dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of the 95% reference set for strain typing from whole genome data To evaluate the 95% reference set raw sequences from different genome-sequencing projects were downloaded from the NCBI SRA database. To evaluate the 95% reference set, raw sequences from different genome-sequencing projects were downloaded from the NCBI SRA database to evaluate a representative genome set of DTUs diversity. Considering the short size of minicircles, only genome projects with no fragment size selection previous sequencing were analyzed. Furthermore, the genomes analyzed were reported as previously typified. The files corresponding to the 29 T. cruzi strains of the six main lineages were analyzed. The accession numbers are listed in Table  2. The reads from the whole-genome sequencing projects were processed and analyzed using the Galaxy platform (https://usegalaxy.
For instance, if the sequencing reads from a given genome are mapped to 99 mHVR reference sequences from TcI and only one from TcII, this indicates that this genome belongs to the TcI lineage. B-The total number of bases for the reads that mapped to the reference sequences of each lineage. The same analysis was performed for A and B, with a coverage of 270 bases at 10X depth. These procedures were applied to each of the downloaded datasets. A strain level analysis was also made by determining the proportion of mapped 95% reference mHVRs that cluster with each strain in the dataset.
Analysis of dataset performance on mHVR amplicons To evaluate the typing resolution of the reference datasets, every strain was typified by using a reference dataset that excluded the strain that was being typified; for example, typing of Sylvio strain is made by a reference set constructed without Sylvio reads. This process was performed *The sample was reported as non-clonal in the NCBI database, which resulted in TcI in the analyses. 1 Sequenced with an Illumina HiSeq 2000. 2 Sequenced with Ion Torrent. https://doi. org/10. 1371/journal. pntd. 0011764. t002 for the 62 strains in this study, and the sensitivity and specificity for typing each DTU were evaluated. In addition, to evaluate the potential suitability of this workflow for typing biological samples, a PCR simulation algorithm was developed in R (https://github.
02. In addition, mock samples composed of reads of two different strains from different DTUs were evaluated to determine the sensitivity of the reference sets for detecting co-infections. Different proportions of different DTUs were evaluated (95%-5%, 90%-10%, 10%-90%, and 5%-95%). Strains with the highest number of reads were selected to build the mock datasets. The datasets for each strain were sampled according to the expected proportions for each DTU in the mock sample (e. g. , 90% of the reads of PalDa20cl3-TcI and 10% of MNcl2-TcV). The mock sample was used as the input in the PCR simulation algorithm using m 0 = 100 and e = 0. 99, with 100 replications. The simulated datasets were typified as described above, and the generated matrix of cutoffs was used to discard false positives. The sensitivity for detecting the less abundant DTU in the sample was evaluated.
Notably, all evaluated strains were accurately typified using this approach, except for two strains reported as belonging to the TcIII lineage (Ikiakarora and 231). Furthermore, both CL Brener genomes exhibited a high degree of concordance in their typing, despite having been sequenced using different sequencing technologies. In addition, the proportion of mHVR clusters shared between different genomes and strains in the 95% reference dataset was addressed (S2 File). Different patterns were observed within some DTUs for different genomes, suggesting potential utility for intra-DTU typing.
The suitability of reference sets for typing despite PCR stochasticity Reference sets of mHVRs are potentially useful for identifying DTUs in biological samples such as blood. However, PCR stochasticity caused by a low amplification efficiency, or a low number of initial DNA molecules may cause the frequency of each mHVR cluster to not represent the real frequency in the sample. To assess the suitability of the reference sets for typing, artificial samples were simulated for each strain in the dataset under different simulated PCR conditions for efficiency and different numbers of initial DNA molecules. The reference sets  remained useful for typing when PCR had an efficiency of 80%-99% per cycle, starting with 10-100 molecules, with the 85% reference set producing better sensitivity results.
This result was unexpected because TcVI has a relatively low genetic diversity, and consequently, it would be expected to be fairly represented in the 95% reference set. It is important to note that the specificity of the Southern blot method was evaluated with a reduced dataset (less than 20 strains)  [34]  and that hybridization can be sensitive to probe incubation conditions. Consequently, the discordance may be attributed to the cross-reaction of the mHVR TcVI probe in the Southern blot. A potential drawback of our method is related to minicircle inheritance. Maxicircles from T. cruzi kDNA have been suggested to be inherited uniparentally, whereas we previously proposed that minicircles are inherited biparentally in hybrids  [21, 47]. If minicircles are inherited biparentally, they are expected to behave similarly to the nuclear genes.
Laboratory experiments using artificial samples created by mixing DNA from different known strains would be necessary. Numerous typing assays have been proposed for T. cruzi. However, there is an increasing need for simpler and more cost-effective methods. In this regard, the proposed typing workflow based on mHVR amplicon sequencing is suitable for simultaneously typing hundreds of samples and based on sequences, unlike other techniques that use the same target. Furthermore, this typing workflow is more economical than other techniques  [11, 16, 17, 19, 20, 49, 50]  and the bioinformatics analysis is relatively simple because it only needs to upload the data on a Google colaboratory notebook (no specific hardware and no bioinformatic skills are required) and follow the steps until the typing results are obtained.
Blue bars: TcI, orange bars: TcII, gray bars: TcIII, yellow bars: TcIV, violet bars: TcV, and green bars: TcVI. (JPG) S1 File. Evaluation of the PCR simulating algorithm by comparison against a duplicate experimental PCR of mHVRs from the LL015P68R0cl4 strain (TcVI). (PDF) S2 File. Proportion of mHVR clusters shared between different genomes and different strains in the 95% reference dataset. (XLSX) S3 File. Reads obtained from blood samples and percentages of clustering against each DTU. (XLSX).
Within the One Substance One Assessment initiative, the establishment of a Common Data Platform on Chemicals is expected to enhance data and knowledge sharing, reuse and integration across sectors. There is also an emphasis in the EU on tracking substances of concern and their uses to best control potential emissions across products and material lifecycles. This aligns with the Safe and Sustainable by Design Initiative  (Patinha Caldeira et al. , 2022). The "Substances of Concern In articles as such or in complex objects (Products) established under the Waste Framework Directive (2008/98/EC)", or SCIP, database from ECHA (https://echa. europa. eu/scip-database) provides key information to achieve the Safe and Sustainable by Design Initiative. Such information enables the incorporation of information on the lifecycle of substances and materials into exposure assessments.
g. , legal limits, usage levels, experimental, monitoring and surveillance, amongst others etc. , for the dietary exposure assessment across the types of chemicals considered and for a number of different purposes. EFSA collects data to support exposure assessments into the EFSA Scientific Data Warehouse, for instance an annual data collection of chemical occurrence from EU Member States, the EC, industry, consumer associations and academia. Many data are collected, for instance in 2021 more than 26 million records were collected for pesticides residues, 12 million records for veterinary drug residues, etc. EFSA's Data Warehouse also hosts the Comprehensive European Food Consumption Database for more than 20 EU countries and pre-accession countries, containing representative food consumption data for individuals across a range of ages, including sensitive groups such as pregnant and lactating women (https://www.
efsa. europa. eu/en/data-report/food-co nsumption-data). Such data are used for EFSA's dietary exposure assessment that may be reported either as mean exposure or as high-level exposure (e. g. , 95th percentile). Exposure results are usually reported per age group (infants, toddlers, other children, adolescents, adults, elderly and very elderly) and country. Exposure assessment can also provide data on which foods contribute most to a particular exposure, which helps the risk manager make appropriate decisions. There are a number of developments in exposure assessment at EFSA to address a number of issues including One Substance One Assessment. The developments include provision of a number of open access tools such as FAIM (allowing the input food additive data to provide a chronic exposure assessment), FACE, FEIM, PRIMo, DietEx (https://www.
Estimation of the systemic exposure dose (SED) is performed in a tiered approach with the first tier using a conservative, external exposure model and tending towards overestimation. The second tier uses a more refined exposure model for the internal exposure dose, in which dermal absorption plays an important role. The NoG provide guidelines to conduct in vitro dermal absorption studies with a number of basic criteria to ensure the quality of the results (including physico-chemical properties that may be indicative of very low dermal absorption). Guidance is also given for oral and inhalation exposure. Dermal absorption and SED may also be derived from toxicokinetics and by applying different PBK models. For PBK models to be used and considered reliable, the ratio between simulated and observed data should be within a factor of two, in addition, sensitivity and uncertainty analyses must be performed.
Topics relevant to all, or nearly all, sectors Use of exposure-based waiving including development of low bioavailability criteria for hazard data waiving or 'no classification' • A consensus on the definition and character of an exposurebased assessment • Harmonisation of definitions of low/medium/high internal exposure and bioavailability definitions • Definition of exposure/bioavailability cut-off criteria and how they may be applied • Build confidence and consensus on how low bioavailability calls and cut-offs could be used to waive hazard data requirements and for no classification decisions, a case study on polymers could be developed in this context   S1.
These have resulted in very strong exposure assessments as well as models linking use scenarios to exposure. A number of cross sector models are also used e. g. , the ECETOC Targeted Risk Assessment (TRA) (ECETOC, 2018), RIVM's ConsExpo (https://www. rivm. nl/en/consexpo) and the International Association for Soaps, Detergents and Maintenance Products (AISE) Reach Exposure Assessment Consumer Tool (REACT) (https ://www. aise. eu/our-activities/regulatory-context/reach/consumer-sa fety-exposure-assessment. aspx). The safety assessments are supported by consumer and worker safety guidance and communication. An example of product stewardship was provided for the safe use of enzymes, used ubiquitously in laundry and automatic dishwashing • Creation or generation of example data to build confidence in human MABEL estimation to understand exposure to veterinary medicines in human users.
Fragrance Consideration of (aggregate) exposure is routinely applied in the safety assessment of fragrance ingredients both for human and environmental endpoints. The International Fragrance Association (IFRA) Standards (https://ifrafragrance. org/) are a risk management measure that incorporates exposure within three steps of a six step process: 1) IFRA members provide volume of use data which are shared with RIFM (https://rifm. org/), whilst RIFM collects concentration data on fragrance ingredients in a wide range of consumer products, 2) RIFM prepares a safety assessment dossier combining exposure with toxicological data and 3) an independent Expert Panel evaluates the information to determine if the current reported use exposure is supported. The RIFM Safety Assessment Program is guided by two criteria documents in which exposure is key, one for discrete fragrance materials  (Api et al.
Veterinary medicinal products Input from the animal health sector (veterinary medicinal products) was provided for human safety assessments and the role of 3Rs in exposure assessments. Veterinary medicinal products (VMPs) are regulated in the EU by the EMA through the Committee for Veterinary Medicinal Products (CVMP). Regulation (EU) 2019/6 (European Commission, 2022) requires toxicology and residue studies be performed for human food safety for livestock products, and User Safety Assessment to be conducted for livestock and companion animal products. The human food safety evaluation of new animal drugs used in food-producing animals ensures that food derived from treated animals is safe for human consumption. The human food safety of VMPs is governed by VICH guidelines which require studies to be undertaken to establish a toxicological database in laboratory animals for acute, subchronic, chronic, genetic, reproductive and developmental toxicology, microbiological safety, and special studies to establish an Allowable Daily Intake (ADI) and Acute Reference Dose.
A variety of opportunities for the implementation of the 3Rs were presented. A database of toxicology studies is mandated by VICH and national authorities, similar to Human Pharmaceuticals and Agrochemical sectors. Innovative methods to determine MRLs are being implemented with engagement from the regulators. For example, toxicogenomic, toxicokinetic, pharmacological, and exposure data may be incorporated into development programs to reduce testing. In addition, exposurebased waiving of toxicological safety testing can be requested based on PK studies demonstrating the lack of oral bioavailability, pharmacokinetics, degradation leading to a lack of activity (e. g. , for biotherapeutics). There is also increased use of BMD modelling of (sub-) chronic data to determine PoDs, rather than repeating testing.
, veterinary medicines, human medicines and food substances. The information in Table  8  recognises the overall aim to have exposure-based safety assessment which will be facilitated (in part at least) by the use of case studies from different sectors on how this could be achieved. It was recognised that some uses or approaches are similar in different sectors, for different regulatory purposes. One of many examples is the use of TTC, and the potential advantages of such common approaches could be highlighted through the sharing of experiences and methodologies. There is also a clear need to share data and tools e. g. , databases of exposure measurement, tools and models to calculate exposures (see Table  7  for examples). The PF also recognised the need to facilitate change in regulation policy and guidance from hazard-based/ animal-based assessments (and consequent cut-off/restrictions) to a safety (exposure/hazard)-based policy.
g. , PARC) to estimates against biomonitoring develop one or more use case examples. • A framework for aggregate exposure is required in many • Identification of opportunities relating to human exposure sectors for cross-sector fertilisation which may include: o Tools to translate external vs internal exposure with PBK being a common area of interest for most sectors o Investigation of sensitive population exposure • Creation of a database of use patterns on consumer products across different sectors for use by industry and regulators Application of biomonitoring data • Various biomonitoring projects have been successful at defining the presence of compounds, however there is a greater need to determine if exposure will lead to adversity (capitalising on data from existing projects) and role of PBK modelling to link internal exposure to external dose • Development of the problem formulation for biomonitoring studies, e.
, 2017). Among the software packages developed, the High-Throughput Toxicokinetic (HTTK) open source R package, a platform created by the U. S. EPA's National Center for Computational Toxicology is easy to use for a wider range of chemicals and readily available. Due to the lack of adequate TK data for emerging BPs, the objective of our experimental study was to evaluate and compare the toxicokinetic properties determining the internal exposure of twelve unconjugated (active) BPs, i. e. , BPA, BPS, BPAF, BPB, BPF, BPM, BPZ, 3-3BPA, BP4-4, BPAP, BPP, and BPFL after oral exposition. These twelve analogs were selected because of their industrial use  (Caballero-Casero et al. , 2016; "ECHA," n. d. ; Liao and Kannan, 2013)  and their detection in recent biomonitoring studies  (Chen et al. , 2018; Gys et al.
, 2021; Lucarini et al. , 2020; Mok et al. , 2022; Zhang et al. , 2020). Two routes of administration were performed, intravenous administration to evaluate TK parameters such as plasma clearance and volume of distribution and oral route to determine the oral bioavailability of these 12 BPs. In the context of reducing in vivo animal toxicological testing, mixtures of these twelve BPs were administrated intravenously and by gavage to piglets, a species relevant to the human in terms of digestive function  (Gonzalez et al. , 2015; vom Saal and Welshons, 2006). Our mixture approach was validated for BPA and BPS by comparing the TK parameters obtained in this study with those previously determined when they were administered individually  (Gayrard et al. , 2019). We also used the HTTK package to predict TK parameters of these BPs and compare them with our in vivo experimental data.
The IV administrations were carried out via an indwelling catheter inserted into the auricular vein just before the administration (0. 1 mL of solution by kg BW) and oral administrations were carried out by gastric intubation (2 mL of solution by kg BW). The IV and oral BPs doses were selected based on previous TK data  (Gayrard et al. , 2019)  allowing to quantify plasma concentrations for a few hours, i. e. , sufficiently long to observe the terminal phase slope and allow calculation of TK parameters considering the performance of the assay.
The wavelengths for each compound were reported in Table  S2. Chromatographic data were monitored by Empower® software (Water, Milford, MA, USA). The method was validated in urine according to the European Medicine Agency Guidelines (European Medecines Agency Science Medicines Health, 2011) with two calibration curves: high concentrations, from 0. 5 to 20 µg/mL, and low concentrations from 0. 01 µg/mL to 2 µg/mL were quantified at several wavelengths (Table  S2 ). Blanks and quality control samples were used to monitor potential contamination during analysis and precision of the  method. The accuracy ranges of three QCs (0. 03, 0. 15 and 1. 5 µg/mL, low calibration range) were 89-114 %, with mean intra-and inter-day coefficients of variation lower than 16 % for all BPs. The accuracy ranges of three QCs (1.
Then, the supernatant was eluted on BEH Phenyl column using H 2 O/methanol gradient [0. 3 mL/min, 40 • C] and detected in negative electrospray ionization using multiple reaction monitoring (MRM) mode. Chromatographic data were monitored by Targetlynx® software (Water, Milford, MA, USA). The MRM transitions of BPs and their respective cone voltages and collision energies were detailed in Supplemental Material Table  S5. Two injection volumes were used, 20 µL for concentrations lower than 10 ng/mL and 10 µL for concentrations higher than 50 ng/mL. For the intermediate concentrations, a double injection was performed. Blank samples were used to check the absence of contamination during assays and the method was validated according to the European Medicine Agency Guidelines in piglet plasma over the calibration range between 2.
The residual variance was modeled using a combined additive and proportional error model, using equation (4): Cobs ij = Cpred ij ( 1 + ε 1ij ) + ε 2ij (4) where Cobs ij and Cpred ij are respectively the j th observed and predicted concentrations for the i th individual and ε 1ij and ε 2ij the multiplicative and additive residual errors. The predictive ability of the model was checked graphically by plotting Visual Predictive Check (VPC) plots to compare the observed data with the prediction interval (20th-80th percentile) of data simulated using the model and obtained from 200 replicates (Figure  S1-S3 ). Secondary parameters were calculated from the population primary parameters. The plasma clearance values (Cl) of BPs were estimated using obtained using equation (  5 ): Cl = V c × K e (5) where V c is the volume of the BPs central compartment, and K e is the first-order BPs elimination rate constant from the central compartment.
e. with the administration of only BPA/BPS  (Gayrard et al. , 2019). This previous dataset was described in Table  S8. To assess the possible cocktail effect, we included in the NLME model a categorical covariate corresponding to the mode of administration (single versus mixture dosing), on two key TK parameters, i. e. , Cl and F. The statistical significance was evaluated by considering the confidence interval of these estimate parameters as obtained using a bootstrap method.
High-throughput toxicokinetics The R package HTTK (version 2. 1. 0, https://CRAN. R-project. org/package=httk) on R studio software (version 1. 2. 5001) was used to predict key TK parameters for each BP taken individually  (Pearce et al. , 2017). The toxicokinetic models within the R HTTK are parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. A one-compartment model is used to compare predictions with in vivo data. Total clearance is equal to hepatic metabolism, calculated with a well-stirred model using scaled in vitro intrinsic hepatic clearance, and passive glomerular filtration (non-metabolic renal clearance). The effective volume of distribution is calculated by summing the plasma volume and the products of each tissue volume and the in vitro ratio of unbound to total concentration in plasma and tissue, as given by the following equation: Vss = V P + fu fuT × V T (10) Where V P is the plasma volume, V T is the tissue volume, and fu and fuT are the fraction of unbound drug in plasma and tissue respectively.
TK prediction by HTTK package The HTTK package enabled the prediction of TK parameters for only 5 BPs (BPS, BPFL, BPA, BPAF, and BPB, Table  3 ). The total clearance of BPS is about 20 to 30 times lower than that of BPA, BPAF and BPB, but is 10-times higher than that of BPFL. The steady state volume of distribution varied among these five BPs, with those of BPS and BPFL being 7-30 times lower than those of BPA, BPAF and BPB. These predicted TK parameters resulted in half-lives unexpectedly long for this class of phenols, ranging from 6 to 215 h. These TK parameters predicted with the HTTK model were very different from those we estimated in vivo in piglets. This was especially true for plasma clearances, HTTK model predicted values were 25 (BPAF and BPB) to 3552 (BPFL) times lower than those we actually evaluated in vivo with the consequence of major differences in half-life times, with HTTK model predicted values being 10 (BPS) to 260 (BPFL) fold longer than those we evaluated in vivo (see Table  2 ).