dataesr/scientific-paragraphs-categorization huggingface dataset:
3%) patients, respectively. During the last 4 years with an exhaustive dataset of VRC MIC determination (2016-2019), the rate of VRC-resistant A. fumigatus remained stable, with 3. 8% (8/183), 4. 8% (10/209), 3. 0% (6/201), and 3. 6% (8/223) (ns), with an overall median MIC at 4 [2; 8] mg/L (Figure  1 ). An exhaustive dataset was obtained during the last 3 years for ITC and showed a consistent rate at 9. 7% (20/206) in 2017, increasing to 16. 8% (34/201), and 17% (30/176) in 2018 and 2019, respectively (ns). The median MIC of ITC-resistant isolates was 14 [3; 32] mg/L. Among these resistant strains, 20/95 (21%) isolates and 10/38 (26. 3%) had ITC MICs and VRC MICs in the ATU range, respectively, corresponding to 20/656 (3. 0%) and 10/927 (1. 1%) of all tested isolates for ITC and VRC, respectively (Figure  1 ).
, 2009). For example, sick calves reduced the number of non-nutritive visits (without milk intake) to the feeder, but not the number of nutritive visits (with milk intake), which highlights the fact that animals will reduce the frequency of sampling behaviors with the onset of illness  (Weary et al. , 2009). For growing pigs (barrows fed ad libitum meals), the time spent at the feeders increased until they reached 95-105 days of age, at which point it stabilized at 85 min per day  (Brown-Brandl et al. , 2013). In the same study, the gilts spent around 14 min less per day at the feeders than the barrows. References for sows in this area are rare, sow feeder data will thus need to be analyzed to determine whether nutritive or non-nutritive visits to the feeders as well as time spent at the feeders could be relevant indicators of health and welfare disorders.
Automatic detection of behaviors The automatic detection of aggressive sequences in fattening pigs is possible through computer vision-based algorithms  (Oczak et al. , 2013; Viazzi et al. , 2014; Chen et al. , 2017). This method is even more accurate, as its initial step is to include only the aggressive pigs in the dataset  (Chen et al. , 2017) , and not all moving pigs  (Oczak et al. , 2013; Viazzi et al. , 2014). With this method  (Chen et al. , 2017) , the mean accuracy (number of true positive and true negative recognition units / total number of recognition units) is 97 % for high aggression and 95. 8 % for medium aggression (defined by  Jensen and Yngvesson, 1998). Aggressive episodes are therefore very well recognized. Sensitivity (true positive recognition units) is 97. 4 % and 90.
4. Effects of feeding strategies on lysine and phosphorus intake, nitrogen and phosphorus excretion, for gestating sows (data from  Dourmad et al. , 2015 and Gaillard et al. , 2020a). nutritional model presented in  Gaillard et al. (2019). This approach considers the influence of the type of diet (pellets or mash) and the addition of phytase for P digestibility. The model makes it possible to adjust dietary P supplies to sow and litter performance. However, P requirements for growth are estimated based on animal BW or protein gain, which has certain limitations. More mechanistic models have recently been developed, in which mineral concentrations (P and calcium) can vary independently of protein and lipid mass  (Letourneau-Montminy et al. , 2015) , but until now they had been developed for growing pigs only.
Supervised and unsupervised techniques The ML algorithms are usually classified between supervised learning techniques and unsupervised learning ones. The majority of high-performance and practical methods use supervised learning methods that rely on labelled data (samples) to train the model. Both approaches require a sufficient amount of data for this function. If data volume is not problematic in the context of animal monitoring (multiple sensors recording all day long), the availability of a sufficient number of samples may be questioned when considering supervised learning techniques. Labelled data actually results from a human data annotation task of videos, images, and audio datasets. This is an expensive task which requires qualified staff in animal welfare.
Essential oils (EOs) are increasingly consumed as food supplements. The few published recommended doses available generally lack details both on the methodology used and concentration limits for substances of concern, including genotoxic carcinogens. We propose a tiered approach based on the toxicological evaluation of maximized concentrations of each constituent present in the EO investigated. The genotoxic potential of each constituent is assessed using literature data or QSAR analyses. Genotoxic constituents are evaluated according to the methodology provided in the ICHM7 guideline. A Toxicological Reference Value (TRV) is associated to each nongenotoxic constituent, using one of the following methodologies (decision-tree successive steps): extraction from recognized databases or clinical studies, application of adequate safety factors to NOAELs established in animal studies, read-across analyses and when none was possible, TTC of Cramer classes.
Data bases Chemical information was extracted from Sci-Finder© CAS database, PubChem and ECHA websites. Toxicological data on the individual constituents of the EOs were extracted from EFSA, EMA, EPA, FEMA, HSDB, IARC, INRS, JECFA, NTP websites. Scopus©, MedLine, Embase©, PubMed and ScienceDirect© search tools were used to retrieve toxicological and clinical information from literature, using the trivial name of the constituent associated with the key words: non-human toxicity, animal toxicity, genotoxicity, mutagenicity, teratogenicity, reprotoxicity, endocrine disruption, clinical trial, clinical study, human data, human exposure, human toxicity. Posologies were obtained from 12 out of 164 EMA herbal monographs  (EMA, 2020)  for medicinal use, and from the German Monographs (Commission E)  (German Monographs, 1994)  for the few EO initially evaluated by German authorities but no longer maintained.
In silico analyses and softwares Each compound lacking published toxicological information to establish a Toxicological Reference Value (TRV) was analysed by  Tox-Tree© (version3. 1. 0-1851-1525442531402 , available at: https://source forge. net/projects/toxtree/) to determine its Cramer classification. Mutagenicity was assessed using Derek© (version 6. 1. 0). When a possible mutagenic activity or an out-of-application domain was identified with Derek©, an additional genotoxicity analysis was performed using TopKat©  (version 18. 1. 100. 18065)  and Vega (version 1. 1. 5 48, available at: https://www. vegahub. eu/download/vega-qsar-download/ ) for confirmation. Each QSAR analysis was checked and the conclusion was finally left to the expert's judgment.
Read-across analysis Read-across principle consists in using available data of a data-rich substance (the source) for a data-poor substance (the target), provided that the target can be considered similar enough to the source substance to use the same data as a basis for the safety assessment, based on an expert chemist judgment. Chemicals were thus grouped based on their structural and functional similarities, including their metabolic fate, and TRVs can be derived from the source TRV, corrected by the molecular weight of both the source and the target substances. The EFSA performed read-across analyses in the course of their evaluation of several food additives, and among them food flavouring substances which happen to also be EO constituents. Similarly, readacross evaluations were performed by applicants in the context of REACH registration of chemical substances  (ECHA, 2016).
, 2019) , -for environmentally related compounds  (Nelms et al. , 2019) , -for antimicrobials  (Yang et al. , 2020) , -for fragrance-related materials  (Patel et al. , 2020). We have now adopted the TTC values proposed by Patel  (Patel et al. , 2020)  for fragrance-related materials. This decision is based on the following reasons. (1) This database was established according to the original Munro database  (Munro et al. , 1996)  using oral experimental animal data on cancer and non-cancer toxicity endpoints, enriched with cosmetic products (base referred as 'federated',  Yang et al. , 2017)  and 476 fragrance chemicals to reach a total of 1327 compounds. (2) This database was corrected following EFSA's evaluations (WHO/EFSA, 2016) and included 111 class II compounds leading to the derivation of a more robust TTC value, as the original class II TTC from the 1996-Munro database was derived from 28 compounds only.
(3) Many constituents of EOs are fragrance chemicals (aromatic-ring-containing molecules, alkane-derived molecules, terpenoids …) used also as flavouring NA: not available. substances in the food industry and are at least volatile molecules, thus, according to our opinion, the resulting database is the most relevant for the evaluation of EO constituents. Values from the aforementioned databases are summarized in Table  3. Consequently, we used the following TTC values derived from the Patel database  (Patel et al. , 2020) : -49. 1 μg/kg/day (2946 μg/person/day) for class I, -12. 7 μg/kg/day (762 μg/person/day) for class II, -2. 9 μg/kg/day (174 μg/person/day) for class III. The TTC of 49. 1 μg/kg/day for class I compound was higher than the original value of Munro-1996 database (see Table  3 ) because some evaluations were corrected  (Yang et al.
, 2017)  and less toxic cosmetic compounds were added. But this database still contains organophosphorus and carbamate esters present in the original Munro-1996 database. Nevertheless, the addition of less toxic class III molecules leads to an increase of the class III TTC value from 90 to 174 μg/day. Of note, removing organophosphorus and carbamate esters with anti-cholinesterase activity present in the  Munro-1996  data resulted in a class III TTC value of 180 μg/day  (Kroes et al. , 2004). As organophosphorus and carbamates are unlikely to be observed in EOs, the TTC value of 174 μg/day seems thus conservative enough. On the other hand, compounds identified as genotoxic, either based on literature data or based on QSAR analysis but lacking carcinogenicity data, were assigned a TTC value of 0.
, 2020)  49. 1 (2946) 12. 7 (762) 2. 9 (174) 1327 Antimicrobials  (Yang et al. , 2020)  33. 5 (2010) 5. 4 (324) 1. 3 (78) 1357 Environmentally-relevant compounds  (Nelms et al. , 2019)  37. 3 (2238) 34. 6 (2076) 3. 9 (234) 1304 Food-related substances  (Reilly et al. , 2019)  17 (1020 1 ) NA 1. 5 (90 2 ) 329 Cosmetics (federated database)  (Yang et al. , 2017)  46 (2760) 6. 2 (372) 2. 3 (138) 966 Food related substance  (Munro et al. , 1996)  30 (  1800 ) 9 (  540  which is a conservative estimate of potential cancer risk, corresponding to an estimated excess risk of 1 in 10 6 for lifetime cancer  (Kroes et al. , 2004). So far, there is no general recommendation to limit the oral use of EOs, except for EOs containing genotoxic compounds which should be limited to 2 consecutive weeks according to regulatory recommendations  (DGCCRF, 2019b; EMA, 2019a).
Recommended doses were expressed in number of 25 mg drops per number of intakes per day. For each constituent, systemic exposure dose (SED), was calculated by multiplying the recommended dose expressed in drops/intake/day, by the drop weight (25 mg), the concentration of the constituent in the EO expressed in %, and the oral absorption of the constituent using 100% as default value in the absence of human data for oral bioavailability. Subsequently, this quantity was divided by the default body weight value, i. e. 20, 38 or 60 kg respectively, for the corresponding targeted populations, i. e. 6-year-old children, 12-year-old children, and adults (Fig. 1a ). The Margins of safety (MoS) calculated for each of its constituents, i. e. the ratio between the TRV and the SED should at least all be equal to 1 to validate an acceptable daily recommended oral dose (Fig.
General approach The EO is considered as a mixture defined by its reconstructed chemical composition which was established as a preliminary step (see M& M section). Starting from this composition, the EO is evaluated constituent by constituent, assuming additivity rather than synergy between the potential effects of each constituent. This approach relies on a decision tree based on "available"/"not available" switches for each individual constituent based on the availability of clinical or toxicological data in the literature as shown in Fig. 2. If none is available, the methodology switches to a risk assessment methodology based on the constituent structural features, either by using a read-across analysis or the Cramer TTC, rather than a methodology based on experimental data to derive a TRV.
Determination of a toxicological reference dose. When a compound was confirmed to be neither mutagenic nor part of the cohort-ofconcern, according to the decision tree described in Fig. 2 , an ADI established by recognized organisations was searched for in databases. When none was available, a NOAEL established for a repeat-dose toxicity study was searched for in databases to derive a TRV. This approach is illustrated thereafter. 6. 1. 2. 3. 1. Limonene. Both the JECFA and the EFSA have allocated an ADI 'not-specified' for limonene, considering there was no need of a numerical ADI for this compound which was judged safe in its current uses. Limonene has been tested in 4-week, 13-week and 2-year oral toxicity studies in mice and rats by the NTP  (NTP, 1990). In summary and in agreement with the EFSA (EFSA, 2015a), the dose of 300 mg/kg days/week, corresponding to 215 mg/kg/day, administered to female rats in the carcinogenicity study has been established as the NOAEL of limonene.
, 2020) , which was used as a default TRV value for the determination of the recommended dose. The number of compounds analysed by ToxTree and the corresponding repartitions in classes I, II and III is provided in Table  5  for the 6 EOs analysed. ToxTree analyses showed mainly classes I and III compounds and lesser compounds in class II, as typically observed in other databases (WHO/EFSA, 2016;  Patel et al. , 2020).
Acknowledgements Our research is 100% funded by the  Pierre Fabre Research Institute. At the time of this work, all the co-authors were employees of the  Pierre Fabre Research Institute. Marion Feuillée  and  Elise Roux  were interns at the  Pierre Fabre Research Institute  when they contributed to this work and left the company after their internship. Therefore, their respective current addresses were provided. Françoise Fraboul  was also employed at the  Pierre Fabre Research Institute  when she contributed to this work, but later retired and now works as a consultant. We thank  Philippe Schambel  for performing QSAR metabolic analyses.
Solid tissues (mantle and gill) were either fixed in 96% ethanol or directly used for DNA extraction. For hemolymph samples, collected from adductor muscle (1ml syringe, 26G needle), cells were centrifuged (10 min at 2500g) and the supernatant removed and replaced by 96% ethanol. DNA extraction was done with the NucleoMagTM 96 Tissue kit (Macherey-Nagel) in combination with a Kingfisher FlexTM extraction robot (serial number 711-920, ThermoFisher Scientific). We followed the kit protocol with modified volumes of some reagents: beads 2x diluted in water, 200μL of MB3 and MB4, 300μL of MB5 and 100μL of MB6. A total of 106 biallelic single nucleotide polymorphisms (SNPs) described in Simon et al. 2020 were analysed. However 32 SNPs produced too much missing data and unsatisfactory fluorescence level and were removed for subsequent analyses.
The list of 74 nuclear SNPs used and their sequences are provided in Table  S2. They were identified to be strongly differentiated between species and lineages of the Mytilus edulis complex of species in the study of Fraisse et al. (2016) , in order to develop an ancestry-informative panel of SNPs, and its efficacy was confirmed by the analysis of large datasets  (Simon et al. 2020 (Simon et al. , 2021)). Genotyping was subcontracted to LGC genomics (Hoddensdon, UK) and performed with the KASPTM method  (Cuppen, 2007 , Semagn et al. 2014 ). This method puts two "allele specific" primers with two different fluorochromes in competition during a PCR amplification and gives two values of fluorescence level, one for allele X and the other for allele Y. Homozygous genotypes have a high fluorescence for allele X or allele Y only, while heterozygous genotypes have an intermediate fluorescence for each allele (Figure  2 ).
The genotype classification at each SNP of each sample was determined by two LGC genomics experts. Assays with too low fluorescence were classified as badly amplified ("bad") and those with an unbalanced intermediate fluorescence, that were compatible with neither homozygous nor heterozygous genotypes of a standard diploid genotype, were classified as "uncalled" (Figure  2 ). In order to assign mussels to species (M. edulis, M. galloprovincialis or hybrid), we performed a Principal Component Analysis (PCA, dudi. pca(), package adegenet in R,  Jombart et al. 2011)  based on the 74 nuclear SNPs and used the first axis coordinate to infer a hybrid index  (Väinölä and Strelkov 2011; Brisbin et al. 2012).
Furthermore, SNP alleles were oriented in order for allele Y to be defined as the most frequent in M. trossulus and allele X the most frequent in compound M. edulis and M. galloprovincialis reference samples (see Table  S2 ). To do this, we used allele dosage measured by the following formula y'i ,j = yi ,j /(xi ,j + yi ,j) where yi and xi are the fluorescence values of allele X and Y respectively for the ith SNP of jth sample (Figure  2 ). We called y'i ,j the "Variant Allele Fluorescence Fraction (VAFF)" (analogously to the Variant Allele Fraction statistics used with NGS dataset, but with the fluorescence signal used instead of the read coverage). To filter the data out from badly amplified low fluorescence level SNPs and samples, we calculated an index of amplification following this formula ai ,j = Xi ,j + Yi ,j, where Xi ,j and Yi ,j are the allele X and allele Y original fluorescence values.
Genotype inference was performed independently by two users and cross-checked. To have a global comparison of cancerous genotypes with Mytilus references, we performed a maximum parsimony (MP) tree using MEGA v7. 0. 26  (Kumar, Stecher and Tumura 2016)  with all sites and Subtree-Pruning-Regrafting method. SNP genotypes were coded with two nucleotides per position (a single SNP was represented by two positions in a "pseudo-fasta" file) to be able to represent heterozygous and homozygous genotypes and then we combined all SNPs in a single "pseudosequence" for each sample before converting to fasta format (as. fasta() package bio3d v2. 4-1 in R,  Grant et al. 2006).
h. mtCOI sequences Individuals STBRI_69, L19_8, L9_3, L11_4 and CamaretB_1 affected by MtrBTN2, were sequenced for a parallel research project and short reads produced by Illumina sequencing were available (Novaseq, paired-end 150 bp). We skimmed mitochondrial reads by mapping them to a set of eight Mytilus mitogenomes, using bwa-mem2 (v2. 0, Md et al. 2019)  and SAMtools (v1. 10, Li et al. 2009 ) with a minimal mapping quality of 5. The mitochondrial genomes from female and male M. galloprovincialis (FJ890849 and FJ890850, respectively), M. edulis (DQ198231 and AY823623, respectively), M. trossulus (AY823625 and HM462081, respectively), and M. californianus (JX486124 and JX486123, respectively) were retrieved from GenBank and used as references. Note that the M. edulis female mitochondrion is attributed to a M.
trossulus individual in GenBank but corresponds to an introgression of the M. edulis mitochondrion in the Baltic Sea. For each cancerous individual, mitochondrial reads were then assembled with MEGAHIT (v1. 2. 9,  Li et al. 2016). Contigs produced with megahit were finally processed with MitoFinder  (Allio et al. 2020)  to automatically retrieve contigs corresponding to mitochondria and annotate them (using the invertebrate mitochondrial code as organism type). Then, we extracted COI sequences from all mitochondrial contigs for each individual, and aligned them with sequences of  Yonemitsu et al. (2019) ,  Skazina et al. (2021)  as well as M. trossulus mtCOI sequences gathered from Genbank  (Breton et al. 2006 , Lesser et al. 2010 , Marko et al. 2010 , Layton et al. 2014 , Zbawicka et al. 2014 , Crego-Prieto et al.
a. Transmissible cancer genetic diagnosis of individuals sampled on two tissues We first analysed samples of two tissues from n = 1749 mussels in order to evaluate the prevalence of genetic chimerism in this dataset and the ability of our indexes to identify mussels infected by a transmissible cancer either of M. trossulus origin or other Mytilus spp. origins. We found 11 positive MtFF outlier individuals (M. trossulus fluorescence fraction), 5 for both tissues and 6 for hemolymph only (Figure  3A , Table  S5 ). Ten of them were also positive at both PUF (proportion of unbalanced allelic fluorescence, 5 for both tissues and 5 for hemolymph only) and 2TD (2-tissue genetic distance), and the last one was positive at PUF but not 2TD (Figure  S2 , Table  S5 ). MtFF values were higher in the hemolymph samples than the mantle samples but the difference was sometimes low (Table  S5 ).
Acknowledgements This work was supported by  Montpellier Université d'Excellence  ( BLUECANCER  project),  Agence Nationale de la Recherche  ( TRANSCAN  project,  ANR-18-CE35-0009 ), the  French DPMA (Direction des pêches maritimes et de  l'aquaculture, DPMA-2017-MORBLEU Conventions N°:  16/1212569 & 17/1212952 ) and  Conseil Régional de Normandie, LabEx CeMED (Junior Research Team funding).
g. , herbivores) terms. We restricted the results of our search to the topics Ecology, Zoology, Environmental, and Behavioral sciences. The survey included articles published up to October 2019. Our selection followed three steps (Figure  2 ). We first retained for further consideration only articles for which data on the sizes of home ranges were provided for ungulates and at the population level. We restricted our review to herbivorous ungulates, hence removing articles on the wild boar (Sus scrofa), an omnivorous species, but included articles on elephant (Loxondonta africana), which is an herbivorous subungulate. This led us to keep 216 articles out of the 7,454. Second, we retained only articles providing a continuous relationship between the sizes of individual home ranges and an independent ecological metric measured at the individual home range level.
The results in the article were considered supporting the hypothesis concerning ecological drivers if at least one metric was retained. The probability to support a hypothesis was evaluated using the data collected across all case studies from the review. Pictograms downloaded from http://www. phylopic. org or from the personal collection of authors. canadensis) provided the sizes of home ranges for individuals assigned a priori to habitat categories defined by their dominant vegetation type, which were synthetic proxies of differences in all ecological drivers (resource, landscape heterogeneity, risk). We could therefore not derive results concerning the relationship between the size of an individual's home range and an ecological metric of resource, landscape heterogeneity or risk from these articles that would compare with the other retained articles.