dataesr/scientific-paragraphs-categorization huggingface dataset:
(I)Frequency of CNV breakpoint reuse across Tasmanian devil cancers. Number of reused breakpoints between pairs or trios of devil cancer groups are shown, with colours indicating the number of breakpoints within each category which are also reused within individual devil cancer cohorts. DFT1 cell line CNVs, ends of chromosomes, M5 CNVs, and reuse within individual samples are excluded from count. Simulated data are derived from 2,000 neutral simulations of datasets of the same size. Simulated data are coloured for breakpoint reuse within group using the same colour key as used for real data, as shown on the plot. None of the reused breakpoints were found in the genomes of normal devil tissues, including those of matched hosts (S1 Table). Non-DFT, spontaneously arising non-transmissible devil cancers that are neither DFT1 nor DFT2.
Acknowledgments We are grateful to the  Wellcome Sanger Institute Core IT team  and the  Cancer, Ageing and Somatic Mutations IT team  for their assistance. We thank volunteers who assisted with Tasmanian devil field work. Robyn Taylor  provided technical assistance, and  Andrea Strakova , Maire Nı ´Leathlobhair,  Hannah Bender ,  Anne-Maree Pearse , and  Graeme Knowles  contributed advice and helpful discussions.
This work was supported by  Wellcome  grant ( 102942/Z/13/A ) to EPM, https://wellcome. ac. uk/; a  Philip Leverhulme Prize  from the  Leverhulme Trust  to EPM, https://www. leverhulme. ac. uk/; National Science Foundation  grant ( DEB-1316549 ) to EPM and MJ, https://www. nsf. gov/; Eric Guiler Tasmanian Devil Research Grants  to EPM, https://www. utas. edu. au/giving/areas-tosupport/research/grants-and-scholarships; Australian Research Council  grant ( DE170101116 ) to RH, https://www. arc. gov. au/; and a  Herchel Smith Postgraduate Fellowship  to YMK, https:// www. herchelsmith. cam. ac. uk/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Table. (. F) Facial distribution of DFT1 tumours. Diagram was constructed by superimposing facial tumour locations of 96 DFT1s. Raw data are available in S1 Data in https://doi. org/10. 5281/zenodo. 4046235. CNV, copy number variant; DFT1, devil facial tumour 1; km, kilometre; MRCA, most recent common ancestor; SNV, single nucleotide variant. https://doi. org/10. 1371/journal. pbio. 3000926. g001.
Thus, the choice of syngenic mouse ensures an intact immune system, making it a relevant model for studies in immunotherapy. Target occupancy (PD biomarker) of one of the two targets (Target 1) was measured in peripheral blood mononuclear cells (PBMCs) and in tumour samples from the same animal model. The acute and chronic PK/PD and PK/efficacy data generated were modelled to identify the efficacious concentration and the extent of target occupancy needed for efficacy both in tumours and in PBMCs. The doses needed to achieve the identified target occupancy in human PBMCs and to reach efficacious  a For the one exceptional drug (cladribine, case study 3; see Box 3 in the main text) in this category, the Phase II dose was based on clinical evaluation rather than on preclinical PK/PD modelling.
II ), assuming that these were invariant across species. PD biomarkers in Phase I population: Occupancy of Target 1 was measured in human PBMCs in dose-escalation studies. Comparison to clinical observations: Exposure of Compound A observed in the clinic was within twofold of predicted at all dose levels, even though a significantly higher clearance was observed at the lowest dose, compared with clearance in other doses possibly because of TMDD. Moderate effect was observed in the clinic in the predicted biologically effective dose (BED) range. Lesson learnt: Integrating PD and efficacy data from an appropriate animal model into PK/PD and PK/efficacy models for translation and having a relevant PD biomarker measured in Phase I clinical trials can lead to successful prediction of the active dose range for Phase II trials.
Ia ). The translated human PK/PD model integrating predicted human PK and irreversible binding constant (k irreversible ) supported a safe, pharmacologically active, once-daily dose range, at receptor occupancy of 93-97% (Fig. Ib ). PD biomarkers in Phase I population: In the Phase I trial, receptor occupancy was measured in PBMCs rather than in tumour tissue. At the doses tested, the measured data confirmed the receptor occupancy predicted. Clinical observations: Compound C was found to either stop the disease from progressing or reduce the tumour burden in 86% of patients, including a complete response seen in a patient receiving the highest dose of the predicted range. Lessons learnt: Lack of a translatable efficacy model in preclinical species need not be a concern, if, for a clinically validated target, the target engagement needed for efficacy can be obtained from an approved drug in the market.
Acknowledgements The authors would like to acknowledge our colleagues in Translational In vivo Pharmacology for generating the data used in the analysis,  Samer El Bawab  and  Anup Zutshi  for the many useful discussions, as well as  Dietmar Gross ,  Simone Hansmann ,  Irina Kareva ,  Eva Sherbetjian ,  Klaus Urbahns , and  Christian Wagner  for their contributions. We would like to thank  Luciano Rossetti  for his executive sponsorship of this work.
Non-genetic information (epigenetic, microbiota, behaviour) that results in different phenotypes in animals can be transmitted from one generation to the next and thus is potentially involved in the inheritance of traits. However, in livestock species, animals are selected based on genetic inheritance only. The objective of the present study was to determine whether non-genetic inherited effects play a role in the inheritance of residual feed intake (RFI) in two species: pigs and rabbits. If so, the path coefficients of the information transmitted from sire and dam to offspring would differ from the expected transmission factor of 0. 5 that occurs if inherited information is of genetic origin only. Two pigs (pig1, pig2) and two rabbits (rabbit1, rabbit2) datasets were used in this study (1,603, 3,901, 5,213 and 4,584 records, respectively).
The test of the path coefficients to 0. 5 was performed for each dataset using likelihood ratio tests (null model: transmissibility model with both path coefficients equal to 0. 5, full model: unconstrained transmissibility model). The path coefficients differed significantly from 0. 5 for one of the pig datasets (pig2). Although not significant, we observed, as a general trend, that sire path coefficients of transmission were lower than dam path coefficients in three of the datasets (0. 46 vs 0. 53 for pig1, 0. 39 vs 0. 44 for pig2 and 0. 38 vs 0. 50 for rabbit1). These results suggest that phenomena other than genetic sources of inheritance explain the phenotypic resemblance between relatives for RFI, with a higher transmission from the dam's side than from the sire's side.
| Material Two pig and two rabbit datasets were used in this study. The different datasets were collected from separate populations with different histories of selection for FE (Table  1 ). Rearing conditions are described in  Déru et al. (2019)  and  Gilbert et al. (2007)  for pigs and in  Garreau, Hurtaud, and Drouilhet (2013)  for rabbits. Briefly, the first pig dataset (pig1) includes data from French Large White maternal line male pigs, raised at the INRA UEPR France Génétique Porc phenotyping station (Le Rheu, France). Piglets from different selection farms were received at the test station at weaning (3 weeks of age), penned in postweaning facilities until 9 weeks of age and then moved to growing-finishing pens equipped with singleplace electronic feeders fitted with a pig scale (Genstar, Acemo Skiold).
Pigs remained in the same group of 14 animals from 3 weeks of age until the end of the test. Among a total of 1,663 pigs, 880 pigs were fed a two-phase conventional dietary sequence and 783 pigs were fed a two-phase high fibre dietary sequence. Pigs had ad libitum access to feed and water. Feed intake and body weight gain were recorded from 30 to 120 kg of live weight. Pigs were then slaughtered, and carcass yield (CY) and lean meat percentage (LMP) were recorded for 1,603 animals. The average daily gain (ADG) was computed as the difference between BW at the end (BW end ) and the beginning (BW start ) of the test period divided by the number of days elapsed, and the average metabolic body weight (AMBW) was computed as  (Noblet Karege & Dubois, 1991). The second pig dataset   (Labroue, Gueblez, & Sellier, 1997) ].
Ultrasonic backfat thickness (BF) of selection candidates was measured on live animals at 95 kg as the average of six ultrasound measurements, at three locations on both sides of the spine, on the neck, the back and the hips. For non-candidates, BF was measured on the carcass at slaughter. The AMBW was computed the same way as for the pig1 dataset, resulting in a fixed value for the candidates for selection (the test period being between fixed weights: 35-95 kg). To account for the difference in the test period between the two groups, ADFI, ADG, BF and AMBW were standardized and zerocentred within groups, as proposed by  Aliakbari, Delpuech, Labrune, Riquet, and Gilbert (2019). BW 1. 6 end -BW 1. 6 start (1. 6×(BW end -BW start )) The two rabbit lines were the paternal lines AGP39 and AGP59 of Hypharm, a French breeding company.
| Methods Data were analysed using the transmissibility model with maternal genetic effects, which is, for the different datasets, submodel of the following global model: where y is the ADFI over the growing period for pig1, the standardized zero-centred ADFI over the test period for pig2, and the standardized WFI for AGP39 and AGP59, β is the vector of fixed effects; t is the vector of transmissible values; p is the vector of permanent environmental effects (included in the models for data with repeated measurements); m is the vector of maternal genetic effects; l is the vector of litter effects (week by litter combination for rabbit data); e is the vector of residuals; X,Z,W,S, and R are the corresponding known incidence matrices. For pig1 and rabbit data, all random effects were distributed as centred normal distributions with variance-covariance matrices equal to A 2 m for the maternal genetic effects, I p 2 p for the permanent environmental effects, I l 2 l for the litter effect, I e 2 e for the residual effects, where I are identity matrices of appropriate size.
For pig2 data, to account for potential variance heterogeneity between candidates for selection and non-candidates (i. e. , RFI is a different traits in the two categories), random effects were distributed as centred normal distributions with variance-covariance matrices equal to A ⊗ G m for the ma-  and 2  m NC are the maternal genetic variances for the candidates and non-candidates for selection, respectively, and ternal genetic effects, G m = ⎡ ⎢ ⎢ ⎣ 2 m CS m CS-NC m CS-NC 2 m NC ⎤ ⎥ ⎥ ⎦ where 2 m CS , m CS-NC is their covariance effects, ⎡ ⎢ ⎢ ⎣ I lCS 2 l CS 0 0 I lNC 2 l NC ⎤ ⎥ ⎥ ⎦ for the litter ef- fect, ⎡ ⎢ ⎢ ⎣ I eCS 2 l CS 0 0 I eNC 2 l NC ⎤ ⎥ ⎥ ⎦ for the residual effects. The transmissible value t was normally distributed with matrix M 2 t (M ⊗ G t for the pig2 dataset), where M is the unknown trans- mission relationship matrix.
e. , models that do not include transmissible and genetic effects) using likelihood ratio tests (LRT) and maximum likelihood estimation. Transmissible values and maternal genetic effects were included in the model for all species while other random effects were selected using the transmissibility model with the constraint that s = d = 0. 5 (i. e. , animal model), which runs much faster than the unconstrained transmissibility model. Selection was performed by comparing step-by-step nested models using LRT (REML estimation). In addition, variance heterogeneity between groups for the different random effects and correlations (different from 1) was also tested using LRT for the pig2 dataset using the animal model. All LRT tests for parameters on the boundary of their parameter spaces (test of variance equal to zero, correlation equal to 1, sire and dam path coefficients equal to 0.
5) were performed by accounting for the change in the asymptotic distribution of the likelihood ratio statistic under H0 (i. e. , mixture 1 2 2 p-1 + 1 2 2 p , where p is the number of parameters tested)  (Foulley, Jaffrezic, & Robert-Granie, 2000; Self & Liang, 1987; Stram & Lee, 1994). To model ADFI in the pig1 dataset, fixed effects were the type of feeding regime (two classes) and the batch effect (36 levels), and ADG, AMW, LMP, CY were fitted as covariates. A litter random effect was not included in the models because its variance did not differ significantly from 0. For the pig2 dataset, fixed effects were the sex (three levels), pen within group (32 levels), batch (99 levels), group*herd (four levels), group*pen_size (10 levels), group*ADG (ADG as a covariate), group*BF (BF as a covariate) and AMBW as a covariate for the non-candidates for selection.
Selected random effect was the litter effect. The variances of the transmissible values were not different between groups. Transmissible and maternal genetic correlations between groups did not differ significantly from 1. Consequently, a unique vector of transmissible values was considered for the two groups and the maternal genetic correlation was fixed to 1 in the analysis. For all the other random effects, the variances differed significantly between the candidate and non-candidate groups. For the two rabbit datasets, fixed effects were combined effects of week*batch (210 levels for AGP39 and 240 levels for AGP59), week*sex (10 levels for AGP39 and 12 levels for AGP59), week*litter size (35 levels for AGP39 and 42 levels for AGP59), week*WADG (WADG as a covariate) and week*WMBW (WMBW as a covariate).
Selected random effects were the week*litter combination and permanent environmental effects. The parameters of the transmissibility model (variance components, the sire and dam path coefficients of transmission) can be estimated with the restricted maximum-likelihood method (REML) using ASReml and the OWN Fortran program developed by  David (2018). Parameter estimates were used to compute dam transmissibility: d 2 t 2 t + 2 m t + 2 l t + 2 p t + 2 e t and sire transmissibility: s 2 t 2 t + 2 m t + 2 l t + 2 p t + 2 e t , which corre- spond to half the heritability in the animal model. To test the hypothesis of non-genetic inheritance: H0 "sire and dam coefficients of transmission are equal to 0. 5" was tested against the H1 hypothesis "at least one of the coefficients of transmission (sire or dam) differs from 0.
| RESULTS Parameter estimates obtained with the animal and transmissibility models are provided in Table  2. For the animal model, the direct heritability of RFI (twice the sire or dam transmissibility) ranged from 0. 10 ± 0. 02 to 0. 42 ± 0. 09. Depending on the dataset, the maternal genetic variance obtained with the animal model represented 11%-52% of the direct genetic variance and was not significantly different from 0 for the pig1 dataset. The LRT that compared the animal and the transmissibility models showed that the null hypothesis "sire and dam path coefficients of transmission are equal to 0. 5" (i. e. , RFI is transmitted by genetic inheritance only) was rejected for the pig2 dataset only. For this dataset, the sire and dam path coefficients of transmission were both lower than 0.
5, and the sire coefficient was lower (although not significantly given the SE) than the dam coefficient (0. 39 T A B L E 2 Parameter estimates obtained with the animal and transmissibility models for the different species vs 0. 44). Even if not significant, we observed the same trend for the pig1 and AGP39 datasets: a lower value of the sire path coefficient of transmission compared with the dam path coefficient of transmission (0. 46 and 0. 38 vs 0. 53 and 0. 50, respectively). On the contrary, the sire and dam path coefficients estimated in the AGP59 line were both equal to 0. 5 indicating that for that rabbit line the transmissibility model was equivalent to the animal model, resulting in similar estimates for the different variance components of the models. For the three other datasets, the variances of the transmissible value tended to be higher than the variances of the direct genetic effects obtained with the animal model, especially for the pig2 dataset for which it was nearly two times higher.
The sire and dam transmissibility estimates for these three datasets tended to be higher than those obtained with the animal model, indicating a stronger parent-offspring regression than that considered with the animal model. When the breeding values were compared with the sire and dam transmissible values (Table  3 ), we found that, as expected given the sire and dam path coefficient estimates, the breeding and transmissible values were the same for the AGP59 dataset. For the other datasets, despite the very high correlation between the breeding values and the sire or dam transmissible value (correlation higher than 0. 98), we observed that the two models would probably not have resulted in the selection of the same animals. Indeed, the percentage of animals in common in the 10% best animals based on their breeding value or their transmissible value was not very high for the sire's side (87%-93% depending on the species) and still lower for the dam's side (83%-90% depending on the species).
It should be noted that it would have been possible to consider maternal transmissible values instead of maternal genetic effects in the models, that is to account for non-genetic inheritance for the maternal effects. We did not use this approach because maternal genetic effects are generally not considered in models for RFI in growing animals  (Berry & Crowley, 2012; Do, Strathe, Jensen, Mark, & Kadarmideen, 2013; Drouilhet et al. , 2013)  and were therefore not the focus of this study. We considered a null covariance between the transmissibility value and the maternal genetic effects, since this parameter cannot be estimated with the data structure of the different datasets  (Gerstmayr, 1992). It should be also noted that any other non-inherited factors that might induce different covariances between offspring and dam or offspring and sire may affect the conservativeness of the LRT that compares the animal and the transmissibility models (i.
e. , wrongly conclude to non-genetic inheritance). When applying the transmissibility model, particular attention must therefore be paid to such sources of confusion of non-genetic inheritance that must be taken into account in the model if known. For instance, mitochondrial inheritance can cause deviation from the law of transmission assumed in the animal model, leading to a higher covariance between dam and offspring than between sire and offspring. The heritabilities obtained with the animal model were in line with previous studies. Gilbert et al. (2007)  reported heritabilities of 0. 14 and 0. 24 for candidates and non-candidates for selection, respectively, in a subset of the pig2 dataset (four generations of divergent selection) using a two-step approach to estimate the genetic parameters of RFI.
The higher heritability in the non-candidates for selection can be explained by a more accurate predicted feed intake compared with candidates. The higher heritability for the pig1 dataset compared with the pig2 dataset is probably due to different modalities of data quality control (only animals with "good" performances over the entire test period were retained for the analysis). This high heritability is in line with that reported by  Do et al. (2013) (0. 36-0. 40). The moderate heritabilities reported in rabbits are close to the value reported by  Drouilhet et al. (2013) (0. 16). Our results using the transmissibility model to study RFI in different species were not entirely consistent. Indeed, in one dataset (AGP59), sire and dam path coefficients of transmission were equal to 0. 5, whereas in the three other datasets, the sire path coefficient of transmission tended to be lower than the dam path coefficient, although significantly different from 0.
5 in the pig2 dataset only. Close inspection of the data structure (relationships between phenotyped animals) of both AGP59 and AGP39 datasets did not provide any insights that might explain this difference. An explanation could be the length of the test period which was longer for the AGP59 line compared with the AGP39 line. The impact of environment experienced by the animal on non-genetic heritable effects may therefore have been more pronounced in the AGP59 line, resulting in a modification of the non-genetic inherited effects that consequently differed more from those of the parents. The power to detect non-genetic inheritance with the transmissibility model increases with the size of the population, the deepness of the population structure (i. e. , number of different family links), the relative importance of non-genetic inherited variance, the difference between sire and dam path coefficients of transmission and the magnitude of their difference from 0.
5  (David & Ricard, 2019). Our results were in line with these considerations. In the pig1 dataset, sire and dam path coefficients were not very different and both close to 0. 5 (dam higher, sire lower). Consequently, the relative importance of non-genetic inherited variance must be much higher than genetic inherited variance and/or a huge amount of data are necessary to detect non-genetic inheritance in such situations. Even if the relative importance of inherited variance was small for the rabbit AGP39 dataset, the discrepancy between sire and dam path coefficients of transmission was higher than for the pig1 dataset, and, even if close to each other, the sire and dam path coefficients obtained for the pig2 dataset differed the most from 0. 5, which provided a more favourable situation for detecting non-genetic inheritance.
Indeed, we were at the limit of significance for the AGP39 dataset and significant for the pig2 dataset. Increasing the size of the datasets will result in the gain in power required to detect non-genetic inheritance. However, that will also mean longer computing time and probably memory issues (to give an idea of the actual computing time, the transmissibility model ran for 9 hr before convergence for the AGP59 dataset on a Linux system with an Intel ® Xeon ® E5-2698v3 processor). To overcome this difficulty, we shall consider revising our program for estimating the parameters of the transmissibility model, which is currently based on ASReml, and to create a stand-alone software dedicated to the transmissibility model. Research is underway on this subject. Finally, it should be noticed that standard errors of estimates were generally slightly higher in the transmissibility than in the animal model, which is certainly a consequence of the additional parameters to estimate.
When path coefficient estimates are different from 0. 5 (all datasets except AGP59), we observed, as a general trend between the animal and transmissibility model, a decrease of the residual variance, maternal genetic variance and a higher transmissibility variance compared with the genetic variance. This confirms the confusion that exists between maternal genetic and non-genetic inherited effects. The lower genetic variance compared with the transmissibility variance is explained by the use in the animal model of a set value for the path coefficients of transmission (0. 5), that is generally too high. Consequently, to find the best fit for the covariances between the different types of relatives in the population, the genetic variance estimate is smaller than the transmissibility variance.