dataesr/scientific-paragraphs-categorization huggingface dataset:
The database For completeness, the entire database of 169 substances, i. e. , the here introduced 41 substances (set 1) and the original version with 128 substances (set 2), is provided as a Supplemental Table  1 , in which substances of set 1 are highlighted. This version also includes a few corrections and updates for substances of set 2, especially for in silico software versions. Substances are identified by substance name, CASregistry number, and SMILES. In addition, primary use categories, which were assigned based on uses by CE member companies, are included. It contains information on the mechanistic reactivity domains, an indication, if a substance requires activation  (Patlewicz et al. , 2016; Urbisch et al. , 2015) , skin sensitisation predictions of DEREK Nexus (version 6. 0. 1; Nexus version: 2.
Data analysis We descriptively compared the two sets of substances in terms of their use categories and physico-chemical properties, including a Mann-Whitney test for each property (Graph-Pad Prism 7. 0, www. graphpad. com). Distributions of the individual properties for all substances were graphically described by a combination of half-violin, box-and dot-plots (using the R package 'ggplot2'  (Wickham 2016) , version 3. 2. 1). We chose the width parameter to obtain a single-peaked violin-shape, as it was assumed that the underlying distributions would be unimodal. The predictivity of the individual NAMs was evaluated in detail. Applying the standard prediction models (PM) or dichotomised versions thereof, predictivity was described for the six NAMs by specificity, i. e. , the proportion of the non-sensitising substances predicted as nonsensitisers, sensitivity, i.
Description of the expanded database The expanded database (Supplemental Table  1 ) contained in total 169 substances, i. e. , set 1 with 41 new substances and set 2 with 128 substances  (Hoffmann et al. , 2018). The two subsets were compared by a descriptive analysis of the use categories. Since the primary use category (Table  1 ) was the main selection criterion for set 1, the two datasets differed considerably in this regard. For example, set 1 contained many more dyes (54% vs 5% in set 2) and UV-filters (12% vs 1% in set 2), while there were fewer fragrances (5% in set 1 vs 45% in set 2) and no preservatives and pharmaceuticals (Fig. 1 ). This difference is also reflected to some extent in the CPCat use categories, which may assign a substance to several categories (see Supplemental Table  1 ).
Reference and NAMs results The 41 substances of set 1 are listed in Table  1. In addition, their primary use categories, dichotomised results for the six NAMs as well as the human and LLNA reference data are presented. All further information, such as physico-chemical properties and the primary NAM results, are provided in the Supplemental Table  1. No major technical applicability issues were met, with the exception that DPRA results could not be obtained for several substances, primarily dyes, due to cysteine co-elution. Human category indicating a human skin sensitisation risk could be determined for 19 substances. Most of these (16) fell into categories 5 and 6, which we considered as not warranting a classification, i. e. , deemed to be non-sensitisers. For 32 substances a single LLNA result was available.
For the five substances with two LLNA results, the median-like location parameter was calculated as described by  Hoffmann et al. (2018). For the remaining four substances, LLNA results could not be identified. In these cases, results from GPMT were used, assuming that, if tested in the LLNA, the results in terms of hazard would be identical. Results of the in vivo tests covered the entire skin sensitisation potency spectrum, including the extremely sensitising oxazolone and 18 non-sensitising substances. Most LLNA-sensitising substances (15) were moderate skin sensitisers (EC3 in the range of 1-10%). The complete database (set 1 + 2) comprised 169 substances, 165 with LLNA reference data, 4 with GPMT reference data and 141 with human reference data.
g. , both test methods addressing the AOP key event 3  (Ashikaga et al. , 2010; Piroird et al. , 2015). These differences need to be accounted for  when assessing individual NAMs, comparing predictivity among NAMs and when combining them in a testing strategy. Therefore, we calculated the number of positive and negative predictions for both sets, both reference standards and the six NAMs in our database (Table  2 ). While the ratios of positive to negative prediction over both substance sets was approximately in the range of 1. 5-2. 5 for both reference standards, the DPRA, KeratinoSens™ and PPRA, this ratio ranged between 3. 4 and 5. 0 for the h-CLAT, U-SENS™ and the SENS-IS. In addition, there were clear differences between the two sets of substances. The proportion of LLNA negatives was much higher in set 1 compared to set 2, but the proportion of negative predictions was substantially lower for the DPRA, PPRA, h-CLAT and U-SENS™ (as indicated by a higher ratio).
Comparison of the predictivity of NAMs Several parameters were calculated for the two substance sets individually and the combined set to comprehensibly describe the predictivity of the NAMs for discriminating sensitisers from non-sensitisers. Therefore, we compared the predictions obtained with the standard PMs of the NAMs, which were developed with LLNA data, against the LLNA reference data and the human potency reference data. In addition, the two references were compared (Table  3 ). For NAMs with PMs that have more than two categories, i. e. , the DPRA, PPRA and the SENS-IS, all categories indicating reactivity and sensitisation potential were combined and considered as positive. Human predictivity for set 1 needs to be evaluated with care due to the small sample size. The only parameter which allows some interpretation is the specificity (sample size of 13 or 16), which was generally lower as compared to set 2.
When combining the sets, this consequently resulted in lower specificity as compared to set 2 alone. Due to the low number of human positives in set 1, sensitivities of the combined set were like those of set 2. All NAMs and the LLNA had similar (balanced) accuracy when compared to the human data of set 2. However, specificities and sensitivities differed, being rather balanced for the DPRA and KeratinoSens™ and skewed towards sensitivity for the h-CLAT, U-SENS™ and SENS-IS, supporting the observation that prediction models were optimised for different objectives (Fig. 3 ). The same skewness was observed for the LLNA predictivity. Considering the combined set, the (balanced) accuracy was highest for the SENS-IS, especially as it was the most predictive NAM for set 1. LLNA predictivity for set 1, which was based on a sample size from 32 (DPRA) to 41, was in generally lower as compared to set 1, except for SENS-IS.
Comparison of NAMs addressing AOP KE 1: DPRA and PPRA While NAMs addressing the same AOP event are considered mechanistically similar, we investigated how that similarity is reflected in our database. The first pair of similar NAMs are the DPRA and the PPRA, which model the molecular initiating event of the skin sensitisation AOP, i. e. , covalent binding to skin proteins, by measuring peptide depletion in chemico. Both NAMs measure the depletion of two model synthetic peptides either containing cysteine or lysine OECD TG 442C (OECD 2021a;  Ryan et al. , 2020). In addition, the PPRA determines cysteine depletion after a potential activation step using horseradish peroxidase for characterising reactivity of hapten-as well as pre-/pro-hapten sensitisers. Furthermore, the ratios of the peptide amount to the test substance amount and the lysine buffer differ between the two NAMs.
Of the 23 substances in our database that were identified as requiring activation, the DPRA identified 14 substances and the PPRA classified 21 as reactive. These seven substances explain some of the difference in the discordant predictions for both human and LLNA as graphically represented in Fig. 4A  (light grey squares). The two substances geraniol and farnesol, which are structurally related and therefore could be activated in similar ways, were not detected as reactive by any of the two methods. These findings confirms that the technically simpler DPRA is the obvious default choice to address KE1, e. g. , in a DA, and that the PPRA has a particular advantage when assessing suspected pre-/prohaptens.
Analysis of the octanol/water partition coefficient The discussion of the predictive value of the h-CLAT for poorly water-soluble substances as compared to LLNA data initiated by  Ashikaga et al. (2010)  resulted in a proposal of considering negative predictions for substances with a logKOW >3. 5 as unreliable  (Takenouchi et al. , 2013). This led to the fact that negative h-CLAT results for such substances should not be considered in a regulatory context  (OECD, 2018b). Therefore, we investigated negative NAM predictions of substances in both sets with positive LLNA reference data and of substances with positive human reference data. We determined the relative frequency of these negative predictions in subsets of substances grouped by their logKOW to allow for a comparison of test methods and to identify trends.
, 2016; Urbisch et al. , 2015). In addition, the PMs were optimised differently with respect to specificity and sensitivity, which severely complicates a comprehensive direct comparison of NAM predictivities. Nor were the PMs optimised to be used in combination with other information, e. g. , in DA, so that combination of predictions will likely fall short of the optimal complementary use of the information provided by the individual NAMs. With a focus on applicability domains, we expanded our database as provided in  Hoffmann et al. (2018)  to further explore six skin sensitisation NAMs DPRA, KeratinoSens™, h-CLAT, U-SENS™, SENS-IS and PPRA  (Reisinger et al. , 2015). A set of 41 substances (set 1) of high relevance for cosmetic industry, comprising dyes, UV-filters, surfactants, fragrances, and actives, has been selected that were, at least to a large extent, not part of the substance sets to develop and optimise the individual NAMs.
Comparison between females of the same age (in years) as their female groupmate with subsequent lower social rank score and females whose groupmate with subsequent social rank was different (in all cases: younger) in age. Only higher-ranking females (ranks 1-3) were considered for analysis; the number of females is given in the bars (data from 3 yr). (b) Comparison of the percentage of litters in which infanticide occurred (single kits or whole litter) on the total number of litters born per breeding season between groups with high or low heterogeneity in the age structure of the females' rank hierarchy (modified after  Rödel et al. , 2008b). High and low heterogeneity were determined based on the age (in years) of the 3 highest ranking females per group (social rank 1-3); the number of social groups is given in the bars (data from 5 yr).
Acknowledgements: I am grateful to  Dietrich von Holst  for his kind and enduring support and for helpful discussions. I also thank all my colleagues and students who helped with the field work and data collection over the years. Furthermore, I thank  Angela Trocino  and another anonymous reviewer for their constructive and helpful comments on the manuscript. The study was financially supported by grants from the  German Research Foundation DFG  ( Ro 2431/2-1, 3-1 ).
Disseminated/metastatic was defined as cancer that had spread to areas of the body distant from the primary tumor; or lymphomas that involved two or more lymph nodes on both sides of the diaphragm and/or one or more extra-nodal sites (Stages III, IV, and V); or any non-lymphoma hematological malignancy. Undetermined was used in a small number of cases where it was not possible to accurately determine the extent of disease, despite a complete cancer staging workup. These definitions allowed for all cancer-diagnosed cases (whether solid or hematological) to be classified by extent of disease. Clinical data for each visit were collected on standardized case report forms (CRFs). Full source-document collection (including but not limited to clinical intake and progress notes, imaging reports, surgery reports, and histopathology reports) and verification were performed for each subject.
Queries were issued to clinical sites to clarify or correct data items, as appropriate, before clinical database lock. Final clinical determinations, such as benign versus malignant tumor and cancer type, stage, and size, were performed by board-certified veterinary medical oncologists on the central study team and recorded prior to unblinding of liquid biopsy testing results. This independent clinical adjudication process was informed by all of the written clinical information available, including the diagnosis provided by each subject's managing veterinarian(s); however, primary data derived from imaging tests (e. g. , actual images) and pathology tests (e. g. , actual slides or pictures of slides) were not independently reviewed by the central study team.
Genomic DNA (gDNA) was extracted from WBCs, and from tissue samples, using QIAamp DNA Mini Blood Kit (Qiagen). Amplified DNA libraries were generated for each subject from the matched cfDNA and gDNA extracts. Libraries were prepared by incorporating universal adapters and barcodes into sample DNA via ligation and universal PCR amplification, and were subjected to next-generation sequencing on an Illumina NovaSeq 6000 for somatic variant analysis. All sequencing reads were aligned to the CanFam3. 1 reference genome  [33]  using the BWA-MEM algorithm  [34]  with default parameters. Somatic variant calling from cfDNA and gDNA sequence data was performed using a custom bioinformatics pipeline leveraging Sentieon TNscope  [35] , ichorCNA  [36] , and internally-developed algorithms that are based on the coverage at specific genomic regions or on fragmentomics profiles.
Another subgroup analysis focused on cancer-diagnosed subjects from the testing set representing a predefined list of eight common canine cancers  [40]  that account for most cancer deaths in the species: lymphoma, hemangiosarcoma, osteosarcoma, soft tissue sarcoma, mast cell tumor, mammary gland carcinoma, anal sac adenocarcinoma, and malignant melanoma. Separately, detection rates by cancer type were determined for a large number of distinct cancer types using data from all cancer-diagnosed subjects across the training and testing sets. The detection rate for the subgroup of subjects diagnosed with multiple concurrent primary cancers was also determined in this manner. Additional analyses were performed to determine the effect of pre-analytical factors such as the time difference between sample collection and processing (time in-transit), and the levels of hemolysis and lipemia, on the test's performance in the cancer-diagnosed as well as the presumably cancer-free subjects in the testing set.
Genomic alterations detected in cfDNA from the plasma sample included copy number variants (CNVs) across the genome, and single nucleotide variants (SNVs) in two genes that are frequently mutated in human and canine cancers: TP53 and PIK3CA  [43]. Both mutations, TP53 R265Q and PIK3CA N1044K, have human orthologues (TP53 R213Q and PIK3CA N1044K, respectively) that have been observed in multiple human cancers as recorded in the COSMIC (Catalogue Of Somatic Mutations In Cancer) database  [44]. In particular, PIK3CA N1044K is a known oncogenic mutation that is targetable in human breast cancer by an FDA-approved drug  [45]. At the time of receipt of the Cancer Signal Detected result in April 2021, the subject continued to have a normal physical exam and no clinical signs noted by the owner. Because of the Cancer Signal Detected result, the subject underwent a confirmatory cancer evaluation in May 2021, five months after the initial blood sample was collected.
Discussion A novel, NGS-based MCED liquid biopsy test has demonstrated the ability to identify cancerassociated genomic alterations in canine patients representing a wide spectrum of cancer types. To the authors' knowledge, this is the first NGS-based test in the veterinary space that leverages a robust training and testing study design for algorithm development and performance validation, respectively. A large, representative training set was used to optimize and lock the test's algorithms and thresholds. This locked pipeline was separately applied to an independent testing set for validation of clinical performance. Use of an independent testing set for validation is critical, as reporting performance from the same dataset used in development may result in inflated estimates of test performance due to overfitting.
Another limitation is the fact that only one sample (collected around the time of initial cancer diagnosis) was tested for each cancer-diagnosed subject as part of this validation study; as a result, the performance of the test for post-diagnosis longitudinal use cases (such as minimal residual disease detection, recurrence monitoring, and treatment response monitoring) has not been determined. Over 1,000 serial blood samples were obtained from cancer-diagnosed subjects as part of the prospective collection that powered this study, along with clinical data such as the nature and timing of therapeutic interventions, time to recurrence, and extent of disease at recurrence. Future validation studies using these samples are planned to demonstrate the performance of the test in disease-monitoring use cases.
Another potential limitation of this study was that clinical status designations (such as benign vs malignant tumor, and cancer type, stage or size) were collected as provided by the managing veterinarian(s) according to the standard of care at each collection site. This information was independently reviewed and adjudicated by the central study team based on source documents (including but not limited to clinical intake and progress notes, imaging reports, surgery reports, and histopathology reports) prior to database lock. However, the original radiologic images and pathology samples were not independently reviewed by the central study team. Thus, there exists the possibility that the clinical status designations attributed to each subject may not have captured the true clinical status of every subject in the study, given the well-documented variability in interobserver concordance for imaging, pathology, and other clinical assessment areas  [69] [70] [71] [72] [73] [74].
Prediction of a specific Cancer Signal Origin for patients with a Cancer Signal Detected result would further enhance the utility of the test, as it would help focus the cancer evaluation process and ideally lead to a definitive diagnosis earlier and at lower cost for the pet owner. This study demonstrated the ability of the test to provide CSO prediction for canine hematological malignancies, the most common of which are lymphomas. As more subjects are tested and adjudicated, and larger datasets are accumulated for additional cancer types, bioinformatics algorithms may be developed and optimized to provide accurate CSO predictions for additional cancer types, including those that are less commonly encountered in dogs. Such capabilities have already been documented for MCED tests developed for use in humans, with accurate CSO prediction demonstrated for various cancer types in large numbers of subjects representative of each cancer type  [50, 51].
Acknowledgments The authors thank all the dogs in the CANDiD study and the humans who love and care for them. Without their support and contributions, this study would not have been possible. The authors would also like to thank  Jason Loftis  and  Dominique Lau  for assistance with developing the tables, figures, and graphics in this manuscript, as well as  Vu Q. Nguyen ,  Amanda Nguyen , the entire  PetDx laboratory team  for assistance with data generation. Additionally, the authors would like to thank  Oncovet Clinical Research, VCA Clinical Studies , the doctors and staff at  Flint Animal Cancer Center Colorado State University , the  College of Veterinary Medicine at the University of Missouri, Veterinary Specialty Hospital of San Diego, Veterinary Specialty Hospital of North County, Southeast Veterinary Oncology and Internal Medicine, Bridge Animal Referral Center, The Animal Medical Center, the team at the University of Guelph Institute for Comparative Cancer Investigation at the Mona Campbell Centre for Animal Cancer OVC HSC U of Guelph, Governor Animal Clinic Inc.
Experimental design The experimental procedure was carried out between mid-September and mid-November 2018 and is summarised in figure  1. During period 1, to assess baseline glucocorticoid level of each individual, we collected faeces every four days during four weeks and measured FCM concentrations. Faeces were collected immediately after defecation was observed and kept at +4°C for a maximum of 1 h before being stored at -20°C until steroid analysis. At the end of period 1, each roe deer was subjected to a standardised capture stress protocol involving restrained immobilisation  [29]. During capture, we collected faeces from the rectum, collected blood samples, injected an anti-rabies vaccine (Rabisin ® , Merial, France, 1 ml) subcutaneously, and fitted collars equipped with tri-axial accelerometers (see Capture protocol and data collection for details).
Capture protocol and data collection Roe deer were directed into their hut by slowly approaching them and then pushed through a trap door into a retention box. Once in the box, animals were tranquilised with an intramuscular injection of acepromazine (Calmivet®, Vetoquinol, France; targeted dose of 0. 075 mg/kg)  [31]. Individuals were weighed with an electronic balance to the nearest 100 g. In addition, we characterised each individual behavioural profiles using three behavioural traits, docility to capture, activity level and neophilia, at three different moments. We point out that, here, behavioural profiles do not refer to personality or behavioural syndromes, which would require repeated measures of each behavioural traits considered, and to partition phenotypic (co)variation at the among-individual versus residual levels, which was not possible to do with our data.
First, docility was indexed during handling as follows: struggling (score of 1), not struggling (score of 0). This has been shown to be repeatable over time (r=0. 26) with a tendency to be heritable (h=0. 17) in roe deer  [32]. The second trait, spontaneous daily activity  [33]  was measured using accelerometery data recorded at 20 Hz from tri axial accelerometers (Daily Diary tags, Wildbytes Ltd. , Swansea University) mounted on animal collars. We calculated the Vectorial Dynamic Body Acceleration (VeDBA) metric  [34] , using a 2 second smoothing windows and the DDMT software (Wildbytes Ltd. , Swansea University). VeDBA values were summed for each individual, date, and hour of the day ('total VeDBA') and averaged through the four weeks between the two capture events (period 2) to index daily activity.
In addition, we calculated AICc weights (AICcw) to measure the relative likelihood that a given model was the best among the set of fitted models. The normality of model residuals was tested (Shapiro-Wilk test) and visually assessed. Goodness-of-fit was assessed by conditional and marginal R 2 values and standard residual plot techniques  [47]. All analyses were carried out with R version 3. 6. 0 [48] , using the lmer function from the lme4 package  [49]  and the plspm function from the plspm package  [42].
Acknowledgments We thank  Eric Bideau  and  Benoit Coracin  for their help throughout this study regarding roe deer manipulation and monitoring. We also thank  Edith Klobetz-Rassam  for EIA analysis, veterinary students for their assistance during capture events, as well as  Laura Gervais  for constructive discussions and comments that helped to improve this manuscript. We are also very grateful to the  ECODIV department of the INRAE  for its financial support to the Gardouch experimental station.