dataesr/scientific-paragraphs-categorization huggingface dataset:
Data from the pedometers were extracted weekly. Video recordings were reviewed by a single observer. The observer reviewed the 6 h of video recording for each cow, counting the total number of steps observed each minute. The definition of a step was determined by reviewing second by second pedometer data and corresponding video recordings. In a similar study investigating step activity in tie-stall housed cows, a step was defined as when the rear foot lifted completely off the ground and was returned to the ground in any location with or without moving the entire body  [5]. In the initial review of the videos, it was found that it was not necessary for the foot to lift off the ground and, as such, an additional component to this definition of a step was added. In addition to when the foot completely leaves the ground, a step was also counted when the foot moves quickly (<1 s) and at a distance roughly equivalent to two hoof lengths or more in any direction without leaving the ground.
Statistical Analysis The analysis was run on hourly step averages for both the pedometer and observation data. Pedometer step data for the left leg of two cows were excluded due to technological error and, as such, only 18 cows were used in the analysis of left step data. Left and right leg pedometer data were analyzed separately using a multivariate mixed model with the number of steps as a dependent variable and technology and time as fixed effects. Data was run in SAS (version 9. 4, SAS Institute Inc. , Cary, NC, USA) using PROC MIXED to obtain covariance estimates from which correlation between observation and pedometer data could be computed. The same multivariate mixed model was used to obtain estimates to calculate the correlation between the left foot and the right foot with step as the dependent variable and leg and time as the fixed effects.
Bacterial Strains The list of isolates used in this study is presented in supplementary data set S1, Supplementary Material online and includes 23 B. ceti isolates from stranded striped dolphins from the Eastern Tropical Pacific of Costa Rica as well as several previously described isolates: Four from the Mediterranean Sea, nine from the North Atlantic Ocean, one from France, four Brucella pinnipedialis from the North Atlantic Ocean, and one Brucella sp. from California. These were analyzed alongside with reference strains from other Brucella species (Brucella abortus, Brucella canis, Brucella melitensis, Brucella microti, Brucella neotomae, Brucella ovis and Brucella suis).
DNA Molecular Studies DNA was extracted with DNeasy Blood & Tissue kit from QIAGEN or Promega Wizard Genomic DNA Purification kit, and stored at À70 C until used. Multiple loci variable number of tandem repeats (MLVA-16) analysis and the corresponding cladograms were generated according to described protocols (Le  Fleche et al. 2006; Al Dahouk et al. 2007; Maquart et al. 2009; Isidoro-Ayza et al. 2014 ) using the MLVA-NET database (http://microbesgenotyp ing. i2bc. paris-saclay. fr/ (last accessed July 24, 2017);  Grissa et al. 2008). Values obtained for each MLVA marker are in supplementary data set S2, Supplementary Material online. DNA polymorphism at the omp2 locus was performed as described  (Cloeckaert et al. 2001). Other genotyping techniques such as multiplex PCR Bruceladder, MLST, PCR detection of ST27 or bcsp31, HRM RT-PCR and PCR targeting specific IS711 elements, were performed either as previously described (references in data set S1, supplementary Material online) or in silico (supplementary data set S3, Supplementary Material online).
Whole genome sequencing (WGS) was performed at the Wellcome Trust Sanger Institute on Illumina platforms according to in house protocols  (Quail et al. 2009 (Quail et al. , 2012)). For WGS assembly and alignment sequencing reads were de novo assembled using Velvet Optimiser  (Zerbino and Birney 2008)  and contigs were ordered using abacas  (Assefa et al. 2009)  against B. abortus 9-941 under accession numbers NC_006932 and NC_006933 at the National Center for Biotechnology Information (NCBI). To detect missassemblies, raw data were mapped back against the genome assemblies using SMALT v. 0. 5. 8 (http://www. sanger. ac. uk/sci ence/tools/smalt-0; last accessed July 24, 2017). All sequencing data have been deposited at the European Nucleotide Archive (ENA) (http://www. ebi. ac. uk/ena/; last accessed July 24, 2017) under the accession codes listed in Supplementary data set S1, Supplementary Material online.
Phylogenetic Reconstruction To construct a multiple sequence alignment for phylogenetic reconstruction, whole-genome sequence data from two Ochrobactrum species and the Brucella isolates from different hosts (Supplementary data set S1, Supplementary Material online) were aligned by bwa and mapped with SMALT v. 0. 5. 8 against B. abortus 9-941, with an average coverage of 98. 81%. Single Nucleotide Polymorphisms (SNPs) were called using samtools  (Li et al. 2009) , and 311,780 variable sites were extracted using snp sites  (Page et al. 2016). The resulting alignment was used for maximum likelihood phylogenetic reconstruction with RAxML v7. 0. 4  (Stamatakis 2006 ). The phylogenetic tree was rooted using Ochrobactrum anthropi ATCC49188 and Ochrobactrum intermedium strain type LMG3301. Within this data set the B.
ovis lineage shared the most recent common ancestor with Ochrobactrum, therefore it was subsequently used to root phylogenies constructed using only Brucella. All analyses relevant to reference annotation (e. g. , dN/ dScalculation and SNP ascription to coding sequences-CDS) were relative to B. abortus 9-941 (accession numbers NC_006932 and NC_006933). The alignment and the tree files were used to generate a tab file containing coordinates of SNPs position relative to the root; all three files were used to produce a visual reconstruction of the SNPs distribution along the genome per branch (as seen in supplementary fig. S5 , Supplementary Material online).
Pseudogene Analysis To detect pseudogenes in B. ceti, we selected five phylogenetically representative draft genomes from marine mammal brucellae (B. ceti bmarCR17 -P1 cluster-, B. ceti bmarMR26 -MR cluster-, B. ceti M644/93/1 -A1 cluster-, B. ceti M187/00/1 -A2/B cluster-, and B. pinnipedialis M2466/93/4 -C2 cluster-) and automatically transferred the annotation of the manually curated draft genome working strain B. abortus 2308 Wisconsin (Su arez-  Esquivel et al. 2016). Pseudogenes were defined as any gene containing deletions or insertions that removed start or stop codons, or at least one in-frame stop codons and/or frame shifts compared with orthologs in B. abortus 2308 Wisconsin or reference genomes as described above. Pseudogenes were detected manually using Artemis and ACT. Pseudogenes from marine mammal brucellae with no homologs in terrestrial Brucella were compared against the NCBI nonredundant protein database using BlastX.
Specific Search for Regions of Interest In order to examine relevant phenotypic genes (virulence related, outer membrane, lipopolysaccharide [LPS] and flagellar genes), regions of interest were examined through bwa alignment and SMALT mapping. The number of SNPs, insertions and deletions in each one of the genes was recorded. The number and position of the insertion sequence IS711 were searched in the analyzed genomes by mapping the reads to the 842bp IS711 of B. ovis (accession number M94960). Those reads that showed 99% mapping, were then mapped against the reference WGS B. ovis ATCC 25480 in order to judge where IS711 might be inserted. The reads that mapped >90% to the WGS were filtered to 50Â coverage and used to produce a visual representation displaying the identified sites per genome and approximate location according to B.
Brucella ceti Clusters According to Geographical Region and Host Type To study host preference in nondomesticated Brucella hosts, we performed genotypic analysis of B. ceti isolated from dolphins from the Pacific Ocean and the Mediterranean Sea (table 1), and compared the results with those of isolates obtained from marine mammals (dolphins, porpoises, and seals) from the Atlantic Ocean. These findings were then related to host and geographical origin. MLVA-16 results were analyzed in the context of a worldwide Brucella databank and indicated that isolates from marine mammals showed dispersion and clustering according to the host from which they were isolated (fig. 1 ). Five B. ceti clusters were observed; two correspond to isolates mainly from different dolphin species (clusters A1 and A2) inhabiting the North Atlantic Sea.
A third one is represented mostly by isolates from porpoises (cluster B) from the same sea  (Maquart et al. 2009)  Brucella pinnipedialis isolated from the North Atlantic Sea was divided in three different MLVA-16 clusters that also related to host preference: Two were represented by isolates mainly from harbor seals (Phoca vitulina) (clusters C1 and C2) and one was represented by isolates from hooded seals (Cystophora cristata, cluster C3;  Maquart et al. 2009). In addition, a human Brucella sp. isolate from New Zealand (Brucella sp. 02611), with no zoonotic link, an isolate from an aborted dolphin (Brucella sp. F5/99), and isolates from a stranded bottlenose dolphin from the Adriatic Sea (Cvetni c et al. 2016) define a distinct cluster  (Maquart et al. 2009)  herein named cluster H. To determine if the dispersion and clustering observed by MLVA-16 could be reproduced by using higher resolution methods and establish possible explanations for this, we performed WGS of Brucella isolates from marine mammals from the North Atlantic, Eastern Tropical Pacific and Mediterranean Sea and analyzed them together with publically-available high quality Brucella genomes (table 1, supplementary data set S1, Supplementary Material online).
The number of studied genomes (n ¼ 50) was adequate to describe basic genomic characteristics of the genus, because the pan and core genome reached a plateau value within the data set (supplementary fig. S2 , Supplementary Material online). The overall genetic structure of Brucella from marine mammals is in tune with the classical pathogenic Brucella from land mammals. Some conserved traits are: Presence of two chromosomes, absence of plasmids, no major recent recombination events, similar GIs/anomalous regions, and conservation of genes encoding virulence factors (fig. 2 , supplementary figs. S3, S4, and supplementary data set S3, Supplementary Material online). Phylogenetic analysis using O. anthropi ATCC49188 and O. intermedium LMG3301 as an outgroup showed that B. ovis shared the most recent common ancestor within this data set with Ochrobactrum, so it was subsequently used to root phylogenies constructed using only Brucella isolates.
All together, these results expand the panorama observed in previous genotypic studies  (Audic et al. 2011; Garofolo et al. 2014; Wattam et al. 2014; Maquart et al. 2009)  and indicate a correlation between the evolutionary traits of Brucella isolated from marine mammals, its geographical origin and preferred host. In order to benchmark other molecular techniques described for identification or typing of Brucella, we compared results generated using ten different techniques to the WGS classifications of the marine mammals Brucella isolates. Of these, multiplex PCR Bruce-ladder, adopted by the OIE for identification of Brucella species (OIE 2009) was able to classify but not discriminate all marine isolates (Supplementary data set S2, Supplementary Material online). Phylogenetic analysis based on DNA polymorphism at the omp2 locus essentially replicated the genomic and phenotypic analysis results (supplementary fig.
Multiple Sources and Consequences of B. ceti Genome Variation To establish if there were genetic traits that could be related to Brucella host preference and virulence using isolates from wild animals, a detailed analysis of the genome structure of B. ceti clusters as compared with other Brucella genomes was performed. Analysis of amount of SNPs found in genes encoding virulence traits such as the type IV secretion system virB, some of its effectors (see below), LPS, membrane lipids, BvrR/BvrS two component system regulatory network and flagella did not show significant variations among the isolates (Supplementary data set S2, Supplementary Material online). Genome alteration through the active transposon insertion sequence IS711, used as a Brucella genus fingerprint (Ocampo-Sosa and Garc ıa-Lobo 2008), was examined.
B. m i c r o t i B. n e o t o m a e annotation in the four B. ceti representative genomes, one representative B. pinnipedialis, B. abortus, B. ovis, and B. suis genomes and compared pseudogene traits according to genome (fig. 4A ). In all genomes, the proportion of pseudogenes was higher in chromosome II than in the larger chromosome I (supplementary data set S4, Supplementary Material online). A total of 706 pseudogenes were found among these genomes and only two were shared among them. The mutation site within each gene was often conserved, suggesting that they occurred once in a common ancestor. The main cause of pseudogenization, was frame shift (410/706, 58%), followed by deletions (90/706, 13%) (fig. 4A  and supplementary data set S4, Supplementary Material online). Their distribution according to former gene product, subcellular location and function is in Supplementary data set S4, Supplementary Material online.
4B ). Frame shift (22/35, 63%) was the main cause of pseudogenization followed by insertions (6/35, 17%) (fig. 4C ). Functional analysis of the cognate wild type genes indicated that several pseudogenes were related to relevant metabolic pathways (Supplementary data set S4, Supplementary Material online). Notably, multiple pseudogenizations had occurred in pathways that alter fatty acid metabolism. Specifically, an acetyl-CoA acyltransferase and an acetyl-CoA C acetyltransferase very likely lost function in the marine mammal isolates. Lack of these enzymes is expected to influence fatty acids synthesis and beta-oxidation. In line with this finding, AceB, a malate synthase, catalyzing the conversion of glyoxylate to malate during the TCA, glyoxylate cycle (Z uñiga-Ripa et al. 2014) has probably lost its function.
Acknowledgments The authors are grateful to  Daphnne Garita ,  Eunice V ıquez ,  Andres Balbin ,  Martha Piche ,  Carla Murillo , and  Reinaldo Pereira  for their technical assistance. Bruno Lomonte  for his help with MALDI-TOFF analysis. Jose David Palacios-Alfaro  and  Alejandro Alfaro  for their assistance with dolphins. Mariano Domingo ,  Geoffrey Foster ,  Jacques Godfroid , and  JoseMar ıa Blasco  for providing marine isolates. Gordon Dougan  for helpful discussions. This work was supported by  FEES-CONARE ,  Costa Rica ,  Ministry of Science and Technology of Costa Rica Forinves  [ FV-004-13 ] and  Wellcome Trust. N. R. V. , A. C. Z. , and C. J. R. were partially sponsored by  UCR  scholarships. Authors from the Sanger Institute are supported by  Wellcome Trust  [098051]. K. B. was founded by a  Wellcome Trust   Postdoctoral Training Fellowship for Clinicians  [ 106690/Z/14/Z ].
One of the three blinded evaluators assessed the dogs in each group before and after surgery (1, 3, and 6 months). Clinical score and gait and bone healing process were assessed. The data were statistically compared between the two groups for pre-and postoperative evaluations. inTrODUCTiOn Cranial cruciate ligament rupture (CrCLR) in dogs is one of the most common orthopedic diseases in veterinary medicine  (1) , leading to stifle joint instability and inflammation and inevitably resulting in the development of stifle osteoarthritis (OA)  (2) (3) (4). Treatment involves surgical joint stabilization and the medical management of pain and inflammation. To restore joint stability, tibial plateau leveling osteotomy (TPLO) is one of the most common surgical procedures performed worldwide  (4) (5) (6) , delivering quick and good clinical outcomes for a majority of the treated dogs  (7) (8) (9) (10) (11).
Semiquantitative Gait Analysis A blinded operator performed the gait evaluation using a pressure walkway (GaitRite ® , Biometrics, France)  (41, 42). Each dog was acclimated to the room and the walkway for 5 min. The run was considered valid if the dog walked on the pressure walkway at a constant walk speed, at a walking pace, with a relaxed and regular gait and without pulling on the leash. Three valid runs of six gaits were required for each dog. The data collected to evaluate the gait included the ratio between the front and hind limbs for total pressure (FH/TP), ratio between the front and hind limbs for number of sensors activated (FH/NS), Symmetry index between non-affected and operated limbs for stance percentage during gait cycle (SI/%), symmetry index between non-affected and operated limbs for total pressure (SI/TP) and symmetry index between non-affected and operated limbs for number of sensors (SI/NS).
Statistical analysis Statistical analysis was performed using GraphPad Prism 6. 0 (GraphPad Software, USA). Baseline comparisons between MSCs and NSAIDs groups on clinical score, weight, age, meniscal tear, and radiographic score were examined using the Mann-Whitney U test. Categorical data (gender) were compared using Fisher's exact test. Changes in the score over time for each parameter in each group were examined using the non-parametric Friedman repeated measures ANOVA on rank sum test. Post hoc comparisons were made using Dunn's test. A comparison of the responses (clinical score, bone healing score, and gait evaluation) between the MSCs and NSAIDs groups was conducted using two-way repeated measures ANOVA, with treatment and time as grouping variables. A post hoc comparison was obtained using Sidak's test.
Impact analysis An impact analysis was used to examine and visualise how the system variables impact on each other. To undertake the impact analysis the farmer, an advisor and the local veterinarian met with a researcher on each farm, the latter taking up the role of the facilitator. Prior to the visits, all researchers were trained in the moderation of group discussions and had tested the procedure on two pilot farms. In some cases a project veterinarian stepped in if the farm veterinarian could not attend the meeting, ensuring a veterinarian's perspective was always available. Each assessment was preceded by a short farm walk and a presentation of data on general farm characteristics and herd health status by the researcher. During the assessment an impact matrix was incrementally completed by quantifying the relationships between pairs of variables, i.
Data analysis The impact matrix data were further analysed using the statistical software package R. For betweenfarm comparison, relative values were determined by dividing Active Sum (AS) and Passive Sum (PS) by the maximum value of both AS and PS per farm to rescale values between 0 and 1. Inspired by the works of  Linss and Fried (2010) , two indices were obtained for each variable: AI = 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐴𝑆 -𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑃𝑆 2 CI = 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝐴𝑆 + 𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑃𝑆 -1 2 Where AI = Activity Index CI = Criticality Index AS = Active Sum PS = Passive Sum Variables with a high score AI are active, i. e. they exercise a lot of influence on other variables without being much affected by them. Conversely, variables with a low AI score are reactive, i. e. they are strongly influenced by other variables while not being very influential.
Acknowledgements The authors would like to thank all farmers, advisors and veterinarians involved in the definition of system variables for their engagement and those participating in the farm visits for permitting us access to their farms and sharing their expertise and ideas. Our thanks go to  Uwe Wilske  ( DSP agrosoft, Germany ) for programming the software tool used for the impact analysis and to three anonymous reviewers whose comments substantially improved the article. This project has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement n°  311824 , project  IMPRO  (Impact matrix analysis and cost-benefit calculations to improve management practices regarding health status in organic dairy farming).
Interestingly, HERV-K Env, but not the control, also modified the expression of EMTassociated genes, with a significant increase in the levels of fibronectin and N-cadherin, and a decrease in E-cadherin. However, the most induced transcription factor was Zeb2, unlike in TGFß treated cells. We also tested the different populations for changes in their motility properties using transwell assays. As shown in Fig  1D , the HERV-K Env expressing populations displayed an increase in cell migration and invasion, similar to what is observed with TGFßtreated cells  (Fig 1E). Altogether the data obtained with the MCF10A cells indicate that HERV-K Env possesses oncogenic properties, and modifies the cell physiology to induce a process related to, but not identical to TGFß-induced EMT.
HERV-K Env and other cell signalling pathways We also assayed HERV-K Env for potential effects on other cell signalling pathways. We first tested for activation of NFκB, which is often implicated in transformation, using the degradation of IκBα as a sign of activation of the pathway. None of the Envs that we tested had any effect on IκBα levels, unlike TNFα stimulation (Fig  7 ). HERV-K Env therefore does not modify cell physiology through the NFκB pathway. We then tested for p38 activation (Fig  7 ). As previously reported, JSRV Env induces the phosphorylation of p38  [36]. The role of p38 activation in JSRV pathogenesis is not clear, but it has been shown to have an inhibitory effect on ERK1/2 signalling. We found that HERV-K Env also activates p38, but our microarray data indicates that ERK1/2 is the major signalling pathway activated by HERV-K Env.
Materials and methods Plasmids HIV-1 derived particles were produced as described  [43] , using a modified CSGW  [58]  expressing HERV-K Env or control proteins and the hygromycin resistance gene. pBabe-H-RasG12V  [59]  was kindly gifted by A. Puisieux. All Env transient expression vectors are CMV-driven. Plasmids for Ampho, IAPE, RD114, GaLV, FeLV and HERV-K Envs have been described previously  [60] [61] [62] [63] [64]. phCMV-JSRV Env was constructed by replacing the G protein ORF in phCMV-VSV-G (GenBank accession no. AJ318514) with the JSRV Env ORF (plus the 3' LTR) present in pCMV3JS21ΔGP  [3]  (a gift from M. Palmarini). The Env gene from the enJSRV-18 provirus was similarly cloned by PCR on sheep genomic DNA using a forward primer on the ATG and the reverse primer indicated in  [65]. phCMV-MMTV Env contains the MMTV Env ORF and the 3' LTR from the pEnv vector  [66]  (a gift from S.
RNA isolation, qRT-PCR, gene expression profiling Total RNA were extracted with the RNeasy extraction kit (Qiagen) and treated with DNase I (Ambion). For microarray experiments, we compared duplicates of RNA from 293T transfected with either HERV-K(HML2) Env or the control plasmid, HERV-K(HML2) Env-LP, collected 24 and 48 hours post-transfection. Gene expression analysis was performed on Agilent SurePrint G3 Human GE 8x60K Microarrays (Agilent Technologies, AMADID 39494). Data were extracted using Feature Extraction software (v10. 5. 1. 1; Agilent Technologies) and normalized using an empirical Bayes method. Top-ranked genes were selected for an absolute fold-change !2 using a False Detection Rate (FDR) <0. 05. DNase-treated RNAs were reverse-transcribed using the MLV reverse-transcriptase (Applied Biosystems).