dataesr/scientific-paragraphs-categorization huggingface dataset:
Exposures may also be adjusted for food processing effects and for the greater concentration variability in units of consumption compared to the composite samples used in the monitoring program. In addition, modelled exposures may be inspected for co-occurrence of substance combinations as exposure mixtures, and they may be compared to human monitoring data. Hazard identification is concerned with identifying the active substances related to a given health effect. AOP networks may be used to identify relevant effects connected to the adverse outcome of interest in the assessment. Identification of active substances as belonging to the relevant AG may be just specified as data, or it may be derived from available toxicity data or from in silico models (QSAR and/or molecular docking). Effect representations data will link observed responses to effects of interest and may specify benchmark responses, which are toxicologically relevant levels for those responses, to be used in the modelling of dose-response data to obtain benchmark doses (BMDs).
BMDs as well as other points of departure (POD) specified as data can then be used for hazard characterisation, either directly or after applying assessment factors for POD type, species differences and/or within-species variability. Kinetic models (or simple absorption factors) may be use to extrapolate between external and internal doses. We use the term hazard characterisation (HC) as a generic term which can be any form of POD or health-based guidance value, depending on the purpose of the analysis. For the assessment of mixtures, RPFs are calculated as the ratio of HC for an index substance to HC for a specific substance. The toolbox distinguishes between two types of runs: the nominal run and the uncertainty analysis loop. The nominal run represents a single calculation in which the aim is to compute the most likely, if possible unbiased estimates for the model at hand.
User work is organized in workspaces. A workspace is a collection of work items that are logically grouped together. A workspace has a name, description and, optionally, a number of tags. Users are the owners of their own workspace folders. An action is started with the module of the corresponding action type (e. g. dietary exposures), but also links to other modules that are needed for its completion (e. g. consumptions per food as measured, consumptions, foods, etc. ). An action can be available in two forms: 1) a data selection action and 2) a calculation action. A data selection action comprises the selection of already available data of that action type, optionally the specification of selections on that data, and in some cases some pre-processing of data. A calculation action is an action in which the data of that action is calculated based on relevant input and specific calculator settings.
Data -Example data organised in the EuroMix project The toolbox contains a repository for relevant data. Data can be uploaded by individual users or by representatives of a larger user group. Data can be shared with other users or user groups. The data administrator for each data set can decide on use, read, or read/write permission for users. The data collected during the EuroMix project (Data/EuroMix repository) are shared with project participants and other stakeholders and are summarised in Table  1. These data collected in the EuroMix project were the basis for the examples shown below. (Luckert et al. , 2019; Schreiber et al. , 2019ab)  Consumptions 11 files with food consumption data in 10 countries  (Crépet et al. , 2019a)  Food recipes 5555 records specifying food ingredients in the FoodEx1 system or conversions  (Boon et al.
epa. gov/bmds) and PROAST (http://www. rivm. nl/en/proast) (EFSA, 2017). In module Dose response models of the toolbox, dose response data organised in the data repository can be fitted using the various dose response models  (Slob, 2002; Slob and Setzer, 2014; EFSA, 2017) , either using an internalised version of the PROAST software or using the web-based PROAST version (https://proastweb. rivm. nl). In the toolbox connections can be specified between effects, e. g. the adverse outcome or related biological effects, and responses, which are the measured quantities, in vivo or in vitro, that are available for BMD modelling. For example, it was proposed to consider the AdipoRed response after 72 hours in a HepaRG test system as one of the most appropriate and costeffective responses in relation to the AO steatosis  (Lichtenstein et al.
, 2019a, Lichtenstein et al. , subm. ). We therefore provide more detailed example calculations based on in vitro dose response data for these chemicals. Dose response relations for the intracellular lipid accumulation after 72 hours were measured using the AdipoRed assay in the in vitro HepaRG test system for three substances in the steatosis AG  (Luckert et al. 2019). Using the Proast model (https://www. rivm. nl/en/proast; Slob and Setzer, 2014) , that is integrated in the MCRA toolbox, a 6-parameter parallel-curve exponential dose response model was fitted to the data. Three parameters represent the lower and upper asymptote and common slope, one parameter is the BMD for the index substance (here imazalil), and the remaining two parameters represent the RPFs for the other two substances relative to the index substance.
In this paper concentration data according to EFSA's standard sample description (SSD1) (EFSA, 2010) were used with FoodEx1 food classification (EFSA, 2011). The toolbox however has no fixed coding systems, and could equally well use the newer SSD2 system (EFSA, 2013b) provided a table with code conversions for the foods-as-eaten is available. The module Dietary exposures also implements advanced methods for long-term exposure commonly used in nutrition science, such as the logistic-normal normal (LNN) model for episodical intakes  (Goedhart et al. , 2012; Roodenburg et al. , 2013; Boon et al. , 2014; van der Voet et al. , 2015). In the module Exposures dietary (external) exposures can be translated to internal exposures, using simple absorption factors or integrated kinetic models. Non-dietary exposures from e.
Examples An example is given of dietary exposure from 144 pesticides with a POD (NOAEL or LOAEL) for steatosis  (Kyriakopoulou et al. , 2017). Consumption data were available for a Dutch population of children (1-19 years)  (van Rossum et al. , 2011). These were combined with merged European food control and monitoring data 2010-2014  (Crépet et al. , 2019a ) using a previously established database of food conversions  (Boon et al. , 2015)  and BfR processing factors (https://www. bfr. bund. de/de/a-z_index/verarbeitungsfaktoren-8400. html, downloaded 01-09-2015). As reported in section 3. 2, the PODs were used to calculate external RPFs. For the handling of left-censored data (concentrations reported to be below a limit of reporting) the EC-EFSA method based on observed occurrence patterns was applied  (van Klaveren et al.
, 2019). For linking external to internal level exposures, the EuroMix Generic PBTK was used, with parameterisations for the human including variability of the parameters  (Tebby et al. , 2019 ). In a third example, the module Human monitoring analyis was used to combine questionnaire data on food consumption and personal care product use with monitoring data on bisphenol A (BPA) in food, and compare the resulting predicted exposures with measured urine levels. Human biomonitoring data for BPA were available from a Norwegian survey which measured bisphenols in urine and asked participants for their diet and their use of personal care products  (Husøy et al. , 2019). Predicted exposures were based on the recorded consumptions and personal care product use in combination with measured or modelled BPA levels  (Karrer et al.
Hazard identification: Assessment groups, probabilistic memberships from in silico data Running the Active substances module for the 573 pesticides listed in the EuroMix Chemical Inventory, 144 substances (25%) were found to belong to the AG for steatosis based on available PODs for steatosis. The remaining 429 pesticides (75%) may or may not have steatosis as a noncritical effect, but it was not recorded in the dossiers. Therefore QSAR models may be useful to consider possible AG membership. We first illustrate non-probabilistic (crisp) options for membership assignment. From a larger collection of 29 available QSAR models collected in the EuroMix data, the five QSAR models that relate to the AOP network for steatosis were automatically identified (Table  2 ). Note that three of these models directly relate to the adverse outcome, whereas the remaining two relate to other effects (molecular initiating events or key events) that occur upstream in the AOP network.
Hazard characterisation: Dose response modelling and relative potency factors The preparation of the NOAEL and LOAEL data of 144 pesticides related to steatosis has been described in  Crépet et al. (2019a). Hazard characterisations varied between 0. 25 µg/kg bw/day (ethoprophos) and 20 mg/kg bw/day (metosulam). A complete overview is given in the supplementary material, Appendix B. Based on the hazard characterisation of 40 µg/kg bw/day for the index substance imazalil, the RPFs varied between 160 (ethoprophos) and 0. 002 (metosulam). For the three substances also used in the second example, the RPFs from in vivo NOAELs were 1, 0. 148 and 3. 33 for imazalil, clothianidin and thiacloprid, respectively. For imazalil, clothianidin and thiacloprid, dose response relations for the AdipoRed response after 72 hours were measured in the in vitro HepaRG test system.
Cumulative dietary exposure and risk drivers for steatosis Following the traditional approach, the cumulative dietary exposure of Dutch children (1-19 years) to steatosis-related pesticides was calculated at the external level, using external RPFs for dose addition. In Figure  8  we show an example of cumulative exposure assessment based on NOAEL-or LOAELbased RPFs for the 144 of 573 pesticides that were related to steatosis according to the POD data (see 3. 2 and Appendix B). It can be seen that imazalil in citrus fruits are main risk drivers, where it can be noted that processing factors for the peeling and/or juicing of citrus fruits were missing and therefore suggest a possibly useful refinement of the model. For further details see  Crépet et al. (2019b). Comparing different models for long-term exposure, the model-assisted estimates of the LNN model were found to be lower than the OIM estimates in the upper tail (Table  5 ), which confirms that the OIM method underestimates the median, but overestimates the upper tail percentiles  (Dodd et al.
On the one hand, the use by less-experienced users can be optimised by offering clearly described tiers including presets of options, avoiding the need to specify all settings by hand. On the other hand, the modular design of the toolbox makes it suitable for developing interoperability with other web-based databases and models, and for adding new functionalities. A practical example would be to add the use of expert-elicited uncertainty distributions to adjust the results of risk assessments  (EFSA, 2019cd). Another extension would be to include read-across approaches for hazard assessment as have been recently proposed  (Escher et al. , 2019). In conclusion, the MCRA model and data toolbox has been found useful to perform case studies of hazard identification, hazard characterisation, exposure assessment and risk characterisation, as shown in this paper and in other deliverables in the EuroMix project  (Crépet et al.
Category Module Description Exposure mixtures Exposure mixtures are mixtures of substances that contribute relatively much to the overall cumulative exposure (potential risk drivers). The occurrence and concentrations of compounds in the same samples may be correlated, which is of importance for acute exposure assessments (Note that chronic assessments only use mean concentration values). Theoretically, this could be modelled and fitted to datasets. However, in practical applications (regarding pesticide residues) the number of positive values is commonly too low to allow such detailed modelling. Co-exposure of compounds is defined as the pattern of compounds occurring together on a single individual day. Co-exposure can enter the risk assessment through the use of mixtures of substances on a single food or by combining different food sources on a single day (through consumption).
Sample collection A total of 643 anurans, belonging to 38 species of 20 genera from 13 families were collected at night using active sampling methods and their blood screened for the presence of dactylosomatid parasites as described previously  (Netherlands et al. , 2015). The majority of the samples were collected from South African anurans (n = 618) with the remaining samples collected from European anurans (n = 25). Anurans were collected from several sampling localities in South Africa and Belgium (Supplementary data S1). This study received the relevant ethical approval from North-West University's AnimCare ethics committee (ethics number: NWU-00372-16-A5). Ezemvelo KZN Wildlife issued research permits OP 526/2014, OP 839/2014, OP 4374/2015, OP 4092/2016, and OP 4085/2017. Agentschap Natuur & Bos issued the permit (ANB/BL/FF-V17-00091) for collection and sampling of amphibians in Belgium, with strict collection protocol followed as instructed by the permit office.
Light microscopy screening Blood was collected and processed following standard methods  (Netherlands et al. , 2015 (Netherlands et al. , , 2018)). Stained blood smears were screened at 1000× using a Nikon Eclipse E100 compound microscope and images of parasite stages captured and measured using the imaging software NIS Elements Ver. 4. 60. Trophozoites, primary meronts and merozoites, and secondary meronts, merozoites and gamonts were measured for the dactylosomatid species. Measurements comprised the parasite's length and width. Measurements are in micrometres (μm), with the average, standard deviation and range given (in this order). Parasitaemia was calculated per 100 erythrocytes, with ~10 4 erythrocytes examined per blood smear.
DNA extraction, PCR amplification and phylogenetic analyses Eleven (n = 11) ethanol-preserved blood samples from Ptychadena anchietae (n = 6), Sclerophrys gutturalis (n = 4), and Pel. lessonae (n = 1) parasitised with dactylosomatids were used for molecular work. Samples were selected based on a high intensity of dactylosomatid infection without other haemogregarine blood parasites present (i. e. Hepatozoon sp. ) assessed using light microscopy screening. Genomic DNA extraction, polymerase chain reaction (PCR) amplification and sequencing of PCR products were all completed following the methods detailed in  Netherlands et al. (2018). The software package Geneious R11 (http://www. geneious. com (Kearse et al. , 2012) ) was used to assemble and edit resultant sequence fragments. Species identity was verified against previously published sequences using the Basic Local Alignment Search Tool (BLAST)  (Altschul et al.
, 1990). Sequences obtained were deposited in the NCBI GenBank database under the following accession numbers [GenBank: MN879389 -MN879399]. Sequences for species of Hemolivia Petit, Landau, Baccam and  Lainson, 1990 , Hepatozoon Miller, 1908 , Karyolysus Labbé, 1894 , Haemogregarina, and Dactylosoma (parasitising amphibian, avian, reptilian and mammalian hosts) were downloaded from GenBank and aligned to the sequences generated in this study. As in previous studies on haemogregarines, Adelina dimidiate Schneider, 1875, Adelina grylli Butaeva, 1996 [GenBank: DQ096835-DQ096836] and Klossia helicina Schneider, 1875 [GenBank: HQ224955], from the suborder Adeleorina were selected as outgroup. Sequences were aligned using the Clustal W 2. 1 alignment tool  (Larkin et al. , 2007)  implemented within Geneious R11.
When possible, the individual anurans the haematophagous invertebrates were feeding on were also collected. In November 2017 and January 2018, while collecting Scl. gutturalis (Supplementary data S1) for a separate study on microfilariid nematodes  (Netherlands et al. , 2020)  in Sodwana Bay, site SB-1 (S27. 488591°; E32. 664259°), mosquitoes were observed feeding on calling Scl. gutturalis and collected using an aspirator. Collected mosquitoes housed in plastic jars (350 ml) with moist cotton wool were transported back to the NWU frog lab, to keep humidity levels up and provide mosquitoes with a source of water when necessary. Mosquitoes were euthanised between 1 and 7 days post-infection (dpi) with carbon dioxide (CO 2 ), identified using  Jupp (1996)  and dissected under a stereomicroscope using modified entomology pins.
7% from Pty. anchietae deposited in the protozoan collection of the National Museum, Bloemfontein, South Africa, under accession number [NMB P 534]. Parahapantotype, 1 × blood smear: Pty. anchietae, parasitaemia of 2. 0%, deposited in the protozoan collection of the National Museum, Bloemfontein, South Africa, under accession number  [NMB P 535]. Additional material: 2 × blood smears: Scl. gutturalis with a parasitaemia of 0. 2%, respectivly, deposited in the Protozoan Collection of the National Museum, Bloemfontein, South Africa, under accession number. Representative DNA sequences: The sequence data specifically associated with D. kermiti (upon which the present biological description is based) have been submitted to GenBank and are as follows: Nuclear 18S rDNA (nu 18S) partial sequence: MN879389 -MN879398.
Dactylosoma sp. from Belgium Host: Pelophylax lessonae (Camerano, 1882) (Anura: Ranidae). Site in host: Peripheral blood. Vector: Unknown. Localities: Haacht, Belgium (N50. 979434°, E4. 659686°). Voucher material -1 × blood smear with a parasitaemia of 0. 3% from Pel. kl. esculentus deposited in the protozoan collection of the National Museum, Bloemfontein, South Africa, under accession number  [NMB P 538]. Representative DNA sequences: The sequence data specifically associated with Dactylosoma sp. ex Pel. lessonae have been submitted to GenBank and are as follows: Nuclear 18S rDNA (nu 18S) partial sequence: MN879399.
Phylogenetic analysis Sequence fragments between 1677 and 1744 nt long were isolated from the dactylosomatids collected from Ptychadena anchietae (n = 7), Sclerophrys gutturalis (n = 3) and Pelophylax lessonae (n = 1). Species of Dactylosoma formed a highly supported clade branching basally among the ingroup taxa that was sister to a large clade of species of Haemogregarina, Hemolivia, Hepatozoon and Karyolysus. Dactylosoma   (Sergent and Sergent, 1904)  [GenBank: KF992698] with 0. 7% (Table  S2 ).
Discussion In the present study, a total of 643 anurans were collected and screened for the presence of dactylosomatids. Out of 38 species from 20 genera of 13 families, only three species of three genera and three families were found infected. Morphological and molecular data indicate that the dactylosomatid found parasitising Pty. anchietae and Scl. gutturalis from South Africa represents a new species, D. kermiti n. sp. The species of Dactylosoma found parasitising a single Pel. lessonae from Belgium, possesses several phenotypic characters of the synonymised D. splendens, originally described form Pel. kl. esculentus from Paris, France  (Labbé, 1894). However, based on the data from the current study this species cannot be classified to species level.
Phylogenetic analysis The 18S rRNA gene is a well-used marker for a variety of apicomplexan parasites with large datasets available for members of Adeleorina. This gene contains conserved regions for a wide range of organisms, with scattered variable regions providing sufficient evolutionary information to infer close and distant phylogenetic relationships  (Medlin et al. , 1988). Nonetheless, the 18S rRNA gene has been shown to possess limited resolution between closely related species within Adeleorina  (Barta et al. , 2012). Thus, while this gene provides sufficient information on the phylogenetic relationships between different genera, it is less informative for species differentiation. Although closely related haemogregarines such as H. clamatae and H. catesbianae that have an interspecific divergence below 0.
5% based on the 18S rRNA gene were confirmed as separate species based on comparison of the internal transcribed spacer 1 (ITS-1) region  (Boulianne et al. , 2007). On the other hand, mitochondrial DNA has been shown to provide better resolution for species-level diagnostics and molecular phylogenetics of the intestinal eimeriid coccidia and would be a good alternative for future studies  (Ogedengbe et al. , 2011). As expected the species of Dactylosoma from the present study and D. ranarum [GenBank: HQ224957] formed a well-supported clade, distinct from the other genera of which sequences were available (Fig. 4 ). These three species can be separated based on specific phenotypic characters and the conservative 18S rRNA marker. Model-corrected interspecific divergence ranges between 0. 2 and 0.
ranarum and D. kermiti n. sp. was 0. 5% (Supplementary data S2), and 0. 2% between D. ranarum and Dactylosoma sp. ex Pel. lessonae. Several other haemogregarines also have an interspecific divergence below 1%, namely Hepatozoon cf. clamatae [GenBank: HQ224963] and Hepatozoon cf. catesbianae [GenBank: HQ224954] with 0. 3%; Hepatozoon chinensis Han, Wu, Dong, Zhu, Li, Zhao, Wu, Pei, Wang, Huang, 2015 [GenBank: KF939620] and Hepatozoon ayorgbor Sloboda, Kamler, Bulantová, Votýpka et Modrý, 2007 [GenBank: EF157822] with 0. 3%; Hemolivia parvula (Dias, 1953) [GenBank: KR069083] and Hemolivia mauritanica.
Acknowledgements We are grateful to  Prof M. Coetzee  and  Dr G. M. Kvifte  for assisting in the identification of the dipteran species,  W. Pretorius ,  J. E. Quinn ,  F. M. Phaka ,  J. Snoeks  and  S. Deneve  for help in the field, P. and  C. van Jaarsveld , for accommodation and site identification, and to  Prof. T. Huyse ,  Prof. J. Snoeks ,  Prof. T. Artois  and,  Prof. F. Volckaert  for comments on a previous version of this manuscript. We are also grateful to the two reviewers for comments and suggestions that have contributed to the final version of this manuscript. Ezemvelo KZN Wildlife ,  South Africa  and  Agentschap voor Natuur en Bos ,  Belgium  are thanked for research permits. This paper forms part of a  VLIR-UOS   TEAM  funded project ( ZEIN21013PR396 ), co-funded by the  National Research Foundation of South Africa  ( NRF  project  CPRR160429163437 , grant  105979 ,  NJ Smit, PI).
The financial assistance of the NRF towards ECN supported by the  DAAD-NRF doctoral scholarship (Grant   UID: 108803 ), and the  VLIR-OUS university scholarship  ( ID 0620854/Contract 000000076310 ), is acknowledged. Elsevier is thanked for the early career grant and its contribution to the open access publishing of this manuscript. Opinions expressed, and conclusions arrived at, are those of the authors and not necessarily those of the funding bodies. This is contribution number # 362  of the  North-West University (NWU) Water Research Group.
We compare the results of the Geant4 simulations to reference data of the United States National Institute of Standards and Technologies (NIST), included in the NIST XCOM database available online  (Berger et al 1987 [41] ). The NIST XCOM database was chosen as it is often used as a reference in medical radiation physics. It provides photon attenuation coefficients between 1 keV and 100 GeV for all the elements of the periodic table, calculated with a theoretical approach based on Hubbell et al 1980  [42]. The quoted uncertainty for the reference data is about 1% for high energies and away from the atomic edges, while it can be substantially larger, up to 10-20%, for energies close to the atomic absorption edges (Cirrone et al 2010  [43] ). From here it was decided to adopt an uncertainty (σ ref ) of up to 10% and 1% for energies below and above 100 keV, respectively.