dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional analysis Enrichment analysis was performed using tools available at WEB-based GEne SeT AnaLysis Toolkit (WebGestalt)  [24, 25]. Entrez gene IDs were used as unique gene identifiers. Target gene lists for main effects and interactions and a background gene set consisting of all 9530 genes were used to identify enrichment in GO, KEGG, Transcription Factor Target, Microarray Target, Protein Interaction Network Module, and Phenotype Analysis in WebGestalt using Fisher's exact test and BH correction for multiple testing. A pathway is an interconnected arrangement of processes, representing the functional roles of genes in the genome. The biological processes in which individual genes may participate were identified using the "Gene Ontology" database AmiGO (http://amigo. geneontology.
org). The DEG were assembled into networks using Ingenuity Pathway Analysis (IPA © ) (http://www. ingenuity. com). This application includes algorithms that automatically identify the biological pathways and functions of selected genes. It is based on a large bibliographic database with various types of interaction already identified between pairs of genes. Every biological network extracted by IPA corresponds to the best possible arrangement of the genes, and are associated with a score derived from a p-value (right-tailed Fisher's exact test,log 10 -transformed).
offset term + X b between-animal deviation + X w within-animal deviation (4) Similarly to what was performed in  [11] , multivariate approaches were performed on X w to bring out the most relevant correlations between variables in the dataset, independently from individual variations. First a multilevel PCA was performed on the biological variables to study the overall effect of LPS on plasma metabolites and cortisol over time. Then, a multilevel multiple factor analysis (MFA)  [26]  was used to investigate the overall relationships between clinical biology and transcriptomic data.
Subsequently, these biomarkers may serve collectively as an indication of inflammation in swine. The knowledge gained from this series of experiments may help in the development of a model for further studies. This project received financial support from the French National Research Agency (contract SUSoSTRESS, ANR-12-ADAP-0008) which also funded the PhD thesis of VS together with the Région Midi-Pyrénées. DB was supported by the Embassy of France in Russia (Mechnikov Program). The funding bodies had no role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data; or the preparation, review, or approval of the manuscript.
Acknowledgements We thank the team of "le Magneraud" experimental station who took care of animal breeding,  GeT-TRiX  platform (Toulouse) for expression data production,  GenPhySE INRA Toulouse (more specificaly Nathalie Iannuccelli ,  Katia Fève  and  Juliette Riquet ) for experimental support,  ANEXPLO platform (Toulouse)  for biological assays,  PEGASE INRA Saint-Gilles (more specifically Rafael Comte ) for cortisol assay,  Aurélie Ducan  for hematology analysis,  Aurélie Sécula-Tircazes  for Fluidigm analysis and  Magali San Cristobal  and  Pascal Martin  for helpful discussions on the analyses.
Following MICROBEnrich treatment, electrophoresis was performed and showed this treatment to have been poorly effective in our hands, because strong bands corresponding to the 18S and 28S rRNAs were still detected (Figure  2D ). These data confirmed the predominance of eukaryotic ribosomal RNA in samples, and suggested that mouse mRNA would probably also predominate over bacterial transcripts. However, based on our qPCR demonstrating the presence of mycobacterial RNA in samples, we decided to attempt a deep sequencing approach (RNA-seq) on a sample before and after bacterial RNA enrichment with the MICROBEnrich kit. The obtained RNA-seq data showed an absence of reads aligned with the M. ulcerans genome, even though mycobacterial RNA transcripts were detected by RT-qPCR, a more sensitive technique.
The RNA-seq data confirmed that less than 5% of the reads mapped to rRNA, demonstrating the efficacy of the rRNA removal kit. The datasets for samples M2-1, M2-2 and M2-3 obtained by the optimized method (Method 2) contained 9. 5, 7. 6 and 7. 9 million reads aligning with the M. ulcerans genome, respectively, whereas no reads aligned with this genome sequence were detected with the samples obtained by Method 1 (Figure  4 ). This enrichment procedure therefore provides sufficient coverage of sequences mapping to the M. ulcerans, because a minimum of 2 to 5 million reads from a ribosomal RNA-depleted library is required to provide adequate coverage of the gene expression profiles of bacteria in RNA-seq experiments  (Rienksma et al. , 2015). Finally, two biological replicates, M2-1 and M2-2, were used to evaluate the reproducibility of the gene expression profiles obtained with RNA-seq technology.
Primer/probe sequences. Sequencing data were processed with Illumina Pipeline software (Casava version 1. 9). All 65-bp reads were aligned against the complete genome and plasmid sequences of M. ulcerans (Agy99 strain) obtained from the Burulist database with Bowtie software, and against the mouse genome (GRCm38) obtained from the Ensembl database with STAR software. Data were normalized and analyzed in R, with the Bioconductor packages.
, 2013). Cysteine proteases HmCP-1,4 and 6, enriched from adult H. contortus gut membrane provided a lower level of protection  (Knox et al. , 2005). Barbervax ® induces circulating antibodies which are ingested by the parasite when it feeds and which inhibit haemoglobinase activity in vitro  (Ekoja and Smith, 2010)  and probably in vivo. Because the gut-membrane antigens are not exposed to the host immune system during natural infection, Barbervax ® relies on the induction of antibodies to "hidden" antigens  (Knox et al. , 2003). Therefore, it is speculated that the Barbervax ® proteins are not under selective pressure during natural infection, but whether vaccine-induced immunity influences levels of gene expression is currently unknown. The high level of genetic diversity observed in genomic datasets of H.
Real-time PCR Total RNA was extracted from triplicate samples of five female worms from the same populations as the sequenced males. 3 μg total RNA was used per oligo(dT) cDNA synthesis (SuperScript® III First-Strand Synthesis System, ThermoFisher, 18080051) with no-reverse transcriptase controls included for each sample. cDNA was diluted 1:100 for quantitative RT-PCR (RT-qPCR) and 1ul added to each reaction. RT-qPCR was carried out following the Brilliant III Ultra Fast SYBR QPCR Master Mix protocol  (Agilent Technologies, 600882)  and results analysed using MxPro qPCR Software, Version 4. 10. Gene expression was normalised to ama (HCOI01464300) and gpd (HCOI01760600)  (Lecova et al. , 2015). Primer sequences are listed in Table  S2.
RNAseq data handling and differential expression analysis RNAseq data were mapped onto the reference genome using a gene index built with Bowtie2  (Langmead and Salzberg, 2012)  and TopHat v2. 1. 0 (Trapnell et al. , 2009)  with maximal intron length of 50 Kbp and an inner mate distance of 30 bp that identified 48. 8% of the reads being mapped unambiguously to a gene feature. Counts of reads spanning annotated gene features were subsequently determined with HTSeq v0. 6. 0  (Anders et al. , 2015). To ensure our biological conclusions are not sensitive to details of the statistical methods used, we implemented two different analysis frameworks for the RNA-seq count data, using the DESeq2 v1. 12. 4 framework  (Love et al. , 2014)  and the voom function as implemented in the LIMMA package v3. 28.
21  (Law et al. , 2014)  in R v3. 3. 1 (R Core Team, 2016). Genes found to be significantly differentially expressed (DE, adjusted p-value <5%) by both VOOM and DESeq2 analyses were retained. A gene ontology enrichment analysis was performed using the TopGO package v2. 26. 0  (Alexa, 2016). Gene identifiers of the vaccine core components, namely MEP-3  (Smith et al. , 2000) , MEP-1,2,4, PEP-1  (Britton et al. , 1999)  and PEP-2  (Smith et al. , 2003)  as well as H11, were retrieved via a BLAST search of their nucleotide sequence against the H. contortus MHco3. ISE reference assembly  (Laing et al. , 2013)  in WormBase ParaSite  (Howe et al. , 2016). The expression levels of candidate housekeeping genes  (Lecova et al. , 2015)  were also retrieved using the gene identifiers associated with their GenBank records (Table  1 ).
3. 2. Transcriptional response of worms to host vaccination is dominated by higher expression of proteases and protease inhibitors We investigated any changes in H. contortus gene expression in worms surviving in vaccinated sheep relative to those surviving in controls. On average 11M (standard deviation of 1. 79M) reads were available for each library (Table  S1 ). In PCA of the normalized RNAseq read counts, the first two axes explained 53% of the total variation, 37% of which was resolved along the 1 st axis that separated the experimental groups  (Fig S1). Two pools of worms sampled from control sheep, C_4 and C_6, showed atypical behaviour that was resolved along the 2 nd PCA axis (Fig  S1 ). These samples were discarded from the dataset for subsequent analyses, resulting in a comparison of 6 V samples and 4 C samples.
Acknowledgements We thank  Stephen Doyle  for advice and comments on the manuscript and the biological services staff at  MRI  for their expert animal care. JAC, NH, AM, AT and MB are supported by the  Wellcome Trust  via their core funding of the  Wellcome Trust Sanger Institute  (grant  206194 ). JAC, NH, AT, MB, KM, RL, ED and CB are supported by  BBSRC  grant  BB/M003949/1  (BUG), GS has received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills  (grant agreement n°  267196 ) and  AgreenSkills+ fellowships  (grant agreement  n°609398 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methods: The single-trait SAD model assumes that a random effect at time t j can be explained by the previous values of the random effect (i. e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. Results: For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively.
program that we have developed (available online at https://zenodo. org/record/192036#. WEAYLdLhBaQ). The orders of (cross-)antedependence and the degrees of the polynomial functions of time for (cross-)antedependence parameters and innovation variances can be selected by comparing nested models using the likelihood ratio test and by comparing non-nested models using the Bayesian or Akaike information criterion (AIC)  [23, 24]. To reduce the number of models that need to be compared, we suggest selecting the specification (order and degree) of the antedependence and innovation variances by first using a single-trait SAD model and then the cross-antedependence specification by using a multipletrait SAD model. For the single-trait SAD model step of the selection process, we suggest to start with a SAD 100, then test an increase of the degree of the function of time (γ) for the innovation variance (i.
Data application The multiple-trait SAD model was applied to data from two polytocous species (pigs and rabbits) to study the relationship between the number of young born alive per litter (litter size, LS) and the average birth weight (ABW) per litter, calculated as the sum of the weight at birth of all the individuals of a litter divided by LS. The pig dataset included 4345 litters from 1817 Large White sows over a maximum of five successive farrowings. The mean LS was 12. 1 ± 3. 6. Piglets were weighed at birth in all litters, the mean ABW was 1514 ± 31 g. The rabbit dataset included 8706 litters from 2286 L-1777 does  [25]  over a maximum of five successive kindlings. Kittens were weighed at birth for 3490 litters. The mean LS and ABW were 9. 5 ± 3. 1 and 83 ± 14 g, respectively. The descriptive statistics of LS and ABW per parity are in Table  1.
Conclusions In this paper, we have outlined a multiple-trait SAD model to simultaneously analyze repeated measurements of several traits. This flexible approach was developed to provide an advantageous approach to model the covariance matrix of random terms with few parameters. When used to study relationship between LS and ABW over five parities in two species, the multiple-trait SAD model showed that a total of 16 or 20 parameters was sufficient to model the random term covariance matrices. This is much less than the number of parameters required to obtain estimates when unstructured covariance matrices are assumed for U and P (up to 110 parameters). Further- more, we showed that multiple-trait SAD models provide a better fit to the data than multiple-trait RR models. We offer a freely-available online Fortran program that can be used to implement this SAD model in ASReml.
Data collection We examined TriMag data recorded using Daily Diary (DD) logging units  [23] , deployed for a variety of reasons but not specifically for consideration of the TriMag data as documented here. The DD devices weighed between approximately 16 and 60 g, with dimensions from 33 × 38 × 10 mm to 80 × 37 × 25 mm, depending on the battery and housing design. These devices were attached to free-living vertebrates (a fish, 3 bird and 3 mammal species) and aspects of their behaviour analysed through comparison of accelerometry and magnetometry data. In all cases presented here, we refer to the x, y and z axes of the magnetometer, which correspond to the dorsoventral, lateral and longitudinal axes of the animal, respectively. Orthogonal TriMag data were recorded in gauss (G) using the ultra-compact high-performance eCompass module consisting of a combined 3D digital linear accelerometer and ST digital magnetometer (LSM303DLHC, STMicroelectronics www.
On first inspection though, the TriMag data immediately show the postural angular rotation associated with both the major body rolls (at a frequency of ca 0. 3 Hz) and the flipper movement (at a frequency of ca 2 Hz) (Fig. 3b ), without interference from dynamic movement. We used a template-matching procedure (using Framework4 software as detailed in  [39] ) to quantify the potential value of TriMag and acceleration signals to detect and correctly classify the three behaviours described above. Using the TriMag template, more instances of the specific behaviours were detected than could be found with the acceleration equivalent template. Thus, for the Himalayan vulture, the TriMag signal correctly identified 32 of 63 cases of thermal soaring (51%) while the acceleration signal found 0 (0%).
Calibration of TriMag sensors and movement protocols As with tri-axial accelerometer and gyroscope measurements, the sensor outputs for each of the three tri-axial magnetometer axes may relate to the outputs of the other two. This means that, in order to obtain the complete picture of TriMag rotational movement, users should consider all three TriMag channels simultaneously. A standard calibration of a device containing TriMag sensors might be to hold the tag horizontal, facing North, and then rotate the unit through 360°in the yaw, then the pitch, then the roll axis. This would then effectively allow production of three graphs, each of time versus magnetic field strength (one for each Fig. 2  A time series plot of 6 Hz tri-axial acceleration (a) data and its associated tri-axial magnetometry data (b) in a swimming whale shark (Rhincodon typus) (equipped in Australia).
3  A time series plot of 40 Hz tri-axial acceleration data (a) with its associated tri-axial magnetometry data (b) of a washing Magellanic penguin (Spheniscus magellanicus) (equipped in Argentina cf. [55] ). The tag was attached to the bird's back with tape  [38]  associated with the normal yaw rotation and four additional rings for each of the 90°postural rotations (Fig. 6 ). If a tag containing a TriMag is mounted on an animal that normally operates in a particular posture, such as a walking or standing deer, full directional rotation of that animal in heading (i. e. yaw) will create a ring on the surface of the m-sphere (in the same manner as described above, Fig. 5 , blue ring, Fig. 6 , centre). We define this ring as the Normal Operational Plane (NOP) since it relates to the normal operational posture adopted by the animal when operating on the plane parallel to the Earth's surface (i.
e. yaw plane). Any deviation from the NOP on the surface of the m-sphere is therefore the result of change in animal pitch and/or roll. The position of the NOP itself will vary according to both the inclination angle of the magnetic field at that location, and the manner in which the tag has been attached to the animal. However, data on the m-sphere can be normalised post-hoc to reposition the NOP about the pole of the sphere on a horizontal plane to aid visual interpretation (see below) and essentially calibrate the tag to the animal's movement. The only circumstance where directional rotation of the animal will not clearly define a NOP is at Earth's magnetic poles because magnetic field runs perpendicular to the Earth's surface.
M-sphere outputs for interpreting the behaviour of freeliving animals Plotting TriMag data on m-spheres highlights patterns in heading and postural rotation that are not obvious either by the consideration of acceleration data, or absolute values of heading. For example, examination of 10 min of randomly selected periods of active movement data for a European badger (Meles meles) and an Arabian oryx (Oryx leucoryx) show highly variable, species-specific m-sphere coverage. Some postural variation is evident in the acceleration data (Fig. 7 , left), but the m-spheres (Fig. 7 , middle) reveal very different patterns of movement for the two species. The badger data (collected at 20 Hz) lie about a specific region of the NOP (Fig. 7a , dashed ring, drawn from calibration of the tag prior to deployment), indicating firstly, that the heading of the animal was fairly invariant for this period, and secondly, since the radial spread of data about this section of the NOP is relatively large, that the body posture in terms of roll and pitch varied appreciably for any given heading (cf.
Fig. 5 ). This is apparent even without consultation with the acceleration data because tri-axial plotted data that remain on the NOP correspond to no departure from the horizontal orientation. In comparison, the oryx displayed a comparatively welldefined, thin ring about the NOP, indicating relatively little change in body posture compared to the extensive variation in heading (but see below for discussion of the small variation in this). 6 a The complete rotation of a DD with TriMag sensor about its yaw axes on a level surface (blue circle; centre) creates the Normal Operational Plane (NOP, dashed outline). The tag was tilted -90°and 90°in both its pitch and roll axes and the full yaw rotation repeated (cf. Fig. 4 ). This shows how the specific pattern of data on the m-sphere, or the 'm-print' of the behaviour, here created by a rotation, moves across the surface of the m-sphere as posture is altered (dark grey m-print rings correspond to rotations in pitch postures; pink m-print rings correspond to rotations in roll postures).
e. grazing, and 'head-up' behaviour at these sites (the tags were collar-mounted and thus sensitive to neck angle). The combination of heading and postural rotation provided by TriMag data, allows behaviour to be distinguished by simultaneous consideration of the two. This enhances biological interpretation of logger data beyond that possible with tri-axial acceleration data alone. For example, animal heading may be associated with particular postures, if for instance birds coming in to land recapitulate their landing trajectory, or where a 'head-up' or vigilant posture occurs at certain locations with patterns in directionality. In multiple associated tagged animals, this approach could quantify 'information gathering' via scanning behaviour or the cohesive movement of social groups, for example, where individuals make decisions on who to follow.
The general omission of heading in behavioural texts to date may be largely due to the difficulties of resolving it, especially when tri-axial data is interpreted using 3 separate time-series plots. Visualising TriMag data on the m-sphere for an extended period of time concatenates behaviours, producing a broad-brush picture of headings and postures, and potentially concealing valuable information on the sequence of rotations associated with a given behaviour. As with the calibration movements, a repetitive behaviour that involves stylised body-roll and/or body-pitch produces a specific trajectory of points on the m-sphere, here termed the 'm-print' (e. g. Fig. 8 ). The m-print can therefore be used to identify behaviour according to the conserved sequence of angular rotation, as well as to quantify consistency in behaviour as a measure of performance.
Distribution of data about the NOP (black dashed circle) may be due to variation in postural rotation about a single heading, as is the case for the badger, or by directional rotation in the yaw axes, as seen in the oryx. The frequency distribution of TriMag data (right) highlights the predominant posture or direction associated with the behaviour be derived, such as track tortuosity, which, while conventionally used for 2-dimensional movement, is likely to be useful in m-sphere representations.
Dot product metrics As with the NOP, the position of the m-print may shift about the sphere according to the animal's operational plane and general posture. It may therefore be appropriate to extract general postural information from the TriMag data  (Williams et al. , in preparation)  to quantify variation between different cases of a single behaviour. This is derived from the centroid of the m-print, defined as the average location of its points of the sphere (Fig. 8 ), and equivalent to the mean or median values of its coordinates in each of the three axes. Taking a vector of this point from the origin of the sphere, the average posture can be calculated as the dot product (θ) between this vector and the centroid vector of the NOP (that which is equivalent to a level posture). Thus; A⋅B ¼ A j j j j B j j j jCosθ ð1Þ Or θ ¼ acos x Ã x 2 þ y Ã y 2 þ z Ã z 2 ð Þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi x 2 þ y 2 þ z 2 ð Þ p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi x 2 2 þ y 2 2 þ z 2 2 ð Þ p # " ð2Þ where A and B are the vectors of the m-print and NOP centroids, respectively, and their corresponding coordinates are defined as (x, y, z) and (x 2 , y 2 , z 2 ).