dataesr/scientific-paragraphs-categorization huggingface dataset:
5. 1). Partial-least square discriminant analysis (PLS-DA) was performed with mixOmics package  (Rohart et al. , 2017). Metabolite relative concentration was used as variable matrix (X). Groups (Day 7, Day 11 CTRL, Day 11 ETEC) were used as predictors (Y) and timerepeated measurement were considered by using a multilevel approach. Univariate statistical analysis was also performed on each metabolite relative concentration with the R packages lme4 and car. A mixed-model one-way ANOVA (lmer and ANOVA functions) with group (Day 7, Day 11 CTRL, Day 11 ETEC) as a fixed effect and fermentation experiment as a random effect was used. A post hoc test was used to compare the mean relative concentrations with Tukey correction. P-values were corrected for multiple testing (false discovery rate).
4 µL of forward and reverse primer, 9. 2 µL of milli-Q water, and 2 µL of cDNA. Tenfold dilution series of each primer pair were used as standard curves to determine primer efficiencies. Real time PCR efficiencies were calculated according to the equation: E = 10 (-1/slope). The appropriate reference gene and the Log2 fold change of each gene, compared with the IPI-2I cells which had not been exposed to bead medium supernatants, were determined by GenEx software 3. A mixed-model one-way ANOVA (lmer and ANOVA functions) with time point (days of fermentation) and ETEC treatment as fixed effects and fermentation number as a random effect was used to compare the significance between gene expression profile of the IPI-2I cells which were exposed to bead medium supernatants containing or not the ETEC Ec105 strain using the R packages lme4 and car.
RESULTS Analyses of the microbial profile and SCFA proportions of the three pooled fecal inocula for the runs #1, 2, and 3 are available in Supplementary Figure  1. Redox potential was followed throughout the whole fermentation runs and was representative of a feed deprivation stress by displaying an important increase during this period such as detailed in  Gresse et al. (2021)  (data not shown). Gas composition was also monitored every day during the fermentation runs. At the end of the stabilization period [Day 7, the mean relative proportions of H 2 , O 2 , CO 2 , N 2 , and CH 4 for the six bioreactors were, respectively, 2. 1 ± 1. 2, 0. 3 ± 0. 1, 73. 7 ± 0. 4, 5. 9 ± 0. 8, and 6. 8 ± 1. 1% (data not shown)].
It can be noticed that the LT gene was quantified in much lower quantity from day 11 in all samples from run #1 (Figure  8 ). Finally, a low copy number of LT gene could also be detected in the CTRL condition at mean values of 2. 9 ± 1. 6, 3. 4 ± 1. 6, and 3. 9 ± 0. 2 log 10 of gene copy/g of sample, respectively, in the bioreactor medium, mucin beads, and bead medium, however, these values are close to the detection limit (Figure  8 ). The activity of the ETEC strain in the MPigut-IVM was evaluated by quantification of virulence gene expression using RT-qPCR. Even though the expression of virulence genes was detected in the bioreactor medium and bead medium in the ETEC condition on the day 10, 11, and 15, none of the targeted virulence genes (EAST1, LT, and K88 genes) was expressed in the CTRL condition (data not shown).
The orientation of the limb can be obtained by integrating the angular velocity of the gyroscopic signal over time, paying attention to the accumulation of drift errors  [36]. In order to limit the risk of drift, the integration is carried out over reduced time windows  [37] , corresponding in our study with the duration of the stride or stance phases (641. 5 m/s and 201. 5 m/s on average, respectively, at a 4 m/s trot  [23] ) for the measurements of the protraction/retraction movements of the limb during the stride or the stance. The gyroscopic data are recorded in the IMU's local coordinate system. The measurement of the absolute angles of protraction and retraction of the limbs requires knowing the initial position of the limb, the neutral position, corresponding with a protraction/retraction angle of zero degrees.
Data Acquisition The data acquisition was performed on a treadmill. Each horse trotted three times on a treadmill at a speed of 4 m/s, a common speed at which the horse is observed during clinical examinations  [39, 40]. They then trotted three times at a faster trotting of 6 m/s. Motion capture sessions included the tracking of 3D passive markers and IMUs  [23]. Each of the eight horses was equipped with 10 reflective markers placed on an anatomical landmark  [23]. In addition, two IMUs, one on the dorsal part of the canon bone and the second on the withers, were used  [23]. At least 25 strides were recorded at a stabilized speed during each trial.
Data Processing of the IMUs The calculation of the canon angle by the IMU was performed by the integration of the gyroscope data, which required an integrative constant to be determined. The first step in processing the IMU data was also to define a reference point where the canon bone was at a vertical position corresponding with a limb protraction/retraction angle of 0 •. For this, two methods were used and compared: (i) The canon bone was considered in a vertical position at 50% of the stance phase  [12]  ("50%_Stance" method). (ii) The canon bone was considered in a vertical position at the moment when the withers reached their lowest point ("minWithers" method). In the case of (i), the index corresponding with 50% of the stance phase was calculated from the FootOnMOCAP(i) and FootOffMOCAP(i) data previously defined on the MOCAP data for each stride i ranging from 1 to n (n > 25).
This gave the index 50% _Stance(i) for each stride recorded (Equation (  7 )). 50%_Stance(i) = (MOCAPFootOff(i) -MOCAPFootOn(i))/2. (7) In the case of (ii), the vertical displacement of the withers was calculated from the MOCAP data and the IMU data. For the MOCAP data, the vertical position of the withers corresponded with the coordinates of the marker of the withers on the Z axis (the vertical axis in the laboratory reference system). From the IMU, the vertical displacement of the withers was obtained by a double trapezoidal cumulative integration of the acceleration on the Z axis of the IMU  [43, 44]  with the application of a Butterworth high pass filter (1 Hz) to remove the drifts generated by the double integration. The dorso-ventral displacement curves of the withers at the trot obtained from the MOCAP and IMU data formed two oscillations repeated at each stride corresponding with the stance of each of the horse's forelimbs  [44, 45]  (Figure  5 ).
The detection of the minimum position of the withers was performed for the oscillation corresponding with the stance of the right forelimb (Figure  5 ) on the displacement curve of the withers obtained from the MOCAP data and the displacement curve of the withers obtained from the IMU data. The detection of the minimum position of the withers was made only on the oscillation corresponding with the stance of the right forelimb because it was the limb equipped with the IMU. The correlation coefficient between the indices corresponding with the minimum position of the withers obtained from the MOCAP data and those obtained from the IMU data showed a perfect correlation (R = 1 with α = 0. 01), leading us to choose to use only the indices obtained from IMU data for the continuation of the data processing.
Thus, the frame minWithersIMU(i) was obtained for each stride i ranging from 1 to n (n > 25) (Figure  5 ). Once the 50%Stance(i) and minWithersIMU(i) indices were obtained for each stride i recorded, the calculation of the orientation of the canon bone from the data of the IMU positioned on the canon bone could be achieved in two steps. First, the signal from the gyroscope on the Y axis (  ) at each frame was integrated to determine the temporary values of the canon angle based on the IMU data for each stride i (AngleCanonIMU_temp(i)) as follows (Equation (  8 )) (Figure  6A ): AngleCanonIMU 𝑡𝑒𝑚𝑝 (𝑖) = { 1 𝑓 ∫ 𝜔𝐼𝑀𝑈 𝑦 (𝜆)𝑑 𝑗 FootOnIMU(𝑖) 𝜆 | 𝑗 ∈ [FootOnIMU(𝑖) FootOnIMU(𝑖 + 1) -1 ]}. (8) The numerical calculation of the integration was performed by a trapezoidal method. Let us note that AngleCanonIMU_(){} was the k-th element of AngleCanonIMU_().
At this stage, let us remark that at each stride i, we had An-gleCanonIMU_temp(i){1} = 0° (Figure  6A ). We could then add the integrative constant to deduce the canon angle according to option (i) and (ii), respectively, named AngleCanonIMU_50%S (Equation (  9 )) (Figure  6B ) and AngleCanonIMU_minW (Equation (  10 )) (Figure  6C ) and defined as follows: AngleCanonIMU 50 %𝑆(𝑖) = AngleCanonIMU_𝑡𝑒𝑚𝑝(𝑖) -AngleCanonIMU_𝑡𝑒𝑚𝑝(𝑖){50%Stance(𝑖) -FootOnIMU(𝑖) + 1} (9) AngleCanonIMU () = AngleCanonIMU_() -AngleCanonIMU_(){minWithersIMU() -FootOnIMU() + 1} We could then compute the protraction and retraction angle during the stance phase according to option (i) and (ii) as follows (Equations (  11 )-(  14 )): Once the 50%Stance(i) and minWithersIMU(i) indices were obtained for each stride i recorded, the calculation of the orientation of the canon bone from the data of the IMU positioned on the canon bone could be achieved in two steps.
Statistical Analysis The results of each method with the IMU data were then compared with the MOCAP data using Bland-Altman plots  [46]. For each method, the accuracy was defined by the mean difference (bias) between the values of the developed method and the reference values obtained from the MOCAP data and the precision such as the standard deviation of differences (SD). The limits of agreement corresponded with the confidence interval where 95% of the differences were calculated  [46]. The bias and the SD were used to estimate this interval (Equations (  19 ) and (  20 )). The results were expressed as the difference in degrees between the angles measured using the method applied to the IMU and those determined with the MOCAP. AgreementLimitHigh_X = Bias + 1. 96 × SD (  19 ) AgreementLimitLow_X = Bias + 1.
However, the minWithers method required the use of an additional IMU to those of the limbs, fixed at the withers. The use of this additional IMU could then be a limit to the system. Indeed, this additional data had its own noise to signal ratio and uncertainty due to the attachment system of the withers in particular, which could contribute to increasing the measurement variability. Considering this observation, the use of the 50%Stance index for the measurements of the maximum protraction and retraction angles of the limb during the swing phase had advantages because it did not require the addition of an IMU on the withers. Our result suggested that it also had disadvantages, in particular by the loss of precision during the measurements of the protraction/retraction angles at a stance, which could be a limit to the system.
Introduction Two relatively new developments in the field of toxicology and environmental health are the development and use of adverse outcome pathways (AOPs)  (Villeneuve et al. , 2014)  and the application of evidence-based approaches such as systematic reviews  (Griesinger et al. , 2008; Hoffmann et al. , 2017; Stephens et al. , 2016; Whaley et al. , 2016)  and systematic evidence maps 1   (Wolffe et al. , 2019). An AOP describes a sequence of temporally and causally linked events at different levels of biological organization, which follows exposure to a stressor (e. g. , chemical, physical, etc. ) and leads to an adverse health effect in humans or wildlife. AOPs are used to organize mechanistic information and support the application of mechanistic data in chemical safety assessment  (Groh et al.
Second, humans must then train computers to digitize this information using controlled vocabularies (such as Universal Medical Language System). However, while these "digital" vocabularies include definitions and synonyms, they lack the relationships between terms found in ontologies that are needed to identify connections between exposure and effect findings that might have otherwise been missed. Dr Angrish concluded that mapping the digitized data using an AOP ontology model is a possible solution (and an active area of research) with the added advantage that this approach combines expert-and data-driven evidence integration within an AOP framework.
This may be independently assessed or addressed as part of the assessment of internal validity. -Sensitivity: whether or not the study design impacts the ability of that study to detect an effect (i. e. , if only high doses were used and low doses are of interest, it will not be relevant to your research question). In addition to the apical endpoint studies that need to be critically appraised, mechanistic data also need to be analyzed. Mechanistic data generally focus on upstream indicators and may be part of a larger study that investigated the health effect of chemical exposure, but the data are needed to illuminate the AOP. Mechanistic data can be found from a wide variety of study types: animal or human in vivo studies, in vitro studies of either animal or human cells, in silico or modeled data.
In particular, researchers could use evidence-based systematic methods to explore direct and indirect connections between outcomes of regulatory concern and the AOP analytical construct (i. e. , KE-KER(s), etc. ). Dr Angrish next introduced systematic review methods in environmental health sciences and provided an example of how systematic review workflows and systematic mapping methods could be used to formulate a literature search, screen studies, and extract data (i. e. , test system/species, exposure methods, experimental design, endpoints, chemicals, etc. ). In the example, Dr Angrish demonstrated how the overall workflow could be formulated around a particular molecular target and outcome of interest. Experimental studies and extracted data organized into literature inventories can be mapped to the AOP schema, but a challenge lies in the semantics.
Once the flexibility of an IATA has been removed and the information sources (e. g. , in silico, in chemico, in vitro, in vivo methods) and data interpretation procedures are fixed, the IATA becomes a defined approach (DA) (OECD, 2017b). In a DA, expert judgement is removed and independent users of the DA should come to the same conclusion for the same chemical. A "complete" AOP (including a MIE, all KEs, all KERs, and an AO) is not necessary for IATA development. Rather, the appropriate degree of completeness of the AOP will vary highly with the problem formulation. Three examples were provided to illustrate how AOPs can be used to guide IATA development under different circumstances. In the first example, Dr Browne discussed human skin sensitization, which is a relatively simple and well-understood toxicological process supported by a complete AOP, with multiple guideline methods that measure all KEs.
Was administered dose or exposure level adequately randomized? 2. Was allocation to study groups adequately concealed? 3. Did selection of study participants result in the appropriate comparison groups? 4. Did study design or analysis account for important confounding and modifying variables? 5. Were experimental conditions identical across study groups? 6. Were the research personnel blinded to the study group during the study? 7. Were outcome data complete without attrition or exclusion from analysis? 8. Can we be confident in the exposure characterization? 9. Can we be confident in the outcome assessment? 10. Were all measured outcomes reported? 11. Were there no other potential threats to internal validity? The study design determines which questions are applicable. The evaluation is endpoint-specific (so the questions are answered multiple times for studies with multiple endpoints), and the rating is on a 4-point scale.
Building evidence-based AOPs The discussion focused on building AOPs in an evidence-based way. To that end, the participants were presented with the AOP concept and schema and then discussed what sort of information should be sought if AOPs were to be constructed using a literature-driven (rather than expert-driven) approach. Major chal-In the second example Dr Browne described, AOPs are used to help organize and integrate data for evaluating endocrine disruptors. International regulatory requirements for demonstrating the endocrine disrupting potential of a chemical vary by region, but all include linking an adverse effect to an endocrine MoA. As most available methods do not measure both of these types of data in a single assay, evaluating endocrine activity requires integrating evidence from multiple assays.
From a retrospective analysis of human carcinogens identified by the International Agency for Research on Cancer, 10 key characteristics were proposed for identifying and grouping mechanistic data to assess potential carcinogens  (Smith et al. , 2016). The key characteristics were not identified or organized using AOP frameworks, and, in fact, building "complete" AOPs or AOP networks to capture the various MIEs and KEs represented by the key characteristics would take years. Nonetheless, the key characteristics provide information on several levels of biological organization, and along with animal experimental and human epidemiological data, can be organized using an AOP construct without understanding all intermediate events or causal relationships. Such an approach has been proposed as a way to organize mechanistic data to predict therapeutic response of personalized cancer therapies  (Morgan et al.
g. , peer-reviewed publications, meta-data, public databases, AOP-Wiki, etc. ) at the KER level or across several KERs. Participants emphasized the importance of transparency throughout the process for evaluating certainty within AOPs, especially when there is limited or no evidence between KE pairs. When understanding the certainty of the underlying evidence, the perspective of the end-user should be taken into account: Does the certainty relate to the entire AOP model in order to inform questions about an exposure/population outcome relationship or to elements within the AOP to understand the uncertainty of a relationship between KEs? When considering the relationship between KEs, participants established the following elements for assessment: study limitations (RoB), inconsisten-lenges to applying GRADE practitioners' experience in literature-driven approaches to the AOP context include the use of a different technical vocabulary by AOP developers (e.
Plenary concluding discussion Due to the requirements of the regulatory environment and for historical reasons, laboratory animal models continue to serve as the standard in toxicology in order to extrapolate to human outcomes in the absence of direct human data. But it remains an indirect approach and, thus, offers a less than perfect prediction of human outcomes. Since neither in vivo animal nor in vitro mechanistic data are considered purely predictive of human outcomes at this point, various types of mechanistic evidence should be combined within an assessment framework to provide robust, high-confidence outcomes. AOPs provide such an integrated framework but have traditionally been defined through an expert review process that can be inconsistent and incomplete. Applying systematic methodologies to AOP development would result in higher certainty in the evidence evaluation underpinning each AOP, which would then increase the utility of that AOP for supporting regulatory decisions.
The AOP framework was designed to provide a transparent structure to organize knowledge/data and can function as the scientific basis for toxicity extrapolations via the underlying mechanisms of toxicity. AOPs can serve as a guide for new assay development to fill important gaps and add information about measurable KEs leading to AOs. Retrospective systematic literature reviews of in vivo and in vitro mechanistic studies promise a way to provide a bridge to human outcomes as well as increase the transparency, consistency and objectivity in the development and assessment of AOPs as well as the resulting toxicity predictions. AOPs combine in vitro or in vivo data by mapping the available data to the associated KE within the biological mechanism. Therefore, the framework naturally integrates different types of data, but it does not assess the individual types of data or provide guidance on how the information from different types of data should be combined.
Systematic methodologies can fill this gap within current AOP development practices. The first ideas on how to assess the RoB in in vitro (mechanistic) studies and how to apply (the domains from) GRADE to AOPs, notably to KERs, are being developed, but further research is necessary to establish the details of the required tools (Box 1). As many individuals and organizations do not have the resources to delve into large and growing literature databases, the development of broad systematic evidence maps has the potential to allow far more researchers to use the scientific literature effectively and to formulate specific questions that can be answered by systematic reviews  (Wolffe et al. , 2019). In order to construct such systematic evidence maps, the scientific community needs to restructure the way they conduct, report and publish results.
At the moment, the primary raw data are stripped from experiments as they are prepared for publication. These data need to be accessible to anyone and kept as machine-readable meta-data, rather than being lost. Systematic evidence maps built on such transparent and complete data would allow researchers to find gaps in the knowledge and notice novel associations that were not apparent before. The beauty of the AOP paradigm is that it provides a framework for capturing the extracted information, but the chal-cy (unexplained heterogeneity between individual studies), indirectness (how well the evidence reflects the question asked), imprecision, and publication bias. No additional domains were identified. The identified factors strongly suggest that current evidence assessment domains used within the GRADE framework  (Guyatt et al.
Box 1: Key messages The AOP framework represents a significant development in how mechanistic information is assembled for safety assessment in toxicology and environmental health: -AOPs allow the integration of human data, animal data, and data from new approach methods (NAMs) within a single framework. -AOPs are developed using a transparent framework. To support this, the AOP KnowledgeBase was developed, which includes two modules: the AOP-Wiki and Effectopedia. -The problem for which an AOP is used will determine the level of detail required when describing the molecular initiating event, key events, adverse outcomes, and the relationships among them. In every case, however, systematically and transparently assembled literature data could support the assessment of the certainty in AOPs.
The sedation level was also assessed based on a 4point criterion, ranging from 0 to 3, at different times after injection. Data were analyzed in SPSS software using Kruskal-Wallis, Chi-square, multivariate regression, and generalized estimating equation (GEE) tests. The signi cance level was set at P <0. 05. Results: The naloxone-diazepam combination reduced the number of seizures, severity, and duration of seizures compared to the tramadol use alone and reduced the number of higher-intensity seizures to a greater extent than other treatments. Naloxone alone reduced the number and duration of seizures, as well as the duration of mild seizures. Diazepam decreased the severity and duration of seizures. However, it increased the number of mild seizures. In comparison with the tramadol alone group, the acute quercetin group had a lower duration of mild and severe seizures, and higher numbers of mild and moderate seizures.