dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to lack of normality of distribution, the effect of sex and social status (investigated in both PH and DH groups) and species on yawning frequency (YF) and affiliative, defensive and aggressive interactions was evaluated by a non-parametric test (Wilcoxon two-sample test, two-sided, PROC NPAR1WAY). The P level was set on 0. 05; however, in the case of P <0. 1 (tendency level), the results were also presented if they seemed to be interesting. The relationship between yawning and social interactions was studied with Spearman correlations (PROC CORR). The SAS 9. 3 statistical package (SAS Institute, Inc. , Cary, NC) was used for the analyses. The data in text are presented as medians and quartiles.
Acknowledgments This work was supported by a grant from the  Caisse Centrale de la Mutualité Sociale Agricole  (to  Martine Hausberger ). Aleksandra Górecka-Bruzda  thanks the  University of Rennes 1  for the funding of her scientific stay at  UMR CNRS 6552 Ethologie Animale et Humaine. The authors thank  Professor Zbigniew Jaworski  and the staff of  Research Station of Polish Academy of Science in Popielno  for their technical assistance related to Konik horses, the  Association Takh  and the  Tour-du-Valat biological station in France  for giving access to the population of Przewalski horses and related information and  Maïc Moulinot  and  Alice de Boyer des Roches  for taking part to the observations of the Przewalski horses. We also thank three anonymous referees for their helpful comments on the manuscript.
Acknowledgements The study was supported by funds from the  Centre National de la Recherche Scientifique (CNRS). N. Hanna  was supported by a fellowship from the  Lebanese CNRS. We thank  V. Jubier-Maurin  for critical reading and helpful comments on the manuscript. We acknowledge  P. Bouhours  for the preparation of bacterial media,  E. Hauser  and  M. Korrichi  for helpful technical assistance, and  P. Clair  ( qPCR Platform ,  UMS Biocampus, University of Montpellier 2 ) for assistance in RNA analysis and RT-qPCR. We are grateful to the  United States Department of Health and Human Services, National Institutes of Health (NIH) ,  Western Regional Center of Excellence   (WRCE) Pathogen Expression Core  ( Drs. M. McGee ,  R. Friedberg  and  S. A. Johnston ,  Arizona State University, Tempe, AZ ) for developing and printing the B.
An understanding of the patterns of animal contact networks provides essential information for the design of risk-based animal disease surveillance and control strategies. This study characterises pig movements throughout England and Wales between 2009 and 2013 with a view to characterising spatial and temporal patterns, network topology and trade communities. Data were extracted from the Animal and Plant Health Agency (APHA)'s RADAR (Rapid Analysis and Detection of Animal-related Risks) database, and analysed using descriptive and network approaches. A total of 61,937,855 pigs were moved through 872,493 movements of batches in England and Wales during the 5-year study period. Results show that the network exhibited scale-free and small-world topologies, indicating the potential for diseases to quickly spread within the pig industry.
Understanding of animal movement networks in terms of these network features can support compartmentalisation approaches for disease control, which are encouraged by the World Organisation for Animal Health (OIE) and the World Trade Organisation (WTO)  25, 26  and may support the possibility of continued international trade even in the event of a disease outbreak in a particular country or region. A previous study investigated pig movements in the UK over a 12-month period, using pig surveillance data from three Quality Assurance (QA) schemes  27. However, compared with the RADAR database, QA records lack information on pig movements involving small and semi-professional holdings, gathering areas and markets; and these types of premises may indeed have a particularly strong influence on disease transmission  13, 18.
Other authors have also highlighted that farm connectivity may have been underestimated as a result of non-reported movements (due to farmer self-reporting and recall bias, non-inclusion of non-QA registered holdings)  27. Consequently, there are significant knowledge gaps in relation to the overall structure of pig movement networks in Great Britain, which compromises the ability to design effective risk-based disease surveillance and control strategies. The aim of this study was to explore the pig movement networks throughout England and Wales with a view to characterising spatial and temporal patterns, monthly network topology and trade communities. This analysis was based on data covering 5 years (2009-2013) which had been extracted from the RADAR database. The results from this study will make an important contribution to the knowledge base informing the design of risk-based disease surveillance and control programmes for the British pig industry.
Number of premises. Pig holdings accounted for most of the premises (up to 98. 6%) in the movement database (See Supplementary Table  S1 ). In 2009, there were 22,310 pig holdings, 217 gathering areas, 174 slaughterhouses and 16 markets that reported moving pigs. From 2009 to 2011, pig holding numbers increased by 13. 8% while from 2011 to 2013, they decreased by 26. 4%. The number of gathering areas (i. e. area of common land used for grouping pigs for temporary time periods before they are moved to other premises), slaughterhouses and markets also first increased (from 2009 to 2011) and then decreased (from 2011 to 2013) over the study period. The distribution of herd sizes was right-skewed, with 2,115 (5%) holdings identified as "large" (more than 5,000 pigs), 1,514 (4%) as "medium" (between 500-5,000 pigs) and 37,645 (91%) as "small" (less than 500 pigs).
Discussion This study presents an analysis of the space-time patterns of movements of pig batches and total pigs moved per year between pig holdings, gathering areas, markets and slaughterhouses in England and Wales, based on data covering a 5-year period (2009-2013) extracted from the RADAR database. Results indicate an overall decline in the number of movements of pig batches from 2010 onwards, and in the number of pig holdings from 2011, while the total number of pigs moved during the study period increased. This can be explained by changes in pig farm size distribution, in particular an increase in the number of large pig farms and the disappearance of small farms, as previously reported by the Agriculture and Horticulture Development Board (AHDB) Pork, and is in agreement with the general trend observed in other pig-producing EU countries  28, 29.
The RADAR database allowed access to detailed information on pig movements in England and Wales, permitting exploratory analyses of the contact network. Amongst the movement records in the database, less than 4% had missing data on geographical coordinates or premises type and these were excluded from the computations. This will have resulted in some degree of underestimation of the number of contacts between premises. Some premises were also incorrectly georeferenced. Twenty-one movements originating from slaughterhouses were recorded; these might be due to misclassification of the type of premises or recording of illegal movements. Underreporting of pig movements by pig owners still remains possible, however they are difficult to quantify due to lack of data. Around 17% of small pig holding and pet pig owners interviewed in England reported not being registered with the Department for Environment, Food and Rural Affairs (Defra) despite it being compulsory  31.
In particular, those coming back from the slaughterhouses have shown a great potential for transmission of diseases  41, 47. However, information regarding lorry movements was not available, reducing the potential for more in-depth analyses. Moreover, no further differentiation was possible among pig holding premises, preventing the differentiation of hobby pig keepers from professional producers in this dataset, other than by using the size of premises as a proxy. Distances were measured as Euclidean distance between premises as an approximation of actual road travel distance. A more accurate alternative would be to introduce GPS (Global Positioning System) recording devices onto pig lorries. These are increasingly being used in private and public transportation vehicles including courier vehicles, ambulances and buses to geographically locate vehicles and precisely track their movements  48, 49.
Methods Data collection. Pig farmers are required by the UK government to pre-notify movements from their farms, and to report movements onto their farm within three days of the movement having taken place  43. All pig movement data are then collected by the Rapid Analysis and Detection of Animal-Related Risks (RADAR) team at the Animal and Plant Health Agency (APHA) and analysed through the RADAR analytical system  50. Data on pig movements in England and Wales were extracted from the RADAR database, covering the time period from January 1 st , 2009 to December 31 st , 2013. Each movement record -defined as a movement of a batch of pigs between two different premises on the same day -consisted of the date of movement, the departure premises identifier, the locations of the departure and arrival premises, the type of departure and arrival premises and the number of pigs moved.
Data were aggregated temporally by month to estimate the general network properties, since this represents a likely epidemic duration prior to detection of important swine diseases (such as CSF and FMD) and implementation of movement bans  18, 44. This time period has been frequently used in previous pig network analyses  7, 36, 37  , and thereby will facilitate between-country comparisons. Data were aggregated by year to detect communities. Weights were assigned to each edge according to the number of pig batches moved between two nodes (premises) within each period (each month for the general network properties and each year for the communities). Descriptions of the network terminology are outlined in Table  2. To characterise the network topology, the clustering coefficient (CC), the average path length (APL) and the degree measures were computed for each month of the 5-year study period, and their distributions were plotted over time.
Acknowledgements The authors gratefully acknowledge the  Animal and Plant Health Agency (APHA Weybridge, UK)  for supplying the dataset, particularly  Helen Gartner  ( RADAR Team, APHA Weybridge, UK ) for providing additional detailed information about the dataset;  Martin Smith  ( AHDB Pork, UK ) for providing detailed information on the English pig industry;  Kim Stevens  ( RVC, UK ),  Timothée Vergne  ( RVC, UK ) and  Anne Bronner  ( ANSES, France ) for advice in relation to spatial maps and geocoding. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n°311931 (ASFORCE). The  Pirbright Institute  receives grant aided support from the  Biotechnology and Biological Sciences Research Council of the United Kingdom.
, 2004). All the lambs were euthanised ten days after treatment and examined post-mortem. The abomasum and small intestines (the first three metres) were processed as described by  MAFF (1986). Worm counts were conducted on aliquots of one tenth of the washing volume of the contents (500 ml). Worms present in the large intestines were sampled directly during post-mortem examination. Species were identified by examining 30 male worms, if available, from each organ. The trials were carried out in compliance with animal welfare requirements and did not cause any pain according to French regulations on experiment ethics. Worm burdens in the treated and untreated control group were compared using a Mann-Whitney test, with significance accepted at p<0. 05. Data were analysed using a negative binomial regression.
Controlled efficacy test with the infective larvae from flock 1 All the lambs were free from infection before experimental infection. An anthelmintic treatment was administered 35 days post-infection, when all the lambs were excreting strongyle eggs. Before anthelmintic treatment, mean egg excretion varied from 383 to 400 epg per group and mean live weight varied between 23 and 25 kg per group. The plasma disposition kinetics of IVM and MOX are shown in Figure  1  and Table  2. For IVM, the mean observed Cmax was 15. 27 ng/ml, obtained at 0. 5 days post-administration. For MOX, Cmax was 9. 64 ng/ml, also obtained 0. 5 days post-administration. Tmax was at 0. 5 days for both IVM and MOX. Both drugs were detected at significant levels (> 1 ng/ml) until the fourth day post-treatment. These data show that IVM or MOX were At slaughter, worm burdens in the control group varied between 220 and 670 worms in the abomasum (T.
The bodyweight, body condition and amount and type of nutrition were therefore different, and these factors are known to influence IVM plasma concentration  (González Canga et al. , 2009). For the experimental infection, the parasitology data were consolidated by measuring the drug concentration in the host plasma after drug treatment. Plasma concentration is closely related to IVM concentration in target tissues such as the abomasum  (Lifschitz et al. , 2000). The plasma disposition kinetics are similar to those previously reported in infected lambs  (Lloberas et al. , 2013). These pharmacokinetic data demonstrated that In flock 1, both IVM and MOX are used regularly, a practice that can lead to selection for resistance, as demonstrated in experimental conditions by  Van Zeveren et al. (2007).
Acknowledgements This study was supported by the  Montpellier University Teaching Hospital (CHU Montpellier , " Equipe Performante Recherche " contract). We would like to thank  INSERM ,  IRD  and  CNRS  for technical support. We thank  Elisabetta Andermarcher  for assistance in preparing and editing the manuscript, and  Nicolas Veziris  for helpful discussions. The authors declare they have no conflict of interest.
9%, 15. 0% and 3. 4%, respectively. Serious treatment-emergent adverse events occurred in 4. 4% of lesinurad 200 mg +allopurinol, in 9. 5% of lesinurad 400 mg+allopurinol and in 3. 9% of allopurinol-alone groups, respectively. Conclusion Lesinurad added to allopurinol demonstrated superior sUA lowering versus allopurinolalone therapy and lesinurad 200 mg was generally well tolerated in patients with gout warranting additional therapy. Trial registration number NCT01493531.
Study design CLEAR 2 was an international, phase III trial to investigate the efficacy and safety of two lesinurad doses (200 or 400 mg oral, once daily) in combination with allopurinol, versus allopurinol combined with placebo (the control arm), in patients demonstrating inadequate response to standardof-care allopurinol (ClinicalTrials. gov Identifier: NCT01493531). The study was conducted in 12 countries in Europe, North America, South Africa, Australia and New Zealand between December 2011 and July 2014. CLEAR 2 included a screening period of approximately 28 days, including a run-in of approximately 14 days on gout flare prophylaxis and 12-month double-blind treatment (figure  1 ). The study was conducted in accordance with Independent Ethics Committee E6 Good Clinical Practice, the Declaration of Helsinki (October 2008) and all applicable local regulatory requirements.
05 (nominal p value), two-sided, without multiplicity adjustment. Results for the primary end point of sUA response are expressed as proportions and p values. Patients with missing values at month 6 or month 12 for any reason were considered non-responders (non-responder imputation, NRI). 22 Key secondary end points were analysed using negative binomial regression (gout flares) or CMH test (tophus response). Mean rates of gout flares were adjusted for day -7 renal function, tophus status at screening and length of exposure to randomised study medication. The time points and analytical methods used in the study were agreed with multiple regulatory agencies. Safety data are listed by treatment arm and are not subjected to statistical testing. TEAEs are coded by system organ class and preferred term and are listed according to incidence, severity, relation to study medication and relation to discontinuation.
001 at each time point. ALLO, allopurinol; LESU, lesinurad; sUA, serum uric acid. 1] [42]  The proportions of patients with TEAEs classified as CV events during study were low and similar in treatment groups. Incidences of MACE events-that is, serious CV events including CV deaths, non-fatal MI and nonfatal stroke-were similarly low. Three patients experienced MACE in the study, all receiving lesinurad 400 mg. Low rates of MACE events during gout treatment were also reported in the open-label Long-term Allopurinol Safety Study Evaluating Outcomes in Gout Patients (LASSO) study, which reported a rate of 0. 58% over 6 months for MACE during allopurinol treatment (incidence rate 1. 42/100 patient-years). 6 imitations of CLEAR 2 include the limited data on allopurinol doses >300 mg, the relatively low proportion of women enrolled, low number of patients with evaluable tophi and the relatively short-term follow-up period that limits the ability to adequately study flares and tophi.
Rates of gout flares and tophus resolution over the longer term are being investigated in an extension study (NCT01808131). In conclusion, lesinurad (200 and 400 mg), a novel SURI, in combination with allopurinol significantly increased the proportion of patients achieving the target sUA of <6. 0 mg/dL (<357 mmol/L) by month 6 and other sUA end points compared with allopurinol-alone therapy. There were no statistically significant treatment-group differences favouring lesinurad for rate of gout flares or complete tophus resolution. The combination therapy was generally well tolerated, particularly at the 200 mg lesinurad dose approved by the US Food and Drug Administration and European Medicines Agency, except for higher incidences of predominantly reversible sCr elevation compared with allopurinol-alone therapy.
Contributors TB, RTK, PPK and AS: Criterion Funding This clinical study was funded by  Ardea Biosciences , a member of the  AstraZeneca group. The study sponsor had a role in the design and conduct of the study; collection, management, analysis and interpretation of the data and review and approval of the manuscript. grant/research support from  Ipsen ,  Menarini  and consultant for  AstraZeneca ,  Ipsen ,  Menarini ,  Novartis ,  Savient ,  Sobi ,  Takeda  and Cymabay. RTK: consultant for  AstraZeneca ,  Crealta Pharmaceuticals  and  Takeda. PPK: research grant:  AstraZeneca. JK (former employee), MF, NB, CS (former employee) and SB: full-time employees of  Ardea Biosciences , a member of the  AstraZeneca Group. SA: full-time employee of AstraZeneca Pharmaceuticals. AS: consultant for  Novartis, AstraZeneca, Menarini.
Data Eggshell color was measured on individual eggs during four periods on purebred hens (period 1: 25 to 35 weeks, period 2: 36 to 55 weeks, period 3: 56 to 75 weeks and period 4: 76 to 95 weeks of age) and during three periods in crossbred hens (30 to 45, 50 to 65 and 70 to 85 weeks of age). Purebred hens were individually housed, whereas crossbred hens were housed as paternal half-sibs in group cages that contained between four and 17 hens. Eggshell color was measured with a reflectometer (Minolta) using three parameters: L* measures lightness (0 is black; 100 white), a* measures hue as a function of the red-green scale (<0 is green; >0 is red) and b* measures hue as a function of the blue-yellow scale (<0 is blue, >0 is yellow)  [15]. The three measures were combined into an eggshell color index as L*-a*-b* and multiplied by 10.
Data were collected on purebred hens between 2006 and 2013 and on crossbred hens between 2009 and 2013. The raw data contained 221,467 records for purebreds and 96,106 for crossbreds. For purebreds, we limited the data to have at least five records per hen in order to estimate permanent environmental effects and to have at least 40 daughters per sire resulting in 167,651 records for analysis. For crossbreds, at least 40 records per sire were required resulting in 79,345 records after editing. Crossbred and purebred hens had a sire from the same line, whereas their dams originated from four different female lines. In total, 279 sires had purebred daughters and 880 sires had crossbred daughters, while 71 sires had both purebred and crossbred daughters. The sire pedigree was traced back 5 generations and contained 2491 animals.
The DHGLM model For crossbreds, we used the sire model adjustment  [18]  to account for the fact that the residual variance contains three-quarters of the genetic variance of eggshell color itself. Simulations showed that standard DHGLM would underestimate the genetic variance in V E and the pro- posed adjustment resulted in unbiased estimates of genetic variance  [18]. In this study, when we used the standard DHGLM, the genetic variance in V E was indeed less than with the adjusted DHGLM, but the difference in estimates was smaller than theoretically expected. This may indicate that the Mendelian sampling variance is heterogeneous between sires. Disentangling Mendelian sampling variance and V E is, however, impossible for the crossbred data in this dataset. Although the genetic variance changed when using either standard DGHLM or adjusted DHGLM, the estimated genetic correlation between V E in purebred and crossbred layer hens was the same.
Thus large datasets with more than 200,000 records would be needed. Therefore, for traits that are measured only once per animal, such as growth rate in pigs, it might be challenging to obtain such large data sets. Fortunately, for such traits h 2 v seems larger  [4, 31]. When the h 2 v is equal to 0. 03 instead of 0. 01, about 170 purebred and crossbred offspring from 200 sire families are required alleviating the requirements on the size and structure of the dataset. With repeated observations such as eggshell color, fewer offspring per family are required. With 10 repeated observations, approximately 60 purebred and 60 crossbred offspring per sire are required with 200 sire families. It can be concluded that for estimating r pc for V E , very large datasets are needed. In this study, the DHGLM methodology was used to estimate the genetic correlation between V E in purebreds and crossbreds, but the same methodology can be used to estimate the genetic correlation between V E in different environments to investigate genotype-by-environment interactions.
In a previous study  [21] , we investigated V E for fish raised in fresh and seawater and found geno- type-by-environment interactions for V E , especially after log-transforming the data. Due to different micro-environmental factors in these environments, genotype-byenvironment interactions for V E may arise. The method of Bijma and Bastiaansen  [14]  can be used to design experiments or to evaluate how datasets should be created to estimate genotype-by-environment interactions for V E.
Acknowledgements This study was supported by  Breed4Food , a public-private partnership in the domain of animal breeding and genomics. Institut de Sélection Animale, Hendrix Genetics  is acknowledged for providing the data. Abe Huisman  and  Addie Vereijken  are acknowledged for discussing results of this analysis and  Abe Huisman  for his role in initiating this study. Piter Bijma  is acknowledged.
For both A. vasorum worm counts and cumulative A. vasorum larval output, log-counts of the treated group were compared to the log-counts of the untreated Control Group using an F-test adjusted for the allocation blocks used to randomize the animals to the Treatment Groups. The MIXED procedure in SAS Version 9. 4 was used for the analysis of variance, with the Treatment Groups listed as a fixed effect, and the allocation blocks listed as a random effect. All testing was two-sided at the significance level α = 0. 05.