dataesr/scientific-paragraphs-categorization huggingface dataset:
) were retained. The correlation coefficient was then correlated with the number of individuals transferred between two group compositions using a Spearman correlation test with permutations (library R "Coin", R 3. 24). iii. How do sociodemographic factors influence individual centralities? GLMMs (R package 'lme4';  Bates et al. , 2014)  were used to test whether the eigenvector centrality and the strength of associations were affected by the following independent sociodemographic variables: the age of individuals, their sex, their dominance rank and the number of familiar individuals they were associated with in the group. The experimental units we used were the eigenvector centrality for a first GLMM and the strength of associations for a second GLMM, per individual and per group composition.
This means that for each combination of factors, all models were tested and ranked according to the best AIC. ΔAICc is the difference in AICc between a given model and the model with the lowest AIC. The AIC weight indicates the probability that a given model will be the best among candidate models. Models with a ΔAICc <10 were considered equally possible candidates, and their statistics were averaged. The null model (random effect: identity of individuals) was included as a possible candidate but was never among the models with lowest AICc. The results also indicate relative variable importance (RVI), which is the number of times a variable is present in the best models. Model inference and averaging were carried out with the R package 'MuMIn'  (Bartoń, 2013). Node label permutations were also performed  (Croft et al.
Acknowledgements We would like to thank  Cécile Bayeur  and  Jean-Claude Bieber  from the  "Parc Naturel des Vosges du Nord", France and Frédéric Lonchampt from Strasbourg, France  for granting us access to the Highland cattle. Our thanks to  Arthur Letzelter  and  Fabien Kauffer  for their help obtaining data for the socio-demographic factors of animals, and to  Louise Frey  and  Audrey Claus  for their help in data scoring. We thank  Joanna Lignot  ( Munro Language Services ) for English editing and  Alecia Carter  for her helpful comments. Sebastian Sosa is funded by the  National Natural Science Foundation of China  ( 31470456 ).
Methods FungiScope V R is a registry study on rare invasive fungal diseases (IFD) currently active in 74 countries. Its methodology has been described elsewhere  (Seidel et al. 2017). Data of scedosporiosis and lomentosporiosis cases were extracted and included for analysis. In addition, we performed an electronic literature search for case reports in PubMed on August 18, 2017 using the search filter "(Scedospori Ã OR Pseudallescheri Ã OR Lomentospori Ã ) AND ((invasive OR disseminated OR infection) AND (case OR patient OR report))". Articles in English, French, German, Italian, and Spanish were chosen for further selection. Records presenting cases of invasive Scedosporium spp. or L. prolificans infection were selected on the basis of the title and abstract. Reference lists of the identified articles were checked for further studies.
prolificans together with one case that had a mixed infection with S. apiospermum  (Lackner et al. 2014). Statistical analyses were performed using SPSS 25 (IBM Corp. , USA). Characteristics of the patient population were compared by calculating the frequencies, means and medians. For the comparison analysis, Chi 2 , Fisher's Exact Test or t-test were used as appropriate. Kaplan-Meier method was used to estimate survival and curves were compared statistically using the log rank test. The p value 0. 05 was considered statistically significant. Mean survival of time to death was calculated using the mean and 95% confidence interval (CI 95%). Minimum inhibitory concentrations (MIC) were compared by calculating medians. 2013;  Agatha et al. 2014 , Abela et al. 2018; Campa-Thompson et al. 2014; Kepez Yildiz et al.
prolificans than for Scedosporium spp. Clinical manifestation differed between scedosporiosis and lomentosporiosis as well as between patient populations. Infection with Scedosporium spp. frequently disseminates to distant organs, often to the CNS even in immunocompetent patients, without primary bloodstream infection. L. prolificans infection often presents as fungemia, while brain infections are reported less commonly  (Horr e et al. 2000; Mellinghoff et al. 2002). Tropism for blood vessels and haematogenous spread is considerable in both diseases  (Kowacs et al. 2004 ). Disseminated disease is independently associated with worse outcome, fungemia being the key determinant. In our dataset, fungemia proved fatal in all immunocompromised patients and in all but one immunocompetent patients.
Olorofim is a new antifungal agent that shows promising antifungal activity in vitro. In our analysis, olorofim had low MICs against all Scedosporium spp. and L. prolificans strains, confirming recently published results  (Wiederhold et al. 2017; Biswas et al. 2018). A clinical efficacy study on olorofim is upcoming. Similarly, APX001 (E1210) a novel antifungal agent that disrupts fungal cell wall assembly, showed good in vitro activity against Scedosporium spp. and L. prolificans and is currently tested in phase I clinical trial in patients with acute leukaemia (clinicaltrials. gov 2017;  Castanheira et al. 2012). Route of entry of Scedosporium spp. and L. prolificans is frequently through the respiratory tract, which explains the high incidence of lung lesions. In this case series, lung involvement predicted worse outcome of scedosporiosis in patients with malignancy in univariate analysis.
Furthermore, disseminated infection was associated with worse outcome in patients with Scedosporium spp. infection post SOT and in patients with L. prolificans infection and malignancy. In our analysis, we did not identify additional potentially important predictors. Proper control of confounders is difficult due to heterogeneity of hosts and diverse clinical patterns of IFD, leaving us with small numbers not allowing multivariate analysis. Trends determined in univariate analysis need further confirmation in a larger dataset. Infections caused by Scedosporium spp. and L. prolificans resemble other mold infections on cytological and histological examination, challenging timely diagnosis and targeted treatment  (Guarro et al. 2006). High virulence of certain species, in particular L. prolificans, and less predictable susceptibility patterns urge for prompt pathogen identification.
We detected low voriconazole MICs and slightly higher MICs for itraconazole and posaconazole in most Scedosporium spp. strains. In comparison, for L. prolificans MICs were higher for all antifungals and low voriconazole MICs were determined in individual cases only. Amphotericin B or echinocandin MICs were low in few Scedosporium spp. strains, which may offer a treatment option in respective patients. Supportive experimental in vivo data are scarce with varying results on efficacy of single and combination therapy with amphotericin B or echinocandins  (Rodriguez et al. 2009; Lackner et al. 2014). The clinical predictive value of in vitro susceptibility of amphotericin B or echinocandins and thus, the additional therapeutic benefit for patients is still unknown. Successful treatment of L. prolificans infections seems almost impossible.
Acknowledgements We thank  Sabine Wrackmeyer  for her private donation to support the project. We thank  Alexandra Laska  and  Maren Ziegler  ( Wisplinghoff Laboratories, Cologne, Germany ), and  Katharina Rosam  ( Medical University of Innsbruck, Austria ) for their technical assistance, and  Susann Bloßfeld  ( University Hospital Cologne, Germany ) for her administrative support.
Morphological description and measurements of the sclerotized pieces of the haptor and the male copulatory organ (MCO) were carried out according to Dossou & Euzet (1993) using a Leica DM2500 microscope and LAS 3. 8 software. Drawings of the sclerotized pieces were carried out using Coral Draw X4 Software (Ver 14. 0. 0. 701; Corel Corporation, www. corel. com). Type specimens were deposited at the Royal Museum for Central Africa (RMCA, Tervuren) and at the Muséum National d'Histoire Naturelle (MNHN, Paris).
Statistical analysis Data were analyzed using PROC general linear model proce dure in SAS (SAS Institute Inc. , Cary, NC, USA) as a randomized complete block design. The individual pig was used as the experimental unit for all response variables in the model, which included feeding level as the main effect. Orthogonal polyno mial contrasts were used to examine linear and quadratic effects of feeding level on growth performance, ATTD values, energy, and nitrogen balance in growing pigs. Differences were con sidered significant at p≤0. 05, whereas tendencies were discussed at p>0. 05 but p≤0. 10. Linear regression analyses were conduct ed by OriginPro 8. 5 (OriginLab, Northampton, MA, USA) to determine the relationship between ADG (g), RE p (g/d) or RE f (g/d) and MEI (kJ/kg BW 0. 60 d).
Item Feeding level (% of ad libitum  1) Data are means of 6 replicates per treatment. 2) The estimated ad libitum ME intake was 2,400 kJ/kg BW 0. 60 d. 3) Linear and quadratic contrasts for feeding level. 4) Net energy requirement for maintenance was obtained from Noblet et al  [15]. Liu et al (2019) Asian-Australas J Anim Sci 32:1397-1406 ratio and level of dietary energy intake.
En ergy retention, when expressed as protein, fat or total, linearly increased as feeding level increases (p<0. 001). To determine the effect of feeding level on the energy retention for fat and protein in growing pigs, linear regressions of REp and REf in growing pigs were performed and presented in Figure  1  and 2. The equations (parameters±standard error [SE]) for RE p and  1) Data are means of 6 replicates per treatment. 2) The estimated ad libitum ME intake was 2,400 kJ/kg BW 0. 60 d. 3) Linear and quadratic contrasts for feeding level.
Item Feeding level (% of ad libitum  1) Data are means of 6 replicates per treatment. 2) The estimated ad libitum metabolizable energy intake was 2,400 kJ/kg BW 0. 60 d. 3) Linear and quadratic contrasts for feeding level. on energy utilization in growing pigs, and no differences were observed in energy digestibility during feed restriction. Dif ferent feeding levels had no effect on the value of ME:DE ratio, which agrees with previous observations in growing pigs  [9]. The ME:DE ratio is related to the protein content of the diet and the amount of nitrogen in urine  [27, 28]. In the current study, nitrogen excreted in urine as a proportion of nitrogen intake had no association with dietary energy intake, which may explain the results of the ME:DE ratio  [25]. The ADG in the present study was 773 g/d at the 100% feeding level, whereas ADG in previous studies ranged from 560 to 900 g/d  [2931].
Statistical Analysis All the statistical analyses were conducted using the Statistical Analysis System (SAS version 9. 2; SAS institute Inc. , Cary, NC; USA). Significance was established at P < 0. 05, and the exact P value is provided for all those results with a P < 0. 1. The results are presented as mean ± standard deviation (SD) or SE (standard error) unless otherwise indicated. The Shapiro-wilk test (with PROC UNIVARIATE in SAS) was used to examine the normality of the distributions. The experimental unit for the lesions, physiological indicators and carcass and meat quality parameters was individual, whereas for the behavioural observations and productivity data (weight, feed intake and food conversion ratio) the pen was considered as the experimental unit. The least square means of fixed effects (LS-means) with Tukey adjustment was used for comparisons when analysis of the variance indicated differences (P < 0.
Carcass weight was considered a covariate in those parameters where it was proved to have a significant effect. The lactate and CgA data collected at the slaughterhouse after two on farm fasting periods was also analysed using a GLM model with enrichment type, fasting time and its interaction as fixed effects. The skin lesions and behavioural data were analysed using non-parametric generalised linear models for repeated measurements (GLIMMIX or GENMOD procedure, respectively). The Poisson distribution was used and for some parameters a negative binomial, depending on the deviance  [26]. Lesions before providing enrichment were considered as a covariate. Enrichment material and time, and its interaction were used as fixed effects, and the pen as a random effect for skin lesions. The behavioural data from the scan samples was analysed as a percentage of the scans in each category in relation to the total number of scans per day, while data from focal sampling was analysed as the total number of counts observed in each category per pen and day.
Study Duration and Dataset The study was carried out over a full year from September 2016 to October 2017. During this period, one dolphin was born (not included in analysis), and two dolphins of 55 and 32 years died (52 and 57 days of data were collected from these individuals before their death). Data was collected every day, multiple times daily, where the number of consecutive days of data taken for each dolphin varied between 272 and 365 days due to the study starting at different points at each facility (mean: 317 days; excluding two dolphins that died during study). This translates to a total of 15,635 days of data, with approximately 99,600 separate scores conducted on the dolphins' WtP during training sessions.
Behavioural and Health-Related Animal-Based Measures The aim of the study was to collect multidisciplinary daily information to establish a large dataset of welfare-related data from a variety of dolphin facilities. The facilities were located in four different countries, where the animal care staff who would collect the information had a range of zero to moderate previous experience with scientific data collection. For this reason, the data collection methods had to be simple and able to be standardised across the international facilities. Therefore, qualitative scoring scales were used for three of the measures, with a fourth measure (Kg of fish eaten as a percentage of total offered) established as a quantitative measure since there was little room for error when recording these data (unlike attempting to quantify the animals' behaviour).
Occurrence of Rake Marks Measuring the presence of new rake marks on individual dolphins was used here as a proxy indicator for real-time changes in the social context of the group. During their daily sessions with the dolphins, the trainers visually scanned the animals' bodies for any new rake marks that were visible. In order to standardise the evaluation of rake marks across facilities, we originally used a Likert scale with three categories: no new marks, a small number of new marks (about the size of one to two human hands), and a large number of new marks (covering the area of three hands or more). However, when the data were collected we had a very low sample size of the large rake mark category, which led us to treat the rake mark score as a binary "occurrence" in order to make it more meaningful.
In some rare cases, when the veterinarian visited the facility for several times per week, we used the weekly median value of the score, and thus the weekly averages remained on an ordinal scale. In a first step, we analysed associations between the WtP score (dependent variable) and either (a) the Health score, (b) the percentage of daily food eaten or (c) the occurrence of new rake marks (dependent variables in separate statistical models. For (a), due to the ordinal structure of the dependent variable, we used a cumulative link mixed-effects model for ordinal data using the clmm function provided by the R package ordinal  (Christensen, 2015). Post hoc comparisons (after sequential Bonferroni correction,  Holm, 1979) , as shown in Figure  3A , were done using principally the same model, but based on a subset of the data restricting the analysis only to 2 of the ordinal categories of the dependent variable.
For Figure  3B , we used a generalised linear mixed-effects model (GLMM) for proportional data, and for Figure  3C  we used a GLMM for binomial data. This was done by using glmer function of the R package lme4  (Bates et al. , 2017). For all models, we included the identity of the dolphin as a random factor to account for the individualbased repeated measurements across consecutive weeks. We also included the identity of the facility and the identity of the group as further random factors to account for potential effects of the same origin (either facility origin or group origin) of the animals. Furthermore, we tested all remaining, possible associations between the different score variables using the different functions described above (see section Results for details). In a second step, we analysed whether the three different scores, WtP, percentage of daily food eaten and the occurrence of rake marks already showed any apparent changes shortly before the veterinarian determined a 'departure from good health' (DGH) in a dolphin.
We considered restricting the analysis to only the 22 cases where the Health score decreased from a 0 to 1 to increase standardisation, but we principally found the same significant results, and so decided to keep in all cases of DGHs to maintain a larger sample size. Daily WtP scores and the percentage of daily food eaten were averaged over a 3-day period prior to and over a 7-day period following the diagnosis of a DGH by the veterinarian, to test whether these parameters could indicate the onset of DGHs. Furthermore, we assigned a 7-day control period ending 1 week prior to the diagnosis of the departure from good health (see grey bars in Figures  4A, B ). For the occurrence of new rake marks, we assigned the absence/presence (binomial data structure) during the different periods. For the WtP score and the percentage of daily food eaten we ran a linear mixed-effects model LMM, and for the occurrence of new rake marks we ran a generalised linear mixed-effects model GLMM for binomial data, by comparing the three periods using lmer and the glmer functions of the R package lme4, respectively  (Bates et al.
DISCUSSION The present study investigated how certain behavioural, health, social and food intake parameters might relate to overall dolphin welfare, through collecting long-term data from multiple dolphin groups and facilities. Firstly, it was found that animals with a higher Willingness to Participate (WtP) in training sessions had a significantly better health status, took a higher percentage of the food they were offered, and were less likely to have new rake marks. WtP therefore may be a good welfare indicator for captive bottlenose dolphins. In addition, we looked at the above welfarerelated parameters around the time period where a departure from good health was recorded by veterinarians, and found that the WtP score had already significantly decreased prior to the veterinarian's visit during which decreased health was detected, suggesting its use as an early predictor of health problems.
ACKNOWLEDGMENTS This research would not have been possible without the participation and enthusiasm of the dolphin trainers and managers at  Parc Astérix ,  Boudewijn Seapark ,  Dolfinarium Harderwijk , and  Attica Park , who all integrated the scoring methods into their daily schedules in a very meticulous and professional manner, continuing with the data collection for a whole year. A big thanks also to several other facilities who took part in initial trialling and data collection for the project. We greatly appreciate the discussion with  Francoise Wemelsfelder  ( Scotland's Rural College ) regarding the methodology and its relation to Qualitative Behavioural Analysis.
Microsatellite genotyping Individual samples were genotyped with a set of microsatellites initially developed for S. haematobium by Webster et al. [22] ; however, S. haematobium and S. bovis are closely related sister species so the microsatellites were known to cross-react with S. bovis (Webster B, personal communication). Of the 18 microsatellites tested, 14 successfully amplified on our S. bovis samples and were thus used in the present study. The remaining four markers (C131, Sh7, Sh10 and Sh8) succeeded on less than 20% of the specimens and were thus discarded from our dataset. Microsatellite amplifications were performed using the Qiagen® Multiplex PCR kit, (Qiagen, Hilden, Germany). The forward primers were fluorescently labeled using 6-FAM, VIC, NED and PET dyes (Applied Biosystems, Foster City, USA) as per  [22].
PCRs were carried out according to the manufacturer's standard microsatellite amplification protocol except for the final volume of 10 μl including 2. 5 μl of DNA template. Thermal cycling was performed with an initial hot-start activation of 15 min at 95 °C, followed by 30 cycles of 94 °C for 30 s, 56 °C for 90 s and 72 °C for 60 s, with a final extension at 60 °C for 30 min. Microsatellite reactions were performed using a TECNE TC-Plus Thermal Cycler (Cole-Palmer, Stone, UK). The microsatellite reactions were sent to Genoscreen (Lille, France) for genotype determination. Peak calling and genotype determination was done using GeneMarker Software. We used automatic size determination of the allele size using the Fragment Animal Analysis option of Genemarker Software with the GS500 size standard.
Population structure and genetic diversity Microsatellites The genetic diversity of the schistosomes from each origin, defined as populations, was assessed by computing the expected heterozygosity (He), number of alleles (A), allelic richness (Ar) and FIS at each microsatellite locus using FSTAT v. 2. 9. 3. 2. [25]. These parameters were compared between the populations using the pairwise Friedman rank tests. The genetic differentiation between sites was assessed using pairwise FST according to Weir & Cockerham  [26]  using FSTAT v. 2. 9. 3. 2. Next, we checked for potential genetic structure among individuals using a principal components analysis (PCA) as implemented in the Genetix software. We also performed an analysis of molecular variance (AMOVA) to evaluate the partitioning of the overall genetic variance among individuals according to three hierarchical levels: (i) within host; (ii) among hosts within the site of origin; and (iii) among sites.
The AMOVA was performed using Arlequin software v. 3. 5  [27]. Finally, we determined the uppermost level of genetic structure of all individuals using the Bayesian clustering approach implemented in the Structure software  [28]. In particular, we tested the number of clusters from K = 1 to K = 10. Three runs were computed for each K each of which consisted of 10 6 iterations after a 'burn-in' period of 250,000 iterations, using an admixture model and the other parameters were set by default. The log likelihood for each K was then averaged over the three runs using the corrsieve package in R. The ΔK-values were then computed in R to determine the most likely K number among the K tested as described in Evanno et al. [29]. Two K-values (K = 2 and K = 4) were identified as the most likely number of genetic clusters.
For each of these two K-values, an additional 10 runs were computed using 10 6 iterations and setting the same initial parameters as those described previously. The probability for each individual to belong to each K was then averaged over the 10 runs (Q values). These Q values were then graphically represented using Clumpp  [30]  and the Distruct  [31]  software. Finally, we took advantage of the fact that the microsatellite markers used in this study are informative for both S. haematobium and S. bovis to compare the genetic diversity between the two species based upon the 14 shared markers. To do so, we used a raw dataset from a previous article focusing on S. haematobium  [22]. This raw dataset includes individual genotypes using 16 microsatellites (including the 14 markers used in this study) for 50 S.