dataesr/scientific-paragraphs-categorization huggingface dataset:
Morphological evaluation Cercariae and rediae were examined live under the light microscope Olympus BX51 and BX41 for primary identification based on their morphology, following the keys of Faltýnková et al. [34, 35]. Series of photomicrographs of live individuals and formalin fixed samples were taken with a digital camera on Olympus BX51 and BX41 microscopes; in locations with microscopes with no camera (Alaska, Finland), hand drawings of live cercariae were made. Measurements for each isolate were taken from the digital images with the aid of QuickPHOTO CAMERA 2. 3 image analysis software. Metrical data in the descriptions are based on live specimens and fixed material (formalin and/or ethanol). All measurements in the descriptions are in micrometres and are presented as the range, followed by the mean in parentheses.
DNA-based identification During the present study, 68 novel sequences, including 19 of 28S rDNA and 49 of nad1 were generated for 49 cercarial isolates belonging to the family Echinostomatidae (Table  3 ). Molecular delineation of the isolates and their species identification was performed based on the analysis of nad1 sequence data via comparison to previously published data for echinostomes (Table  4 ). Analyses of the 28S rDNA sequence data were conducted to explore relationships among collected taxa and to identify several species for which nad1 sequences were not available in GenBank. Cercariae of Echinoparyphium rubrum  (Cort, 1917)  and Moliniella anceps (Molin, 1859) were molecularly identified based on the 28S sequence data analyses. For Echinostoma revolutum we follow the concept of Georgieva et al.
3 ). The intraspecific divergence between the four European isolates of this species was 0-0. 3% (0-1 nt) (Alignment 3; 402 nt). Isolates from North America differed from the European isolates by 2. 8-3. 1% (10-11 nt). The remaining isolate collected in Ireland formed a separate branch within the clade consisting of Neopetasiger sp. 1 and Neopetasiger sp. 2 previously reported from G. albus in Germany  [118]. The interspecific divergence between sequences of Neopetasiger sp. 5 and the other species of this genus included in the analyses was 18. 5-32. 5% (66-116 nt). Neopetasiger sp. 2 appeared to be more closely related to Neopetasiger sp. 5, whereas N. neocomense showed the highest sequence divergence. Comparison of nad1 sequence data between the isolate of Hypoderaeum conoideum (Bloch, 1782) of the present study and two isolates of this species available in GenBank (AY168949  [76] ; and MH282580  [95] ) showed low divergence (0.
1 , clades of "E. rubrum", "Echinoparyphium sp. 2" and clade with Echinoparyphium sp. A, 1A, C, D, Lineage A); (ii) the same name was used for different species (Fig. 1 , Echinoparyphium sp. 1A (MH369046 and MH369047), and Echinoparyphium sp. A (MH369069, MH369051, MH369083 and MH369081)); and (iii) different species names for sequences have the same GenBank accession number (i. e. , MH369130 and MH369158 correspond to Echinoparyphium sp. Lineage 3 and Echinoparyphium sp. Lineage 4; MH369080 corresponds to Hypoderaeum sp. Lineage 1 and Hypoderaeum sp. Lineage 2). Our analyses demonstrated that the genus of cercariae reported as Hypoderaeum sp. Lineages 1 and 2 (MH368958 and MH369080) was misidentified. These cercariae belong to the species of Echinoparyphium (Fig. 1 ). Additionally, the previous identifications of Echinostoma spp.
were not followed (Fig. 2 , see clades of "Ec. revolutum" and "Ec. trivolvis"). Another problem was related to the data of the host range of echinostomes. Although the species of snail hosts were provided in the paper, these names were not associated with the specific isolates, which precludes identification of the host of each species considering that some of the isolates were misidentified. A total of 14 cox1 (n = 4) and ITS2 (n = 10) sequences were generated for snail isolates (Table  3 ). Molecular identification was achieved via comparison of novel sequences with those previously published and available in GenBank. Generally, the sequence divergence was low corresponding to the intraspecific level. The cox1 sequence of P. acuta from Iceland differed from P. acuta from Greece (KF737936;  [4] ) and the USA (KJ769124;  [57] ) by 0.
The sequence divergence was low (0-0. 7% (2 nt)), corresponding to the intraspecific level. The intraspecific difference between cox1 sequences of Gyraulus cf. parvus generated in our study was 0. 4% (2 nt). They differed from sequences of Gyraulus parvus from the USA (LC429535;  [115] ) and from Canada (MG421286, MG421343, MG421564;  [134] ) by 4-6. 1%  (22-33 nt). The ITS2 sequences of Gyraulus cf. parvus generated in the present study were identical and differed from a sequence of G. parvus in GenBank from Canada (MN644828;  [114] ) by 0. 5% (3 nt).
Our new sequences for nad1 are highly similar to sequences deposited in GenBank by Gordy and Hanington  [51]  and identified as Echinoparyphium sp. A (MH369158 and MH369047), and Hypoderaeum sp. Lineage 2 (MH369080), all from Physa gyrina in Alberta, Canada. Alas, the same sequences were identified under different names by Gordy & Hanington  [51] , therefore, to avoid further confusion, we chose to name our species Echinoparyphium sp. 2. The present cercariae were found in one single locality in Iceland (Nordic House, Vatnsmýri bird reserve, Reykjavík), a popular area for nesting birds and with an established population of P. acuta, an invasive snail originating from North America  [91] , dwelling in Iceland for more than finding Echinoparyphium sp. 2 consistently for the last two years in snails, indicating that the trematode's circulation in the environment is well established.
Moliniella Representative DNA sequences: MZ404683 (nad1); MZ409815 (28S). Remarks: The new material from Ireland corresponds well in morphology to M. anceps as in the key of Faltýnková et al. [34]. The only sequence (28S) of M. anceps available in GenBank was provided by Tkach et al. [126]  of a metacercaria ex P. corneus in Lithuania. This one and our 28S sequences were identical. Cercariae of this species were found not to be too common in Stagnicola corvus and L. stagnalis in central Europe; however, metacercariae are quite common  [34]. The present study provides the first nad1 sequence for M. anceps and the first record of this species in Ireland, which is the westernmost distribution of the species. Remarks: The morphology of the present species corresponds well to that described by Georgieva et al.
Prior to our study, in Europe P. acuta was reported as a host for cercariae from the "furcocercous" group in Spain  [131]  and cercariae of Fasciola hepatica L. , 1758 in France  [27]. However, both cases were not molecularly characterised. Our study reports the third record of P. acuta as a host for trematodes in Europe with supporting molecular and morphological evidence. Two species of Neopetasiger were recorded in the present study. Neopetasiger islandicus was found parasitising Gyraulus cf. parvus in two localities in Iceland (Table  2 ). The previous records  [47, 77]  and our data demonstrate that the distribution of this species in Iceland is associated with the distribution of their definitive hoststhe population of horned grebe Po. auritusbut also with the distribution of the first intermediate host, i.
Although in recent years the family Echinostomatidae has received considerable attention, particularly in molecular genetic studies, there is still a need for such studies to gain data for accurate species identification, especially within the species complexes ("revolutum" complex and "trivolvis" complex). This should be achieved by following similar guidelines for "best molecular practice" recommended in trematode systematics  [9]. DNA barcode sequencing became an essential resource for trematode identification particularly when working with their larval stages. These can be identified reliably to species or genus level via comparison to accurately identified sequence data available in public DNA databases. When publishing new DNA sequence data, it is important to consider previous studies and follow uniform data presentation and subsequent numbering of unidentified species.
Acknowledgements. The present study was funded by the  Czech Grant Agency  (project No. 18-18597S ), the research fund from the  University of Iceland , and by the institutional support of the  Institute of Parasitology, BC CAS CR  (RVO:  60077344 ). We are grateful to  A. Karvonen  ( University of Jyväskylä, Finland ) and  O. Seppälä  ( University of Innsbruck, Austria ) for providing infected snails from  Huumonjärvi, Finland , and to  D. Larsson  ( University of Alaska, Fairbanks ) for help with sampling and providing lab space in Fairbanks, Alaska. We are also grateful to  Libuše Kolářová  ( Charles University, Czech Republic ) for help with sampling in Ireland.
e. body mass and haematological traits) and immune traits in offspring (roe deer fawns at 8 months of age) Cuthill (2007)'s recommendations. We calculated effect sizes on the whole data set and in the two populations separately to compare the direction of the effect between populations. All analyses were carried out in R version 3. 2. 3 (R Core Development Team, 2015) and using the function lmer from package lme4  (Bates et al. , 2015).
K E Y W O R D S bio-logging, error measurement, false negatives, false positives, focal resource use, proximity loggers, wildlife encounters, wireless sensor networks In ecology, a contact is defined as the spatial and temporal proximity of 2 individuals at or below a threshold distance, within a defined interval of time  (Cross et al. 2012). Contacts can be quantified through different methods based on the collection of data from an array of bio-logging sensors. Specifically, Global Positioning System (GPS) locations can be used to map areas of spatiotemporal overlap within animal home ranges  (Long et al. 2015)  and to infer co-location rates from which proximity patterns can be inferred  (Pepin et al. 2016). When used in conjunction with on-board cameras, GPS-collars permit the observation and characterization of contacts between animals  (Bombara et al.
For both experiments, we measured the inter-logger distances and compared these to the contact data recorded by the WildScope proximity loggers (Figure  1 ). Specifically, from a fixed observation point, we first measured the distances A and B between the observer and focal dyads of loggers (pair of horses; deer and fixed logger) using a rangefinder (Bushnell, Overland Park, KS, USA), and the associated bearings using a compass. We then computed the inter-logger distance D as: F I G U R E 1 A) Scheme  D A B A B = + -(2 × × × cos ), 2 2 (1) where α denotes the angle between the segments A and B. We rounded D to the closest meter (i. e. , spatial resolution of the experiment equal to 1 m) and bounded it to 70 m, which was the maximum distance observable within the limits of the fenced areas.
While taking these measurements, we recorded the timestamp of each observation at the temporal scale of one minute, using a digital wristwatch synchronized with the logger internal clock. We considered as valid only those minutes of observations where the focal dyads did not change their mutual position. We then matched the timestamps of the empirical observations with those of the contacts detected by each logger. For each experimental setting and observed D, we thus obtained a time series of observations at one-minute temporal scale, to which we associated an occurred (1) or missing (0) detection according to the data recorded by each logger at any minute of observation. For each distance D, we then derived the contact success (CS) as the rate between the minutes of detected contacts (DC) and the sum of minutes of detected and not detected (NDC) contacts: CS DC DC NDC = + (2).
ACKNOWLEDGMENTS We acknowledge the work of  S. Nicoloso  and  M. Corrà  in manufacturing WildScope. We thank  C. Pucci  and the staff of  Stelvio National Park  who allowed us to test collars on their animals. The research was conducted with the support of the  Forestry and Wildlife Service of the Autonomous Province of Trento. This project was partly funded by  Autonomous Province of Trento (PAT) , under grant BEARNET, protocol N. S044-5/2012/226570. F. Ossi  was granted 3 yearly scholarships financed by the  European Union  (European Social Funds),  Aosta Valley Autonomous Region , and the  Italian Ministry for Work and Social Politics.
RT-qPCR Total RNA was isolated from caeca using TRIzol extraction (Life Technologies, Carlsbad, CA, USA). cDNA was synthetized using M-MLV Reverse Transcriptase (Promega, Madison, WI, USA), with random hexamer primers and oligo(dT)15 primer (Promega). cDNAs were then amplified by qPCR (CFX96, Bio-Rad, Hercules, CA, USA) using iQ ™ SYBR ® Green Supermix (Bio-Rad). Parasite load into caecal tissues was determined using specific primers to the E. tenella housekeeping genes Et18S, Etprofilin and Gallus gallus housekeeping genes g10 and gapdh (Table  1 ). Et18S is commonly used as a housekeeping gene during the parasite cycle  (Walker et al. , 2015) ; we added Etprofilin for which the expression was not modified in our experiment (data not shown). Microgamete and macrogamete gene expressions were determined using Etfoa1, (microgamete-specific gene; ETH_00025255) and Etgam56, (macrogamete-specific gene, ETH_00007320) both obtained from ToxoDB release 34 (Table  1 ; Eurogentec, Seraing, Belgium) and the previously cited E.
ACKNOWLEDGEMENTS We are thankful to  T. Chaumeil ,  M. Renouard ,  S. Lavillatte ,  P. Cousin ,  A. Faurie ,  O. Dubes ,  M. Girault  from the  Infectiology of Farm, Model and Wildlife Animals Facility (PFIE) , for the animal care and skills with the animal experiments. The authors are grateful to the breeders and the  society Boyéaccouvage (La Boissière en Gatine, France)  for the supply of fertilized eggs. We also acknowledge  Y. Le Vern  for flow cytometry sorting of transgenic oocysts ( INRAE, Universitéde Tours, UMR ISP, Platform IMI, F-37380, Nouzilly, France ). We are grateful to  P. Queŕé ( INRAE, Universitéde Tours, UMR ISP F-37380, Nouzilly, France ) who kindly reviewed and gave suggestions for the manuscript.
Statistical Analysis Microsoft Excel 2019 was used to conduct simple descriptive statistics including mean and standard deviation (SD) for body mass, capture parameters (drug doses, induction time, capture time, recovery time), anesthesia variables (Tr, HR, RR, SpO 2 ), and all variables from blood gas, hematological, and serum biochemistry analyses, for every animal and within protocol groups. Kurtosis, skewness and their standard error were also calculated to confirm that the data within each protocol group were normally distributed. R-3. 5. 3  (20)  software was used to conduct a Kruskal-Wallis test by rank to investigate significant differences among protocol groups. A Dunn's test with simple Bonferroni method for pvalue adjustment was used as post-hoc. Paired samples Wilcoxon test was used for the blood gas, hematological and serum biochemistry variables between the first and the second sample.
When a sow entered the feeding station, an antenna detected and identified its RFID ear tag, and its feed allowance, as stored in the computer, was delivered. Sows from seven batches were not fed this way when the gestating rooms were under reconstruction in 2013. The solution was to keep the gestating sows in rooms in the fattening unit, where it was not possible to control the individual feed allowance. During gestation, sows were fed a gestation diet in the form of pellets. The nutritional composition of ingredients in the feed was assessed using Evapig®software  [8] , based on chemical analyses (dry matter, crude protein, starch, crude fibre, ash and fat contents, depending on the ingredient) performed by the feed company. Technical staff of the demonstration farm created a formulation to meet the nutritional recommendations (i.
Modelling the Mean Daily Energy Requirement The ME requirement, estimated using a factorial approach, equalled the sum of the daily maintenance requirement (ME m ), daily requirement for physical activity (ME act ) above the moderate level already considered in ME m , and the daily energy retention in maternal tissues (ME ERs ) and in conceptus (ME ERc ). Data collected at the beginning of gestation were used to describe the initial characteristics of the sow (BW initial and BT initial ). The age at farrowing (Age final , day) was calculated as the sum of age at insemination (Age initial , day) and the mean duration of the gestation period in the herd (D gest , day). The relationship between BW final and Age final in the herd was used to calculate the expected net body weight of the sow at the end of gestation.
7 × (1 -exp [-1. 962/1000 × Age final 1. 085 ]) (10) Next, the relationship between BW final and Age final was modelled from sows that were studied for at least six parities over the reproductive cycles (SAS, v9. 4, proc NLIN). Four sow populations were defined based on each sow's year of birth, with one population defined every 4 years. Their root mean square error of prediction (RMSEP) was calculated for each relationship. The mean and SD of measured BW final and BT final were calculated per batch that followed the precision feeding program and subjected to analysis of variance (SAS, v9. 4, proc GLM), with the housing systems used before or after the station was under reconstruction as the main factor. Other investigations were based on individual data collected from sows from the nine batches that were not fed individually during the reconstruction period.
Definition of Optimal Backfat Thickness at Farrowing from Longevity Data from 1221 primiparous sows born from February 2005 to April 2013 were collected from reproductive cycles 20-56 to study the relationship between BT final at first parturition and longevity in the herd. When there were more gestating sows in the batch than places available in the farrowing unit, these sows were culled, and their data were removed from the analysis. Data from primiparous sows that farrowed during reproductive cycles 51-56 were also removed from the analysis because they could not reach 7th parity by that time. Ultimately, BT final and the longevity of 720 primiparous sows were available, as was the housing system in which they were reared during the first gestation period. The mean longevity per rounded BT final was calculated by housing system.
Discussion The equations based on the four populations recorded every four years indicate that it is not necessary to update the equation used to predict BW final every four years; it can be updated less frequently. The Brody and Weibull models fit the data similarly. The precise calibration of the sow profiles is based on the dataset used to describe mature BW. The original Weibull equation (Equation  (10) ) initially fit the data obtained from the first reproductive cycles, but it seemed that it was evaluated too soon. After only a few reproductive cycles, the amount of available data from old sows was too limited, which resulted in underestimating the mature BW final (mean of 273. 4 kg from Equation (10), compared to 320. 1 kg with the Brody model and 325. 2 kg with the Weibull model). Because the mean BW was slightly underestimated, so was the energy requirement for maintenance and physical activity.
Acknowledgments: The author thanks the technical staff of the demonstration farm,  Sylvie Lechaux ,  Didier Pilorget ,  Kelig Rocher  and  Angélique Debroise , the IFIP technical staff,  Jean-Claude Vaudelet ,  Delphine Loiseau ,  Frédéric Guyomard ,  Romain Richard  and  Lorena Gire , for the husbandry of the animals and their technical support and  Jean Noblet  for his scientific support. The author also thanks the two anonymous reviewers who improved the article.
Moreover, little data is available on the heritability of these biomarkers and their correlation with other HS phenotypes and with production traits, making it difficult to postulate on the most relevant biomarkers to include in genetic studies. Because HS reduces feed intake, it can be difficult to determine whether these responses are the consequences of direct effects of HS, or of indirect effects linked to the reduced feed intake  (44, 45, 48, 49). Moreover, most of these measures are obtained from blood sampling methods that are costly in production conditions and that are invasive to animal welfare. Nevertheless, understanding the metabolic and cellular response to HS is essential to eventually find proxies or alternative measurements (such as salivary measures) for these parameters that can be used in genetic studies.
These observations were censored per compound and excluded from the modelling dataset (Fig. 9 ). The final model showed low residual error (Supplementary information, Figure  S2 ) and low bias. Consequently, PK parameters like k e, t 1 2 e (as calculated from ln(2)/k e ) and Q could be accurately identified with low standard errors (Table  2 ). Moreover, we calculated the AUC 0-48h values based on fitted functions that represents the total compound exposure across time for the immersion, PC and IP microinjection and the combined treatment conditions. To define the Relative Exposure (RE 10/2 ) of the compound after immersion at 10 µM, we calculated the ratio of AUC 0-48h immersion vs the AUC 0-48h PC microinjection (2 mg/kg) (Fig. 10a ) as defined in Eq. (  5 ): In general, the results show that in case of short incubations (3 h) the relative exposure was low to very low (range: < 0.
We therefore show evidence that IY injections should be avoided when screening small compounds for toxic or pharmacological effects, as this route possibly results in a disproportionate number of false negative outcomes. Clearly, the outcome does not preclude the possibility of an effective compound transfer from the yolk to body tissues when IY injections are performed during a very early embryonal stage (i. e. , 0-3 hpf) as compared to the time point used in this study (i. e. , 3 dpf). However, this issue is beyond the scope of the present study and requires further systematic characterization of the body distribution of IY-injected fluorescent dyes as a function of embryonal development. Next, we quantified the integrated fluorescence intensity of delineated whole-body contours of the eleutheroembryos after immersion and after the PC and IP microinjections, and modelled the data using a one-compartmental PK model.
Determination of experimental lipophilicity (LogD o/w ). The lipophilicity of the fluorescent compounds was determined following the EPA guideline OPPTS 830. 7550 Partition coefficient (shake flask method)  46  using Danieau's solution and n-octanol as the two immiscible phases. Analyses were performed using an Agilent Infinity 1290 UHPLC system (Agilent Technologies, Waldbronn, Germany) consisting of an autosampler, quaternary pump and DAD-detector, operated with Open Lab software (version C. 01. 10, Agilent Technologies). FAMA, R6GA and CY3A were separated in RPLC mode on an Acquity BEH C18 column (100 × 2. 1 mm, dp = 1. 7 µm) from Waters (Milford, MA, USA) at a flow rate of 0. 4 mL/min. Gradient elution was performed starting at 3:5:92 (v/v) ACN: 200 mM ammonium formate (adjusted to pH 3 with formic acid):MilliQ water, and changed to 82:5:13 (v/v) ACN: 200 mM ammonium formate (pH 3): MilliQ water in 11.