dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to the orthogonal symmetry, intrinsic to the group of cubic structures, the stiffness matrix C can be reduced from 81 to 9 constants. Six different boundary conditions are needed for the identification of constants. For each condition, one component of the strain tensor is equal to 1, while the other five are zero. For each strain condition, stress tensor results are obtained from the reaction forces on the appropriate faces. This constraint tensor directly provides the quantification of a line of the C ijkl matrix. From the constraint tensor data, the constants of the engineer are deduced. This homogenization procedure is applied to lattices with perfect geometry. It is noticeable that the manufactured structures will present differences due to certain topological deviations. These calculated equivalent mechanical properties need to be corrected by a calibration procedure based on experimental data and therefore a digital registration  [22].
5 mm. The mesh is however refined at the level of the abutment cylinder which corresponds to the surface of application of the pressure modeling the cutting forces (see Sect. 2. 2). This mesh size of 0. 1 mm is defined according to the diameter of the tool (3 mm) with a radial engagement of 0. 2 mm and the time increment chosen to discretize the rotation of the tool by 4. 5 Â 10 À5 s. The pressure is variable in space and time and is implemented in Abaqus software by a load subroutine. The peripheral milling operation is carried out using a 3 mm diameter tool, with 4 teeth, a lambda helix angle of 30°, and a cutting angle a of 8-10°. Other parameters are fixed: the radial engagement a e is 0. 2 mm and the cutting speed Vc is 140 m/min, leading to a feed rate of 1600 mm/ min. A machining pass, corresponding to a complete revolution of the 4 mm cylinder, will therefore have a duration of 1.
ACKNOWLEDGMENTS The authors thank the  Infections and Cancer Biology Group (International Agency for Research on Cancer, Lyon, France) ,  IROPICAN study team (Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran), and Deputy for Research of Tabriz University of Medical Sciences, Iran  for all their supports. This study was financially supported by the  National Institute for Medical Research Development (NIMAD)  under Grant No. 940045 ,  Iran , and  IARC. The authors declare no conflict of interest. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily.
Data collection Saliva sampling Operator 1 sampled the saliva of the pig in pen 1, while operator 2 sampled the saliva of the pig in pen 2 for one and a half minute, using a Salivette ® swab (Sarstedt, Numbrecht, Germany) fixed to a clamp, without stressing, touching, or talking to the pig, to avoid any possible influence of the handling. Saliva was sampled to assess cortisol concentration and alpha-amylase activity. As soon as the sample was collected, it was placed in an ice box to keep it at approximately 4 ℃ until it was centrifuged (3 min, 1000 rcf ) and then frozen at -20 °C prior to analysis. The timing of saliva samples was performed according to the literature on alpha-amylase activity and cortisol concentration in pig saliva  [33] , also considering the kinetics of cortisol passive diffusion from blood to saliva (~ 15-30 min) described in other farm animals  [60, 61]  (Table  1 ).
Behavioural sampling All the behaviours (walking, immobile and/or ground sniffing, reciprocal look, agonistic behaviour, agonistic signal-threat, fighting, fighting starter, encounter duration, need to stop de encounter, total social nosing, positive and negative social nosing, proximity, tail movement and latency of first contact with and without aggression) were analysed from the video recording according to an ethogram (Table  2 ) by two independent observers at the end of the study using BORIS software  [68]. A score about aggressiveness was fulfilled. The score 0 was not aggressive: no aggressive contact or aggressive signal/threat, or only one aggressive signal/threat; 1 was aggressive: one aggressive contact and one or more aggressive signals/threats or two or more aggressive signals/threats, and not necessary to stop the encounter; and 2 was very aggressive: more than one aggressive contact and/or more than two aggressive signals/threats or need to stop the encounter due to it (fighting starter).
Statistical analysis Data analysis was carried out using SAS 9. 4 software (Copyright © 2002-2012 by SAS Institute Inc. , Cary, NC, USA). The significance threshold was fixed at 5%, which is the standard threshold. The design of the study resulted in two types of data that required different analyses: 1. The data collected per pig correspond to behavioural and physiological data. 2. The data collected per encounter, i. e. , the duration of the encounter, the need to stop the encounter and the latencies of first contact with and without aggression. Before starting the analysis, the inter-observer reliability was evaluated for all the behavioural parameters using the Pearson correlation coefficient, when the assumption of normality was verified, or using the Spearman correlation coefficient, when normality was not verified for at least one of the two observers.
Analysis of data collected per encounter The effects of treatment and day were studied for the encounter duration and the need to stop the encounter. A 2-way ANOVA was used for the encounter duration for which the assumptions of normality and homoscedasticity were verified. A logistic regression was performed for the need to stop the encounter. Two-way ANOVA and Levene's test were performed with the GLM procedure, normality was checked with the UNIVARIATE procedure, and logistic regression was computed with the LOGISTIC procedure. Latency variables (of the first contact with and without aggression), whose data correspond to delays in the occurrence of an event (in other words, survival data), were analysed. Survival analyses were used to compare treatment groups with the help of the Kaplan-Meier estimator (using the LIFEREG procedure) and the Cox model (using the LIFEREG procedure).
Data collection The data that are needed to feed the model include data on disease dynamics, test results from the CP in place and information on the context situation i. e. prevalence of relevant risk factors in the country of interest  (Van Roon et al. , 2020A). As a first step in the development of the data collection tool, all six participating countries filled in the RISKSUR tool  (The RISKSUR Project, 2015)  to identify differences between various BVD CPs with respect to freedom from infection. The RISKSUR tool was intended for building and optimizing surveillance programmes and therefore was modified within the STOC free project to also collect information about the context and aspects of CPs. The differences between CPs that were identified by filling in the adapted RISKSUR tool were used as input for a first draft questionnaire on aspects of freedom.
In this questionnaire all aspects that can influence, either directly or indirectly, the confidence of freedom from infection in a BVDV CP were queried. The next step was to list all variables for which quantitative data is needed to calculate the confidence of freedom with the STOC free model. All variables were included in a large data collection table in which all participating countries were asked about the availability of quantitative data, the format of the EFSA Supporting publication 2022:EN-7263 The present document has been produced and adopted by the bodies identified above as authors. In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the authors in the context of a grant agreement between the European Food Safety Authority and the authors.
The present document is published complying with the transparency principle to which the Authority is subject. It cannot be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. data, the source(s) of the data and strengths and limitations of the data. This data collection table was first optimized for use for BVDV and later extended to other cattle diseases i. e. JD and IBR. In a collaboration with the SOUND control project (www. sound-control. eu), the data collection table was generalized so that it could be applied to all countries throughout Europe. Over 30 countries were asked to fill in the table and valuable lessons were learned about data availability and the format in which data were available.
All users of the table agreed that a data quality tool is essential in the comparison of the confidence in the probability of freedom. Aspects that were considered important were data sources and accessibility, completeness of data, timeliness of data and data accuracy. These aspects were incorporated in a data quality evaluation tool. The data collection tool with the data quality evaluation tool were made available online through Limesurvey  (Van Roon et al. , 2021; Rapaliute et al. , 2021). The longitudinal data 1 required for the STOC free model are described in a document on https://github. com/AurMad/STOCfree (Figure  1 ). R functions to read the data and use it in the STOC free model are freely available as part of an R package that can be installed from this same site. An example of such data is provided in Figure  2.
The information required by the tool was filled in by all partner countries and resulted in a first and second version of a questionnaire (deliverable 2. 1 and 2. 2), which ultimately evolved into the data format needed for the STOC free model (STOC free data). Guidelines for the identification and sources of data were developed. The aim of these guidelines was to indicate the availability and the quality of data for parameters that could potentially be used as input parameters in the STOC free model. In addition, the guidelines provided definitions of the required parameters and information on the type and format of the data for the model. The assessment criteria included availability of quantitative or qualitative data, the sources of the data and the strengths and EFSA Supporting publication 2022:EN-7263 The present document has been produced and adopted by the bodies identified above as authors.
, 2020B). In collaboration with a COST action, SOUND control, the data collection tool was extended to two other cattle diseases i. e. infectious bovine rhinotracheitis (IBR) and Johne's disease and was adapted to make it applicable throughout Europe. Subsequently, the tool was pilot tested by a Western (Netherlands) and Eastern (Albania) European country and a data quality evaluation module was included. Aspects that were considered important were data sources and accessibility, completeness of data, timeliness of data, and data accuracy. This version of the tool was improved based on the pilot results and feedback that was provided by the SOUND control consortium. In the subsequent step the tool was made available online through LimeSurvey, although at this point only for testing purposes by the STOC free and SOUND control consortia  (Rapaliute et al.
, 2021). A scientific paper was written about the key learnings during the development of a generic data collection tool to support assessment of freedom from infection in cattle herds  (Van Roon et al. , 2021). The STOC free model was developed using simulated data  (Mercat et al. , 2022). It was then tested and validated using French data with bulk milk BVDV test results and risk factors related to the number of cattle introduced. The specifics of the model development and the results of the French case were published  (Madouasse et al. , 2021). The final code of the model is freely available as an R package on https://github. com/AurMad/STOCfree. The STOC free model provided some challenges in combination with the case disease BVD. The STOC free model is an SIS-model (Susceptible-Infectious-Susceptible), specified as a Hidden Markov Model to account for test uncertainty.
Hence, the probability of freedom may be low while the uncertainty will be high with only two test results. 5. Missing test results -In the absence of a test result, the model takes the last known result as measuring the true status (provided high sensitivity and specificity), and simulates all possible dynamics from this on. In such case, the higher the incidence of infection is, the higher the probability of infection following an initial negative result or missing test results. In addition to knowledge on the disease CP and basic knowledge on disease modelling, the use of the STOC free framework also requires some knowledge about the use of the R-package. The provided information in the R-package guides the user through the data interface and the model. A default dataset and default prior distributions for BVDV are provided.
05) at T POST60 , and positively correlated with α-(p < 0. 05) and β2-globulins (p < 0. 05) at T POST5 and T POST60. The linear regression model confirmed the statistically significant correlations found among selected parameters (Figures  2 3 4 ). Table  1. White blood cells (WBCs), red blood cells (RBCs), hematocrit (Hct), hemoglobin (Hb), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC) and platelets (PLTs) measured in ewes before shearing (T PRE ) and after 5 and 60 min (T POST5 and T POST60 ) from the end of shearing. Data are expressed as mean values ± standard deviation (SD).
The independence and normal distribution of residuals and random effects was checked for each model (data not shown). Multiple comparisons of means were also performed with post-hoc Tukey tests. The effect of the presence of inguinal cryptorchidism on NSAIDs distribution was assessed using Fisher's exact test, and the effect on pain scores was assessed using mixed models and Tukey tests. In the descriptive approach, data were expressed as the mean and standard error. Results of mixed models were expressed as the estimate and standard deviation.
Study 2-Analgesic Effect of NSAIDs According to the Pain Score Of the 36 horses that were selected in Study 2, further exclusions were necessary due to incomplete pain evaluations, most likely due to lack of availability from the evaluator at T2 (n = 4, including 3 ketoprofen and 1 meloxicam), early post-operative colic episode (n = 1 ketoprofen) or an isolated episode of hyperthermia (n = 1 ketoprofen) requiring additional analgesic administration. Complete data on thirty horses were finally used for statistical analysis (Figure  3 ). The mean age was 6. 2 years (range 1. 5-20 years), and represented breeds included French Warmbloods (12/30), mixed-breed leisure horses (7/30), Lusitanians (3/30), Spanish Purebred (2/30), one Arabian, one Irish Cob, one pony, one French Trotter, one Thoroughbred and one French chaser.
Study 2-Analgesic Effect of NSAIDs According to the Pain Score Of the 36 horses that were selected in Study 2, further exclusions were necessary due to incomplete pain evaluations, most likely due to lack of availability from the evaluator at T 2 (n = 4, including 3 ketoprofen and 1 meloxicam), early post-operative colic episode (n = 1 ketoprofen) or an isolated episode of hyperthermia (n = 1 ketoprofen) requiring additional analgesic administration. Complete data on thirty horses were finally used for statistical analysis (Figure  3 ). The mean age was 6. 2 years (range 1. 5-20 years), and represented breeds included French Warmbloods (12/30), mixed-breed leisure horses (7/30), Lusitanians (3/30), Spanish Purebred (2/30), one Arabian, one Irish Cob, one pony, one French Trotter, one Thoroughbred and one French chaser.
However, we acknowledge that this aspect could be evaluated in future studies. Here, we adopted a manual segmentation approach, although an automatic segmentation, such as in  [14, 24] , could have been more accurate. Nevertheless, because of the formalin effect on T1/T2 contrast, the latter approach does not seem to perform well with our data (these aspects are elaborated in the next sections). Regarding the analysis on the group of 11 dogs, on each structure, the mean volume (M) and its standard deviation (SD) were evaluated. The volume was provided by ITK SNAP after the segmentation. In these analyses, we did not compute the global brain tissue volume, since we focused on the specific features of single subcortical structures. In a group-level analysis, such a covariate could have been included to account for the brain size across different subjects.
Longitudinal study Here, the operators performed the previous segmentations longitudinally. The percentage (%) changes in volume were computed for the phases: in-vivo, post-mortem, ex vivo 1 month, and ex-vivo 12 months. Then, volume changes were compared across the operators. We considered the following conditions: in vivo vs post mortem (defined as (post mortem-in vivo)/in vivo � 100), the post mortem vs ex vivo-1-month volumes (expressed as (ex_vivo_1 -post mortem)/ post mortem � 100), the in vivo vs ex vivo 1 month volumes (expressed as (ex_vivo_1 -in vivo)/ in vivo � 100) and, finally, ex vivo 1 month vs ex vivo 12 months (expressed as ((ex_vivo_12 -ex_vivo_1)/ ex_vivo_1 � 100)). As in the previous stage, ITK SNAP software (version 3. 8. 0, 2019) was employed for the manual segmentation and the statistical analyses were performed with MATLAB.
Mean_11 dogs CI_11 dogs Compared to histopathological analyses, the MRI-based segmentation of formalin-fixed brains has several advantages. First, structural abnormalities can be assessed within the entire brain without altering the original structures. Second, the fixation allows to analyze the data multiple times in any plane  [19, 34] , preserving the structure of the tissues. In fact, after death, the brain undergoes microbial degradation, autolysis, and breakdown of cell membranes. The chemical fixation tends to preserve the macromolecular structure, providing the longitudinal stability required for extensive scanning times. Nevertheless, a series of artifacts and changes are expected. It is known that the formalin fixation may alter the relaxation times. This is due to the induced tissue dehydration, crosslinking, and reduced transmembrane water exchange  [34]  that lead to a T1/T2 shortening.
Acknowledgments Gratitude to all participants of the First WHO meeting of the  Network of Buruli ulcer PCR Laboratories  in the WHO African Region who were as follows:  Noudehouenou Senou :  Laboratoire de Reference des Mycobacteries de Cotonou, Benin; Alida Agoua: Laboratoire de Reference des Mycobacteries de Cotonou, Benin; Yannick Kamdem: Centre Pasteur du Cameroun; Earnest Njih Tabah: National Leprosy, Buruli Ulcer,Yaws and Leishmaniasis Control Programme Disease, Epidemics and Pandemics Control, Ministry of Public Health, Yaounde Ćameroon ;  Aka Nguetta :  Pasteur Institute Cote d'Ivoire, Platform of Genomic and Molecular Biology, Abidjan, Cote d'Ivoire ;  Nadine Mintsey :  Institut national de Recherche Biomedicale (INRB) ,  Kinshasa ,  DRC ;  Anthony Ablordey :  Bacteriology Department Noguchi Memorial Institute for Medical Research College of Health Sciences, University of Ghana, Accra, Ghana; Pam Vincent Gyang: Nigerian Institute of Medical Research, Lagos, Nigeria ;  Camila Johnson :  National Public Health Institute of Liberia (NPHIL), Monrovia, Liberia ;  Wemboo Afiwa Halatoko :  Institut National d'Hygiène (INH), Lome, Togo ;  Mosi Lydia: West African Center for Cell Biology of Infectious Pathogens, University of Ghana, Accra, Ghana ;  Ignatius Ejiofor: St Joseph's Hospital Adazi Nnukwu ,  Anaocha Local Government Area ,  Anambra State , Nigeria;  Marcus Beissner :  Division of Infectious Diseases and Tropical Medicine, University Hospital, Ludwig-Maximilians University Munich, Germany; Laurent Toe: ESPEN Laboratory, WHO Country Office, Ouagadogou ,  Burkina Faso ;  Dr Hubert Wang :  WHO Country Office, Yaounde, Cameroon ;  Dr Besong Samuel :  WHO Country Office, Yaounde, Cameroon.
Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.
HF calls (screams, squeals) are common in negative contexts, while LF calls (grunts) prevail in neutral and positive situations  17. Thus, HF calls could be used as an indicator of negative affective valence  15. Yet, there is also a large within call-type variation (e. g. duration, formants, energy distribution  [18] [19] [20] [21]  ) that could be used as additional way to assess emotional valence and arousal, and to identify the contexts in which the calls were emitted. The aim of this study was to identify the features of pig vocalizations that are most indicative of emotional state and context, in order to thereby provide a basis for the development of a tool able to automatically assess valence and detect particular situations from real-time acoustic input. Towards this aim, we performed an analysis of an extensive and unique dataset of vocalizations emitted across many different situations from the birth to slaughter of commercial pigs (7414 calls produced by 411 pigs).
In the clusters where the vast majority of points are of a single valence, the presence of several irregular points demonstrates outlier vocalizations in the dataset, which might be calls for which the valence was incorrectly assumed. The t-SNE mapping of the context-trained neural network (Fig. 2b ) shows remarkably clear clusters, despite the large range in the number of vocalizations per context class (e. g. Surprise: 17, Isolation: 2069). However, the smaller classes have generally less clear boundaries, likely due to the neural network's lower incentive to recognize them during training because of the class imbalance  23. Notably, several of the larger context categories have split into two or more clusters (like Reunion, and arguably Isolation). In these cases, the network appears to be discerning subtypes within the context categories beyond what it was trained to recognize.
These distinctions are due to the composite nature of the dataset; for instance, 'Reunion' experiments were conducted by two different teams. It is therefore unclear whether these experiments, using slightly different protocols, produced markedly different vocalization types, or if the environmental noise captured by the recording teams causes this subdivision. Inversely, it can be seen that some contexts that were expected to be distinct produced indiscernible calls (e. g. negative and positive conditioning; Fig. 2b ). However, further analyses suggest that the environmental noise likely did not affect the valence and context classification (see Supplementary Text, Supplementary Figure  S5 , and Supplementary Tables S4-S5 for further information on this analysis).