dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was funded by the  FINOVI foundation  under a  Young Researcher Starting Grant , the  Cystic Fibrosis French Foundation Vaincre la Mucovicidose  grant  RF20130500897  and the  ANR  (grant  n˚ANR-15-CE15-0011 ) to SS, and by grants  BIO2016-75030-P  from  Ministerio de Economı ´a y Competitividad (Spain)  and  S2017/ BMD-3691  ( InGEMICS-CM ) from  Comunidad de Madrid and European Structural and Investment Funds  to VJC and MM. SS is supported by an  INSERM  staff scientist contract. J. M. C-S is supported by a predoctoral contract from  UCM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Despite a reasonable negative predictivity (i. e. 82%), there were some concerns hampering regulatory adoption of the approach, arisingat least in part -from the definition of the three criteria and how they are applied  (Bourcier et al. , 2015). Further inspection of the dataset used for developing the NegCarc approach led to the hypothesis that predictions of carcinogenic potential could be improved by also taking into account pharmacologic properties  (ICH, 2013). This hypothesis was evaluated by van der Laan and colleagues and proven to be valid in two previous projects, one of which was an EPAA project (van der  Laan et al. , 2016a, b). They confirmed the association of negative findings for the three criteria with negative carcinogenicity. Moreover, they showed that negative and positive predictivities increased to 92 and 98%, respectively, when pharmacological properties were also taken into account  (van der Laan et al.
The EPAA project on carcinogenicity of agrochemicals The current EPAA project on NGTXC builds on the previous experience with the approach for pharmaceutical products and the evaluation of testing requirements in different sectors. The overall goal of this project is to provide evidence that data from subchronic repeated dose toxicity studies can be leveraged to predict human relevant carcinogenic potential of agrochemicals with reduced or no need for a carcinogenicity study. The focus is on agrochemicals, which, according to current regulatory requirements, involve the mandatory conduct of long-term carcinogenicity studies in two species (OECD Test Guideline 451 (OECD, 2018) as part of their risk assessment. In a first step, a database of reports for over 400 agrochemicals evaluated by the US Environmental Protection Agency (US EPA) and the European Chemicals Agency (ECHA; Classification & Labelling inventory) was used to identify and categorise tumours induced by chronic administration of the chemical.
For some of these agrochemicals, information was incomplete or insufficient. The remaining 352 chemicals were categorised into (i) genotoxic carcinogens (  25 ); (ii) NGTXC (170); and (iii) non-carcinogenic chemicals (157). Of the 170 NGTXC with sufficient quality data, information on tumours was listed, including sites, sex, and species. A more detailed description of these findings has been published separately  (Heusinkveld et al. , 2020). Tumour sites were mainly in the liver and thyroid gland but others were evident in a wide range of tissues, including endocrine tissues and haematopoietic/lymphoid tissues. Where available, data related to mechanisms underlying tumour formation were collected from reports and/or from literature or, alternatively, they were deduced from the histopathological changes observed.
Potential To ensure engagement of various stakeholders and to collect valuable feedback, a workshop was held to discuss the mechanism-based approach for predicting the carcinogenic potential of agrochemicals. To illustrate what the mechanism-based approach may look like in practice, four "real-life" case studies from the database were developed and anonymised. These case studies were selected based on good availability of data; and they were merely used as examples to facilitate discussion of the proposed mechanism-based approach. They were presented to the participants. Each case study focused on a specific MOA and comprised data from in vitro and short-term in vivo studies for a specific agrochemical, while information on absence or presence of tumour formation was not included. For the purpose of this discussion, the focus was on information known about MOAs for tumours observed in rodent studies.
Acknowledgments The authors wish to thank  Fabrice Broeckaert ,  Kathryn Guyton  and  Andrea Terron  for their valuable contributions during the workshop. They thank  Eric Gremmer  for providing a graphic design of the mechanism-based approach to cancer risk assessment. The workshop 'Mechanism-based approach to cancer risk assessment incorporating 3Rs principles' was funded by the  European Partnership for Alternative Approaches to Animal Testing (EPAA). Disclaimerthe authors are experts representing their own opinions and not their institutions. H. Braakhuis ,  H. Heusinkveld ,  N. Hewitt , and  M. Luijten  were compensated by EPAA for the time spent in the preparation, review and submission of this manuscript. About the EPAA: EPAA is a Public-Private Partnership across 7 industry sectors and between the  European Commission and Industry stakeholders.
Data collection Data from the cross-sectional study was available for 16 Belgian, 14 French, 19 German and six Swedish farms. Details of the data collection in farms in the cross sectional study was described in Sjölund et al. [17]. Briefly, data on AMU of the participating herds was collected for one year preceding the herd visit in Belgium, Germany and Sweden and for the last batch produced in France. Information on AMU originated from invoices from prescribing herd veterinarians or feed companies, treatment records or from direct interview with the farmer. Since 15 farms (additional farms: France n = 6; Germany n = 6; Sweden n = 3) did not participate in the cross-sectional study and were recruited specifically for the intervention study, these farms were asked to provide data on AMU and disease incidence from the year preceding the study (Fig.
1 ). During the intervention study data on disease incidence and AMU was collected for one year in Belgium, France and Germany. Herd veterinarians in Belgium and France provided receipts of all antimicrobial expenditures of the farm during the year of follow-up. In Germany either the herd veterinarian or farmer provided copies of the obligatory application and dispensing records. Swedish veterinarians retrieved the AMU data from herd treatment records as described by  Sjölund et al. (2015)  for three consecutive farrowing batches. For sows, AMU was only collected during the nursing period  [19]. The information on the receipts and records included commercial product names, amount and size of the antimicrobial packages. In Belgium and France all prescribed antimicrobials were re-allocated by the farmer to a given animal category, namely suckling pigs, weaned pigs, fattening pigs or breeding pigs (i.
e. sows, gilts and boars). In Sweden this was retrieved from the treatment records and in Germany the dispensing and application form provided information on the treated animal category. To define a population at risk to receive antimicrobial treatments, the average number of present pigs per age category (i. e. suckling pigs, weaned pigs, fattening pigs and breeding pigs) was collected from the farm Fig. 1  Overview of the study setup management system or by interviewing the farmer. To harmonize and improve data quality across the participating countries all collected data was entered in a Microsoft Office Access© (version 2010) database.
Herd specific intervention plan and compliance In Belgium, France and Germany, an initial herd visit, together with the farmer, the herd veterinarian and the project researcher was organized to define a herd specific intervention plan. In Sweden the herds were either visited initially by the herd veterinarian of the G&D or by one of the two project researchers. Data and results from the cross-sectional study were used as a basis for discussion about options to reduce AMU. The alternatives were summarized in six general categories for improvement namely i) external biosecurity status (measures to prevent pathogens from entering into herd premises or a group of animals), ii) internal biosecurity status (measures to prevent the spreading of pathogens within herd premises or a group of animals), iii) herd vaccination scheme, iv) feed or water quality or composition, v) pig health care (management/focus on individual, vi) pig stable climate and other zootechnical measures.
Disease incidence For information on the disease incidence, farmers were asked about the average frequency of treatments (e. g. antimicrobials, electrolytes, probiotics etc. ) against certain disease symptoms per age category over the preceding year. Annual data on the disease incidence from both the cross-sectional study and the intervention study was available in 15 Belgian, 13 French and 20 German farms. In Sweden data on diseases was collected batchwise and expressed as a percentage. Thus, the Swedish data was incomparable with the data from the other countries and not included in the analysis. In the intervention study, farmers were asked to provide these data by the end of the study. Median disease incidence scores before and after the intervention study were compared for each age category.
The comparison of selected outcome variables before and after intervention (AMU, disease incidence) was possible, because detailed data from the year proceeding the interventions study was available for each participating farm. One of the main limitations of the present study relates to the way of applying a direct comparison between groups (non-parametric comparison of medians). During the study most of the technical information, antimicrobial usage and information on the disease incidence were collected on two time points: the last batch produced before the implementation of the intervention and at the end of the study. The last batch produced, was used as the reference to evaluate the impact of the intervention. In order to improve data quality and analysis different statistical tools, such as statistical process control (SPC) could be used.
Acknowledgements The participating farmers and herd veterinarians are greatly acknowledged for contributing to the study. Moreover, we want to thank the rest of the  MINAPIG consortium  for designing and supporting the project:  Ann Lindberg ,  SVA, Sweden ;  Elisabeth Okholm Nielsen ,  Danish Agriculture and Food Council, Denmark ;  Hugo Seemer ,  Boehringer Ingelheim, Germany and Vivianne Visschers, ETHZ, Switzerland.
Biomechanical testing These two types of fixations were tested independently but following the same mechanical test methodology. Eight static tensile tests were performed using a traction system (AGS-X Shimadzu, Japan) with a pre-test of 20 mm/min traction until the load reached 30 N, straightening the system. The tensile test consisted in a 25 mm/min traction until failure and the sampling rate for data acquisition was set at 100 Hz. A total of 8 experimental set-ups were randomly considered: 4 testing the mechanical resistance to tearing of the proximal tendon fixation made using sutures (S1D&G, S2D&G) (Figure  1a ) and 4 testing the mechanical resistance to tearing of the distal fixation made using an interference screw (C1D&G, C2D&G) (Figure  1b ).
Statistical analysis Statistical analysis was performed using MedCalc @ 12. 1. 4. 0 (MedCalc Software®, Ostend, Belgium). The number of horses in each group was chosen arbitrarily. Normality of distribution was assessed using the Shapiro-Wilk test. Normal data were expressed as mean ± standard deviation (SD) whereas skewed data were expressed as median and interquartile range  [IQR]. Demographic data was compared between groups using Student's t test. An analysis of variance (ANOVA) for repeated measures was used to detect any signi cant variations of PTA and MAP within 1 min and 5 min at each time-point for both Colic and Elective groups. In case of signi cant variation, post-hoc Tukey multiple paired comparisons were performed. Variations of PTA and MAP within time were compared between groups using two-way ANOVA with Bonferroni correction for post hoc analysis.
When AIC c values of two competing models were within two units, we retained the simplest model (i. e. , the model with the fewest parameters) to satisfy parsimony rules. Analyses were conducted in R v. 3. 6. 0 using the nlme package (v. 3. 1-140,  Anderson and Burnham 2002, Pinheiro et al. 2019). Associated 95% confidence intervals for model estimates were calculated using the delta method  (Powell 2007). Repeatability and associated standard errors (SE) were estimated from 1000 bootstrap iterations using rptR (v. 0. 9. 22, Stoffel et al. 2017).
ACKNOWLEDGMENTS We thank two anonymous reviewers for helpful comments on an earlier version of this manuscript. We would also like to thank the  ONCFS (OFB)  for performing data collection and for granting access to data. We are especially grateful to  Serge Brandt ,  Cyril Rousset , and  Gilles Capron ,  Hervé Bidault ,  Jean-Luc Hamann , and  Vivien Siat  for helping to capture and.
It is therefore vital to supplement general spectral descriptives with a more fine-grained analysis of NLPs. This is technically challenging but feasible; for instance, several papers have shown that NLPs are involved in conveying intense distress in the pain vocalizations of human infants  [13, 14]  and adult actors  [15]. Here, we use a novel acoustic analysis and resynthesis software, soundgen  [21] , to experimentally add NLPs to human nonverbal vocalizations. Using this innovative methodology, we empirically test two accounts of the role of NLPs in the perception of vocal affective signals: (i) NLPs are non-specific and attention-grabbing indicators of high arousal, and thus not necessary specific to negative emotional and motivational states, or (ii) NLPs preferentially convey negative affect.
Data analysis The original dataset, scripts for acoustic analysis, audio stimuli and html files for running the experiments are available in the electronic supplementary material and from http://cogsci. se/ publications. html. Data from all three experiments (40 978 trials after excluding the 15% of Skip responses in Experiment 3) were analysed without aggregation, in a single multilevel model with three interacting main effects: call type (roar/scream/moan/gasp), measure (valence/valence in a positive context/valence in a negative context/intensity in a positive context/intensity in a negative context) and manipulation (no NLPs/subharmonics or sidebands/chaos). The outcome variable was the rating of a sound on a continuous scale, modelled with zero-one-inflated beta distribution  [26]  with four separately modelled parameters: (i) mu: the mean of beta distribution capturing non-extreme responses between 0 and 1; (ii) phi: the precision of beta distribution; (iii) zoi: zero-one inflation, the probability of answering 0 or 1 rather than a number in the interval (0, 1); (iv) coi: conditional one inflation, the probability of answering 1 rather than 0.
To make the model computationally tractable, its structure was slightly simplified relative to modelling all four parts with the full list of fixed and random effects. Model selection was performed on a subset of data using WAIC, Watanabe-Akaike information criterion  [27] , which suggested that modelling phi did not improve the model's predictive power. This part was therefore omitted, together with the random effects in the models of zoi and coi. The model of mu contained a random intercept for each participant and each unique vocal stimulus (N = 144, 48 prototypes × 3 manipulations). In addition, the effect of adding NLPs in each experiment was allowed to vary across stimuli by specifying the corresponding random slopes for each of 48 prototype sounds. All models were fit using the R package brms  [28]  with conservative priors.
The acquired physiological signals and the associated emotion-related labels were used in order to train machine learning models for the task of predicting the respective Valence and Arousal values, thus predicting the emotional state of the respective participant. The rest of this chapter is divided into six sections. Section 3. 2 gives a brief overview of several recent studies within the field of affect recognition and humanhorse interaction. Section 3. 3 presents a comprehensive description of the experimental approach followed in this study, while Section 3. 4 provides a description of the acquired data analysis process and the machine learning approach followed. Experimental results are provided in Section 3. 5 and finally the results are discussed in Section 3. 6 and conclusions are drawn in Section 3.
Experimental data acquisition Electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals were captured throughout the whole duration of the experiment using wireless, portable low-cost and low-weight sensors, while a laptop computer was utilised for signal recording and visualisation. The portable sensors were selected for their convenience during outdoor use as other types that require cables may restrict user movement during interaction with the horses. Furthermore, the small formfactor of the sensors and being wearable made them invisible to the participants. This fact, along with setting the laptop used for signal recording to not emit any sounds or show movement in the screen, ensured that there was minimal external interference due to the presence of equipment, thus avoiding bias.
Participants Out of the 23 participants recruited for the field experiments, only the data from 19 were considered for further analysis, as the captured data for 4 participants were unusable due to erroneous data recording. These 19 participants consisted of 12 males and 7 females that reported having normal health and were between 19 and 64 years old, with the average age being 38. 05 years with a standard deviation of 13. 14 years. Furthermore, 8 participants reported having no prior experience with horses, 6 participants reported having prior experience with the horses used in this study, and 5 participants reported prior experience with other horses.
, N, with k being the k-th reported emotion and N being the number of different emotions reported. Finally, thresholding was used in order to convert the numerical V and A values to emotion labels. Valence was set to Positive for V > 0 and to Negative for V < 0, while Arousal was set to Low for A < 0 and High for A > 0. As a consequence, the problems of predicting the Valence and Arousal dimensions of the emotional state of a participant were converted to binary problems. As a result, each feature vector in this study was associated with a Valence and an Arousal label. After computing the Valence and Arousal labels, their distribution was examined in order to establish the class balance of the created dataset. From Figure  3. 2, it is evident that the dataset is moderately unbalanced for Arousal since 70.
2% of the samples refer to High Arousal and only 29. 8% to Low Arousal. Furthermore, the dataset is highly unbalanced for Valence as only 12. 3% of the samples refer to Negative Valence, while 87. 7% refer to positive Valence. The distribution of Valance labels in this study is consistent with the outcomes of the Hama et al. study  [30]  which suggested that human-horse interaction is usually a pleasant experience for humans that leads to positive emotions. The distribution of Valence and Arousal labels was also examined in relation to the participants' prior experience with horses. As can be seen in Figure  3. 3, the distribution of Arousal labels was similar for both participants with and without prior experience with horses, with ∼70% reporting High Arousal and ∼30% Low Arousal. Contrary to that, participants with prior experience reported a higher percentage of ratings referring to Positive Valence (92.
During the application of the LOO cross validation, one sample is used for testing the trained model and the rest of the samples for training the model. This process is repeated until all the samples have been used for testing and the overall performance is computed as the average performance across all iterations. Furthermore, due to the unbalanced dataset, classification accuracy would not be suitable as a performance metric. To compensate for the class imbalance, the classification F1-score was selected as a performance metric since it is more suitable in cases of uneven class distribution. Furthermore, the value of the F1-score metric depends on the class that is considered as positive. To this end, the reported F1-scores in this study correspond to the average F1-score between the two classes.
Classification F1-score for Valence reached 78. 27% using the 1-NN classifier, while an F1-score of 65. 49% was achieved for Arousal using the LDA classifier. The best performing fusion approach for Valence yielded an F1-score of 76. 72% using the 1-NN classifier using the fusion of the ECG, EMG, and EEG (PSD) features, as well as the fusion of the ECG, EMG, and EEG (Spectral) features. Furthermore, the best performing fusion approach for Arousal yielded an F1-score of 61. 62% using the LSVM classifier and the fusion of all the computed EEG features. Considering the class imbalance within the dataset and to ensure that the acquired results were significantly different from the results for random voting, majority voting, and class ratio based voting, the acquired results were compared to the analytically computed results for these three cases, reported in Table  3.
The use of the bismuth singlecapsule in this setting has markedly increased effectiveness, achieving up to 94% in our cohort in first-line treatment, thus confirming this regimen as the current therapy of choice in this population  [1, 9, 34]. Despite these relevant findings, our study has several limitations. The main one is the low proportion of patients subjected to culture testing. It is unquestionable that promoting the creation of big-size databases to update local antibiotic resistance information could improve effectiveness. Nonetheless, the current study was developed to evaluate the routine practice of gastroenterologists in Spain, where the empirical prescription is generally advocated up to three eradication attempts  [9]. Another limitation of the present study is its observational design, with the consequent potential higher risk of bias.
Materials and Methods This study is a sub-analysis focused on the Spanish centres actively participating in the "European Registry on H. pylori Management" (Hp-EuReg), an international (27 countries), multicentre (300 investigators), prospective non-interventional registry that started in 2013 and promoted by the European Helicobacter and Microbiota Study Group (www. helicobacter. org) [35]. The established Scientific Committee, national coordinators, gastroenterologist recruiting investigators and a list of variables and outcomes are detailed in the previously published protocol  [36]. This protocol was approved by the Research Ethics Committee of La Princesa University Hospital (Madrid, Spain) and prospectively registered at ClinicalTrials. gov (NCT02328131). Written informed consent was obtained from each patient included in the study.
Data were recorded in an Electronic Case Report Form (e-CRF), collected and managed using REDCap, and were subjected to quality review. REDCap is an electronic data capture tool hosted at "Asociación Española de Gastroenterología" (AEG; www. aegastro. es), a non-profit scientific and medical society focused on gastroenterology research  [37, 38]. The aim of the current sub-analysis was to evaluate in the Hp-EuReg the effectiveness of first and second-line treatments in Spain. Secondary aims included the evaluation of strategies designed to increase effectiveness, prescription trends over time, and safety.