dataesr/scientific-paragraphs-categorization huggingface dataset:
5% of them not going through a sorting centre moved only once: from the cow-calf producer to the fattener (Fig. 4a ). The remaining 95% moved at least twice: from the cow-calf producer to their first sorting centre, and from their last sorting centre to the fattener. In between, the calves potentially also moved between centres. However, the dataset only informed us about the first sorting centre each weaned calves went through, but not about their complete itinerary. To circumvent this lack of information from the database, we chose to infer the rest of the itinerary with the following rule of thumb: the distances travelled had to be as short as possible. We therefore considered the best case scenario distance-wise for the historical data. While the actual itineraries were likely longer than what is assumed, this method ensures that any improvement brought about by our algorithm can be viewed as a lower bound for the actual improvement When all the weaned calves of a same batch were initially sent to the same sorting centre (i.
e. 84% of the batches of the dataset), we assumed that it was the only one they went through. Therefore, they moved twice: from the cow-calf producer to the centre and from the centre to the fattener (Fig. 4b ). When the weaned calves of a same batch were initially sent to multiple sorting centres (i. e. 13% of the batches), we assumed that they were only moved one more time to be reunited in one of these sorting centres before being sent to the fattener. The centre where they were reunited was chosen so that the sum of the total distance travelled by all the calves in the batch, from their cow-calf producer to their fattener, was minimum. Therefore, those initially sent to the centre where they were reunited were considered to move twice, and those initially sent to other centres moved three times: from the cow-calf producer to their original centre, from this centre to another one, and from this last centre to the fattener (Fig.
Acknowledgements This work was supported by The  Research Program "For and on Regional Development" PSDR 4 Grand Ouest  funded by  INRAE  and the regions Bretagne,  Normandie ,  Nouvelle Aquitaine  and  Pays de Loire  and project  ANR-368 16-CE32-0007   CADENCE. The data were provided by the beef producer organization  Terrena Production Bovine  as part of the  SANT ' Innov  project.
The ABIMMUNE Project Two projects aiming to develop immunomodulatory drugs as adjunct to antibiotic drugs in the context of pneumonia are described in this work. In the first one, ABIMMUNE, the lipopolysaccharide (LPS)-derived compound monophosphoryl lipid A (MPLA) was used in combination with amoxicillin (AMX) in order to treat mice infected with Streptococcus pneumoniae. This project is described in detail elsewhere  [60, 61] , and the extensive pharmacometric analysis applied on the data can be found in a companion paper elsewhere in this issue  [62]. An extensive in vivo dataset was generated, investigating multiple levels of murine data to comprehensively gain information about the efficacy of the combination of AMX and MPLA. A murine infection model was established with immunocompetent Swiss and Balb/cJ mice being infected intranasally with S.
pneumoniae during anesthesia. Then, 12 h post infection, treatment was initiated with a single dose of AMX via oral gavage or MPLA intraperitoneally. In total, four study groups were investigated: untreated mice, treatment with AMX, MPLA, or the combination of both drugs. While a single dose tier was defined for MPLA, multiple dose tiers of AMX were investigated. Several markers were assessed with typically one sample per individual mouse in independently performed studies: AMX serum concentrations over time, bacterial numbers in lung and spleen over time, survival, change in body weight, mRNA expression of markers of the immune system, cytokine serum concentrations, histological scores of immune-system-related markers, and cell recruitment of immune-system-related cells to the lung. To quantitatively analyze the investigated combination of AMX and MPLA, pharmacometric analysis of the generated data was performed.
Lessons Learned from ABIMMUNE and FAIR The aim of this concept paper was to use two case studies to elucidate the use of translational modelling and simulation approaches in early drug development, specifically in the case of immunomodulatory drugs in the context of bacterial pneumonia. In the first project, ABIMMUNE, pharmacometric modelling was used to quantify a PK and a PD interaction between the TLR4 agonist MPLA and the antibiotic AMX. Indeed, by using powerful hierarchical modelling approaches, sparsely sampled murine trials could be combined into one dataset and exploited in full. In this way, the magnitude of the bacterial killing effect of the immunomodulator or antibiotic alone, as well as that of the combination, could be quantified. Furthermore, the pharmacometric PK/PD model was linked to a TTE model, which could quantify the effects of treatment in the sense of survival.
g. , model misspecification. At the beginning of a drug development project, a small amount of information with high uncertainty is available, allowing only for simple pharmacometric models combined with non-informative priors in a Bayesian inference framework. As these models are used to design further experiments, confidence in model structure increases as data become available and priors are updated using posterior distributions of previous analyses and/or external data. As the project continues, a knowledge database for the compound is constructed, and a model repository of increasingly more complex models is created. For example, with more data available, basic allometric scaling approaches can be complemented by mechanistic models, such as physiologically based pharmacokinetic models.
Figure1. The developed translational modelling and simulation platform for integration, exploitation, and modelling of an immunomodulatory compound's PK/PD. Starting from the literature and pre-existing datasets, the platform enables the design of appropriate preclinical experiments and novel data to implement the model in order to perform dose finding for clinical trials. This process can be iterated multiple times to predict the PK/PD and doses to be used in phase I trials and beyond. PD: pharmacodynamics; PK: pharmacokinetics; NLME PK/PD: nonlinear mixed effects PK/PD; PBPK/PD: physiologically based PK/PD.
Funding: This work was supported by grants from the  German Federal Ministry of Education and Research  [Grant agreement ID:  031L0097 ] in the context of the  Joint Programming Initiative on Antimicrobial Resistance project  " ABIMMUNE " and  H2020-EU. 3. 1. 3. [Grant agreement ID: 847786] in the context of the  SC1-BHC-14-2019  project " FAIR". Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Pharmaceutics 2021, 13 , 8 of 13 Figure 1. 13131 Figure1. The developed translational modelling and simulation platform for integration, exploitation, and modelling of an immunomodulatory compound's PK/PD. Starting from the literature and pre-existing datasets, the platform enables the design of appropriate preclinical experiments and novel data to implement the model in order to perform dose finding for clinical trials. This process can be iterated multiple times to predict the PK/PD and doses to be used in phase I trials and beyond. PD: pharmacodynamics; PK: pharmacokinetics; NLME PK/PD: nonlinear mixed effects PK/PD; PBPK/PD: physiologically based PK/PD.
9-74. 6%) in Korea  [9] [10] [11]. The revised 2013 Korean guideline recommended bismuth-containing quadruple therapy (BQT) as an alternative first-line therapy when clarithromycin resistance is suspected  [5]. The European and North American guidelines also recommended the BQT as an empiric first-line therapy in high clarithromycin resistant regions  [1, 2]. However, in the nationwide registry database, the prescription rate of BQT as first-line therapy in Korea was low (2. 2%)  [6]. The low prescription rate of the regimen as first-line therapy is related to the fact that the regimen is reserved for a second-line therapy after the first-line STT failure  [5]. The BQT accounts for up to 81% of all prescribed second-line therapies in Korea  [6]. It also has a complex dosing schedule and higher risk of adverse events, which might affect compliance with the regimen  [12, 13].
Randomization, treatment, and follow-up Eligible participants were randomly assigned in a 1:1 ratio to receive either BQT for 10 days (lansoprazole 30 mg, twice daily; tripotassium bismuth dicitrate 300 mg, four times daily; tetracycline 500 mg, four times daily; and metronidazole 500 mg, three times daily) or STT for 7 days (lansoprazole 30 mg; clarithromycin 500 mg; and amoxicillin 1,000 mg; all given twice daily). A web-based system using computer-generated permuted random block with a size of 2, 4, or 6 was used for randomization, and the stratification factor was sex. The Conclusions: Ten-day BQT had higher eradication rates compared to that of the 7-day STT as an empirical first-line treatment for H. pylori eradication in Korea. Trial registration: ClinicalTrials. gov, NCT02557932. Registered 23 September 2015, https ://clini caltr ials.
Quantifying the intensity of animals' reaction to stimuli is notoriously difficult as classic unidimensional measures of responses such as latency or duration of looking, can fail to capture the overall strength of behavioural responses. More holistic rating can be useful but have the inherent risks of subjective bias and lack of repeatability. Here, we explored whether crowdsourcing could be used to efficiently and reliably overcome these potential flaws. A total of 396 participants watched online videos of dogs reacting to auditory stimuli and provided 23,248 ratings of the strength of the dogs' responses from zero (default) to 100 using an online survey form. We found that raters achieved very high inter-rater reliability across multiple datasets (although their responses were affected by their sex and attitude towards animals) and that as few as 10 raters could be used to achieve a reliable result.
This has produced large-scale datasets which could not otherwise be generated (e. g. surveying bird species distributions at national or international levels by using local reports by ornithologists)  (Desell et al. 2015). More recently with the expansion of the internet, researchers have adopted crowdsourcing approaches for mass data analysis tasks which are time-consuming or cannot be automated easily  (Cox et al. 2015). These have been found to contribute positively to both public engagement levels and science generally, with high publication rates and large cost savings relative to employing a worker full-time for years  (Cox et al. 2015). Canine behaviour studies have previously used crowdsourcing for both data collection  (Stewart et al. 2015; Worsley and O'Hara 2018)  and analysis  (Mirkó et al.
to avoid bias in the raters' responses, which can be an issue in studies without blinding  (Tuyttens et al. 2014). The videos were converted to MP4 format using Adobe Media Encoder CC (Adobe 2018). A total of 258 videos were presented in three separate datasets (wave 1: 36 videos, wave 2: 78 videos, wave 3: 144 videos, where wave indicates a dataset). These videos were selected to represent a range of behavioural reactions from low to high activity within the context of the experiment.
Assessing behaviours performed in videos When all the videos had been rated by more than 30 participants, the behaviours the dogs presented in each video were first listed (Table  1 ) and then assessed as a binary response (e. g. head turn? Y/N) for each video. Behaviours from all videos were coded independently by three researchers (co-authors JF, LB, and HRG). Where agreement was not reached unanimously, the majority vote ruled. A principal components analysis (PCA) was performed to reduce the listed behaviours to a set of components (varimax rotation) in SPSS. These were loaded with behaviours that were highly correlated to strength as rated by participants. A linear regression model was then used to assess which components contributed to the reaction strength-ratings, Bonferroni adjusted p = 0.
Participants For wave 1, participants rated between 1 and 36 out of 36 total videos (mean = 34. 2, SD = 7. 2). For wave 2, participants rated between 2 and 78 out of 78 total videos (mean = 72. 4, SD =18. 1). For wave 3, participants rated between 10 and 144 out of 144 total videos (mean = 136. 3, SD = 27. 7). To remove unrepresentative outliers, which were often due in wave 1 to the programme not playing the video and raters thus giving inaccurate scores of "0" because they couldn't see the dog, rating scores were retained if they were within 2 SD of the mean for the stimulus video, calculated in SPSS using the 'Descriptives' function.
Acknowledgements We would like to thank all of our raters, dog owners, and dogs for their participation in this study. We would also like to thank  James Alvarez  for his support of the website and help with the data collection, and  Arik Kershenbaum  and  Angela Dassow  for their help editing the manuscript. This project was funded by the  BBSRC  grant ( BB/P00170X/1 ) ' How Dogs Hear Us '.
Assays available in each selected publication were recorded in a database (Excel file) where the following variables were registered: name of the strain, code of the parasite laboratory clone when it exists, country origin of the strain, DTU to which the strain belongs, parasitic form on which the assay was carried out (epimastigote, trypomastigote or amastigote), drug incubation time, LC 50 (trypomastigotes) or IC 50 (amastigotes and epimastigotes) values expressed in μM, standard deviation of LC 50 or IC 50 values expressed in μM, number of replicates for each assay, parasite viability determination method, publication year, authors and title of the publication.
Data for a total of 59 T. cruzi strains belonging to 6 different DTUs were analyzed (40 TcI, 6 TcII, 3 TcIII, 1 TcIV, 5 TcV and 4 TcVI strains). Strains originated from seven different countries (Brazil 26, Colombia 15, Chile 1, Mexico 10, Venezuela 3, Argentina 2, Bolivia 1, and Nicaragua 1). For 26. 9% of assays the authors used transfected strains or strains cloned in the laboratory. Overall, 11 different methods were used to assess the viability of the parasites; however, manual counting under the microscope (63. 6%), was the most prevalent. Among the 60 articles, the large majority included only one or 2 assays (63. 3%), 23. 3% included 3 to 5 assays, and 13. 3% a greater number, ranging from 6 to 27.
Epimastigotes Table  1  summarizes the information obtained from the 33 articles that included 97 assays (IC 50 values) for epimastigotes from 49 strains distributed by DTU as follows: TcI 69. 4%, TcII 12. 2%, TcIII 4. 1%, TcV 8. 2% and TcVI 6. 1%; note that information for TcIV strains was not available. Most of the strains were from Brazil 42. 9%, Colombia 28. 6%, and Mexico 20. 4%, while data for only one strain from Bolivia, Chile, Nicaragua, and Venezuela (2. 0% each) could be included in the analysis. In the different studies, parasite viability was primarily determined via microscopic counting (76. 3%), while no other method exceeded 8. 2%.
Amastigotes Table  3  summarizes the information obtained from the 30 publications that included 60 assays for amastigotes from 17 strains mostly distributed into TcI 64. 7%, followed by TcVI (2 strains, 11. 8%), and only one each (5. 9%) for DTUs TcII, TcIII, TcIV and TcV. The strain distribution by country was: Colombia 52. 9%, Brazil 23. 5%, Venezuela 11. 8%, and only one strain from Argentina, Bolivia and Chile (5. 9% each). Three main methods for measuring the number of amastigotes per infected cell were used, high-content imaging analysis (40%), microscopic counting after giemsa staining (35. 0%), and the colorimetric method using the β-galactosidase-transfected Tulahuen strain (21. 7%). one assay, 19 data sets were deleted from the meta-analysis (S2 Table ). Ultimately, 189 assays were included.
01 and p < 0. 05 respectively). No other significant pairwise difference was found at 72 or at 96 hours of incubation. Once again, all assays included in the meta-analysis for TcII were performed with the Y-strain. For TcVI only two strains were tested (Tulahuen and CL Brener) while data for 10 TcI strains were available. Additionally, funnel plots were constructed for each data group ("parasite stage/drug incubation time/DTU", S1, S2 and S3 Figs). In all cases, data sets did not fit the expected inverted funnel shape and an asymmetrical distribution of values was recorded in general.
As a result, available data were extracted from numerous articles and required grouping for further analysis. However, most of the groups of assays included in the meta-analysis were composed by fewer than 10 assays, and constitute a source of dispersion as the estimated mean values are less precise than when larger number of assays are analyzed  [54]. All these factors certainly are an important source of the variation reported in LC 50 and IC 50 values and their standard deviations, directly affecting the dispersion recorded on the funnel plots  [55]. Perhaps because axenic culture is simple and cost-effective, most studies focus on epimastigotes instead of on mammalian-infective forms, which are relevant to human infection. Around 20% of the recorded articles focused on epimastigotes exclusively, and twice as many used assays corresponded to epimastigotes than to trypomastigotes or amastigotes.
The published information for in vitro benznidazole susceptibility is heavily skewed towards TcI (35. 1%) and strains from Brazil. Most of the individual assays recorded in the current study corresponded to TcI, TcII, and TcIV (91. 8%). Consequently, the pairwise comparisons of the LC50 or IC50 mean values between DTUs were possible for all three developmental stages of the parasite for only these three DTUs, although with a low sample size in some cases. Data for TcIII were available only for epimastigotes, while no data meeting the inclusion criteria was available for TcIV. In sum, the scarcity of available data prevents an exhaustive exploration of our research question. Additionally, DTU TcII was represented by a single strain (Y-strain) for both mammalian life cycle stages, amastigotes and trypomastigotes.
This work was supported by project  O13066 , from  Pontificia Universidad Catolica del Ecuador  (www. puce. edu. ec),awarded to J. A. C; the ParaFrap PhD South Program 2017-2020 (https:// www. labex-parafrap. fr/en/)awarded to A. V. ; and by annual funds from  IRD  (https://en. ird. fr/),awarded to IRD member S. F. B. The funders had no role in development and evaluation would not be advisable. Our findings highlight the need for establishment of uniform experimental conditions as well as a screening of different DTUs during the optimization of new drug candidates for Chagas disease treatment.
Statistical analyses were performed with R 3. 3. 1  (R Core Team, 2014). The function ''lmer'' in the package ''lme4''  (Bates et al. 2015)  was used to fit GLMMs  (Venables and Ripley 2002)  and the package "lmerTest"  (Kuznetsova et al. 2016 ) used to calculate parameterspecific p-values based on Satterthwaite's approximations. We set the level of significance to α = 0. 05 and parameter estimates are given as mean ± SE.
The three echinostome species recently found in Iceland include Neopetasiger islandicus  Kostadinova & Skírnisson, 2007  afterwards also reported from North America  [126] , Echinostoma revolutum (Frölich, 1802) sensu stricto and Echinostoma sp. IG recorded from Europe  [32, 46, 48]. Due to the apparent overlapping geographical distributions of echinostome species found in Iceland, we further included data from Ireland, Finland, and Alaska (USA) to investigate echinostome species diversity, host-use and distribution over a larger geographical area. We analysed novel DNA sequence data and associated them with morphological characterisations, together with data previously reported from Europe, North America, Africa, Asia and Australasia  [24, 25, 46-48, 51, 84, 118, 122] , with the aim of further contributing to the resolution of echinostome species diversity.
[84]. The section of the nuclear 28S rRNA gene (28S) was amplified using primers digl2 and 1500R  [121] , following PCR conditions as described by Tkach et al. [126]. The amplified DNA was purified using exonuclease I and shrimp alkaline phosphatase enzymes  [136]  and sequenced using PCR primers and internal primers ECD2 and 300F  [88, 89]  for the 28S section. Cycle sequencing of DNA was carried out applying ABI Big Dye™ v. 3. 1 chemistry at the commercial company SEQme (Dobříš, Czech Republic, https://www. seqme. eu) with the use of an AB3730x1 capillary sequencer. Sequences were assembled and edited using Geneious v. 11 (Biomatters, Auckland, New Zealand) and deposited in GenBank. To confirm the morphology-based identification of snail species, the partial mitochondrial cytochrome c oxidase subunit 1 (cox1) gene and the internal transcribed spacer 2 (ITS2) sequences were generated for ten isolates (Table  3 ).
Phylogenetic analyses Four alignments including novel and previously published sequences for echinostomes were built using MUSCLE  [29]  implemented in Geneious v. 11. Alignment 1 (431 nucleotides (nt)) included novel nad1 sequences of Echinoparyphium spp. (n = 25) and sequences of this genus available in GenBank (n = 31). The sequence of Echinostoma revolutum (KC618451) was used as the outgroup. Alignment 2 (417 nt) included nad1 sequences of Echinostoma spp. generated in this study (n = 13) and retrieved from GenBank (n = 27). The sequence of Patagifer sp. (MK534424) was used as the outgroup. Alignment 3 (402 nt) included nad1 sequences of Neopetasiger spp. generated in this study (n = 4) and retrieved from GenBank (n = 10). The sequence of Drepanocephalus auritus (KP053262) was used as the outgroup.
The nad1 sequences were aligned with reference to the amino acid translation, using the trematode mitochondrial code (translation table  21 )  [42, 105]. Alignment 4 (1137 nt) included 28S sequences obtained during the present study (n = 19) and sequences of other representatives of the family Echinostomatidae available in GenBank (n = 36). The sequence of Caballerotrema sp. (KT956941) was used as the outgroup. Taxa used as the outgroups were selected based on the results of the phylogenetic analyses of the Echinostomatoidea and Echinostoma published by Tkach et al. [126]  and Georgieva et al. [46, 48]. Bayesian inference (BI) and maximum likelihood (ML) phylogenetic analyses were conducted using MrBayes version 3. 2. 3. [113]  and PhyML version 3. 0  [56]  software, respectively. Prior to analyses, the best-fitting model was estimated with jModelTest 2.
1. 2 [23]. The general time-reversible model incorporating invariant sites and gamma distributed among-site rate variations (GTR + I + G) was selected for all datasets. Markov Chain Monte Carlo (MCMC) chains were run for 10,000,000 (Alignments 1, 2 and 4) or 3,000,000 (Alignment 3) generations, log-likelihood scores were plotted and only the final 75% of trees were used in BI analysis to produce the consensus trees. Nodal support for the ML analysis of all four alignments was estimated by performing 100 bootstrap pseudoreplicates. Trees were visualised using FigTree ver. 1. 4 software  [110]. Pairwise genetic distances were calculated using the p-distance - Iceland - MZ400492 b - Gyraulus cf. parvus AF352 - Iceland MZ398103 a MZ400494 b - Gyraulus cf. parvus AF354 - Iceland MZ398105 a MZ400495 b - Gyraulus cf.
parvus AF355 - Iceland - MZ400491 b - Myxas glutinosa AF338 - Ireland MZ396110 a MZ400489 b - Physa acuta AF344 - Iceland MZ396244 a MZ400493 b - Radix balthica AF347 - Iceland - MZ400490 b - Radix balthica AF348 - Iceland - MZ400497 b - (Continued on next page) model in MEGA ver. X  [80]. New sequences of echinostomes were deposited in GenBank with accession numbers MZ404641-MZ404689 and MZ409801-MZ409819.