dataesr/scientific-paragraphs-categorization huggingface dataset:
The Staphopia pipeline, specifically developed for S. aureus, detects only nine SE genes available in the VFDB 2016 database  (Chen et al. , 2015)  (sea, seb, sec, sed, see, seh, sek, seq, tsst-1), because this pipeline was mainly developed to analyze antibiotic resistance  (Petit and Read, 2018). SeqSphere software was mainly developed to analyze core-genome MLSTs and can be also used to detect 18 enterotoxins  (Strauß et al. , 2017). However, the task template for S. aureus and based on sequences of a microarray system does not distinguish sea from sep genes, thus considered as the same gene. It is very important to detect all SE-coding genes and to describe all existing SEs to assess the prevalence of toxigenic strains. An increase in the number of toxins detected may mean an increase in the percentage of toxigenic strains  (Le Loir et al.
, 2003; Bania et al. , 2006; Aydin et al. , 2011). For example, the number of toxigenic strains identified in our study (97. 5% of strains) is higher than that reported in precedent studies: although based on the genes coding for the five classical SE genes (SEA to SEE), the toxigenic strain frequency was around 60% in several countries, such as Turkey, Portugal and Italy (62. 6, 60. 1, and 59. 8%, respectively). In Iran and Louisiana (United States), the detection of nine SEs showed that 77. 6 and 84. 9%, respectively, of strains were toxigenic in these regions  (Pu et al. , 2010; Mashouf et al. , 2015). In another study, the SElX-coding gene is present in 95% of strains, and it was localized in the core genome  (Wilson et al. , 2011). Thus, screening for SElX-coding genes in the dataset can reveal a high percentage of toxigenic strains.
, 2016). NAuRA provides access to the complete nucleic diversity of a specific dataset. This can be useful for the development of new, more efficient PCR methods, for example, by designing primers preferentially in the more conserved region of allele sequences. The study of nucleic diversity can also help to choose the reference protein sequence to use for immunoassays methods, for example. In the UniProt database, only nine SEs are annotated and can serve as a standard for method development (The UniProt Consortium, 2018). Moreover, to harmonize analyses, NAuRA can create a database of newly identified variants to include them in subsequent analyses. The complete egc2 cluster is the most frequent egc variant and is distributed throughout the phylogenetic tree based on the core genome, as revealed in trees based on MLVA and PFGE.
There is no approved specific treatment for the infections caused by CoVs and the development of new anticoronaviral compounds is an urgent task. The coronavirus genome (26-32 kb) is the largest among all RNA viruses. [1, 3] It encodes 4 structural and 16 non-structural proteins, many of which have already been exploited as the targets for anticoronaviral drug discovery. [4] part from viral proteins, numerous host-cell factors required for virus replication cycle can also serve as the targets for the development of CoVs reproduction inhibitors. The data on anticoronaviral activity of compounds has been accumulated in large repositories of bioactivity data such as ChEMBL,  [5]  PubChem BioAssay,  [6]  etc. There are more than 3. 7 thousand activity entries related to more than 2. 1 thousand structures extracted from 143 literature sources in the current ChEMBL version 26.
Datasets and Curation CoV-associated molecules (sometimes simply called "CoV" molecules) were retrieved from ChEMBL  [5]  version 26, following the previously described  [8]  procedure. The latest International Committee on Taxonomy of Viruses (ICTV) master species list (2018b. v2) was downloaded from https:// talk. ictvonline. org/files/master-species-lists/m/msl/8266. All viral species names for Orthocoronavirinae subfamily were retrieved from it. The only species name that did not contain 'coronavirus' as a part of the name was Porcine epidemic diarrhea virus. Thus, substrings 'corona' and 'porcine epidemic diarrhea' were searched in the ChEMBL assays table (fields: "description", "assay_organism", "mc_ organism"), after removing all non-alphanumeric characters from both queries and ChEMBL strings.
Entries from ViralChEMBL  [8]  related to Coronaviridae family were extracted and those not already retrieved from the search above were added. Other compound sets were compiled for comparison purposes. They include excerpts from the ViralChEMBL  [8]  database -compound sets associated to virus species other than CoV -including (a) RNA viruses that are not CoVs but are biologically most related to the latter and (b) some viruses of major concern having attracted a lot of research effort so far (see Table  1 ). To ensure that the kept entries have been tested quantitatively, a simple filter was applied to ensure that a numerical activity value was reported in the "standard_value" field, irrespective of whether this was declared to be an exact value, a minimal or a maximal threshold, and irrespective of units.
Notably,  [17]  a set of 1000 candidates with promising docking scores for the SARS-CoV-2 3CL proteinase were selected by "deep docking" from 1. 3 billion commercially available compounds in the ZINC database. This pool was also analyzed in this study, by positioning it in the context of above-retrieved CoV compounds. All compounds were first standardized according to the business rules implemented on the ChemAxon-powered  [22]  server http://infochim. u-strasbg. fr/webserv/VSEngine. html of the Chemoinformatics Laboratory of Strasbourg (returning standardized unique stereochemistry-depleted strings). These were encoded by the ISIDA fragment descriptors employed by the seven complementary  [16]  UMs  [14]  and then projected, herewith determining the responsibility vectors of every compound on each map.
In Silico Profiling of CoV Molecules As a natural consequence of projecting the pool of CoVtested compounds on the seven UMs, some of them can be directly traced back to chemical space zones shown to be preferentially populated by known "actives" on various (most but not exclusively human) biological targets disposing of significant structure-activity data in the ChEMBL database (v. 24). A battery of 749 such activity landscapes can be rapidly used to predict for which of these activities a given compound appears to reside in an "active" neighborhood. Compounds that are consensually located in active neighborhoods by all the seven UMs are predicted "active" with respect to that target. Activity landscapes are originating from previous studies incorporating tested actives, tested inactives and random decoys.
CoV Activity Class Landscapes and their Quantitative Validation Above-collected structure-activity data include > 100 affinity-related (dose-response) measures with respect to the 3 C-like proteinase (3CL) with 176 tested compounds of the SARS-CoV of the 2003 outbreak (the so-far best explored CoV). Within this series, compounds with IC 50 or K i values below 10 μM were considered active (25/176). This set, albeit small, was used to verify, by means of repeated 5-fold cross-validation, whether the herein employed UMs are capable to support predictive activity class landscapes in terms of binding to the viral proteinase. This crossvalidation procedure consists in iteratively using 4/5 of the set to build an activity class landscape, by assigning each map node a likelihood to host actives -as observed by counting the resident active and inactive compounds.
85, in all cases, Figure  10 ) and the rather modest cross-validation performances. It reflects that many compounds are singletons: often unique of their kind in their chemical space zone. Thus, when left out during cross-validation, there are no relevant analogues in the 4/5 serving to train the landscape: the compound will be projected in a blank spot, and its activity will be inferred on hand of landscape nodes that are too remote to return an Figure  6. Chemical space zone (UM# 1) hosting most virtual hits against protease targets (not limited to the four cited above), and some representative structures thereof. meaningful prediction. There is no point in trying to develop machine-learned structure-activity models on such datasets -they would only achieve an overfit (learning individual items "by heart").
Anti-SARS-CoV-2 "Hits" from RP-based Screening of ZINC A pool of potentially novel compounds to be tested, in priority, against SARS-CoV-2 has been extracted from ZINC, in absence of explicit structure-activity data against the virus in cellular or in vivo tests, or against any of its proteins. The above-mentioned SARS-CoV 3CL proteinase set is (a) too small, (b) uses a not very rigorously defined activity label and (c) is not necessarily extrapolable to SARS-CoV-2. Thus, the most rational approach in our opinion was to extract ZINC compounds characterized by the relevant RPs selected for each map -a broader scope, bound to include many inactive compounds, but not at risk of returning only very close analogs of SARS-CoV compounds that may prove to be inactive against SARS-CoV-2. Relevant RPs defining the Relevant Antiviral Space (RAS) on each map included, each, between 12 (maps 1 and 4) and 29 (map 5) of RPs occurring at least four times in the SARS-CoV library, and between 4 (map 4) and 8 (maps 1, 2 and 5) patterns seen at least twice in the DrugBank antiviral pool.
In silico profiling of CoV compounds against reference biological targets with well-established structure-activity landscapes on the UMs was helpful to highlight once more the dominant protease inhibitor-like molecules -predicted to interfere with the proteases amongst the reference targets. Also, the less often occurring chemotypes (bioactive amines, potentially redox-active compounds) were specifically highlighted by profiling results, as they were predicted to "hit" targets (GPCRs, acetylcholinesterase, monoaminoxidase B) which are not related to antiviral activity but might signal in vivo side effect in mammals. However, profiling unexpectedly suggested that some of these compounds might be histone deacetylase inhibitors -a target that seems to be associated to antiviral activity. Unfortunately, at the current moment in time, the existing data are insufficient to support robust predictive structure-activity models for quick virtual screening of electronic databases.
Acknowledgement The  High Performance Computing (HPC) Center of the Strasbourg University  is acknowledged for technical support. Yuliana Zabolotna, Ph. D. student of the  Chemoinformatics Laboratory in Strasbourg , is acknowledged for the curation and mapping of 1. 5 billion ZINC compounds. D. I. O thanks the  State Research Funding for FSBSI "Chumakov FSC R&D IBP RAS " (research topic no. 0837-2019-0002 ) for support.
We retrospectively analysed the data files of 171 adults and 87 children/adolescents with severe haemophilia, except for 14 patients (moderate; minor) (1), to develop a global population pharmacokinetic (PK) model for eight factors VIII (FVIII) that could estimate individual PK parameters for targeting the desired level of FVIII activity (FVIII:C); and (2) to compare half-life (HL) in patients switching from a standard half-life (SHL) to an extended half-life (EHL) and evaluate the relevance of the switch. One-stage clotting assay for the measurement of FVIII activity (FVIII:C, IU/mL) was used for population PK modelling. The software, Monolix version 2019R1, was used for non-linear mixed-effects modelling. A linear two-compartment model best described FVIII:C. The estimated PK parameters (between-subject variability) were: 2640 mL (23.
Study Design, Subjects, Sample Collection and Assay Method This retrospective multicentre (n = 13) study was a characterisation of interindividual PK variability of data collected between 2012 and 2019 from patients treated with FVIII concentrates for mainly severe haemophilia A. The data were taken from current practice. An individual information note was distributed to patients to ensure that they were not opposed to participating in this study. Thus, the database is in compliance with the reference methodology MR004 of the "Commission Nationale de l'Informatique et des Libertés". All of the patients had severe, moderate, or minor haemophilia A and did not present inhibitors during PK analysis. FVIII:C was measured using the one-stage clotting assay principle. For FVIII:C measurement, the couple, reagent kit-coagulation analyser, varied according to the centres: STA R-STA-CK Prest (n = 8), STA R-Pathromtin (n = 1), and ACL TOP-SynthAsil (n = 4).
The blood samples were collected at different times, before infusion (predose) and between 15 min. and 96 h after infusion. The number of instances of blood collection varied between one and 11 samples per patient (median: 4). The lower limit of quantification (LLOQ) was different among centres: 0. 004 and 0. 01 IU. mL -1 (median: 0. 01 IU. mL -1 ). The percentage of FVIII:C below LLOQ (BLQ) of the dataset was 10. 6%. Table  1  shows the details of the modelling dataset.
PK Analysis Population PK analysis of FVIII:C was conducted while using the nonlinear mixed-effects modelling approach that was implemented in Monolix software (version 2019R1, Lixoft, Antony, France, http://lixoft. com/) using the Stochastic Approximation Expectation Maximisation (SAEM) algorithm  [11, 12]. All of the individual PK parameters were assumed to be log-normally distributed. Exponential random effects were used to describe between-subject variability (BSV). Data that were below LLOQ (BLQ) were handled in a right-truncated Gaussian distribution while using the SAEM algorithm  [13]. To take endogenous and predose FVIII:C (corresponding to the residual before the PK data dose) into account, PK modelling was done at a steady-state. Because we know the time interval between the dose corresponding to predose and the PK data dose, we virtually added four doses before these two last doses (respecting this interval administration) to ensure steady-state for all individuals.
Model Qualification Process The most appropriate pharmacostatistical model was selected and evaluated based on the following criteria: decrease of the objective function (calculated by importance sampling) and BIC; inspection of the usual diagnostic plots (OBS-PRED, IWRES, NPDE, pcVPC); a decrease of BSV and a low relative standard error (RSE < 30%) of PK parameter estimates. In the final model, the 95% confidence interval of each parameter was determined from 1000 nonparametric bootstraps based on resampling  [18, 19]  while using R package Rsmlx  [20]. All of the runs were performed five times using the convergence assessment tool of Monolix, which consisted of different randomly generated initial values of fixed effects as well as different seeds to assess the robustness of the convergence.
External Comparison of Half-Life Estimates Between Our Model and Mc Eneny-King SHL Model [21] Mc Eneny-King et al. [21]  developed a generic population PK model for SHL FVIII. They pooled in their model seven FVIII: Advate, Kovaltry, Kogenate, Novoeight, Refacto AF, Emoclot, and Octonate. Each FVIII was represented by at least 30 PK, except for Emoclot (n = 14). We implemented their model in Monolix software in accordance with the structure of the model by fixing all population parameters, omegas, and error model values. The dataset without missing covariates contained 197 patients because one of the covariates of their model was FFM. Subsequently, the individual PK parameters were estimated and extracted (mode of empirical bayes estimates, EBEs) to calculate HL of each individual and compared to our model.
HL Comparison after Switch The dataset contained switched PK SHL to EHL of 44 patients (a total of 88 patients considering two different patients for each pair). We compared the elimination half-lives of patients who switched from Advate, Factane, Kogenate, Novoeight, or Refacto to Elocta. To do this, we extracted from our qualified final model the mode of the EBEs, which are the most probable values of the individual parameter estimates. The association between different predictors as switching from SHL to Elocta, age and weight, and half-life were assessed while using multivariate analysis using multiple linear regression analysis. The stepping method, with p < 0. 05 for entry and p < 0. 01 for removal, was used for predictor selection. Different assumptions were checked on the residues to test the validity of the linear regression model: (1) the minimum and maximum values of the standardised residual are within [-3, +3] values; (2) the data points are independent and tested by the Durbin-Watson test; and, (3) the distribution of the standardised residuals should be normal, with mean = 0 and a constant variance not different from 1, and graphically by means of a histogram, scatterplot, and Q-Q plot.
Covariate Analysis Continuous covariates were centered on the median of the population dataset. Significant EHL (others: 0, Elocta: 1), age, and weight effects were observed on the Clearance (Cl), and weight effects were observed on the central volume of distribution (V1). These covariates were included in the final model and were associated with a reduction in the unexplained IIV of Cl from 45. 3% to 34. 9% and of V1 from 45. 6% to 23. 2%. βweight on Cl corresponds to the regression coefficient. The weight regression coefficient was fixed at 0. 75 for Cl, because it was in the confidence interval of the estimated one (median value: 0. 779, RSE: 9%)  [22]. We decided not to include the covariate Brand because of insufficient data for some brands (Factane, Kovaltry, Afstyla, Novoeight, Refacto) were causing overparametrisation and overfitting observed with high RSE of these brands (see Table  S1.
HL Comparison Between Our Global Model and SHL Generic Model of McEneny-King et al. [21]. We compared the individual prediction capability of McEneny-King et al. 's model with our dataset to the individual prediction capability of our model. For most of FVIII, the half-life estimates were equivalent-the Refacto (Figure  4 ) was the exception-and showed a high correlation for the whole set of brands: R 2 = 95%. When these two models are compared, Figure  5  allows for one to appreciate the delta (difference) between the estimates of HL.
HL Comparison between Our Global Model and SHL Generic Model of McEneny-King et al. [21] We compared the individual prediction capability of McEneny-King et al. 's model with our dataset to the individual prediction capability of our model. For most of FVIII, the half-life estimates were equivalent-the Refacto (Figure  4 ) was the exception-and showed a high correlation for the whole set of brands: R 2 = 95%. When these two models are compared, Figure  5  allows for one to appreciate the delta (difference) between the estimates of HL.
Discussion We developed and qualified a population PK model of FVIII:C from eight different forms of FVIII while using data provided from clinical practice. This work was original because it used reallife routine patient data while implementing the covariate EHL (0:SHL, 1:Elocta) and merging eight FVIII in the modelling process. The population parameters were comparable to those that were estimated in the published generic SHL FVIII model  [21]. Our model had a correlation between V1 and Cl and, despite the heterogeneity of 'coagulation analyser-APTT reagent', it presented a combined error model. This is in agreement with a few models found in the literature  [3, 23]. TBW explained a significant part of IIV on V1 and Cl. The covariate FFM was secondary tested on 197 patients among 258 (height not available for all).
Discussion We developed and qualified a population PK model of FVIII:C from eight different forms of FVIII while using data provided from clinical practice. This work was original because it used real-life routine patient data while implementing the covariate EHL (0:SHL, 1:Elocta) and merging eight FVIII in the modelling process. The population parameters were comparable to those that were estimated in the published generic SHL FVIII model  [21]. Our model had a correlation between V1 and Cl and, despite the heterogeneity of 'coagulation analyser-APTT reagent', it presented a combined error model. This is in agreement with a few models found in the literature  [3, 23]. TBW explained a significant part of IIV on V1 and Cl. The covariate FFM was secondary tested on 197 patients among 258 (height not available for all).
Moreover, our BSV Cl and V1 estimates were slightly weaker than those of their model, whereas our modelling dataset contained EHL. The difference between our population characteristics (age, TBW, number of patients) may explain this, given that age and TBW are significant covariates. We externally compared the individual parameters predictions of our model while using those that were estimated with the generic SHL model of McEneny-King et al. [21]. The results confirmed that their model is capable of predicting individual brands outside the original database, EHL. This shows that the BSV of one factor VIII is equivalent to the BSV of different FVIII. We then compared the data from patients undergoing a switch from SHL to Elocta, the ratio from the A-LONG study  [28]  HL Elocta/SHL was 1. 53 and comparable with ours of 1.
Clinical Application (Example) Currently, tools for calculating individual patient parameters improve prediction control. In prophylaxis for haemophilia A, accurate estimation of HL and time-to-trough are crucial in reducing the risk of spontaneous bleeding. Sampling at optimal times using a population model is necessary to correctly estimate individual parameters  [3]  and minimise the risk of FVIII:C falling below the threshold. We selected a patient who was not included in the modelling dataset to show the process of individual parameter estimations (Figure  7A ), and then used it to simulate adequate dosage using simulX (mlxR package) (Figure  7B, C ). In our case, the patient (a 21-year-old man, 105 kg) took 3000 IU of Afstyla. He came for a consultation and wished to administer an injection every 48 h while being above the 0.
Acknowledgments: We thank the medical staff who collected the data of the study. We thank the laboratory staff of the  Department of Haemostasis  who performed FVIII:C determinations. We thank the company Proofed (proofreadmyessay. co. uk) for their fast and high-quality proofreading service, which improved the English text of the manuscript. We thank reviewers for their helpful comments which improved the quality of the manuscript.
During a phase Ib clinical trial (NanoRAD  [9] ), AGuIX ® was administered up to the dose of 100 mg/kg to patients, which corresponds to the recommended dose for phase 2 multicentric clinical trials (NCT03818386 and NCT02820454)  [9, 18]  with 3 injections at 100 mg/kg at different times during radiotherapy treatment. The enrichment of tumours with nanoagents is a critical issue. The efficiency of the intravenous (IV) injection strongly depends on the fate of the nanoagents in the body. In particular, the impact of nanoagents on metabolic routes such as enhanced permeability and retention (EPR) effect, cell uptake and renal clearance  [22] , strongly depends on their hydrodynamic size and surface charge  [23] [24] [25]. When injected into patients who present blood diseases, nanoagents may cause potential harmful effects following interaction with several blood components  [26].