dataesr/scientific-paragraphs-categorization huggingface dataset:
| Tramadol exposure data The exposure of the whole French population to medicine containing tramadol during the study period was estimated by drug reimbursement provided by the French National Health Insurance System. 25 e number of individuals in France who were dispensed at least once medicine containing tramadol and the level of tramadol exposure in the French population (defined daily dose [DDD]/1000 inhabitants/ day) were computed by year. High-risk tramadol uses were identified through addictovigilance reports with mention of: (1) tramadol use disorder (dependence [withdrawal symptoms or hospitalization for withdrawal]), abuse (daily doses higher than the 400 mg maximal recommended daily dose with deleterious consequences such as seizures, social or professional adverse consequences), and addiction, and (2) illegal procurement.
| Ethics In France, addictovigilance forms part of compulsory safety monitoring regulated by law. 19 Patient informed consent is not required as internationally stated for the collection of data required for safety monitoring. All the data records were collected under the agreement of the National Committee for Privacy. 26 e data included in this analysis do not provide any individual information, except for sex, age, and date of the event. No information allowing potential re-identification (initials, location of residence, exact date of birth) is recorded. Consequently, linking data between spontaneous reporting, forged prescriptions or toxicological analysis of death is not possible. The number of tramadol users in France plateaued from 2014 to 2018 (with a decrease for tramadol associated with paracetamol and an increase for tramadol alone), with 5.
[48] [49]  In France, illegal ways of tramadol procurement were evidenced through addictovigilance, mainly with increased doctor shopping, pharmacy hopping and forged prescriptions. According to the analysis performed in national databases, from 2010 to 2016, tramadol was one of the few opioids for which both the quantity and the proportion obtained by doctor shopping increased. 50, 51 2 | Tramadol-related fatalities according to the profiles of the users The higher rate of fatalities with tramadol than other opioid analgesics in the context of medical treatment (according to the DTA record system) could be explained as tramadol still represented the most common opioid analgesic used in France. 52 ere are very few published studies on unintentional poisoning, which is more frequent in drug abusers than in non-abusers.
In Finland, by combining a nationwide post-mortem toxicology database and the register on reimbursed prescription medicines, fatal poisoning with tramadol was significantly higher in case of non-medical use (procurement without a prescription) and in the same range as for oxycodone between 2011 and 2013. 53 amadol overdose is associated with respiratory depression. 42 talities due to cardiopulmonary arrest have been observed in cases of suicide with very high doses of tramadol (5-8 g) without any coingestion. 54 A review of case reports has indicated that the risk of fatal overdose increases further when tramadol is abused concurrently with alcohol or other CNS depressants, including other opioids. 55  England and Wales, the increase of tramadol-related deaths has previously occurred within increased medical prescribing.
ACKNOWLEDGEMENTS We acknowledge all the participants in the addictovigilance monitoring system, in particular: the pharmacists (OSIAP programme), staff in specialised addiction care centres and harm reduction centres (OPPIDUM survey), analytical toxicology laboratories, and hospital departments of forensic medicine (DRAMES and DTA programmes). The  French Addictovigilance Network  is funded annually by the  Agence Nationale de Sécurité du Médicament et des Produits de Santé, ANSM. The writing of this article was done in a public academic context, without specific financial support.
Dr. Mizukami reported on the status of ATT in Japan: testing by both manufacturers and the NIID is required for inactivated vaccines, while for live attenuated vaccines execution of ATT is required only from the manufacturer. An average of 250 lots are released per year in the country, with more than 500 ATTs performed, resulting in the use of approximately 1000 guinea pigs. Since 2008 an exemption to ATT is considered for vaccines not derived from live viruses or bacteria. For the recombinant Hepatitis B vaccine, a waiver rule first introduced when analysis of data showed 20 consecutive lots to have tested successfully for ATT. After 2013, historical data of other vaccines were evaluated, leading to the waiver of ATT for a series of other vaccines (Influenza HA vaccine, Hemophilus influenza type B (Hib), Japanese encephalitis, Pneumococcal polysaccharide vaccine (PPSV23)).
Gold standard is the true herd status. In the context of the STOC free model, an infected herd is defined as the presence of at least one infected animal. To measure the true status of the herd, it would be necessary to test all the animals within a herd using a perfect test. However, no such data exist in the real-world. Up to now, the STOC free model has only been applied to a single French dataset, which included test results and RFs but no gold standard  (Madouasse et al. , 2022). An evaluation of the performance of this model under different circumstances is therefore lacking. The use of simulated data is an effective way to evaluate the predictive accuracy of the STOC free model given the absence of gold standard information in real-world surveillance data. This approach has been used for the evaluation of latent-class models for the estimation of infection prevalence in dairy herds  (McAloon et al.
, 2019). Data simulation allows a simplified system to be created where the true herd status is known. Simulated surveillance data, i. e. test results and RF information collected at regular intervals, can be used as input for the STOC free model as an alternative to real-world surveillance data. The performance of the model can be then evaluated by looking at errors in herd status classification, by comparing true herd status to the status predicted by the model. Furthermore, compared to real data, using simulated data enables a wide range of epidemiological situations and surveillance modalities to be evaluated. It makes it possible to investigate the potential of the model to be used for different diseases where performance of CPs differs. The objective of this work was to evaluate the capacity of the STOC free model, which takes account of both dynamics of testing and risk factor information, to improve the detection of infected and newly infected herds compared to test results alone (i.
e. the added value of the model in sensitivity). Among infected herds, newly infected are the ones which were not infected at the previous test event. We assumed that the added value of the model in terms of the detection of newly and previously infected herds could be different depending on the epidemiological context (impact of relative risk associated and frequency of risk factor and disease dynamics) and test performances (sensitivity and specificity). Simulated data were used to generate a wide range of realistic CPs (different CP corresponding either to different diseases or to the results of different testing strategies for a disease in different contexts). We quantified the number of additional infected herds detected by the STOC free model compared to test results.
Description and use of the STOC free model The model described by  Madouasse et al. (2022) , represents infection presence at herd level as a latent status over time-steps. The latent status is evaluated at regular time intervals through testing. Tests may be imperfect, i. e. with a sensitivity and a specificity less than 1. The variable of interest (the latent status) has a Markovian dynamic: the latent status at a given time-step depends on both the latent status at the previous time-step and actions taken or RF occurrence since the previous time-step. Risk factors are incorporated as predictors for new infection. The model predicts the probability of infection in the final time-step for each herd in the CP. Data collected before the final time-step are used as historical data for the estimation of the different model parameters, including previous latent statuses.
Evaluation of STOC free model output For each scenario, the STOC free model produced different outputs. The model returns Markov Chain Monte Carlo (MCMC) samples from the posterior distributions model parameters and probabilities of being infected at the last time-step. Model parameters include parameters related to infection dynamics, association between RF and probability of new infection and test characteristics. Estimations of these model parameters are performed from historical data on test results and RFs (in our case, data from the first five time-steps) as well as from the prior distributions for the different model parameters. First, we evaluated the convergence of the MCMC chains as well as the consistency between estimated model parameters and the parameters used for simulating the data.
Verification of parameter estimation. Parameter estimation was verified by comparing the posterior distributions to the empirical parameter values within the simulated populations. In the data simulation process the value of a parameter can differ between the chosen value for simulating a scenario and the resulting simulated population value. For example, because of the stochasticity in the simulations, for a chosen sensitivity of 0. 7 as a simulation parameter there could have been 695 simulated test positives out of 1000 simulated infections. In this case, the empirical sensitivity of 0. 695 was used as the reference to evaluate the posterior distribution for sensitivity.
Identification of cut-off value using Youden's index. Firstly, we used all herd predictions at the last time-step to estimate the cut-off value which minimized classification error (i. e. false positive and false negative). The cut-off choice was determined using the criterion below  (Youden, 1950) , noting that it is a trade-off between sensitivity and specificity: Youden ′ sindex = max(Se + Sp), with Se = TP STOCfree TP STOCfree + FP STOCfree , and Sp = TN STOCfree TN STOCfree + FN STOCfree. We ran this analysis using pROC packages in R software. We compared STOC free model performances to test performances. We firstly compared the number of accurately classified herds (TN + TP) by the STOC free model and by the test. Then, we explored the impact of the simulation parameter values on the additional number of infected herds (TP) detected by the STOC free model compared to test results.
In our study, given the relatively wide prior distributions put on the association between the risk factor and the probability of new infection, this association was estimated from the data. This means that in the scenarios in which test performance was poor, the contribution of surveillance data to estimation and prediction could be expected to be small, which could have made it more difficult for the model to converge. Such estimation issues have already been described in state-space models, when measurement error is high  (Auger-Méthé et al. , 2016). In such cases, increase the sample size (e. g. the number of herds) or adding prior information could reduce this issue. In our study, informative priors were used for measurement error parameters (sensitivity and specificity) assuming that relevant epidemiological quantities would be known beforehand.
019Pr x t,PPDS |x t,parity > 1 as well as probability of sow suffering from some other dis-eases0. 073Pr x t,legs |x t,parity = 1. Both equations were adjusted by country correction factor ( Cor 1 , Cor 2 ) to rep- resent average mortality rates in a given country. A policy iteration method was used to solve the replacement problem. Further details on the bio-economic model are provided by Niemi et al. [32]. The model was programmed in Matlab 2014b  [36]. PeriMor = 0. 072 + 0. 011ln x t.
Parametrizing the model The model was defined to represent parameters specific for six of the countries participating in the PROHEALTH consortium, namely Belgium, Denmark, Finland, Germany, The Netherlands and Spain  [16, 37]. Due to data anonymity requirements, countries were coded as A, B, C, D, E and F; the letter order is not consistent with the country order mentioned above. For each country, specific parameters representing prices ( x t,prices ) and production parameters were determined. Hence, the parameters reflected differences between countries in price and production parameters. The most important production parameter values, such as litter size of a gilt, perinatal and pre-weaning mortality were based on results presented by Chantziaras et al. [16]  and Niemi et al. [32]. The production parameter data were obtained from datasets recorded between 2014 and 2015 and originating from 131 farms across Europe.
Discussion The financial impacts of implementing different animal and housing management interventions aiming to decrease perinatal and pre-weaning piglet mortality were investigated by using data from different sow farms in six European countries. In order to represent the farrowing unit, a stochastic dynamic model was implemented for various European production conditions and used to calculate economic results. We expected that interventions aiming to improve animal welfare would result in increased economic result from a sow space unit across varying production conditions. Our results indicated that some of the examined interventions appeared to be profitable for all pig farmers of the six analyzed countries (supplying enrichment for gestating sows, and assuring animal friendly handling), while the others were economically viable for only selected conditions (installing ventilation and redesign of gestation unit, assistance for piglets).
The challenge in parameterization is related with between-experiment variation in the efficacy of preventive measures. In this study, the bio-economic model was parameterized to represent general country conditions. To provide decision support for individual farms, farm-specific data would need to be obtained. Production conditions, e. g. due to different biosecurity levels, on pig farms are not identical  [57]. Therefore, specific interventions might not be equally effective between farms. Furthermore, as noticed by Niemi et al. [43]  appropriate targeting of the measures is essential for their profitability because an intervention is not automatically always effective, and the selection of an intervention must be solution-oriented. For this reason, analyzing herd productivity and management as well as conducting small management experiments, involving part of the herd, might be advisable before adjusting whole management procedures.
Data origin Pigs feeding database was obtained from our previous study  [8]. In this study, the feeding behavior of 78 barrows (22 ± 2. 5 kg of body weight, BW) from two commercial genetic lines (39 pigs of each genetic line, reported hereafter as Line A and Line B; Agroceres PIC, Rio Claro, Brazil) was recorded in real-time by five automatic feeders (AIPF; University of Lleida, Lleida, Spain). Both lines consisted of Large White × Landrace × Duroc × Pietrain multiple crosses, with Line A pigs having a greater proportion of Pietrain genes than those from Line B, as previously described  [9]. Pigs were housed together in a single room (1. 19 m 2 /animal; without environmental enrichments) with a full concrete floor equipped with an evaporative pad cooling system (Big Dutchman, Araraquara, SP, Brazil) and electric heaters, automatically controlled to maintain the ambient temperature at 22˚C from 18.
Overview of data compilation Feed intake was calculated using the feeding information of each pig (AIPF Software). The feeders were equipped with a monitoring tool that continuously registered each visit of each pig with start and stop times (day, hour, minute, and second) and the amount of feed requested. Feeding information collected on days on which animals were handled (weighed or scanned, six days) or on days that some eventuality happened (equipment malfunctions, momentaneous data record error, etc. , which correspond to 12 days) were removed from the data. The total information from the initial data set consisted of 129,167 records, which were then analyzed using R Statistical Software (version 2. 14. 0; R Foundation, Austria). The presence of outliers was evaluated through graphical analyses and by daily records of anomalies during the experimental period.
Therefore, the first procedure was data filtering. Data of animals whose interval between visits was greater than 720 min (3. 78% of observations from the initial database) were arbitrarily excluded from the database and assumed to be physiologically noncompatible data  [12]. After a graphical analysis of the new database, a second filter criterion was applied. Data of feeder occupancy greater than 30 min (0. 48% of observations) and zero feed intake (0. 35% of observations) were also excluded. Records without feed intake may be associated with animal-feeder interactions, in which quick entry and exit of animals at the feeder are recognized by the software. Therefore, data filtering, although arbitrary and variable according to the database, is a biologically relevant step since it allows a more realistic view and avoids possible incorrect conclusions garnered from the analysis of feeding behavior.
Calculations and statistical analysis After defining the meal criteria, a new database was created using the meal criterion to group visits that have composed the same meal. The new database consisted of 39,680 records. This database was used to calculate the number of meals (n), feed intake rate (defined as the feed intake for each minute spent feeding, g/min), feed intake (g/meal), feeder occupancy (min/ meal), and the interval between meals (min) of each animal. As pig feeding behavior changes throughout the 24-h day  [1] , for a more adequate approach and considering temperature variation during the day, the day was divided into three periods: 22˚C (06-10h) , from 6. 00 to 10. 00 h; 30˚C (10-18h) , from 10. 01 to 17. 59 h; and 22˚C (18-06h) , from 18. 00 to 5. 59 h. Because the periods of the day had different durations, feeding behavior variables were expressed per hour.
The Cramer-von Mises test was used to verify the normality of studentized residuals. The data were analyzed using the linear mixed effects model framework. The implementation was via the MIXED procedure of SAS (version 9. 3, SAS Inst. Inc. , Cary, NC) and were presented hereafter as least squares means. The individual pig was considered the experimental unit and the initial BW (weight of the animal on the first day of the experimental period) was used as covariable between genetic lines when significant in the model. The model included the fixed effects of the period of the day (TP), genetic line (GL), experimental phase (P), and their interactions (TP×GL, TP×P, GL×P, and TP×GL×P) as follow: Yijmk ¼ m þ ai þ bj þ gm þ ðabÞij þ ðagÞim þ ðbgÞjm þ ðabgÞijm þ eijmk where Yijmk is the observed variable (observation for the kth animal from the (i,j,m)th cell), μ is the overall mean, αi is the effect of the ith level of the TP factor, βj is the effect of the jth level of the GL factor, γm is the effect of the mth level of the P factor, and their interactions (that is, ith level of TP with the jth level of GL, (αβ)ij; ith level of TP with the mth level of P, (αγ)im; jth level of GL with the mth level of the P, (βγ)jm; and ith level of TP with the jth level of GL with the mth level of the P, (αβγ)ijm).
Pstart and meal criteria Among the three periods, Pstart difference was observed up to approximately 100 min, a period in which the greatest probability of starting to feed was observed at 22˚C (06-10h) , followed by 30˚C (10-18h) and 22˚C (18-06h) (Fig  1 ). The meal criteria values were 48 min at 22˚C (06- 10h) , 47 min at 30˚C  (10-18h)  and 49 min at 22˚C (18-06h) ; and for line A and line B were 52 min and 46 min, respectively. Therefore, as we had different periods (n = 3) and genetic lines (n = 2), the meal criteria applied in our database was the average of these five results (48 min). After defining the meal criteria, differences between the genetic lines (with a lower probability of line A to start a meal compared with line B) were observed mainly up to 450 min (Fig  2).
Discussion Feeding data from two pig genetic lines with different proportions of Pietrain genes were analyzed to evaluate the genetic selection effect on the feeding behavior of growing-finishing pigs under daily cyclic high ambient temperature conditions. Our major and original finding is that differences in feeding behavior between lines reared under daily ambient temperature variations may partially explain differences in growth performance, which was previously reported  [8]. The greatest probability of animals starting to feed was found at 22˚C (06-10h) , followed by 30˚C (10-18h) and then 22˚C (18-06 h) , suggesting pigs feeding patterns are strongly related to the circadian rhythm. In addition, because pigs from line A had a lower probability of starting a meal than pigs from line B, differences between "hunger" and "satiety" mechanisms could also be attributed to the genetic line effect on feeding behavior.
Preamble The reunion of wildcats was a gradual process in which all individuals were released back into the original enclosure. In detail, after all cats had been removed from their enclosure to apply the CAP spot-on (17 September), the related female (RF) was first released alone in the enclosure for one hour to allow her to explore the environment. The mother was then released in the enclosure on the same day, whereas the other three wildcats were gradually released during the following day (18 September). The reunion process lasted two days, and no behavioural data were collected during this period, as the data collection started in the pre-reunion period and continued in the post-reunion periods. The cats were periodically, although informally, checked and observed during the day to monitor the situation.
Procedure Observations of individual and social behaviours were collected before and after the reunion procedure. In particular, data were collected across three different periods: (1) the pre-reunion period without the related female RF; (2) the post-reunion period with RF; (3) the late-reunion period (see Table  1  for a detailed procedure timetable). Table  1. All the study periods are summarised. Ten days of data collection were conducted for both the pre-reunion period and the post-reunion period (PR1-PR10; POR1-POR10, respectively). The post-reunion period was divided into two periods: post-reunion period A (POR1-POR5) and post-reunion period B (POR6-POR10). Five days of data collection were run for the late reunion (LR1-LR5). R1 indicates the beginning of the reunion process, in which we provided semiochemicals to all individuals and released RF and the mother; R2 indicates the second day, when all five wildcats were together again.
Data Analysis Two comparisons between/among periods were made: 1. Between the pre-reunion period and the post-reunion period. In this analysis, only data pertaining to the four wildcats who had been in the enclosure in both periods were included (n = 4, RF was excluded). 2. Among post-reunion period A, post-reunion period B and the late-reunion period. The data pertaining to all five cats were included in this analysis (n = 5, RF was included). A single-case design was performed to analyse behavioural data of wildcats, using the total duration of each session calculated for each subject as dataset  [46, 47]. Nonparametric statistical tests were used to compare behavioural data of wildcats between and within periods. The significance level was p < 0. 05  [48]. In particular, the Wilcoxon test was used to verify the effect of the reunion process on the behaviour of the study wildcats, comparing the pre-reunion and the post-reunion period.