dataesr/scientific-paragraphs-categorization huggingface dataset:
5% of identity and 98% of coverage with P. rara (QDF64304) isolated from dogs (Canis lupus familiaris), and 87. 2% of identity and 86% of coverage with Physaloptera sp. (AMX28292), P. bispiculata (AMX28291), P. amazonica (QDX15779), and P. hispida (QCF40948). BLASTn analyses of 16S rRNA (416 bp) and 12S rRNA (573 bp) sequences identified the first 60 sequences that corresponded to those of Filarioidea and Thelazidae without any Physaloptera. Nucleotide identity of about 75% with a query coverage of more than 99% were observed among these Spiriruds. Finally, the BLASTn analysis of the partial (675 bp) sequence of the ITS2 showed an identity of 93. 37% (67/71) and a coverage of 10% with the unique GenBank sequence of Physaloptera alata (AY702694) isolated from birds. The interspecific nucleotide pairwise (INP) distance of the 18S rRNA, cox1, 16S rRNA, 12S rRNA, and ITS2 of A.
caucasica prevalences (Table  2 ). Abbreviata caucasica cox1 species-specific primers successfully amplified a partial sequence (504 bp) from 84% (21/25) of samples identified as positive by the qPCR targeting the 12S rRNA gene. There was no significant difference between both assays according to the McNemar test (p = 0. 25). All sequences were identical to each other and showed 100% similarity to those from adult specimens amplified with pan-Nematoda primers. All sequences were deposited in the GenBank database under the following accession numbers: MT231296-MT231316.
To address this question, a molecular comparative characterization of the specimens from the studies of  Schulz (1926)  and  Fain and Vandepitte (1964)   [7, 8]  should be performed to confirm or refute the synonymy of these two species. In our study, we expanded the genetic data available for A. caucasica with sequences of mitochondrial and nuclear DNA (i. e. , cox1, 12S rRNA, 16S rRNA, 18S rRNA, and ITS2 genes), though the genetic characterization was based on cox1 and 18S rRNA genes, due to the limited data on other gene sequences of Physalopterida members in the GenBank database. The molecular analyses carried out in this study such as the phylogenetic comparisons of cox1 and 18S rRNA genes, the TCS network analysis of the cox1 gene, and the Bayesian inference of both cox1 and COI sequences confirmed that A.
In contrast, the newly cox1 A. caucasica specific PCR could be used to assess the identification of A. caucasica from hosts exposed to a wide range of nematode infestations. Since the PCR replicated the same result as the qPCR (p = 0. 25), both tools were highly sensitive and specific in detecting A. caucasica, even the presence of coinfestations, avoiding the hard diagnosis based on egg identification. These tools can resolve problems related to the detection of larval stages from the intermediate and paratenic hosts and therefore avoid the sequencing identification by nematode generic primers. A detection limit as low as 1. 13 × 10 -3 eggs per gram of positive feces, regardless of consistency, solves the problems associated with conventional protocols requiring fresh equipment  [38]. Data generated by qPCR showed a rate ranging from 0.
Study Site and Study Subjects Samples were collected at the Dindefelo Community Natural Reserve, located in the Kedougou region, southeastern Senegal, about 35 km from the town of Kedougou. The vegetation of the reserve is a sudano-guinean savanna woodland  [44] , one of the driest and more open habitats occupied by the species  [45]. All chimpanzees live in multi-female/multi-male communities composed of flexible groups that fission and fuse  [46]. At the time of data collection, some individuals were semi-habituated to observers, but the rest remained unhabituated and thus the exact community composition and size were unknown. Although the total home range of Dindefelo chimpanzees was not known, conspecifics living in savanna woodland habitats have extremely large home ranges (e. g. , >85 km 2 , [47]).
Morphological Analysis of A. caucasica Adult Worms The female worms were processed for morphometric analysis. The body of the nematodes were measured and then cut into three pieces. The central part was subjected to DNA extraction for molecular identification. The cephalic and caudal ends of the worms were fixed and stained in lactophenol solution to observe anatomical structures. Digital images and measurements were made with an optic microscope Leica ® DM LB2 with differential interference contrast. The software Leica ® LASAF 4. 1 was used for the image analysis process including the measuring of nematodes, which are provided in micrometers. The identification was carried out following the description made by Schulz, (1926), Ortlepp, (1926) and  Brede and Burger (1977)   [8, 9, 21]. The observation of structures in the cephalic region, the stout size of the nematode, a thick cuticle finely striated, a large cephalic collarette, the total length, and the distance from the anterior end to the vulva all confirmed the identification of this helminth as A.
Development of PCR Primer Sets The primer sets used in this study are listed in Table  4. First, sequences of the cytochrome c oxidase I (cox1), 16S rRNA, 12S rRNA, and the internal transcribed spacer 2 (ITS2) genes were used to design primer sets targeting nematodes. For each PCR system, a fasta file was constructed from nematode sequences retrieved from the GenBank database. Sequences were aligned using BioEdit v7. 0. 5. 3 software  [50]. The highly conserved areas were submitted in Primer3 software v. 0. 4. 0  [51]. PCRs standardization was performed as described elsewhere  [52]. Primers designed are reported in Table  4. In addition, primers Fwd. 18S. 631 and Rwd. 18S. 1825r, recently designed to amplify a partial fragment of the 18S rRNA gene of nematodes, were also used in this study (Table  4 )  [53].
0. 0 software as recommended  [54]. Sequences amplified from the cox1, 16S, 12S, 18S rRNA, and ITS2 genes were subjected separately to a preliminary analysis using the Basic Local Alignment Search Tool (BLAST)  [55]. The closely related sequences of Physaloptera and nematode species retrieved from the GenBank database were included in the study and a fasta file was constructed for each gene and then subjected to the alignment. In addition, alignment of nematode COI protein sequences was also performed. All alignments were conducted using the ClustalW application within Bioedit v. 7. 2. 5. [50]. The conservation of amino acids between the COI sequences of A. caucasica relative to the sequences of Physaloptera was visualized on CLC Sequence Viewer 7 (CLC Bio Qiagen, Aarhus, Denmark). From the alignment of each gene examined, the interspecific nucleotide pairwise distance (INPD) was evaluated to estimate the genetic divergence between all species included.
Furthermore, the interspecific amino acid pairwise distance (IaaPD) was reproduced for COI-protein sequence alignment. standard errors were obtained by a bootstrap procedure with 1000 replicates using the maximum composite likelihood model  [56]  and Poisson correction model  [57]  for nucleotide and protein sequence alignments, respectively. Evolutionary analyses were inferred on MEGA6 software  [58]. DNA sequences of Necator americanus (AJ920348) and Ascaris sp. (KC839986) were chosen as out-groups for18S rRNA and cox1, respectively, according to the fast-minimum evolution tree on BLAST  [55]. The corresponding COI protein sequence of Ascaris sp. (AGN72537) was maintained as an out-group for the COI-protein alignment. The best model parameters with the lowest score were selected to generate phylogenetic trees of aligned 18S and cox1 sequences as well as COI protein sequences by running the MrBayes algorithm on each model using Topaliv2.
5 software  [59]. The Bayesian phylogenetic tree  [60]  was inferred for nucleotide sequence alignments using the K80 (+G)  [61]  and GTR (+G, +I)  [62]  models, respectively, while the Bayesian phylogenetic tree was inferred on the COI protein sequence alignment using Mtmam (+G)  [63]. All phylograms were generated with five runs for 1,100,000 generations, 25% of burn-in length, and 1000 for sample frequencies. In order to resolve the haplotype variations of Physaloptera species and A. caucasica, the Fasta file of the cox1 sequences was converted to the Nexus format using an online converter  [64]. During the second time, a Templeton-Crandall-Sing (TCS) network phylogram  [65]  was inferred with a 95% connection limit and drawn with 1000 iterations using the PopArt software  [66].
Quantitative TaqMan Real-Time PCR (qPCR) for A. caucasica Detection Sequences of the 12S rRNA gene amplified from the adult worms of A. caucasica were aligned with the closely related sequences of nematodes available in the GenBank using Bioedit v. 7. 2. 5. [50]. The specific regions for 12S rRNA of A. caucasica were submitted in Primer3 software v. 0. 4. 0  [51]  in order to design the following primers Phy. 12S. f. 204:5 -GAATTGGATTAGTACCCAAGTAAGTG-3 /Phy. 12S. r. 305: 5 -TGTTCCAAAAATCTTTCTAAGATCAG-3 (Eurogentec, Liège, Belgium) and TaqMan probe: Phy. 12S. 242p. 6VIC-GCGGGAGTAAAGTTAAGTTTAAACC-TAMRA) (Applied Biosystems TM ), allowing the amplification of a fragment of 101 bp. Both qPCRs were tested in silico within the DNA databases of metazoans (taxid: 33208), vertebrates (taxid: 7742), bacteria (taxid: 2), Canidae (taxid: 9608), Felidae (taxid: 9682), and humans (taxid: 9605).
This experiment was performed for both combinations of forward-reward and probe-reverse of each qPCR using Primer-BLAST  [69]. Subsequently, the specificity was also validated in vitro against the genomic DNA extracted from adult worms of A. caucasica and DNA database including several nematodes, arthropods, laboratory-maintained colonies, hemopathogens as well as human, monkey, donkey, horse, cattle, mouse, and dog as described elsewhere  [52]. All qPCR reactions included 5 µL of DNA template, 10 µL of Master Mix Roche (Eurogentec), and 3 µL of ultra-purified water DNAse-RNAse free. Concentration of each primer, UDG, and each probe was 0. 5 µL. The TaqMan reaction of both systems was run using the same cycling conditions. This included two hold steps at 50 • C and 95 • C for 2 and 15 min, respectively, followed by 40 cycles of two steps each (95 • C for 30 s and 60 • C for 30 s).
The qPCR reaction was performed in a CFX96 Real-Time system (Bio-Rad Laboratories, Foster City, CA, USA) after activating the appropriate dye readers for each qPCR system. A protocol for the quantification of eggs has been established to assess the analytical sensitivity of qPCR in the detection of fecal infestation. A 10-fold serial dilution of DNA extracted from 200 mg of fecal matter containing 113 eggs (Figure  2 ) per gram (i. e. , 22. 6 eggs/100 µL of eluted DNA and 1. 13 eggs/5 µL of qPCR reaction). Standard curves and derived parameters (PCR efficiency, Slope, Y-intercept, and correlation coefficient) were generated using CFX Manager Software Version 3  [70]. The molecular approaches described above were used to screen the presence of A. caucasica and other nematodes in chimpanzee fecal and environmental samples collected in a chimpanzee dormitory.
Statistical Analysis XLSTAT Addinsoft version 4. 1 (XLSTAT 2019: Data Analysis and Statistical Solution for Microsoft Excel, Paris, France) was used for the statistical analysis. Results of qPCRs analysis were used to set a database using the Microsoft Excel ® program (Microsoft Corp. , Redmond, Washington, USA). The effect of localities and fecal consistency on the infestation rates were tested using the Khi2 test or exact Fisher test. One-way analysis of variance (ANOVA) was performed to compare the predicted eggs from fresh and degraded feces. Negative samples and those with a studentized residual higher than 2. 9 were removed before discarding the ANOVA test. McNemar's test was used to compare the detection accuracy of the qPCR and conventional PCR of A. caucasica from the chimpanzee samples.
Acknowledgments: We thank the  Direction des Parcs Nationaux  and the  Direction des Eaux et Forests Chasses et de la Conservation des Sols  for permission to work in Senegal. We thank  Paula Alvarez Varona ,  Daouda Diallo ,  Mamadou F. Diallo , and  Mamadou Samba Silla  for help in the field in the  Dindefelo Community Natural Reserve, Senegal.
Statistical analyses Three replicates were performed for each treatment and for each factor studied. Statistical analyses were performed using GraphPad Prism software, version 5. 0 (GraphPad, San Diego, CA, USA). A two-way ANOVA analysis was performed to show the effects of the treatments on the measured parameters, taking into account the parasitic isolate effect. In parallel, non-parametric statistical tests (Mann-Whitney U tests) were carried out, followed by Bonferroni tests. Principal component analysis (PCA) and linear regressions were performed using XLstat software, version 7. 5. 2. (Addinsoft, Paris, France).
Acknowledgements. We would like to thank the " Région Centre- Val de Loire"  for funding this research. We also thank  Dr. Yan Van Wyk ,  Dr. Gilles Aumont  and  Professor Roger Prichard  for kindly providing the H. contortus isolates, as well as the research staff and the management of the PFIE, in particular  Thierry Chaumeil  and  Maud Renouard  for the careful maintenance of animals and  Marie-Estelle Esnault  and her team for their precious help with the bibliography. PFIE is part of EMERG'IN, the national infrastruc-ture for the control of animal and zoonotic emerging infectious diseases through in vivo investigation. We are also grateful to  Mrs. Marie Fassot-Garnier  for her participation in the experiments. Many thanks to  Professor Claude Motta  ( Faculté de Médecine de Rennes ), for advice on this work.
Statistical Analysis Statistical analysis was carried out using the STATISTICA 10. 0 software (StatSoft, Kraków, Poland). The blood morphology, biochemistry, and hormonal assays obtained on D0 differed among all the investigated groups depending on the physiological status of the individual dogs included, hence the results were analyzed only with respect to the D0 values  [17, 21]. The values within treated groups were compared to D0, to each other, and to the values obtained in control and treated groups, using the paired Student's t-test or Mann-Whitney's U-test depending on distribution. A probability value at p = 0. 05 was considered statistically significant.
Nodes represent individual animals. Edges represent interactions between them. An edge list is created from observations on farm, video analysis or the tracking of animals. The list of all dyadic encounters is established (i. e. , A attacks B; C attacks B, etc. , …). This edge list includes all single dyadic encounters observed. It may encompass a number of repeated dyadic encounters between two pigs, and the centrality measures derived from the dataset may be weighted to account for these repeated interactions. To facilitate statistical analysis, the edge list is summarized in a matrix of social interactions called a sociomatrix or adjacency matrix  (Wasserman and Faust, 1994). the absence of an interaction, and 1 is assigned when at least one interaction has occurred between two pigs  (Wasserman and Faust, 1994; Wey et al.
, 2015). Therefore, it is advantageous to add this information in the model. The chewing activity can be considered as an additional event (3) for biter status with high value of b 3a,t. The chewer status can also be modelled with a hidden Markov chain (corresponding to observed events: chewing group mate's tails or not) linked to the biter status under the form of a probability whereby, by definition, the probability of being a tail biter will be higher if the pig was a chewer earlier. This will facilitate use of the multi-event multi-state model and improve the completeness of the database.
, 2015). Camerlink et al. (2015)  and  Canario et al. (2012)  analysed relations between SBV for growth and DBV for a variety of behaviours including aggression in unstable and stable groups. Their results suggest that housing groups of pigs with a positive SGE on group mates' growth together reduces damaging behaviours. The advantage of this approach is that it requires no behavioural phenotyping and behaviour may be improved indirectly as a consequence of improving growth. Conversely, the specific targeting of improvements in damaging behaviour as a response trait using social genetic models will require new phenotyping tools for behaviour traits enabling access to the large databases of individual records that are required for the convergence of a social model. When a classical direct model is applied to traits depending on social interactions, the extra heritable variation that is attributable to social genetic effects is partially hidden in the residuals, and in other variances such as the environmental group effect that accounts for the identity of the group in which the focal pig is raised.
Data can be analysed using the restricted maximum likelihood methodology, as implemented, for example, in the ASReml software package  (Gilmour et al. , 2015). First, an initial model with direct genetic effects only is fitted to the data: a D is a vector of direct additive genetic effects of the focal individual, with normal distribution of mean 0 and variance A σ 2 AD where A denotes the matrix of additive genetic relationships between pigs and σ 2 AD the direct additive genetic variance. In addition, a vector of random group effects with normal distribution of mean 0 and variance connected to an identity matrix is considered. With social interactions, the model is extended with random social (indirect) genetic effects of group mates, following the methods outlined by  Muir (2005) ,  Bijma et al.
As usual, the direct and social breeding values are random effects following a bivariate normal distribution with zero mean. The offset is needed only when group size varies and/or when the time interval t varies among records. The term ln(n -1) in the offset assumes that the number of bites a pig receives is proportional to its number of group mates (n -1). If this assumption is too strong, one may drop ln(n -1) from the offset, and fit a fixed group-size effect instead. This model can be fitted in standard software for generalised linear mixed models, such as ASReml (see  Biemans et al. , 2019 for an example; Gilmour et al. , 2015). The interpretation of estimates is as follows: e intercept is an estimate of the base line hazard λ 0 , i. e. , the average number of bites an average pig receives per day (assuming t is expressed in days).
Direct breeding values can also be calculated from behavioural observations directly, as has been done for aggression  (Løvendahl et al. , 2005; Turner et al. , 2006)  and tail biting  (Breuer et al. , 2005). In the cases of SNA and CRA, models can be complemented with a pedigree relationship matrix to address the heritable variation of damaging behaviours. More simply, outputs from SNA and CRA models can be used as inputs for classical animal models (for SNA  : Foister et al. , 2018). Datasets on damaging behaviour are often punctuated by missing data and observations that are an imperfect translation of the states of the animals. It is in filling these gaps that multi-event multi-state models can play a role by allowing unobserved states to be imputed, thereby providing more data that can be used in the other models.
Pedigree observation is available for each pig from birth, providing information on its ancestors' identity. At different time points in life, information on the group composition (identity of conspecifics) enables social genetic effect models to be used. In the upper part of the figure the behavioural traits to be analysed are defined. Methods for their analysis are indicated in the circles as follows: CRA, capture-recapture analysis to replace missing observations by imputed biter or victim states. This analysis allows missed information to be retrieved; SNA, social network analysis to estimate centrality parameters; CONT, contagion analysis to analyse the spread of injury. CONT can be used on records or combined with use of a social genetic effect model. CRA and SNA data can be used as input in the genetic model.
In quantitative analyses, a large number of records are needed to obtain estimates with high accuracy. To be efficient, the fusion of models per se might need even larger databases. First, the study of damaging behaviour at a specific life stage in a pig's life, with multiple records to improve the classification of pigs in different categories, such as biter vs. victim, is recommended. This will account for the history of the behaviour in the group and thus fill in missing records (CRA model). Second, since the likelihood that an individual expresses or receives biting may depend also on its experience at earlier stages of its life, longitudinal studies may provide more insight than cross sectional studies. It is important to keep track of all animal movements between groups when integrating life information in longitudinal genetic studies.
ACKNOWLEDGMENTS This paper was based on work from  COST Action CA15134-Synergy  for preventing damaging behaviour in group housed pigs and chickens (GroupHouseNet), supported by  COST (European Cooperation in Science and Technology ; www. cost. eu). LC was grateful to  J. D. Lebreton  for sharing a discussion that supported the idea of using capture-recapture models for the analysis of livestock-damaging behaviour.
Simulation experiments The model was parameterised using Approximate Bayesian Computation (ABC) rejection algorithm  (Beaumont et al. , 2002) , based on the abc R package  (Csilléry et al. , 2012). This approach has the advantage to approximate the likelihood by running the model a large number of times and comparing simulated outputs to the observed data  (Toni et al. , 2009). However, this is a computationally intensive approach which was possible in our case because of access to highly performant computing facilities and because of the small population size of the investigated system. The algorithm is fully described in Appendix B. We first determined the parameters to be estimated based on (i) identifying the most uncertain parameters, from literature survey and field data, and (ii) performing a sensitivity analysis to determine which parameters have the strongest influence on model outputs.
Acknowledgements: This work was performed using the computing facilities of the  CC LBBE/PRABI  and of the CC IN2P3, and performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). The authors want to thank the scientific experts of the ANSES working groups on brucellosis of Alpine ibex in the Bargy massif, and especially  Jean-Pierre Ganière  and  José-Maria Blasco , who helped for the range values of parameters for the sensitivity analysis. The authors also want to thank the field technicians and researchers, especially  Jean Hars , from the  French Wildlife and Hunting Agency (ONCFS) , who performed monitoring and management of the Alpine ibex population.
Modeling Bennu with altimetric data Over a 5-week period in July and August 2019, the OSIRIS-REx spacecraft globally mapped the surface of Bennu from a near-terminator quasi-circular orbit with a radius of <1 km [Orbital B  (1) ], relative to the asteroid center of mass. OLA's scanning capability and high measurement rate (10 kHz) made it possible to achieve global high-density coverage of Bennu during this period, in orbital geometries with low orbital velocities of tens of centimeters per second and ranges near 700 m. OLA collected measurements of Bennu in 5. 5-min continuous blocks (scans), each of which overlapped the previous scan in a sequence that typically consisted of 15 to 20 scans (e. g. , figs. S1 and S2). Each scan was approximately 182 × 175 mrad (azimuth × elevation) in extent with a nominal 100-rad spot spacing, although the scans extended in the direction of the spacecraft ground-track (elevation direction) owing to the spacecraft velocity over the scan duration.