dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection BW (kg) and feed intake (FI -kg) were recorded for 100 fattening pigs. The animals were entire male pigs from the Piétrain NN Français paternal line, free from halothane sensitivity, of the Axiom company. The animals were randomly selected from the 990 animals raised in 2020 at the AXIOM boar testing station and born on one farm. The animals entered the boar testing station after weaning at the rate of one group every 3 weeks and were raised in quarantine rooms and in postweaning rooms for 7 weeks. Then, they were transferred to fattening rooms (t = 0) when they were 76. 5 ± 1. 1 days of age (35. 7 ± 5. 8 kg BW). They were kept in fattening rooms, in pens of 14 pigs, during 71. 8 ± 3. 4 days until the individual candidate test at around 148. 3 ± 3. 4 days of age (107. 2 ± 8.
Data pretreatment Pretreatment was performed on data recorded by AFS at each visit using the procedure proposed by  Revilla et al. (2022). The weight (W t -kg) and the FI t (kg) for each identified pig (ID) and each fattening day (t) were estimated, where t was time in days since the transfer to fattening room. Data recorded on day t = 0 were excluded from the dataset due to AFS calibration and animal adaptation. Missing values for weight and/or feed intake were also estimated using a local regression  (Revilla et al. , 2022). These data were included in the pretreated data file (DataAxiom. csv).
The observation EEq. (1) related CW and CNA. Eq. (2) was a system equation describing the changes in a t (unobserved state variable) from day to day according to a stochastic process. The model has been built using the function dlmModReg of the package dlm  (Petris et al. , 2009)  of the software R (https://www. rproject. org/). The parameters r v 2 and r w 2 were estimated by maximum likelihood with the function dlmMLE. The values of a t were calculated independently for each animal with a Kalman smoother algorithm (function dlmSmooth). The R script (DLM_script. R) contains the workflow to do the estimation of a t across time for a given animal. The identification of the individual (ID), from 1 to 100, to estimate must be specified at the beginning of the script. The various estimated variables are exported in the Result.
csv file: ID, t, W t , FI t , EI t , MR t , NEA t , CNEA t , CW t , a t. The value of a t at t = 1 is not estimable because the consumption at tÀ1 is unknown. The average results for the whole dataset are presented in Table  1. The minimum feed intake value equal to zero was measured on animals #6 and #7, from fattening group 202,025 and pen number 13, without any intake during day 2. This was due to a mechanical problem of the AFS. Figs. 1 and 2 show the example.
Acknowledgements We acknowledge the technicians of AXIOM's testing station at  La Garenne  for their implication for data collection. We also thank  Ingrid David  ( INRAE -UMR GenPhySE -France ),  Anna Stygar  ( LUKE -Finland ) and  Nicolas C. Friggens  ( INRAE -UMR MoSAR -France ) for their constructive discussion and helpful comments on this project.
Search strategy and selection criteria We searched for articles written in English in the PubMed database from May 1, 2019, to May 31, 2021, without any restrictions on the date of publication of the articles included in the search, considering combined terms such as "Africa", "antifungal", "invasive fungal infections", "dermatophytosis", "mycology", "resistance", "agriculture", "laboratory", "access", "treatment", "susceptibility testing", "toxicity", "fluconazole", "amphotericin", "therapeutic drug monitoring", "epidemiology", "burden", "low-and middle-income countries", "cryptococcus", and "HIV". We used guidelines (the WHO guidelines for diagnosing and managing disseminated histoplasmosis among people living with HIV and WHO guidelines for the diagnosis, prevention and management of cryptococcal disease in HIV-infected adults, adolescents, and children) and the essential medicine list from WHO as consult material as well as the 2019 UN Report on Human Development and Multidimensional Poverty Index to better contextualise our data.
a b s t r a c t Respiration chambers are the gold standard technique for measuring methane in ruminants provided that their gas recovery rates are close to 100%. The determination of the gas recovery rate of respiration chamber facilities is a central prerequisite to assess the accuracy of the methane emission quantification. However, data of recovery tests are seldom reported. This paper presents data from gas recovery tests applied to an experimental facility of four open-circuit respiration chambers designed to measure methane emissions from goats. The experimental facility is located at Thiverval-Grignon, France. The recovery test was assessed by placing a known source of methane emission at six locations in each chamber successively. For each chamber, the gas from the chamber and the ambient air were continuously sampled by a Multi-Gas Analyser 3500 gas analyser provided with a multiport unit that switches the sampling between the pipe from chamber and from the ambient air every 90 s.
How data were acquired For each of the four chambers, samplings for determining methane concentrations were taken from the ambient air (background outside the chamber) and from the chamber for each of the six locations of methane emissions. Methane concentrations (ppm) were measured using an Multi-Gas Analyser 3500 gas analyser. Mass flow (m 3 /h) of the conduction system was measured by a mass flow meter Mass-Stream, Bronkhorst.
Description of data collection For the recovery test, data were recorded by the Multi-Gas Analyser 3500 gas analyser that produced a csv file with information on the sampling times, the inlet analysed (ambient air or chamber) and the concentration of methane in ppm. We converted the resulted files into Excel files and added manually a column with the airflow rate (m 3 /h) of the chamber provided by the mass flow meter Mass-Stream, Bronkhorst, and the location of the emission of methane. For the determination of the response time and stability of the gas analyser, the concentration of methane was registered every 5 s. xls display methane (ppm) during the methane recovery test using a controlled source of methane emission (200 mL/min from a bottle with methane at 8%) during 30 min at six locations in each chamber (Fig.
5 Â T90 of the gas analyser). After data collection, the concentration of methane in the chamber was subtracted from the methane concentration measured in the ambient air for each time point. The total amount of methane measured was further calculated by determination of the area under the curve using the trapezoidal rule implemented in the R software (https://www. r-project. org/) (see the script recovery. R in the data repository). The methane recovery was calculated as the ratio between the total amount of methane mea-sured and the known amount of methane emitted. The results are presented in Table  1. Fig. 4  shows the example of the dynamics of measured methane for chamber 4.
Acknowledgements Sylvie Giger-Reverdin ,  Rafael Muñoz-Tamayo  and  Joseph Tessier  are very grateful to  Dr. David Yañez-Ruiz  ( CSIC, Spain ) and  Dr. Carlos Fernández Martínez  ( UPV, Spain ) who kindly allowed them to visit their methane experimental facilities. These visits were central in providing key elements for the design of the experimental facility at  INRAE. Ophélie Dhumez  and  Rafael Muñoz-Tamayo  thank  Mr Sylvain Poullette  ( Gruter & Marchand, France ) for his valuable technical support and timely expert advice in the setting up of our experimental facility.
| Differences between selection lines in infection traits and condition traits (univariate models) To assess line differences (first objective of this study), we analysed separately the changes in the two infection traits (FEC and ΔHE) and in three condition traits (BW, BFT, and MT) during each phase of infection. During PP1 and PP2, data observed during pregnancy and lactation were analysed separately. To assess the effect of line on trait changes (R vs. S) while accounting for the non-independence of the data, we implemented linear mixed models following the standard protocol described by  Zuur et al. (2009)  and using the 'nlme' package  (Pinheiro et al. , 2019)  in R v3. 5. 0 (R Core Team, 2018). Infection and condition traits used as dependent variables were normally distributed, except FEC that was transformed to account for skewness in its distribution.
We thank Annabelle Meynadier (Ecole Nationale Vétérinaire de Toulouse) for technical assistance in formulating the diet and Ingrid David (INRAE, GenPhySE) for technical assistance in using ASREML 3. 0. The work was funded by the projects SusTradeOff (ERA-NET SUSAN, no 696231) and SMARTER (H2020, no 772787).
ACKNOWLEDGING ANIMAL WELFARE AS A MULTI-STAKEHOLDER TOPIC AND ITS DIVERSITY It is evident that the understanding and importance of animal welfare differs for each and every individual, region and culture. Animal welfare is a complex and multifaceted topic, involving  scientific, ethical, economic, cultural, social, religious, and political dimensions (British Farm Animal Welfare Council, 1993; Fraser, 2008) , and also intrinsically linked with the environment and human health  (Pinillos, 2018). Although livestock farms are getting larger and more concentrated in both high-income and low-income countries, the majority of livestock worldwide, primarily living in lowincome countries, are still kept in small scale farms, free-range or backyards  (International Fund for Agricultural Development, 2013).
Ingredient occurrence data using Industry Survey data and consumer database information can also be used (as in the parabens case study  (Ouedraogo et al. , 2020). We are ultimately aiming to perform a risk assessment by calculating an acceptable margin of safety (MOS) where MOS = toxicological POD divided by an exposure estimate in the same units. Tiered risk assessment is an iterative process of refinement of both exposure estimates and toxicological hazard. If, at any tier, an acceptable margin of safety (MOS) cannot be demonstrated, the assessment moves to a higher tier where more data are generated to increase the level of confidence. However at lower tiers in the assessment, there is often more conservatism applied  (Solomon et al. , 2008). The safety assessment is finished if (at any tier of the approach) it has been demonstrated that the MOS is acceptable for the population under consideration, or if at the highest tier the risk is not acceptable and further refinements are not possible, then risk management measures such as restrictions for use must be put in place.
Indeed, there are many factors that are important in the overall exposure assessment and their inclusion may be a consideration for further exposure refinement as needed as shown in Tier 1 of the framework. Knowledge about absorption, distribution, metabolism and excretion (ADME) (via skin, oral or inhalation routes) can provide an internal dose metric. Moreover, the application of physiologically-based biokinetic (PBK) modelling in Tier 2 may help to target internal dose metrics (i. e. chemical in organs, blood, etc) in animals and humans. These kinetic data are considered in Tiers 1 and 2 of the proposed framework but for now, in Tier 0, we only consider external exposure.
Step 3: Collate supporting data on target chemical and define data gap(s) All attempts should be made to collate data for the target chemical (and major metabolites of interest) on physico-chemical properties; existing toxicology and NAM data; and absorption, distribution, metabolism and excretion (ADME) data. An appropriate literature and database search should be performed using the major authoritative sources of information and a search strategy adopted such as those described in IATA (Integrated Approaches to Testing and Assessment) case studies by the OECD (  Van der Stel et al. , 2021). A typical literature and data search might include sources such as those in Table  1. All evidence should be tabulated in a clear, systematic and logical form. If, at the end of this process, the target chemical has relevant data gaps, so that no point of departure (POD) or health reference value for systemic toxicity can be derived, the data gap(s) and the problem formulation must be clearly defined.
Step 4: Analogue(s) a) Identify, b) collate existing data, c) determine similarity hypothesis (with or without MoA data) Step 4 is a crucial step in RAX as it allows for the identification of suitable analogues (source chemicals) to be used as a surrogate(s) for the target chemicals, from which a toxicity benchmark can be read across for the target based on a justifiable hypothesis. The process of analogue identification is multifaceted and may require multiple iterations of refinement depending on the data found in database and literature searches. It is driven by aspects of chemistry and metabolism knowledge, chemical similarity, biological activity (mechanistic) concordance, toxicokinetic similarity, toxicodynamic similarity, availability of evidence to justify and support the similarity arguments and the availability and quality of data for the source compounds.
(2020) ), then a structural analogue from the family may be found when searching in a toxicological database or customised read-across tool such as the OECD QSAR Toolbox, COSMOS DB or AMBIT. It is preferable, even when a homologous series is known, to undertake a generic chemical similarity search using cheminformatics tools and still proceed with care in inferring toxicity, as it is possible that unexpected toxicological behaviour, possibly through biotransformaitions, can arise. Searches based on common modes of action or metabolites would usually be undertaken when some prior knowledge can be assumed or may be made available following in silico or even in vitro profiling. Such information may also include structural alerts for chemical-biological interactions, as linked to molecular initiating events (MIEs) in Adverse Outcome Pathways (AOP)  (Cronin and Richarz, 2017)  as well as any NAM data that already exist.
It is powerful due to its transparency in principle, but demonstration of the assumed mode of action driving toxicity may not be trivial. ii) Chemical structure similarity: Different cheminformatics tools may be used to search databases for appropriate source substances as analogues for the target. Computed similarity scores are popular because of their speed of use and ability to find closely related structural analogues which are not necessarily or obviously in the same chemical class or homologous series. It is vital that the correct approach for assessing similarity is used. Generally these methods are provided to query available databases to find similar structures or may be accessed via websites such as Chem-Mine (https://chemminetools. ucr. edu/), however these can sometimes lack transparency.
Data on the selected analogues may be available in global public data sources, such as from PubMed or authoritative reviews, such as the Cosmetics Ingredients Review (CIR) website, the European Chemicals Agency (ECHA) REACH database (as publication permits), or data may be available in company archives. It is relevant to collect all toxicological endpoint data for target and analogues, e. g. on the range of endpoints as would be included in an evaluation according to the SCCS 10th Notes of  Guidance (2018) , not just the study data type that is relevant to the target chemical data gap, as other data can also be used to substantiate biological and toxicological similarity. Ideally, the study quality should be reviewed and reported for all source analogues, most commonly undertaken as per the methods of  Klimisch et al.
e. a surrogate for an internal dose. Skin penetration could in theory be estimated in silico and a model has been developed for fragrance materials as described in  Shen et al. (2014)  to predict flux (J max ). However, in silico predictions require further development for a broader range of chemicals. Recently,  Hewitt et al. (2020)  described dermal absorption datasets for 56 chemicals, and it is hoped that these data can improve the predictivity of in silico modelling in the future. Gathering as much evidence as possible on the similar and differential ADME properties for both target and analogues will help to inform how confident the POD prediction is for the target, whether adjustment is required and to ensure an appropriate level of conservatism is factored into the risk assessment for differential toxicokinetics between target and analogue and between routes of exposure.
Confidence can be attained by providing sufficient high quality data, evidence and scientific rationale in the RAX documentation, and assessing against a set of defined questions, such that the predictions can be reproduced if necessary, and a full scientific critique can be performed by a body such as the SCCS in Europe. To meet these requirements for transparency and reproducibility, clear documentation, descriptions of searches and results, the databases used and on what date searches were made, etc and reporting following a consistent template structure or agreed framework would be extremely helpful. Examples of how one could document a RAX and address levels of confidence in the evidence have begun to be discussed in  Blackburn and Stuard (2014) ,  Schultz et al. (2015 Schultz et al. ( , 2019) )  and  Escher et al.
Throughout the whole process, uncertainties and the level of confidence in the data should be captured as transparently as possible and integrated to provide and overall level of confidence in the assessment. Examples of questions to address: o What type of category formation was attempted and was it suitable for the context of the read-across? oHow well made was the premise or hypothesis of the read-across argument? oWhat rationale was used to select the NAMs used and how did they support the decision making? oHow was mechanism of action considered supported and assessed? oHow was similarity in TD/effects defined and assessed to support the MoA? (continued on next page) questions outlined in Step 10 of Table  2. The level of confidence should also consider the degree of conservatism vs realism in the accompanying exposure estimation, and especially in the context of the exposure estimate as input data into a PBK model.
The level of confidence analysis in a RAX approach based on chemical similarity alone is qualitative and somewhat relying on expert judgements and narrative statements. The most straight-forward case of NAM is to underpin and increase confidence in a POD for a target chemical, reading across from toxicology data generated for a similar analogue(s), and one can exit the framework when one is sufficiently confident in the POD selected. There has been an expansion in the number of tools and databases in the past decade, including for physico-chemical and ADME analyses, in vitro data and toxicogenomics data etc, that can be used to underpin analogue(s) identification in terms of similarity  (Pawar et al. , 2019) , many of which we used in our parabens case study  (Ouedraogo et al. , 2020). It is oHow was similarity in TK/potency defined and assessed? oWhat were the uncertainties in the toxicological data for read-across data and how did they allow for an assessment of robustness of these data? oHow were NAMs applied and did they assist in the reduction of uncertainty? oWhat are the key strengths of the case study? oWhat are the key limitations of the case study? • If overall confidence is not acceptable e.
g. method reliability, data quality and extent of data, what additional information is required to increase confidence or are additional uncertainty factors required? therefore difficult to see that a single unified approach to searching, analysing and reporting out of these new tools could be done, as it would limit possibilities of using the best scientific tools of the day. Therefore, a general approach for analogue selection based on sound scientific principles that can be supported with a variety of tools and databases could be used so long as they are explained well and justified. The scientific method for analogue selection should be reported using a structured narrative, and description of the adopted search process and tools provided in such detail that the process and conclusions drawn can be reproduced by a regulator and reviewed by a competent and trained professional risk assessor with experience of the tools being used in the submitted dossier.
://ec. europa. eu/jrc/en/scientifi c-tool/toxtree-tool US EPA ToxCast Data https://www. epa. gov/chemical-rese arch/downloadable-computational-to xicology-data Toxicogenomic tools: Comparative toxicogenomics database NTP DrugMatrix http://ctdbase. org/ https://norecopa. no/3r-guide/d rugmatrix a Correct at time of going to press.
Materials and methods A web-based questionnaire was designed and hosted on a dedicated website (www. mercurychallenge. ceva. com) to collect data on cats that were having SBP measured as part of their clinical examination (Figure  1 ). Clinics were encouraged to follow the American College of Veterinary Internal Medicine (ACVIM) guidelines for evaluation of BP,  17  and were given the option of entering up to five individual SBP measurements (with the mean being automatically calculated), but they could also enter any number of values between 1 and 5. Additional data collected included age, weight, breed and sex of the cat; concomitant diseases that had been diagnosed; concomitant treatments that the cat was receiving; results of SBP assessment; time taken to assess SBP; device used to measure SBP; and subjective assessment of the demeanor of the cat when measuring SBP.
Variables with a P value <0. 20 in the univariable linear mixed-model analysis were used in the backward selection to build the multivariable model. Goodness of fit was assessed with the normal distribution of the residuals. It was also hypothesized that the cat's demeanor, the device used and the SBP measured might all independently affect the duration of SBP assessment, and a proportional-odds cumulative logistic mixed model was used to assess the effect of these variables on the duration of SBP assessment. However, only cats known to have had at least five SBP recordings, along with information on the duration of SBP assessment (n = 4250) were included in these analyses. For all analyses, GraphPad Prism (version 9. 2. 0) or R software (version 4. 0. 4) was used, and a P value <0. 05 was considered to be statistically significant.
Population characteristics A total of 10,153 SBP assessments were recorded in the database; of these, 1269 were excluded: 1146 as they were from cats aged <7 years (and therefore did not meet the inclusion criteria); and 123 as the recorded SBP was <80 mmHg (range 10-79) and these entries were regarded as unreliable based on previously published data. 17, 18, 39 his resulted in a total of 8884 unique SBP assessments, which were used as the population for subsequent analyses of factors affecting SBP values measured (Figure  2 ). In 4630/8884 cases, <5 individual SBP recordings (between 1 and 4) were entered into the database, and it was impossible to determine whether at least five recordings had originally been obtained. In a further four cases, although five readings had been entered, the duration of SBP assessment had not been recorded.