of the conditions of interest at the index date (January 1, 2011 to December 31, 2015) were included. Individuals were required to have had continuous enrolment in the database for at least 2 years before the index date to ensure no presence of any underlying medical condition of interest, and for 1 year following the index date (see Fig. 1 ). Individuals who had any underlying medical conditions of interest within 2 years (730 days) prior to the index date or a record of vaccination were excluded.
Limitations As a primary care database, CPRD has a number of limitations in terms of recording medical conditions. Immunosuppressive conditions (such as human immunodeficiency virus [HIV]) are widely underreported in the primary care setting due to a number of reasons. These include patient confidentiality, coding being unable to allow detailed analysis of transplants leading to immunodeficiency, and certain immunosuppressive therapies which may not be completely captured due to their non-primary care usage. Finally, vaccinations received before after the study period were not captured, as laid out within the methodology of the study. Consequently, CRPD data may not capture the true vaccination rates in patients with IPD. Whilst this study is subject to limitations in collecting data from primary care sources, including variations in data reporting and collection across GP catchment areas, this is an important analysis of a robust data source that reflects real world practice to measure VCR within this population.
Acknowledgments The authors thank  Kelly D. Johnson  and  Thomas Weiss  for facilitating internal coordination, protocol development and ethics submission. The authors thank TigerMed for conducting data analysis for the project who received funding from  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ,  USA. The authors thank  Francesca Tavella ,  Hannah Collings , and  Robert Taaffe , employees of  Adelphi Values Ltd. who received funding from  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ,  USA , for medical writing assistance.
Availability of data and materials Source data for this study were obtained from the Clinical Practice Research Datalink (CPRD). These data sources are made available for scientific and medical research after submission of a study protocol to be reviewed and approved by the CPRD Independent Scientific Advisory Committee (ISAC). Owing to ethical restrictions, the data used in this analysis are not publicly available, in line with the data privacy rules set up by CPRD/ISAC. Data access queries can be directed to enquiries@cprd. com.
RNA pellet was dissolved in 100 ml of RNASecure (Thermo Fisher Scientific, Waltham, MA, Cat# AM7006). Additional removal of DNA was required by adding an equal volume Sigma Tri-Reagent (Sigma, St. Louis, MO, Cat# T9424) using manufacturer's instructions with two additional purification steps using Phenol : Chloroform and final elution in 100 ml of RNASecure (Thermo Fisher Scientific, Waltham, MA, Cat# AM7006). One mg of RNA sample were loaded on a Low EEO/Multipurpose Agarose gel (Thermo Fisher Scientific, Waltham, MA, Cat# BP160) and normalized using a Typhoon 9600 FLS (GE Healthcare, Marlborough, MA) with ImageQuant TL Image Analysis Software version 8. 1 (GE Healthcare, Marlborough, MA). All genotype by time point combinations had at least three biological replicates and most had four biological replicates (Supplementary Tables  1  and 2 ).