dataesr/scientific-paragraphs-categorization huggingface dataset:
ITS and cox1 The sequences were checked for possible sequencing errors and concatenated using Sequencher software v. 4. 5. The resulting cleaned sequences were next aligned using Bioedit v. 7. 0. 9 and ClustalW software. Species assignation was done by comparing ITS and cox1 sequences to the GenBank nucleotide database. Concerning ITS, because this gene differs at 5 mutation points between S. haematobium and S. bovis, the sequence chromatograms were carefully checked at these mutation sites to identify possible heterogenous ITS sequences  [32]. For the mitochondrial cox1 data, polymorphism indices (haplotype, nucleotide diversities, Tajima's D* and Fu and Li's D* statistics) were calculated using DnaSP v. 6. 0 software  [33]. Significance of the genetic structuring patterns were tested using AMOVA  [27].
The best-fit model of evolution (HKY +G) was determined using the maximum likelihood value implemented in MEGA v. 6. 0. 6  [34]. A phylogenetic tree was constructed using MEGA v. 6. 0. 6 software using a maximum likelihood method. The support for tree branching was based on 500 bootstrap replicates and rooted with Schistosoma haematobium (AY157209. 1) as the outgroup. All sequences were uploaded to GenBank (accession numbers MH647122-MH647179).
Sequence analyses Only one ITS genotype was identified among the 91 screened schistosomes and this was identified as S. bovis. No double peak was observed in any sequence at the polymorphic sites between S. bovis and S. haematobium. All the cox1 sequences were also identified as S. bovis. Thus, according to these two markers all the schistosomes analyzed were considered as pure S. bovis. A total of 58 cox1 haplotypes (accession numbers MH647122-MH647179) were identified. The haplotype diversity (± SD) and nucleotide diversity (± SD) were 0. 961 ± 0. 012 and 0. 00602 ± 0. 00043, respectively. No difference in haplotype and nucleotide diversity was observed according to the sampling site (Table  6 ). Both Tajima's D* (-2. 24; P < 0. 01) and Fu and Li's D* (-4. 61; P < 0. 02) statistics were significantly negative.
AMOVA analysis revealed that 88. 83% of the genetic variation can be explained by the variation within animals whereas the remaining is due to variation among animals within sites (8. 81%) or among sites (2. 36%) (Table  3 ), which corroborates the results obtained from the microsatellite dataset. The absence of any genetic structuring with regard to the area of origin is confirmed by no or very low branch support and significant mixing of the populations on the phylogenetic tree (Additional file 4: Figure  S2 ).
At the scale of the African continent, the haplotype diversity (h) and the nucleotide diversity (Π) were 0. 94 ± 0. 0067 and 0. 02553  [40]  and 0. 36 ± 0. 014 and 0. 00434  [41]  for S. mansoni and S. haematobium, respectively. Thus the two parameters converge with S. mansoni having both higher haplotype and nucleotide diversity than S. haematobium. Our study shows that S. bovis is more polymorphic than its sister species S. haematobium (based on both nuclear and mitochondrial markers) but also shows that the haplotype diversity is similar to that of S. mansoni (h = 0. 961 ± 0. 012) while the nucleotide diversity does not significantly differ from that of S. haematobium (Π = 0. 00602). Moreover, we found that the Tajima's D* and Fu and Li's D* computed on our S. bovis dataset were significantly negative, which could constitute a signature of population demographic expansion  [42].
INTRODUCTION In the context of precision livestock farming, simple interpretive tools are required to convert raw time series datasets, now routinely recorded in animals, into useful information for onfarm decision-making. Such tools are not only expected to provide farmers with good information on performance level of individual animals, but also to detect pathological, nutritional or environmental problems affecting production traits at individual or herd scales. In dairy systems, it is well known that milk yield can be affected by events such as udder health problems  [1] , lameness  [2] , meteorological changes  [3] , or feed quality  [4]. Such problems induce perturbations in the course of the lactation process and result in a serrated shape pattern of the lactation curve. These perturbations can be seen as deviations of the lactation curve from its typical profile.
The fitting procedure of PLM allows the detection and the characterization of perturbations in milk time-series. The objective of the present paper is  (1)  to introduce the PLM model and the explicit representation of perturbations, (2) to describe the use of PLM to detect and characterize perturbations in milk yield time series with an example in dairy goats, and (3) to illustrate the role of PLM as a phenotyping tool by analyzing the variability in perturbed lactation curves on the basis of the fitting results obtained on the dairy goat dataset.
The structure of the model does not allow a classical identifiability analysis to be performed if n is unknown. However, by using the software DAISY (Differential Algebra for Identifiability of Systems  [23] ), we could assess that for one perturbation the PLM parameters are locally identifiable. To facilitate the identification of the model parameters, we adopted a fitting strategy in two steps: first, performing numerous repeated fittings to estimate the most frequent number of perturbations. In the second step, we fixed as known the number of perturbations detected in step 1 and proceeded to estimate the remaining parameters of the model. This strategy ultimately makes it possible to estimate an optimal number of perturbations and facilitates the estimation of the model parameters. In the following section,   stands for PLM with  perturbations,    stands for the triplet of parameters (, , ) of Wood's model estimated with  perturbations ( ranging from 0 to   ) and   , stands for the quadruplet (   ,  0, ,  Finally, the whole fitting procedure is carried out following the 3 following steps: Step1: Repeat 100 times the fitting sequence with the 'shotgun search' and   = 15.
Unperturbed lactation curve Descriptive statistics of the parameters a, b and c for the unperturbed lactation curves (for both models:   * and Wood model) are presented in Table  2  for the overall dataset, breed and parity. The parameter a, which drives the general scaling of the curve, was not significantly different for the two breeds (Alpine: 2. 49 ± 0. 71; Saanen: 2. 58 ± 0. 73). Consequently, no significant breed effect was found for the peak milk or for the total unperturbed milk production. The same statistical effects were found with the Wood adjustment without perturbation. The parameter a was significantly affected by the parity of the lactation, with first lactations having a lower value for parameter a than the two and more parities (Table  2 ). Consequently, there was a significant parity effect on the peak milk and on the total milk production.
However, it is important to note that atypical curves observed in the dataset were biologically true. A total of 18 out of the 319 analyzed curves were classified as atypical. Generally, these atypical curves come from the same goat in different parities or for primiparous that have not started the second parity. Peaks of milk of the unperturbed lactation curve were on average increased by 27. 47% between the first parity and the second parity, by 9. 46% between the second parity and the third parity, and by -0. 29% between the third parity and the fourth parity (Figure  4 ). The total milk production for the unperturbed curve was increased by 32. 55% between the first parity and the second parity, 5. 20% between the second parity and the third parity, and by 1. 01% between the third parity and the fourth parity.
Indeed, the obtained fitted curve is always very close to what would have been drawn after simply looking at the raw data and wondering what the lactation curve would be without perturbations. This proximity to what could have been inferred was considered decisive, yet subjective. The second reason was related to the issue of finding the number of perturbations. The PLM procedure allows an automated determination of an optimal number of perturbations, without a priori estimates or use of an arbitrarily chosen stopping criterion. Preliminary results have showed that allowing a maximal number of 15 perturbations to be detected in the first step of the algorithm was enough for the considered dataset. The third reason pertained to the model parameters identifiability issue  [22]. Since the fitting is based on a huge number of repeated fittings from which the systematically detected times of perturbations are retained, the 2-step fitting algorithm facilitates the practical identifiability of the model parameters.
Indeed, the overall fitting algorithm was applied several times to the same dataset. Given that obtained parameter estimates were the same between the different runs, not only it strengthens the convergence properties of the algorithm but also it guarantees model parameters identifiability. Fitting results (see Figure  6 ) have shown that, in some cases, parameter estimates characterizing an individual perturbation reached their initial upper boundaries (1 for parameter  0, and 10 for parameters  1, and  2, ). This situation concerns perturbations with a narrow and deep peak-shape. By construction, the value of the parameter k0 (intensity of the perturbation) is a proportion and thus not supposed to exceed 1. For the parameters  1 and  2 , a value of 10 already represents a very abrupt collapse or recovery, respectively.
These results are therefore considered relevant. However, a next step may be to test the model on a larger dataset to assess the need to broaden these boundaries. Furthermore, another working step will consist in developing an application where the settings of the PLM algorithm can be user-defined. For instance, the maximal number of detectable perturbations, the size of the search grid in step one, or boundaries of parameters. Understanding the effects of the environment on farm animals and understanding how they cope with perturbations during crucial times could help to gain insights on resilience and robustness. These complex dynamic properties are highly desirable to face the changes occurring in the livestock sector  [30]. While the conceptual framework to work on resilience and robustness is now well defined in animal sciences, we still need operational metrics  [31].
Indeed, if data are recorded with a low accuracy (due to technical problems of measurements), the outputs of PLM do not have consistency as detected perturbations have nothing to do with perturbations of the lactation curve, but are related to accuracy problem. In addition, PLM has been developed with daily records. It will be necessary to evaluate if PLM can operate correctly with less frequent data. Finally, PLM is based on the concept of a theoretical unperturbed curve of milk production, considered as a potential, and used to determine deviations that reflect perturbations. This rationale for an underlying potential is debatable from a biological point of view. Nevertheless, from a strict mathematical point of view, we considered this approach as valuable to provide a tool for interpreting data.
: [ 0 ;  3 ] (where  0 and  3 are the times of first and last records of the dataset);  0, : [0; 1];  1, : [0; 10];  2, : [0; 10]. For the 'shotgun search', the number of random combinations of starting parameters was set to 100,000. For the 'gridstart  3 consists in estimating   3 and   3,3 with   1,3 and   2,3 fixed equal to   1,2 and   2,2 , respectively. The procedure is applied stepwise until the maximum number of perturbation   is reached. This maximum number is an a priori user defined value to fix a stop criterion. Preliminary tests have shown that setting   = 15 was sufficient. The end of the fitting sequence consists in reordering the   detected perturbations in decreasing order according to the time of perturbation    (the original obtained order of perturbations is based on the opportunities found by the fitting procedure to improve the goodness-of-fit for each added perturbation).
Acknowledgments We gratefully acknowledge the team at the  INRA UMR 791 Modélisation Systémique Appliquée aux Ruminants (Paris, France) experimental installation  for the care of the animals and their work to provide robust performance data. Special thanks to  Dr. R. Muñoz-Tamayo  for his conscientious reading and meticulous corrections of the manuscript. This work was carried out with the financial support of the  ANR-Agence Nationale de la Recherche  -The  French National Research Agency  under the " Deffilait project "  (ANR;  project:  ANR-15-CE20-0014 ). This preprint has been reviewed and recommended by  Peer Community In Animal Science  (https://doi. org/10. 24072/pci. animsci. 100001).
Therefore, the fitting of   provides parameters estimates for the new added i th perturbation and for a new version of Wood model's parameters    (i. e. , each time a new perturbation is added, a new version of the unperturbed lactation is refined). For a given lactation dataset composed of daily milk yield records, the preliminary fitting of  0 (i. e. , the original Wood's model without any perturbation) was first performed to estimate   0. Then, the fitting sequence starts by the fitting of  1 (i. e. , PLM with 1 perturbation) thus providing estimates   1 and   1,1. Then, the fitting of  2 consists in estimating   2 and   2,2 with   1,2 fixed equal to   1,1. Then, the fitting of.
A database of 91 chemicals with published data from both transgenic rodent mutation (TGR) and rodent comet assays has been compiled. The objective was to compare the sensitivity of the two assays for detecting genotoxicity. Critical aspects of study design and results were tabulated for each dataset. There were fewer datasets from rats than mice, particularly for the TGR assay, and therefore, results from both species were combined for further analysis. TGR and comet responses.
Whilst the TGR and comet assays are available for measuring genotoxic effects in multiple rodent tissues, there has been no systematic comparison of their performance in detecting genotoxicity per se. A working group of the Genetic Toxicology Technical Committee (GTTC), part of the Health and Environmental Sciences Institute (HESI), was therefore established to contribute to resolving this deficiency. This manuscript describes the establishment of a database containing 91 chemicals with published TGR and in vivo comet assay data in at least one common tissue, and presents the outcome of various analyses addressing the above question. The results of the analysis may impact regulatory toxicity testing, although this database is relatively small and additional studies are needed to refine the comparison and further clarify the sensitivity and specificity of TGR and comet assay results.
Construction of the database As a starting point, the TRAiD (Transgenic Rodent Assay Information Database  [8] ; this can be accessed by sending a request to Dr Paul A. White, Health Canada, at paul. white@canada. ca), a comprehensive database of TGR dose-response data, was examined. The TRAiD contains thousands of experimental records of transgenic mutation results from different experimental treatments (chemicals, radiation, pharmaceuticals, infectious agents, and mixtures), the majority of which were conducted in the 1990s and early 2000s before the standardization of study design when OECD TG 488 was first published in 2011  [6]. The TRAiD database that was used for this exercise (v. 7. 0, updated September 2015) was originally compiled by Health Canada, has been updated with papers published since 2011, and has been carefully reviewed by members of the GTTC.
Chemicals in the updated TRAiD database that had been tested in the in vivo comet assay were selected for comparative analysis, but alternative sources were also used to construct the database. Additional in vivo comet assay data available from previous database publications  [17, 18, 19] , along with information from the following websites, were used: During the search additional chemicals were identified that had both TGR and in vivo comet data, and these were added to the database. Where data were available on different forms of the same chemical (e. g. free base and salt) the results were combined into a single entry, since in aqueous biological systems it would be expected that the different forms would behave in a similar way. This was done for: Initially the group identified 92 chemicals for which there were data from both comet and TGR assays in at least one tissue.
Methods of analysis of the database Following the quality control steps described above, two different approaches were considered to evaluate the data collected in the database. In the first approach, one experienced individual reviewed all of the collected data and allocated overall calls based on weight of evidence (WoE) and expert judgement. These overall calls were applied to each set of comet or TGR results in each tissue to derive tissue specific calls, which were subsequently reviewed by the entire workgroup. We labelled this the "WoE approach". In the second approach, teams of experienced individuals developed a priori acceptability criteria and then reviewed each set of comet and TGR results, and only derived the tissue specific calls on data that met the acceptability criteria. We labelled the second approach the "data curation approach".
• For TGR, 13 chemicals were studied in rats only, 60 were in mice only, and 18 were in both rats and mice; • For comet, 16 chemicals were studied in rats only, 23 in mice only, and 52 in both rats and mice. Therefore, in order to provide sufficient data for comparison, and since either species is acceptable for in vivo genotoxicity testing, data from rats and mice were combined, and the same rationale was applied to data obtained in males and females. The most widely studied tissues in both assays were liver and bone marrow. However, there were several reasons (given below) to also look at responses in various segments of the GI tract: • OECD guideline 488  [6]  says "The rationale for tissue collection should be defined clearly. Since it is possible to study mutation induction in virtually any tissue, the selection of tissues to be collected should be based upon the reason for conducting the study and any existing mutagenicity, carcinogenicity or toxicity data for the chemical under investigation.
The following principles were applied in arriving at these overall WoE calls. Although the methods described in OECD test guidelines are considered sufficiently robust that negative results can be accepted with confidence, many of the published TGR and comet assay studies in the database were conducted before the respective OECD guidelines were adopted. Even studies published since the guidelines were adopted often deviate from the recommended experimental designs. Moreover, many of the published studies were designed to answer questions other than those for which the regulatory Test Guidelines were developed. Thus, data from studies which were fit for their intended purpose were not always easy to compare to the other studies in our database for the purpose of our analyses. Therefore, scientific judgement had to be applied to the data in order to decide whether the results could be accepted as valid.
2. 2. 1 Step 1: Verifying quality of the database-As described above, the data used for this exercise were collected from all available sources in the literature, and it was evident that the quality of some of the studies was questionable. Hence, a detailed evaluation of the quality of the study protocol and the data in each paper across all 91 chemicals was conducted prior to accepting the data into the curated database. This was achieved by objectively assigning a numerical "quality" score to each study, using the a priori quality criteria listed in Table  1. These criteria were developed independently for each assay although consistency across assays was intended. These quality scores were applied equally across all data in the database using evaluation criteria developed prior to considering the impact of omitting a particular study from the overall analysis.
This ensured that only selected high-quality data in the database would be used for the allocation of overall calls and subsequent analyses. The assignment of quality scores to the TGR and comet studies was carried out separately by two independent expert groups. For the TGR expert working group, two teams of experts reviewed and scored all studies. Any scoring discrepancies were discussed by all members of the TGR expert group and a consensus call was reached for all studies. For the comet expert working group, two individuals went through the database and independently assigned quality scores. When there was a discrepancy, the consensus call was reached by the entire expert working group. To assign quality scores, the criteria in Table  1  must have been met for the study being evaluated.
Studies given higher quality scores met the criteria for all lower categories as well. For example, route of administration must be stated for studies scored both 2 and 3. A list of the final quality scores used in the databases is presented in Figure  1. The final quality score for each paper was then assigned to the responses in each reported tissue. For example, for a quality 2 paper that had positive liver and negative bone marrow findings, the liver would be considered positive. However, bone marrow would be considered inconclusive. It should be noted that the authors were aware that tissue toxicity may be a confounding factor. For chemicals that induced positive responses across different dose levels in the same study, or where the results across different papers in the same tissue agreed with one another, we did not believe cytotoxicity was likely to be a confounding factor.
• For comet, 43 chemicals were studied in both stomach and colon and only 29 (67%) were concordant (both tissues positive or negative). The results mentioned above for stomach and colon exemplify the diverse anatomical and physiological properties of these tissues, i. e. the parenchymal cells are different between the two tissues, chemicals may be modified by the time they reach distal GI tissue impacting actual exposure levels, and duration of exposure due to enterohepatic circulation may be different in the two compartments. Hence, the comet and TGR subgroups analysed stomach and colon separately using the curated database and did not try to combine stomach and colon results into a single category such as "GI tract".
Statistical approaches Since the primary question that stimulated the compilation and analysis of this database was "do the TGR and comet assays detect positive responses for the same chemicals (i. e. do they have the same sensitivity to genotoxicants)", then sensitivity is the analogous descriptive statistic to evaluate. The TGR results were therefore taken as the reference point (as it measures an apical endpoint), and for each of the selected tissues (liver, bone marrow, GI tract or separately stomach and colon) the proportion of TGR-positive chemicals that were also positive in the comet assay (i. e. sensitivity) was determined; 95% confidence intervals were calculated using the modified Wald method  [26]. For the WoE analysis, only overall positive and overall negative results were compiled into 2×2 tables.