dataesr/scientific-paragraphs-categorization huggingface dataset:
The complete acquisition system was placed in saddle bags. An inclined asphalt pavement (slope: 7%, i. e. 4°) of 40 m long was compared to a flat path of about the same length. After warming-up, the horses led by hand alternatively performed uphill and flat trot trials. For each trial, speed and force data were recorded on 10 successive strides. A single trial was selected for each condition, based on the horse's average speed, in order that the.
Customised programmes developed in Matlab (MathWorks) were used to determine, for each limb, the average normal force over the stride (F str ), as well as the normal force impulse, total (I t ) and distinctly for the braking (I b ) and propulsion (I p ) phases of stance, integrating the normal force plot over the time period corresponding respectively to the positive and negative values of the GRF's parallel component (Figure  1 ). Then the following ratios were calculated for each limb, illustrating: (1) the normal force distribution between fore and hindlimbs: F str of the limb (fore or hind) over the total F str (sum of the fore and hind limbs's F str ); (2) the distribution of the normal impulse between th braking and propulsion phases: I b /I t and I p /I t. Linear mixed-effects regression models were used in order to compare uphill and flat conditions, taking into account repeated measurements per horse within each trial and adjusted for speed (SAS version 9.
Biometric assessment Early-life data were provided by the breeding centre, including age and BW of parents at mating, parity, weight gain during gestation, litter size and BW of each puppy at birth. Prior to weaning, puppies were weighed every 2 weeks. Post-weaning, BW was recorded weekly on the same electronic weigh scale (Mettler-Toledo SAS, Viroflay, France; accurate to within 50 g). The withers height was measured at 24 months of age. The morphometric estimation of body fat described by Burkholder and Toll  [21]  has been validated only on adult dogs, so a the pelvic circumference (PC) and patella-to-calcaneus (PCL) was measured every 2 months from 7 months of age, when dogs had a morphology closer to adult one. The body condition score (BCS) was evaluated monthly from 7 months of age by the same investigator, using a 9-point scale (1 for emaciated, 9 for morbidly obese) as recommended by the WSAVA  [22].
Data analysis All statistical analyses were performed using R software (R Foundation for Statistical Computing, Vienna, Austria)  [35]. Graphs were prepared using GraphPad Prism software (GraphPad Software Inc. , San Diego, CA, USA). In order to distinguish dogs in three distinct groups, a principal component analysis (PCA) was performed on quantitative and active variables: FM%, FFM and PC, on dogs aged of 24 months. This was followed by a hierarchical clustering classification (HCPC) which was realized using Ward algorithm with euclidean distance on the two first principal components. The three identified groups were named IW, OW1 and OW2. The BCS is a qualitative variable, so it was included as supplementary qualitative variable in the PCA. In order to assess the impact of parental and gestational factors on FM%, FFM and PC, and parental characteristics were included as supplementary variables in the PCA.
The growth period could be divided into two periods by the point of inflection: the first period corresponding to an increasing growth rate and the second period to a decreasing growth rate. The maximum of weight velocity, calculated as BW max Âk e occurs at the point of inflection (PI) of coordinates t PI ¼ ln α ð Þ k ; BW PI ¼ BW max e. In order to assess the growth parameters (BW max , α and k) of each group, the growth data collected from 0 to 18 months of age were fitted by the Gompertz function using nonlinear mixed effect model tools (saemix package in R software) with the dog as random effect. To compare the evolution of BW of the groups during specific life-stage periods, we split the study duration into five periods. Linear mixed effects models were performed on age, BW, and groups over the following periods: before weaning (0 to 2.
5 months of age), pseudolinear growth (2. 5 to 6 months of age), before spaying (6 to 8 months of age) and after spaying (8 to 10. 5 months of age). Given that the normal weight in 2-week old neonatal puppies is twice that at birth  [37] , GR 2W , % was calculated. We also calculated the relative weight gain after sterilisation (%) to assess the susceptibility to gain weight after the surgery. Post-prandial kinetic data (0-150 min) was transformed into area under the curve (AUC) values based on the difference from baseline. In order to interpret energy intake and expenditure, resting energy balance was calculated by subtracting REE FFM from EI FFM. To take into account the repeated measurements for individual dogs in time, linear mixed effects models (nlme package in R software) were performed with dogs as random term.
Constitution of three groups based on biometric data at 24 months of age In order to categorize dogs according to their status of fatness, a PCA was performed on FM%, FFM (kg) and PC (cm) at 24 month of age. The two first axes explained 95. 3% of the total variation (Fig. 1a ). A hierarchical clustering of principal components revealed three well separated groups (Fig. 1b ), which could be described as dogs with (optimal) ideal weight (IW, n = 9), slightly overweight (OW1, n = 6) and overweight (OW2, n = 9). The distribution of BCS in the groups at 24 months (Fig. 2 ) was in line with the groups' characteristics (Table  1 ). The OW2 group had significantly higher values of BW, FM%, PC (all p < 0. 001) and height to withers (p = 0. 037) than the IW group, although the FFM of the two groups was similar (p = 0.
Study design A prospective, multicenter, non-interventional study was conducted in six European countries (Italy, Spain, France, the Netherlands, Switzerland and the United Kingdom) at a total of 52 sites. Eligible patients were aged 18 years or older and had been prescribed MAL DL-PDT as part of their routine medical care to treat mild-to-moderate AK on the face and/or scalp. The study was conducted in accordance with the Declaration of Helsinki, the International Conference on Harmonization-Good Clinical Practice principles and in compliance with local regulatory requirements. The study was reviewed and approved by the appropriate Independent Ethics Committees, and written informed consent was obtained from all patients prior to study initiation. The study is registered at ClinicalTrials.
Acknowledgments The authors thank all the other investigators who contributed to the study:  JM Amici ,  P Beaulieu ,  H Garat ,  F Neuwirth ,  JL Riboulet ,  E Tisserand ,  G Toubel ,  P Young  and  P Zambelli  from France;  G Argenziano ,  S Calvieri ,  SR Mercuri ,  S Piaserico ,  M Puviani  and  M Venturini  from  Italy ;  A Avermaete ,  L Gonggryp ,  W Habets ,  A Langeveld ,  T Smits ,  M Tjioe  and L van der Spek from the  Netherlands ;  JL Artola Igarza ,  R Botella ,  J Dominguez ,  V Garcia Patos ,  J Hern  andez,  R Ru ız Villaverde  and  A Segurado  from Spain;  O Gaide , A H€ artel,  L Imhof ,  P Itin ,  M Muhlstadt ,  S Nobbe  and  NS Tomi from Switzerland :  U Brudnik ,  M Kaur ,  S Keohane ,  J Lear ,  C Morton ,  M Murison  and  C Perrett  from the  United Kingdom. Editorial and medical writing assistance was provided by  Helen Simpson, PhD , of  Galderma R&D.
A total of 7,668 Coding DNA Sequences (CDSs) was predicted. The analysis of orthologous CDS between Trypanosoma equiperdum OVI and Trypanosoma brucei TREU927 shows that these parasites share a total of 6,805 ortholog clusters, confirming their close relatedness (Figure  1 ). The T. equiperdum OVI draft genome sequence generated in this study constitutes the first genome of a strain classified as T. equiperdum; this represents a new source of knowledge that will be valuable in comparative genomic studies to shed light on the complex biological interplay between the members of the subgenous Trypanozoon, their hosts and their diseases. This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession CZPT00000000. The version described in this paper is the second version, CZPT02000000.
Acknowledgements This research was supported by  ANSES  and by  European Commission  funding for the Reference Laboratory for Equine Diseases other than  African Horse Sickness. Sascha Steinbiss  is funded by the  Wellcome Trust  through their support of the  Wellcome Trust Sanger Institute  (grant  WT099198MA ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The authors would like to thank  Emilie Antier  and  Coralie Pulido  ( ANSES, Lyon Laboratory, Plateforme d'Expérimentation Animale ) for assistance in trypanosomes purification.
Reads were cleaned from adapter sequences with AlienTrimmer  (Criscuolo and Brisse, 2013 ) (version 0. 4. 0) and duplicates and low quality reads using PRINSEQ  (Schmieder and Edwards, 2011) (version 0. 20. 3). The reads were aligned to the reference genome of L. interrogans serovar Manilae strain L495 (total genome size of 4,614,703 bases, GC% of 34. 99, number of contigs is 88, and 4261 annotated coding sequences) downloaded from MaGe platform  (Vallenet et al. , 2013). The alignment was performed by Rockhopper software  (McClure et al. , 2013) , allowing 5% of read length mismatches, and using 35% of read length as minimal seed. The produced alignments were filtered to remove data with 0 scores, sorted and indexed with SAMTools  (Li et al. , 2009). Coverage graphs representing the numbers of mapped reads per nucleotide were generated based on the sorted reads using BEDTools  (Li et al.
interrogans serovar Manilae strain L495 are available on the MaGe platform  (Vallenet et al. , 2013). We compared the distribution of COG classes in leaderless mRNA (whose pTSS are located between 0 and 10 nts) in comparison to genome-wide expected probabilities. To calculate the significance of leaderlessness for each COG category the Fisher exact test was used [SciPy library  (Oliphant, 2007)  for Python] with the following data: in the contingency table, the genes with a detected pTSS were divided into leaderless and others on the one hand, and those that belong to the selected COG category and belong to another category on the other hand. The null hypothesis was that leaderless and non-leaderless genes are equally likely to belong to the selected COG category. A P ≤ 0. 05 indicated strong evidence against the null hypothesis.
We extracted the 80 nucleotides upstream of the identified pTSS in L. interrogans, and performed motif discovery in these sequences using the MEME algorithm implemented in the MEME suite version 4. 10. 1  (Bailey et al. , 2009). We looked for motifs of minimal length five that occur zero or one time per sequence, and are found in at leasts two sequences. We then compared motifs against the Swiss Regulon Escherichia coli motifs database  (Pachkov et al. , 2013) , and against CollecTF, a database of transcription factor binding sites (TFBS) in the Bacteria domain  (Kiliç et al. , 2014). The comparison was performed with Tomtom, a motif comparison tool  (Gupta et al. , 2007)  from the MEME suite. We have also scanned for the bacterial TFBS motifs and for the E. coli regulatory motifs in the 80 nucleotides upstream of the identified L.
interrogans pTSS, using FIMO tool  (Gupta et al. , 2007; Grant et al. , 2011)  from the MEME suite. For the prediction of promoter sequences for the housekeeping sigma factors sigma70, and alternative sigma factors sigma28 and sigma24, the following matrix and spacer between -35 and -10 (in parenthesis) were used: Matrix. 18_ 15_13_2_1. 5 (13-19) for sigma70,  for sigma 24 (matrix resource: http://www. ccg. unam. mx/ Computational_Genomics/PromoterTools/). The 80 nucleotide sequences upstream TSSs were then subjected to prediction of the presence of each sigma factor-type promoter sequence using PromoterHunter software (http://www. phisite. org/main/ index. php?nav=tools&nav_sel=hunter). For the prediction of sigma54 promoter sequences, the PATSER program  (Hertz and Stormo, 1999)  was used to search against 80 nucleotide sequences upstream of the identified pTSS.
Operon Prediction Operon detection was performed using software Rockhopper  (McClure et al. , 2013)  on the total RNAseq data at 30 and 37 • C. Rockhopper detects operons using a naive Bayes classifier based on prior operon probabilities, intergenic distance, and correlation of gene expression across RNA-seq experiments. Potential pTSS was identified for each operon as the pTSS detected on dRNA-seq data (see above) for the first gene of the operon. For operons with no pTSS detected on dRNA-seq data, the value identified by Rockhopper on the total RNAseq data (in the majority of cases equal to the start of the first operon gene) was used.
Putative sRNA Prediction Putative sRNA detection was performed using software Rockhopper  (McClure et al. , 2013)  on the total RNAseq data at 30 and 37 • C. Among the transcripts identified by Rockhopper as predicted RNA, those of the length ≥50 nucleotides were kept. For each sRNA, potential pTSS were identified following the procedure described above, and potential small coding sequences were detected using any of the start codons ATG, TTG, GTG, and the stop codons TAA, TAG, TGA. For each putative sRNA, a search for matching families in Rfam database  (Nawrocki et al. , 2015)  was performed via RESTful interface using urllib2 library for Python. The secondary structure was predicted for each putative sRNA sequence using UNAFold  (Markham and Zuker, 2008; version 3. 8 ). Rho-independent terminator (RIT) sites were detected at positions -25 to 200 nucleotides of stop codon of each putative sRNA using Arnold software  (Naville et al.
Availability of Supporting Data The raw data files for the RNA-seq experiment are deposited in the Gene Expression Omnibus (GEO) database from NCBI  (Edgar et al. , 2002) , Gene accession GSE92976. Additionally, the genome files of L. interrogans serovar Manilae strain L495 used for analysis of RNA-seq data are available in MicroScope (http:// www. genoscope. cns. fr/agc/microscope/home/index. php).
These two conserved motifs, [TA]A[TA]TAGA[AG]TTGT TGAAAAATTAATTCTCCAT[CT][TG][GA]TTTC[TC]ATTT [TC]A and TGT[AG]G[GT]A[AG][TC]T[CA]C[CT]ACA[AT] [AT][TA][TAC], (i) do not have a specific nucleotide position relative to the TSS, (ii) do not resemble motifs and TFBS from the E. coli database, (iii) are part, at least most of them, of an intergenic repeated element, and (iv) are not found in the promoter region of the expressed gene as identified by RNA-seq (our study) and by mass spectrometry  (Malmström et al. , 2009). Taken together, these results suggest that these motifs may not represent DNA-binding sites (Supplementary Table  6 ).
In addition, 29 and 19 asRNA candidates at 30 and 37 • C, respectively, that are opposite to a 5 ′ UTR or 3 ′ UTR were detected (Supplementary Table  9 ). asRNAs overlap either with the 5 ′ end (14-17%), the 3 ′ end (9-11%), or the central region (72-77%) of the gene found on the opposite strand. The vast majority (>60%) of asRNAs overlap with genes coding hypothetical proteins; other targeted genes with a putative known function include the genes encoding lipoproteins LipL32 and LipL21 (Figure  4B ), a TonB dependant receptor, a permease, and an anti-anti sigma factor (Supplementary Table  9 ). Compared to the sRNA sequences in the Rfam database, few L. interrogans sRNAs displayed homology with well characterized sRNAs in other bacteria. Among those are a cobalamine riboswitch, tRNAs, tmRNA, also known as SsrA, RNase P RNA, and 5S rRNA.
ACKNOWLEDGMENTS This work was funded by the  Institut Pasteur  and  FUI 14  ( Fonds Unique Interministériel) COVALEPT and BPIFrance. PH was funded by  Pasteur International Bioresources Network (PIBnet). CP was supported by  National Science Foundation  grant  IIA-1159099. LF (from  Dr. Ana Lucia Tabet Oller do Nascimento 's lab at the  Instituto Butantan, Sao Paulo, Brasil)  is a doctoral recipient fellow from  FAPESP. The Transcriptome and  EpiGenome Platform  is a member of the  France Génomique consortium  ( ANR10-NBS-09-08 ).
, and of links, which are shipments of animals between these units. These networks are directed: animal movements along the network links are considered directed paths for the spread of a disease from one farm to another. Cattle, sheep, pig and poultry markets have already been modelled in several countries  [2, [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] , using either movements reported by farmers through questionnaires, or movements systematically recorded in a harmonised database. Unlike cattle movements, a special feature of swine trade data is that pig movements are reported at a batch scale, without the possibility of tracking animals individually. Moreover, the pig production sector is organised in a pyramidal way, with movements going from the nucleus and multiplying farms at the top, to the production farms at the bottom (from farrowers to finishers).
Data 2. 1. 1. Database description. Since 2010, pig movements in France have been recorded and stored in the National Swine Identification Database (BDporc). This database is managed by swine industry professionals and is recognised by the French Ministry for Agriculture. For the present study, we analysed the data from June 2012 to December 2014. Two levels of information were gathered in the dataset: the characteristics of swine production units and the details of the animal movements between the different production sites. The main features of all swine holdings in mainland France are included in the database: identification number, type of holding (farm, slaughterhouse, rendering company, market, assembly centre, trading company), type of farming activity (boar station BS, nucleus SEL, multiplication MU, farrowing FA, farrowing-to-finishing FF, finishing FI, farrowing-post-weaning FPW, post-weaning PW, post-weaning-finishing PWF, small producers SP), type of production (free-range or not), and location (post code and GPS coordinates).
Movements of pigs were reported at a batch level: groups of animals were sent off the production sites (loadings, further denoted L) and dispatched to either alternative production units or slaughterhouses (unloadings, further denoted U). A single truck could load and unload animals at several production sites: one round corresponds to a series of movements of a truck, from the first loading operation to the last unloading event making the truck empty. Each loading and unloading operation was individually reported for each round with several pieces of information: the farm and the round IDs, the chronological sequence of the operations during the round, the batch size and the animal category (breeding animals, piglets, and growing pigs). 2. 1. 2 Data cleaning and pre-processing. Data included both movements occurring within France and movements from/to foreign countries.
However, imports and exports of animals were recorded at the country level, with a lower data resolution than movements occurring within France. Therefore, movements from/to foreign countries were considered separately to have a global overview of international trade movements, when a thorough analysis of within-France data was performed. A series of cleaning processes were performed on the dataset, discarding records for which the principal pieces of information were unavailable (e. g. round or herd identification numbers, animal category). Farms were categorised into 11 groups according to their major activity; markets, assembly centres and trading companies were gathered into the single "trade operators" category. Direct movements to slaughterhouses and rendering plants were excluded from the analysis as they do not play a major role in pathogen spread.
This type of network can be used to explore the spread of a pathogen both through the introduction of animals to farms and through the indirect route. https://doi. org/10. 1371/journal. pone. 0185858. g001 graph). The distributions of the four main centrality measurements were computed for each holding type: degree, in-degree (number of different holdings from which a holding receives animals), out-degree (number of links going from a node), closeness (number of steps required to access every other node from a given node) and betweenness centralities (number of geodesics going through a node). For each network model, a power-law distribution defined as p(x)*x α was fitted to the observed degree distribution. We used a maximum-likelihood estimator to estimate scaling parameter (α) and the Kolmogorov-Smirnov (KS) goodness-of-fit statistic to test power law fit of the data as described by Clauset et al.
Swine trade description 3. 1. 1. Within-France movements. A total of 21,446 sites were recorded in the BDporc database, among them 97. 9% were farms, 1. 5% slaughterhouses and rendering plants, and 0. 6% trade operators (Table  1 ). The number of farms decreased by 2. 9% between June 2012 and December 2014. The database contained 2,382,510 movement records, from which 9% were discarded after the cleaning process  (16, 44 , and 40% due to missing or incomplete round, foreign movements or missing herd identification numbers, and animal mortality or missing animal category, respectively). A total of 838,777 rounds occurred between June 2012 and December 2014. They were composed of several loading and unloading operations: rounds between farms implied on average 2. 5 holdings (range: 2-32), whereas rounds going to slaughterhouses were on average composed of a single movement.
6% in the total number of unloaded animals. The decline in exchanges mainly affected breeding pigs and trade operators. These data are detailed in S1 Table. The distribution of distances travelled by pigs in a round varied with the animal category. Excluding movements to slaughterhouses, rendering plants and trade operators from distance calculations, breeding pigs travelled on average 270 km (median: 200, range: 0-1,000), whereas growing pigs travelled on average 74 km (median: 42, range: 0-999). 3. 1. 2. Movements from/to foreign countries. A total of 12,065 rounds came from or went abroad over the study period, corresponding to 1. 4% of the total number of rounds recorded in the whole database. Animals sent abroad were mostly growing pigs (59. 4% of animals unloaded abroad), culled sows and boars (28.
This temporal scale was considered appropriate to reflect the global trade behaviour of farms while making it possible to observe evolutions over the study period. Our two models were applied to all movement data recorded in France from June 2012 to December 2014 in the National Swine Identification Database (BDporc). The information provided by this database is managed by swine industry professionals, is recognised by the French Ministry for Agriculture, and can therefore be considered trustworthy. Moreover, a thorough cleaning stage was carried out to manage incorrect or incomplete data. This kind of electronic data is also more accurate than movements reported in questionnaires  [34]. An even more accurate alternative would be to use GPS (Global Positioning System) to geographically locate trucks and precisely track their movements, but this would require the approval of transportation operators to share this kind of data, as well as advanced analytical methods to manage such data.
Movement data are third-party data and are part of the National Swine Identification Database owned by BDporc. The authors did not collect the data and the authors do not claim ownership of the data. The owner of the data can be contacted at: BDPorc, 43, rue Sedaine, CS 91115, 75538 PARIS cedex 11 -FRANCE. The authors did not have any special access privileges that others would not have.