dataesr/scientific-paragraphs-categorization huggingface dataset:
6% of N=203, Figure  3 ), alternating with audible inspired units in two-thirds of the cases, although isolated single expired units were produced occasionally (5. 4%). Some other sound types could be produced as series of same-type units: Hous (85. 7% of N=7, Figure  3 ), Hoots (37. 1% of N=35), Chortles (34. 8% of N=23), Barks (33. 3% of N=3), Screams (20% of N=30), Trills (14. 3% of N=21), and to a lesser extent, Mews (5. 2% of N=461, Figure  3 ), Stutters (3,9% of N=541), Growls (3. 9% of N=178), and Meows (1. 1% of N=703). On the contrary, Howl (N=30) as well as the unvoiced Snuff (N=79), Sneeze (N=67), Hiss (N=12) and Spit (N=4) were never produced in series. Multi-type sounds Sounds made of units of different types (Multi-type sounds = 9. 7% of the dataset, N=259) were more frequently produced as Transitions (i.
This project is part of the  International Research Project VOCOM ("Evolution of Vocal Communication ") coordinated by MH and supported by the  CNRS (French National Center for Scientific Research). This work was funded by the  International Research Project VOCOM , a  Fyssen grant  to KC, a grant from the  University Bretagne-Loire  granted to AL for hiring a postdoc, a grant from the  Association  " Beauval Nature pour la Conservation et la Recherche " (with our deepest thanks to  Baptiste Mulot  for his continued support), as well as financial support from the  University of Rennes 1  and the  CNRS. We thank  Robyn Hetem  for helpful discussions. We are grateful to  Dr Laurie Marker  for permission to conduct research at the  Cheetah Conservation Fund , and to all cheetah keepers and staff of  CCF  for their help.
Risk factors based on the antibody test and two categorical levels such as the location and animal species were investigated individually using univariate logistic regression. Te model of binary logistic regression was performed (in the binary logistic regression model, we used backward stepwise methods) to test the signifcance of variables in the model and to test the signifcance shown by univariate analysis. Te validity of the CFT and I-ELISA techniques was defned by using characteristic feature of sensitivity and specifcity and parameters such as the positive predictive value positive (PPV) and the negative predictive value (NPV) based on RBT results. All statistical tests were conducted using SPSS version 20 (SPSS, IBM) at α � 0. 05 signifcance level.
Study Area, Design, and Sample Size. Considering resource constraints, 50 farms were selected randomly from three diferent districts of southern Oman (Dhofar, East Jabal and the desert region), namely, Salalah, Mirbat, Tumrait, Jibjat, Taqah, Madinat al Haqq, and Ghado from March through April 2015 (see map below). Te study areas were selected based on the data on the animal population obtained from the Ministry of Agriculture and Fisheries [17] and also on information indicating possible Brucella infection in the selected areas.
2. 1. 1 Study Area, Design, and Sample Size. Considering resource constraints, 50 farms were selected randomly from three diferent districts of southern Oman (Dhofar, East Jabal and the desert region), namely, Salalah, Mirbat, Tumrait, Jibjat, Taqah, Madinat al Haqq, and Ghado from March through April 2015 (see map below). Te study areas were selected based on the data on the animal population obtained from the Ministry of Agriculture and Fisheries [17] and also on information indicating possible Brucella infection in the selected areas.
In people and animals, low birth weight (LBW) is recognized as highly predictive of health trajectory from the neonatal period to elderly ages. Regarding the neonatal period, although LBW is recognized as a major risk factor for neonatal mortality, there does not appear to be a clear definition of 'when a birth weight should be considered low' in all species. The aim of this work was to use the scientific literature available to map the various thresholds proposed to define LBW in domestic mammals. Using a standardized methodology, a scoping review was conducted through a literature search in three different bibliographic databases. After a two-step screening of 1729 abstracts and full-text publications by two independent reviewers, eleven studies met the inclusion criteria. Selected publications represented six mammalian species (rat, mouse, dog, pig, cow, and rabbit).
Search strategy Our research question was stated as 'what are the methods used to define LBW using absolute birth weight in non-human mammals?'. A literature search algorithm was developed to capture relevant studies in three online databases (PubMed, Web of Science, and CAB abstracts). The search terms were identified by the authors (AG, CS, SC and AM) and combined into a Boolean query (defin* OR recogn* OR identif* OR cut-off? OR threshold? OR cutoff?) AND ('low birth weight' OR lbw OR iugr OR 'birth weight' OR birthweight) AND (pupp* OR piglet OR calf OR calves OR kitten? OR cub? OR foal? OR monkey? OR mice? OR rats OR 'guinea pig' OR offspring?) that was searched in the titles and abstracts of the articles. Further details on the formulation of this search equation in each of the databases are available in the Supplementary Appendix.
Selection of sources of evidence and general characteristics Searches in the three selected databases with the identified search terms returned 2478 references. After the removal of duplicates, 1729 papers were included in the screening rounds (Fig. 1 ). After the first screening, 133 articles were retained and their full texts analyzed in the second screening, from which 15 were identified as relevant. One additional paper was identified by checking the references of the publications included. Finally, a total of 16 papers were included in the scoping review.
Chastant- Maillard et al. , 2017; with unbalanced datasets such as those dealing with neonatal mortality (around 10% dead newborns compared to 90% newborns still alive at the end of the neonatal period; puppies:Chastant- Maillard et al. , 2017; piglets: Koketsu et al. , 2021; calves: Del Río.
Acknowledgements The authors acknowledge the experimental  unit Pôle d'Expérimentation Avicole de Tours (UE PEAT ,  INRAE , 2018. Experimental Poultry Facility, DOI:  10. 15454/1. 5572326250887292E12 )where the hens were maintained and where the experiments took place. Financial support statement This research received the support of the  PHASE research Department (INRAE)  and a PhD fellowship  INRAE-Région Centre Val de Loire (France).
Molecular barcoding of fish Total genomic DNA was isolated using a QIAamp DNA Mini Kit (Qiagen), as per the manufacturer's instructions. The 5′ region of the mitochondrial cytochrome c oxidase subunit I (COI) gene was amplified with the primers FishF1 and FishR1  (Ward et al. , 2005). PCR reactions and amplification were performed as in  Ayadi et al. (2017). We used CodonCode Aligner version 3. 7. 1 software (CodonCode Corporation, Dedham, MA, USA) to edit sequences, which were 655 bp in length, compared them to the GenBank database content with BLAST, and deposited them in GenBank under accession numbers MG761757-MG761759 and MW788679-MW788687 (Table  1 ). Species identification was confirmed with the BOLD identification engine  (Ratnasingham & Hebert, 2007).
COI sequences of monogeneans Total genomic DNA was isolated using a QIAmp DNA Micro Kit (Qiagen). The specific primers JB3 (= COI-ASmit1) and JB4. 5 (= COI-ASmit2) were used to amplify a fragment of 424 bp of the COI gene  (Bowles, Blair & McManus, 1995; Littlewood, Rohde & Clough, 1997). PCR reactions and amplification were performed as in  Ayadi et al. (2017). Sequences were edited with CodonCode Aligner 3. 7. 1, compared to the GenBank database content with BLAST, and deposited in GenBank under accession numbers MG761760-MG761766 and MW796584-MW796594.
Trees and distances Two matrices were constructed, for delimitation of taxa and phylogeny. The first matrix was built from all our new sequences and the few COI sequences of plectanocotylids already in GenBank. In particular, we used COI sequences from plectanocotylids collected off Sète, on the Mediterranean coast of France (Jovelin & Justine, Note: Note that traceability was ensured by labelling and barcoding fish individuals and their respective monogenean parasites. New data include eight new sequences of fish and 18 new sequences of monogeneans. In addition, some fish were barcoded but monogenean sequences from the same fish were not retrieved. These were registered in GenBank as: MW788683: Br34 Chelidonichthys obscurus 100%; MW788684: Br35 Chelidonichthys obscurus 99. 85%; MW788686: Br38 Chelidonichthys obscurus 100%; and MW788687: Br39 Chelidonichthys obscurus 99.
A second matrix was built from the first by deleting all sequences with indels or missing parts. The second matrix had only 15 sequences, but all taxa of the first matrix were represented. All sequences were perfectly clean: there were 396 positions in the dataset for sequences which were 396 bases in length. After estimating the best model with MEGA7, a tree was inferred using the Maximum Likelihood method based on the Hasegawa-Kishino-Yano model with Gamma distribution (HKY+G) in MEGA7, with 1,000 bootstrap replications  (Hasegawa, Kishino & Yano, 1985). A tree was also inferred with the Neighbour-Joining method with MEGA7 with 1,000 bootstrap replications. Distances were computed on MEGA7 on the second "clean" matrix, using the MEGA7 "group" feature for convenience (groups were defined on the basis of clades found in the analysis).
Molecular characterization of monogeneans A tree built from the first matrix, which included all available COI sequences of Plectanocotylidae (our 18 new sequences and 2 already in GenBank) and 1 Mazocraeidae, provided the following results (Fig. 1 ). The analysis involved 21 nucleotide sequences, and there were a total of 312 positions in the final dataset. The purpose of this matrix with all sequences was to see whether it was possible to attribute the available sequences to separate taxa, and to verify the identity of sequences from GenBank. The answer was positive; the ML tree showed several clades with 100% bootstrap support and each clade corresponded to a nominal species identified from morphology. The Plectanocotyle cf. gurnardi clade included 8 sequences, all new. The P. major clade included three sequences, all new.
The P. lastovizae clade included three new sequences and the sequence labelled "Plectanocotyle sp" in GenBank (AY009169). The T. obscura clade included three new sequences and the sequence labelled "Octoplectanocotyla obscurum" in GenBank (AY009170). Although the analysis provided high bootstrap values for species nodes, the higher nodes had very low values, and Triglicola was not a sister-group to Plectanocotyle. We considered that low bootstrap values were caused by the presence of many indels in some sequences. The second matrix was built by excluding from the first matrix all sequences with indels or incomplete coverage, to improve the accuracy of the final analysis; the matrix, including 15 sequences, was perfectly clean, with 396 positions for 396 base-long sequences. Excluded sequences were the old sequences from 2001  (Jovelin & Justine, 2001 ) and a few of our new sequences.
Sproston followed that opinion, provided a detailed report of the taxonomic contradictions and redescribed specimens at various states of maturity and contraction  (Sproston, 1946). Price followed Sproston  (Price, 1961). Several major authors, including Sproston, Bychowsky and Price  (Bychowsky, 1961; Price, 1961; Sproston, 1946)  pointed out that Plectanocotyle elliptica Diesing, 1850 was never found again and that no Plectanocotyle species was ever found again outside the Triglidae. We can therefore consider that nomenclatural issues have been solved in the past and this indicates that species of Plectanocotyle are parasitic only on Triglidae. We note, however, that P. elliptica is still considered valid in the WoRMS database  (WoRMS, 2021). Within Plectanocotyle, because of the synonymies proposed by Llewelyn, only two species were recognized before our study  (Llewellyn, 1941) : Plectanocotyle gurnardi (Van Beneden & Hesse, 1863)  Llewellyn, 1941 and Plectanocotyle major Boudaya, Neifar & Euzet, 2006 (Gibson & Bray, 2001).
Figure1Molecular phylogenetic analysis of all available members of the Plectanocotylidae. The tree was obtained by the ML method from COI sequences. All available sequences were used (18 new sequences + 3 from GenBank). Each species is represented by a clade with high bootstrap value. The two sequences from monogeneans collected in France (Jovelin & Justine, 2001)  are assigned each to a clade: "Plectanocotyle sp. " to the P. lastovizae clade, and "Octoplectanocotyla obscurum" to the Triglicola obscura clade. Bootstrap values for nodes higher than species are low; Triglicola obscura is not a sister-group to Plectanocotyle in this tree. Scale: base differences per site. Full-size  DOI: 10. 7717/peerj. 12873/fig-1.
New and other localities for Plectanocotyle spp. and T. obscura Since the sequence deposited by  Jovelin & Justine (2001)  as "Plectanocotyle sp. " from material collected off the Mediterranean coast of France coast off Sète, from the same host, is very similar to our sequences of the Algerian material, it is likely that P. lastovizae is present on both the North and South shores of the Mediterranean. Therefore, off Sète, France, is considered an additional locality for P. lastovizae, for which the type-locality is off Algeria. Sequences from a phylogeny  (Jovelin & Justine, 2001)  produced from the same animals are curiously labelled in GenBank with two names; although designated in the text and GenBank "details" as Plectanocotyloides obscurum, they are currently labelled as both Plectanocotyloides obscurum (28S, AF311718) and "Octoplectanocotyla obscura" (COI, AY009170).
The assessment of skin sensitisation is a key requirement in all regulated sectors, with the European Union's regulation of cosmetic ingredients being most challenging, since it requires quantitative skin sensitisation assessment based on new approach methodologies (NAMs). To address this challenge, an in-depth and harmonised understanding of NAMs is fundamental to inform the assessment. Therefore, we compiled a database of NAMs, and in vivo (human and local lymph node assay) reference data. Here, we expanded this database with 41 substances highly relevant for cosmetic industry. These structurally different substances were tested in six NAMs (Direct Peptide Reactivity Assay, KeratinoSens™, human Cell Line Activation Test, U-SENS™, SENS-IS, Peroxidase Peptide Reactivity Assay). Our analysis revealed that the substances could be tested without technical limitations, but were generally overpredicted when compared to reference results.
, 2015 , Kimura et al. , 2015 , Ramirez et al. , 2016; Fujita et al. , 2019a , Natsch et al. , 2020). For all NAMs, large databases have been published by various groups (e. g. , Del  Bufalo et al. , 2018; Hoffmann et al. , 2018; Natsch et al. , 2021; Urbisch et al. , 2015). These databases covered broad ranges of chemical structures and use categories, which provided the basis for the evaluation of the NAMs' applicability domains. Besides substances with non-cosmetic uses, such as industrial or pharmaceutical, various types of cosmetic ingredients have been included e. g. , fragrances and preservatives. However, few more recently developed cosmetic ingredient types, such as actives, surfactants, dyes, and Ultra-Violet (UV) filters were included. With the goal to harmonise the assessment of the individual NAMs, we compiled a database of substances with well-defined LLNA and human reference data and complete data for the NAMs Direct Peptide Reactivity Assay (DPRA), the KeratinoSens™, the human Cell Line Activation Test (h-CLAT), the U937 cell line activation test (U-SENS™) and the SENS-IS  (Hoffmann et al.
, 2018). This database also provided the basis for a systematic assessment of combinations of NAMs, in particular defined approaches (DA)  (Kleinstreuer et al. , 2018). Hence, this high-quality database is pivotal for comprehensive evaluations that support broad acceptance of NAMs and DAs, especially for regulatory purposes, as evidenced by the work conducted by the OECD, particularly resulting in the OECD Guideline No. 497 'Defined Approaches on Skin Sensitisation  (OECD, 2021b). In addition, it provides the data and inputs required to explore next generation risk assessment (NGRA) approaches through case studies  (Gautier et al. , 2020). To include additional ingredients of high importance to cosmetic industry, we expanded the original Cosmetic's Europe (CE) skin sensitisation database by adding 41 substances.
These substances were tested with the same six NAMs, according to the respective OECD test guidelines, or to the published procedures  (Cottrez et al. , 2016; OECD 2018a; OECD 2018b; OECD 2021a)  to assess their performance in terms of applicability and predictivity for this structurally different chemistry. We also expanded the database by adding Peroxidase Peptide Reactivity Assay (PPRA) data for all substances  (Ryan et al. (2020) ). Here, we describe the expanded database focusing on the substances' uses, their physico-chemical properties, aspects of their applicability domain and the evaluation of the predictivity of the individual NAMs. We consider the expanded database as a valuable resource to better understand the advantages and limitations of the individual NAMs. Especially when combined with more focused investigations of others, such as  Basketter et al.
Selection of substances The following selection criteria were used: 1. Substances had to be of high relevance for cosmetics industries, such as dyes, UV filters, surfactants, and others, which are underrepresented in published databases used to assess NAMs  (Hoffmann et al. , 2018; Urbisch et al. , 2015). Due to their specific uses they were expected to be structurally different and to potentially provide useful insight into the applicability domain of the NAMs. 2. LLNA reference data had to be available, specifying at least the test concentrations, the stimulation indices, the vehicle used and the EC3 values. For three UV filters and a dye no LLNA, but guinea pig maximisation test (GPMT) data were available. Due to their importance for the purpose of extending the database to highly relevant substances, we considered them eligible.
Based on the reported high concordance of  GPMT and LLNA (Haneke et al. 2001) , we assumed that the results of both assays in terms of hazard identification would be identical. Note that we will therefore refer to LLNA reference data only. 3. LLNA reference data should cover the entire response spectrum, i. e. , from non-sensitiser to extreme sensitisers. 4. In vivo and human reference data should cover the entire response spectrum, i. e. , from non-sensitiser to extreme sensitisers. 5. The substances had to be clearly defined by name, CAS-registry number, and SMILES code. Consequently, natural extracts or mixtures were not eligible. 6. The substances had to be commercially available. Based on these criteria, 44 substances were identified that fulfilled the criteria. Of these, two dyes were not available and one fragrance ingredient was available in a very low purity only, so that 41 substances were selected.
In vivo reference data For most of the 41 substances LLNA data were publicly accessible, i. e. either in opinions of the Scientific Committee on Consumer Safety (SCCS) of the European Commission (EC) or its predecessors, in scientific publications  (Ball et al. , 2011; De Jong et al. , 2007; ICCVAM 2011; Karlsson et al. , 2011)  or in REACH dossiers disseminated via the website of the European Chemical Agency (www. echa. europa. eu). For eight substances, summary LLNA data according to selection criterion 2 were made available by Cosmetic Europe member companies. For four substances, no LLNA, but GPMT data were found in SCCS opinions or REACH dossiers, which were used instead to determine in vivo reference hazard results. In addition to LLNA data, human data, i. e. , human test data, diagnostic patch test data and other relevant published information, were collected for the substances to derive a human potency category, where possible.
The retrieved evidence was reviewed and was sufficiently conclusive for 19 substances to assign them to one of six human skin sensitisation potency categories according to the approach described by  Basketter et al. (2014)  and  Api et al. (2017). The data used to derive the potency categories are provided as Supplemental Table  2. The reference data in the full database, i. e. those of the 41 substances (referred to as set 1) and the 128 substances (referred to as set 2) already published  Hoffmann et al. (2018) , were compared with data from OECD curated databases (OECD, 2021c (Annex 2)). The comparison showed no need to amend the reference data in our database, since the LLNA EC3 values and human potency categories were identical or very similar for more than 90% of the overlapping reference substances (102 for LLNA, 58 for human).
Computationally generated data Several computational tools were used to further characterise the substances within the database. Physico-chemical properties related for example to skin penetration, i. e. molecular weight (MW), octanol-water partition coefficient (KOW), water solubility (WS), vapour pressure (VP), melting (MP) and boiling point (BP) were calculated using the quantitative structure-property relationship (QSPR) developed by  Zang et al. (2017). We also calculated these parameters using the OPERA QSPR of  Mansouri et al. (2018) , which is implemented in the Integrated Chemical Environment on the website of the US National Toxicology Program (https://ice. ntp. niehs. nih. gov). However, for our set of substances and the six physico-chemical properties no significant differences between the two QSPR were found (data not shown).
0. 1; Nexus version: 2. 2. 2), which were dichotomised by considering the prediction 'non-sensitiser' as negative and all other predictions as positive, and from TIMES-SS (TIssue MEtabolism Simulator model for Skin sensitisation; version 2. 30. 1. 11) by reporting the result for the parent, denoted as TIMES-P, and the most severe result of all metabolites, denoted as TIMES-M. These were generated because some defined approaches use these as input data, such as the integrated testing strategy described by  Takenouchi et al. (2015) , the Bayesian network of  Jaworska et al. (2015) , the stacking model by Del  Bufalo et al. (2018)  and the artificial neural network by  Hirota et al. (2018). Besides the primary use category that was essential for the selection, additional uses were obtained from the CPCat (Chemical and Product Categories) Database of the U.
NAMs As we intended to expand our publicly available database, the substances were tested in the DPRA, the KeratinoSens™, the h-CLAT, the U-SENS™ and the SENS-IS, as briefly described by  Hoffmann et al. (2018) , using either the respective OECD Test Guidelines, where applicable, or the latest protocols of the NAM developers  (Alepee et al. , 2015; Cottrez et al. , 2016; OECD 2018a; OECD 2018b; OECD 2021a). The PPRA was included as a new NAM, as it was potentially of added value, especially for substances needing oxidative or metabolic activation  (Gerberick et al. , 2009; Ryan et al. , 2020). The PPRA expands the DPRA by i) including horseradish peroxidase to potentially activate substances, ii) by testing several concentrations allowing for a concentration-response analyses and iii) by detecting peptide using mass spectroscopy.