dataesr/scientific-paragraphs-categorization huggingface dataset:
To avoid training the Neural Networks on sequences for which we want to predict and interpret the class labels, sequences from model organisms (H. Sapiens, M. , S. Cerevisiae, R. Norvegicus, Melanogaster, D. Rerio, C. Elegans, A. Thaliana) were removed from the dataset prior to training. To interpret the predictions of the trained models, we also removed non-canonical class B proteins from our training set. These were identified as JDPs containing a DnaJ domain, an annotated G/F region (see above) but no DnaJ_C domain. This procedure resulted in a reduced set of 205'252 sequences used for building the classification models.
Construction of a curated J-protein dataset We constructed a JDP sequence database by querying the Uniprot database (union of Swissprot and TrEMBL, release 2021_04)  (36)  to extract all sequences annotated to have a J-domain according to the PFAMfam domain annotation (PF00226)  (37). For each JDP sequence, we recovered its full-length sequence as well as the full taxonomic lineage from Uniprot. We then extracted the J-domain sequence of each JDP by aligning the full-length sequences to the Hidden Markov Model (HMM) of the Pfam J-domain (PF0226) family using the hmmalign utility of the hmmer package  (59). The full domainarchitecture was obtained by querying the PFAM database for each JDP entry in our database and the domain order validated using the domain boundaries reported in PFAM. The domain architectures were used to classify all sequences as either belonging to canonical class A (PFAM architecture "DnaJ;DnaJ_C;DnaJ_CXXCXGXG"), canonical class B (PFAM architecture "DnaJ;DnaJ_C;") and class C (all other sequences).
We further identified the G/F-rich region in all JDPs as being the stretch of 40 amino-acids directly following the JD sequence, if at least 25% of its amino-acids are either Glycines or Phenylalanines. For ease of analysis, we also grouped all domains composing TPR repeat proteins (annotated as TPR_XX) into a virtual TPR domain. Thus, in all proteins containing one or several TPR repeats, we replaced all the TPR domains by a single effective TPR domain (as shown in Fig. 2 ). Subcellular localizations were predicted for eukaryotic sequences using the TargetP software (60), version 2. 0. This resulted in an initial dataset containing 232'516 annotated JDP sequences. We then curated the dataset by first removing all sequences for which the Uniprot and PFAM annotations were not consistent and also removed all sequences which did not have a full characteristic HPD motif.
10 3 3 in year 2003 to 10 5 in year 2018, and projected to reach 10 6 sequences in years 2023-2024 and 10 7 by year 2030. Using stringent homology-search criteria, we extracted a total 223'124 JDP sequences from existing genome databases (see Methods). The associated annotations allowed us to assign all JDPs to different phylogenetic classes at various taxonomic levels. Using sequence domain models from the Pfam database (37), we further characterized the domain composition of the full-length sequences, assigning to each JDP a tag describing the domain architecture of the protein. Proteinlocalization prediction algorithms were used to infer the most probable organelle of residency of eukaryotic JDPs (see Methods).
Introduction: The aim of this international project was to establish a speciesspecific Clinical Breakpoint for interpretation of Antimicrobial Susceptibility Testing of benzylpenicillin (BP) in horses. Methods: A population pharmacokinetic model of BP disposition was developed to compute PK/PD cutoff values of BP for different formulations that are commonly used in equine medicine around the world (France, Sweden, USA and Japan). Investigated substances were potassium BP, sodium BP, procaine BP, a combination of procaine BP and benzathine BP and penethamate, a prodrug of BP. Data were collected from 40 horses that provided 63 rich profiles of BP corresponding to a total of 1022 individual BP plasma concentrations. Results: A 3-compartment disposition model was selected. For each of these formulations, the PK/PD cutoff was estimated for different dosage regimens using Monte Carlo simulations.
VetCAST is the veterinary sub-committee of the European Committee on Antimicrobial Susceptibility Testing (EUCAST), the reference committee for AST in human medicine for the EU  (Toutain et al. , 2017). According to VetCAST, CBs are determined by considering at least an epidemiological cutoff (ECOFF) and a PK/PD cutoff (PK/PD CO ). The PK/PD CO is defined as the highest possible MIC at which a given percentage of animals in the target population (most often 90%) achieve a predefined pharmacodynamic target (PDT) value. In addition, a clinical cutoff can also be considered when clinical data are available to link MICs to clinical efficacy  (Turnidge and Martinez, 2017) , but such data do not exist for BP in horses. A robust estimation of PK/PD CO , which is pivotal for the VetCAST approach, first requires the construction of a valid population PK model from individual animal data collected from a variety of sources in order to quantify typical values of PK parameters and their between-subject variability (BSV).
Raw data collection Raw data collected in four countries (France, Sweden, the USA, and Japan) were considered. Some of the datasets USA1  (Younkin et al. , 2019) , USA2  (Wilson et al. , 2022) , and Sweden  (Olsén et al. , 2013 (Olsén et al. , , 2018) )  have previously been published, but the French data were specifically generated for this project. The Japanese data have not been published. All raw data are available in a Supplementary Excel file. The doses reported in the corresponding publications or in the Summary of Product Characteristics (SPC) of the formulations were presented in terms of either international units (IU) or mass (mg/kg). For the present meta-analysis, all the doses were harmonized in terms of BP (mg/kg), which required the use of different conversion factors (Supplementary Table  1 ).
Doses were expressed in terms of BP according to the second international standard for BP, i. e. , considering 1 mg of sodium BP to be equivalent to 1,670 IU or 1 mg of BP equivalent to 1780 IU. Details on registered name, dosage, and conversion factor for each of the different formulations are given in Supplementary Tables  1, 2. Data from horses (n = 40) were considered for the present analysis; these corresponded to different trials conducted in France (n = 6), in Sweden (n = 15), in Japan (n = 6), and in the USA (USA1, n = 7; USA2, n = 6). In Japan, horses received a single IM dose of procaine BP. In USA1, seven horses received two IV administrations of potassium BP, followed by a single dose of procaine BP  (Younkin et al. , 2019). In USA2, six horses received a single IV administration of potassium BP in association with gentamicin  (Wilson et al.
A total of 63 rich profiles of BP plasma concentration were available for the present meta-analysis, corresponding to a total of 1,022 individual BP plasma concentration measurements. Details on the different experimental designs are given in Supplementary Table  2 , and the demographics of the horses used (body weight, age, sex, breed, etc. ) are given in Supplementary Table  3. All BP plasma concentrations were measured by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The limits of quantification (LOQ) were 5. 5 ng/mL for Sweden, 10 ng/mL for France and USA1, 30 ng/mL for Japan, and 40 ng/mL for USA2. Details of the French and Japanese analytical methods that have not been published previously are given in Supplementary Data 1.
Antimicrobial susceptibility testing (AST) Raw data are presented as a series of spaghetti plots in Figures  1 2 3. All doses are expressed in terms of BP (mg/kg). Visual inspection of Figure  2  indicates that USA1 BP plasma concentrations after procaine BP (panel F) were significantly lower than those from the other three datasets (French, Swedish, and Japanese) for procaine BP, which did not appear to vary among themselves in any major way. The slope of procaine BP for USA1 horses was rather flat. Other spaghetti plots comparing the different formulations with or without scaling for the administered doses are given in Supplementary Figures  1 2 3. They show consistency in the IV data and differences between LA formulations of BP.
Data modeling Data analysis was carried out using Phoenix ® WinNonlin ® 8. 3, Certara, Princeton, New Jersey, USA. The first step was to perform a non-compartmental analysis (NCA) to obtain an initial estimate of basic pharmacokinetic parameters, such as plasma clearance and volumes of distribution from the four IV data sets. The AUC for each profile was also estimated to assess relative bioavailability under the different modalities of administration and formulations, using the French IV data as the reference. These parameters were used as initial starting values for the comprehensive population PK model that was developed using a non-linear mixed-effects model (NLMEM). All raw data were considered, but concentrations reported as below the limit of quantification (BLOQ; less than 5% of concentration measurements) were ignored, without introducing a risk of leading to any structural model misspecification  (Byon et al.
However, in the present analysis, we documented the "source of dataset" as a categorical covariate in order to assess the comparability of the four IV data sets and to compute bioavailability, taking into account the fact that some horses received both IV administration of BP and one (USA1) or more (French) IM LA formulations. The selected covariate model was an exponential model for the six structural parameters of the IV model. The Phoenix covariate Search Shotgun run mode was used to assess the significance of the effect of the covariate on these different PK parameters. The 64 scenarios were ranked based on the Bayesian Information Criterion (BIC)  (Burnham and Anderson, 2004). In addition, the BIC was evaluated to assess the significance of adding or not adding a covariate. A difference of >10 in BIC between models is accepted as "very strong" evidence in favor of the model with the lower BIC  (Kass and Raftery, 1995).
For the present analysis, the BIC was 241. 6 for the model without the covariate and 208. 7 for the best model that included the covariate "source of dataset" only in the case of plasma clearance. This last IV model was selected as the best one to use for subsequent simple runs. In a second step, each IM formulation was analyzed separately using the above-described consolidated IV 3-compartment model to determine the best structural model to describe the absorption process and to obtain initial parameter values (first-order rate constants for absorption Ka and bioavailability F) for the final model. The absorption process was described by a single rate constant for procaine BP. For sodium BP, two sequential rate constants of absorption separated by a lag time (Tlag) were required to describe biphasic absorption, with a rapid initial phase followed by a second slower phase of absorption.
Given the size of the full model and the number of parameters to be estimated, the IV disposition parameters (CL, CL2, CL3, Vc, V2, and V3) were frozen at their optimal values obtained from the analysis of IV data, including the covariate "source of dataset, " for clearance values. However, all the other parameters of the model and OMEGA were re-estimated. In addition, for clearance, the covariate "source of dataset" was introduced into the model for the 8 Swedish horses having received procaine BP and sodium BP IM (no IV route for these 8 horses investigated under a crossover 2 × 2 design) and for the Japanese horses that only received procaine BP. For the 8 Swedish horses, the justification for this covariate was that the estimated bioavailability computed using the clearance of the other 4 Swedish horses having received IV (without IM) was very low (37%).
There was no reason to assume that the 8 Swedish horses having received IM administration of procaine and sodium BP had the same clearance as the other 4 Swedish horses having received an IV administration of sodium BP. The introduction of the covariate "source of dataset" for the clearance of Swedish horses made it possible to calculate an estimated bioavailability of procaine BP for Swedish horses that was similar to that of other horses (French, USA1, and Japanese). For French and USA1 horses, bioavailability was estimated using the actual measured plasma clearance for these horses. For all modeling, the residual model was an additive plus multiplicative (proportional) model. The corresponding Phoenix script for the final model is given in Supplementary Data 2. Parameters and their associated SE (as a measure of the precision of the estimation) were estimated using the quasi-random parametric expectation maximization engine to fit the IV data, and then using FOCE ELS for all other runs.
Non-compartmental analysis IV data from the France (n = 6), Sweden (n = 4), and USA2 (n = 6) datasets were analyzed using an NCA approach to obtain an initial estimate of basic pharmacokinetic parameters such as plasma clearance and volumes of distribution. The USA1 dataset was not analyzable via an NCA approach given that only 3 sampling points were obtained. Table  2  presents the IV results. Table  3  gives the observed AUCs for all tested formulations. For comparability, all AUCs were scaled to a typical dose of 12. 36 mg/kg (22,000 IU per kg). Inspection of Table  2  shows that plasma clearance was lower in Swedish and USA2 horses than in French horses (approximately 25 and 47%, respectively). It can be observed that the half-life was relatively similar for horses in each of the three datasets, the differences in clearance being largely compensated for by differences in the volumes of distribution.
Population modeling: IV data Parameters obtained via the NCA were used as initial values for the population PK model. In a first step, IV data were modeled to estimate basic parameters of the selected 3-compartment model with "source of dataset" as a covariate for plasma clearance. Visual predictive checks (VPCs) evaluating the capacity of the IV model to reproduce the original dataset are presented in Figure  5. Other goodness-of-fit (GOF) plots for the IV data supporting the 3-compartmental structural model, the exponential model for the random component, and the additive plus multiplicative model for the error submodel, which were used to analyze all the data, are given in Supplementary Figures  4A-E. Typical values of the primary structural parameters of the model (theta values), their associated CV%, and the SD of the residual for the IV model are given in Table  4.
Population modeling: IV and IM data Finally, all of the data (IV and IM) were modeled simultaneously; however, given the size of the model and the number of parameters to be estimated, the disposition parameters for French horses (CL, CL2, CL3, Vc, V2, and V3) were used as a reference (i. e. , no covariate). The "source of dataset" covariate for the clearances of Swedish, USA1, and USA2 horses were set (frozen) at the optimal values obtained with the analysis of IV data only (see Table  3 ). The OMEGA component and BSV were reassessed for all parameters having a random component. Figure  6  presents VPC plots for the different routes (IV, IM) and the different IM formulations (procaine, combination procaine and benzathine, penethamate, and sodium BP). Other goodness-of-fit plots are provided in Supplementary Figures  5A-J.
25 mg/L for the French, Japanese, and Swedish formulations, as with fAUC/ MIC as the PK/PD index. The USA1 data were very different, with a PK/PD cutoff of 0. 0625 mg/L for a PDT of 30%. For IM sodium BP (Supplementary Table  19 ), the objective of 40% of the dosing interval was found to be achieved for an MIC of 0. 25 mg/L for a dose of 12. 36 mg/kg at 6, 8, or 12 h intervals. For an MIC of 0. 5 mg/L, the objective of 30% of the dosing interval was also achieved for all regimens. For an MIC of 1 mg/L, only an IM dosage regimen of 12. 36 mg/kg every 6 or 8 h was found to reach a PDT of 40% of the dosing interval in 90% of horses. For an MIC of 0. 25 mg/L, 24-h infusions of a total dose of 12. 36 mg/kg (22,000 IU/kg) were found to to maintain BP plasma concentrations above this MIC for more than 22 h out of the 24-h infusion for 90% of the horses across the three datasets examined (France, Sweden, and USA2).
25 mg/L could be achieved for all horses, validating the most common dosage regimen for IV BP. Visual predictive check (VPC) for the IV route obtained with 500 replicates of each horse. The observed quantiles are the 10 th (lower), 50th (middle), and 90 th (upper), denoted as red lines, with their 90% confidence intervals of predicted quantiles (shaded area). They are rather well superimposed onto the corresponding predictive check quantiles (black lines) over the observed data. The rebound at 8 h is due to the second IV administration in the case of USA1, 6 h after the first one. TABLE 4 Population primary parameters of BP after an IV administration in horses, as obtained with a 3-compartment model with "source of dataset" as a covariate for plasma clearance (estimates, CV%, 2. 5, and 97.
5% percentiles). Vc: volume of the central compartment; V2: volume of the superficial peripheral compartment; V3: volume of the deep peripheral compartment; CL: plasma clearance; CL2: distribution clearance between central compartment and compartment 2; CL3: distribution clearance between central compartment and compartment 3; CMultStdev: multiplicative component of the error model, which should be read as a CV of 15. 3%; stdev0: additive component of the residual error model; dCldSource_of_data: typical value of the "source of dataset" covariate for Sweden, USA1, and USA2. The covariate "dCldSource_of_data" is significant when its 95% confidence interval excludes 0. The covariate "dCldSource_of_data" was significant for Sweden and USA2, but not for USA1, meaning that plasma clearance in USA1 horses did not differ significantly from plasma clearance in French horses (reference).
A PK/PD cutoff is a variable that is essentially of a PK nature and that must meet the statistical requirement to apply to 90% of the targeted horse population. Given our objectives, it was necessary to study the PK of different formulations of BP representative of practices in different regions of the world. This was achieved with data collected in Europe (France and Sweden), in North America (the USA) and in Asia (Japan), particularly in the case of procaine BP, four different formulations of which have been aggregated in our database. Two other LA formulations were also assessed, namely, a combination of procaine BP and benzathine BP and a formulation of penethamate, a prodrug of BP. In addition, data corresponding to repeated IM administration of sodium BP, as an alternative to procaine BP, were also included in the base.
The IV route is also commonly used, in the form of a series of IV bolus doses or, if possible, a continuous-rate infusion (CRI)  (Magdesian, 2015). We also collected data obtained by the IV route and computed corresponding PK/PD cutoffs for different regimens. Importantly, the IV data were the only ones that allowed for direct comparison of the different sources of data, ensuring comparability of the different tested formulations without the risk of a confounding effect associated with the source of the data. The VetCAST approach requires reanalyzing raw data, rather than considering published parameters, in performing Monte Carlo simulations. As such, it is inevitable that the aggregation of datasets obtained with different experimental designs, having enrolled different numbers of horses, and having used more or less sensitive analytical techniques in terms of levels of quantification (LOQ) leads to great heterogeneity in the informative value of each dataset.
Technically, our datasets are regarded as unbalanced. This is a situation frequently encountered in human medicine during meta-analyses of pharmacokinetic data collected during the different phases of development of a drug. A non-linear mixed-effects model is recommended for the analysis of this type of data  (Schoemaker and Cohen, 1996). This is supported by the FDA guidance on population modeling, which indicates that population PK modeling is the only appropriate tool for meta-analysis of data retrieved from different studies with unbalanced designs  (Food and Drug Administration, 1999). A framework for meta-analysis of veterinary drug pharmacokinetic data using mixed-effects modeling has previously been presented  (Li et al. , 2015)  and has already been used to compute PK/PD cutoff values for florfenicol in cattle  (Toutain et al.
Shrinkage was rather low, indicating that the data were sufficiently rich to enable a proper estimate of the BSV, which was high due to the heterogeneity of the horses in the dataset. in the case of the French dataset, thanks to a low LOQ of 10 ng/ mL. This rendered a third phase of the disposition of BP identifiable. This was not the case for the Swedish and USA2 datasets, for which the last quantifiable concentration occurred at 6 h post-administration, leading the authors to analyze these data under a 2-compartment model in their original publications  (Olsén et al. , 2018; Wilson et al. , 2022). When all these data were analyzed simultaneously using the NLME model, the 3-compartment model prevailed. Animals for which data were not collected or quantified beyond 6 h post-administration were treated as censored between 6 and 8 h post-administration.
). Here, no biological covariates were considered in order to compute the most generic PK/PD cutoff values possible. However, we tested the influence of source of dataset as a factor in variability and in order to assess the consistency of the datasets. Only the IV route allowed for such a comparison. Statistically significant differences in clearances appeared between France (the reference source, with a clearance of 481 mL/kg/h) and Sweden (50% of the reference value) and between France and USA2 (87% of the reference value), but not between France and USA1. In the case of Sweden, inspection of Supplementary Figure  S4  indicates that 3 of the 4 horses in the trial had significantly higher plasma concentration profiles than the other horses in the study. The origin of this difference remains unexplained, especially since the Bayesian estimate for the 8 other Swedish horses that did not receive BP by the IV route does not suggest that Swedish horses are different from other horses.
Beyond this difference, the plasma clearances reported in the present publication remain within the same order of magnitude as those already reported in the literature, i. e. , 514 mL/ kg/h  (Horspool and McKellar, 1995)  and 510 mL/kg/h  (Firth et al. , 1986). For plasma clearance, we retained this "source of dataset" covariate in the full model to better estimate the bioavailability of the formulations that were administered to the same horses as those that received BP by the IV route (French and USA1 data). For Swedish horses that received procaine BP and sodium BP and for Japanese horses that received procaine BP, we also introduced the "source of dataset" covariate for clearance, which was therefore estimated indirectly via a Bayesian method. The rationale for this approach is our assumption, based on the results of the NCA, that the Swedish and Japanese procaine BP formulations also had very high bioavailability, like the French and USA1 formulations.