dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: JDF was funded by the  Morris Animal Foundation  Fellowship Training Grant  D18ZO-407. This work was supported by the following grants awarded to  Susan Kutz :  Polar Knowledge Canada  Grant  NST-1718-0015 , the  Natural Sciences and Engineering Research Council Discovery  Grant  RGPIN/04171-2014 , the  Natural Sciences and Engineering Research Council Northern Supplement  Grant  RGPNS/316244-2014 , and  ArcticNet. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical Analysis The analyses were carried out using SAS 9. 4 software Copyright (c) 2002-2012 by SAS Institute Inc. , Cary, NC, USA. The significance threshold was fixed at the classical value of 5%. Interobserver reliability between the 2 independent readers was assessed for each score of the ERT. Correlations were made between salivary cortisol and ERT scores, between startle and avoidance scores and ERT scores, and between salivary cortisol and foster family questionnaire scores. For this purpose, when normality was verified, the Pearson correlation coefficient was calculated. Otherwise, the Spearman correlation coefficient was used. According to Martin and Bateson  [45] , r = 0. 4-0. 7 indicates a moderate correlation (substantial relationship), r = 0. 7-0. 9 indicates a high correlation (marked relationship), and r = 0.
, 2019)  analysed the first three days after parturition, also with a scan sampling methodology but using visual observations, which makes it almost impossible to create postural activity datasets that are uniform in the long term  (Vizcarra & Wettemann, 1996; Nalon et al. , 2014). In the last decade, a number of methods have been developed to automatically measure activity in pigs, mainly using accelerometers  (Ringgenberg et al. , 2010; Brown et al. , 2013; Escalante et al. , 2013; Oczak et al. , 2016b; Matheson et al. , 2017; Canario et al. , 2019; Thompson et al. , 2019; Chapa et al. , 2020). Accelerometers are cheap, and the acquisition and analysis of the signal is relatively simple. However, in practice, the quality of the prediction depends on where the sensor is positioned on the animal, i.
The extracted features could be geometric characteristics of the segmented image  (Oczak et al. , 2016a; Yang et al. 2018 Yang et al. , 2019;; Leonard et al. , 2019; Nasirahmadi et al. , 2019). The second method consists in using available CNNs for image analysis. The CNNs are already pretrained on a large image dataset, such as ImageNet  (Deng et al. , 2009) , which generally contain more than a million images  (Zhang et al. , 2019; Chen et al. , 2020; Marsot et al. , 2020; Gan et al. , 2021; Kasani et al. , 2021). Both methods have been reported to produce promising results in the literature. However, even though software is available to aid the use of CNNs (e. g. Abadi et al. , 2015; Moses & Olafenwa, 2018) , it can still be difficult for new users. In this article, the use of the two methods to extract image features is compared and they are then used to estimate the postures of sows kept in a crate.
For monitoring, Bascom cameras were used; fixed on a metal support 4. 5 m above the ground, with its lens pointing downward and directly above each crate to obtain a rear view of the sow with a tilt angle between approximately 45 and 60 (Fig. 1 ). The cameras were connected to a PC via a wire network for data storage. The videos were manually labelled by three observers, who were trained in using the same ethogram (Table  1 ). Dedicated software was implemented in Python language to play the recorded videos and manually report postural changes and the name of the new posture. The function automatically recorded the frame number of the change and the posture was derived automatically between two changes. It was possible to rewind and slow down the video recording to capture changes in posture accurately, annotating when the sow was in a transitory posture (sitting or kneeling) between two main postures (standing and lying).
The third observer analysed videos without the dedicated Python software and recorded information on the sow's posture manually. In this case, rewinding and slowing down the video recording were again used to capture the information accurately. For the purpose of annotation, eight different postures were distinguished: standing, sitting, kneeling, sternal lying, lateral lying on the right or the left side, and with udder exposed (i. e. , accessible to the piglets) or not. Only four postures were used for statistical analysis of the signal: standing, sitting, sternal, and lateral. The sternal posture includes sternal and lateral lying on the right or the left side. The lateral posture is used when the sow is lying on its right or left side with the udder visible (see Fig. 1 ). The animals were monitored during staff working hours, i.
, 2016) , DarkNet53  (Redmon, n. d. ) , Dense-Net201  (Huang et al. , 2017) , AlexNet  (Krizhevsky et al. , 2012) , SqueezeNet  (Iandola et al. , 2016)  and ShuffleNet  (Zhang et al. , 2018). All the CNN were already pre-trained on the ImageNet dataset  (Deng et al. , 2009)  and were downloaded thought the MATLAB 2020b add-on Explorer. For each CNN, the extracted features were defined as the output value of the last pooling layer. For SqueezeNet for example, the features was the output of the layer named pool10. More details on CNN can be found in the literature, such for example in  (Wang et al. , 2018)  or in  (Khan et al. , 2020). CNNs were already pre-trained on the ImageNet dataset  (Deng et al. , 2009)  and were downloaded using the MATLAB 2020b add-on explorer. For each CNN, the extracted features were defined as the output value of the last pooling layer.
Posture prediction from features -SVM classification model Segmentation and CNN both provide a set of features, i. e. , a set of variables that are supposed to describe the image and help differentiate between the different sow postures. To predict posture from features, we used an SVM classifier. For the CNN-FEM, we used a multiclass Error-Correcting Output Codes (ECOC) model, which breaks a k class classification problem down into kðk À1Þ=2Þ binary classification problems. A linear SVM classifier is then fitted for each binary classification problem  (Allwein et al. , 2000). For the Seg-FEM, we used regular SVM, with an order-3 polynomial kernel, as the ECOC model produced poor results. All the computation in this article was carried out using MATLAB 2020b. Sows 6 and 7 were labelled for two consecutive days, and Sow 8 was recorded and labelled for three consecutive days.
After extraction, the dataset included 41,568 frames. In general, the sternal and lateral postures (resp. 33. 9% and 40. 6%) were well represented in the dataset, while the standing and sitting postures were not (13. 4% and 12. 1% respectively). The number of images for each posture and animal is listed in supplementary material S3. This initial dataset was used to construct different training and testing datasets to investigate the impact of the training set on the quality of the posture prediction model.
Baseline scenario: training and testing from the same bank of images In this baseline scenario, the posture prediction models were trained and tested on a set of images chosen randomly from the initial dataset, without considering the sow number. Two different training sizes, 5% and 30% of the initial dataset, were used. In each case, the training images were chosen randomly and could thus originated from any of the monitored animals, for any posture. The posture prediction models were then  tested on the remaining images not used for training. Only the first day of recording of the animals was used. In this scenario and in the other scenarios, the results obtained were compared when only one posture prediction model was fitted for all animals (group-model) and when one posture prediction model was fitted per animal (individual-model).
Temporal variations The impact of varying environmental conditions during monitoring was investigate by, for example, small changes in the camera angle. The experimental set up was very similar to that in the baseline scenario in that only the testing dataset was different. In this case, the testing dataset was based on the second monitoring day of sow 7 and on the second and third recording days of sow 8. For the individual posture prediction model, the model that was fitted on the same sow was used. For example, to evaluate the second day of monitoring sow 7 in the case of an individual posture prediction model, we used the model fitted on 5% or 30% of the first day of monitoring sow 7.
Individual variations The impact of individual variations on the quality of the posture prediction model was also investigated. As mentioned earlier, the environment differed between sows. For example, a radar was installed on top of the crate for some sows, which were consequently not entirely visible in the resulting image. The camera angles generally differed between sows. Lightning conditions also varied greatly, for example when a sow was located near a window, compared to a sow located far from a window. To investigate the possible impact of these variations on the quality of a posture prediction model, we propose separating the sows used for training from the sows used for testing. In other words, the training image dataset and the testing image dataset should not contain images of the same sow.
For example, a sensitivity of 70% for sternal posture meant that if the animal was in sternal posture, then the model predicted the posture correctly in 70% of the cases. The precision is the positive predicted value. For example, a precision of 70% for sternal posture means that out of all the images predicted as sternal posture, in 70% of them, the sow was truly in sternal posture; but in 30% of the images, the animal was in another posture. To summarise the quality of the method, we calculated the average f1-score as the average of the four postures f1-scores: f 1 À score ¼ P p f 1 À score p 4 : (2) It should be noted that for all the experimental setups, the training and testing dataset were the same for all the posture prediction models. For the experimental setups where the posture prediction models are trained and tested on several datasets, the average f1-score was calculated and used in the statistical analysis.
e. , 70% training, 30% testing). The f1-score of the prediction model was estimated to be 94. 2%. This is comparable to the segmentation model in our baseline scenario with 30% training, in which the f1-score was 93. 3%. Leonard et al. , (2019)  developed an  unsupervised method to distinguish between sitting, standing, kneeling, and lying, for sows in farrowing stalls, using top view depth images acquired with a Microsoft Kinect V2 system. The f1-score was estimated to be 79. 2% on a dataset of 24 individuals, for each of whom 445 images were chosen randomly. But the f1-score is particularly reduced by the kneeling posture. Similar to our study,  Kasani et al. (2021)  evaluated eight different CNN models, with 7556 training images and 1776 images used for testing (80% training 20% testing).
The estimated probabilities could then be combined to estimate the postures. In addition, depending on the frequency of image acquisition, temporal information can be added to facilitate the detection of postures; for example, using the transition probability between postures or to smooth the postural signal. Finally, features from several FEMs can be aggregated to construct a meta-posture prediction model. This work raises two important questions. When does the posture prediction model need to be retrained when a new batch of sows is monitored? Should labelling images wait until the monitoring period ends so as to include images from all the monitoring days in the training dataset? To answer these questions correctly, a larger dataset is needed.
Conclusions Several conclusions can be drawn from this work. Firstly, in general it is not necessary to train one model per animal. When one posture prediction model was used for all sows, the training dataset was larger in size and more diverse, which certainly explained the better performance of this approach. In practice, it is more convenient to build only one training dataset of labelled images. Secondly, the CNN models generally produced a higher quality of prediction than the Segmentation model. However, the Segmentation model may be more robust to temporal variations than some CNN models when the model is trained individually. When the training and testing images were selected from the same pool of images, the difference between Segmentation model and CNN models was relatively small.
The Segmentation model f1-score was 1% lower than the worst CNN model and 2% lower than the best CNN model. The Segmentation model is thus a good option if the CNN models are too complex to implement. Thirdly, an average difference of 9. 7% was observed between the f1-score of the best and worst CNN-PPM. It is thus appropriate to test several CNNs for a given application. Fourthly, it is important to train the posture prediction model on a dataset that includes images similar to those for which the posture is to be predicted. For example, we showed that the f1-score decreased on average by 18. 5% when the posture prediction model was used on a sow that was not present in the training set. For temporal variations, due to, for example, a change in sow behaviour, in lightning conditions, or in the field of view of the camera, it was seen that the f1-score decreased by an average of 16%.
With most CNN models, increasing the size of the training dataset reduced the f1-score, whereas this was not the case with the Segmentation model. With 5% training, the 9% compared to the baseline scenario with one posture prediction model for all animals and 5% training. With 30% training, it decreased by 16. 5%. When one posture prediction model was fitted per animal, the CNN model based on SuffleNet produced the best results, with an f1-score of 79. 8% with 5% training and of 82. 4% with 30% training. Note that in this experimental setup, with 5% training, the Segmentation model f1-score was 65. 7%, i. e. , higher than in the CNN models based on Xception (62. 3%), InceptionV3 (65%), ResNet50 (61. 2%) and DarkNet53 (65. 3%). With 30% training, the Segmentation model f1-score was 67. 2%. Again, it was higher than that of 6 of the 11 CNN models: Xception (65.
Data collection and methods of analyses A systematic literature review was carried out using peer-reviewed articles published between 2006 and 2019. This framework was chosen to have studies in which the reduction of dietary CP was balanced as possible by the addition of common essential amino acids and that the parameters expected to be the object of the meta-analysis were considered. The online database "Pubmed" was used to search for the relevant articles by combining keywords related to diet ["Low protein diet" OR "Low protein level "OR" Decreased protein level "OR" Reduced protein level "] AND to health [" Immunity "OR" Gut health "OR" microbiota "OR" diarrhoea "OR" microflora "] AND to the species ["Pigs" OR "piglets"]. Once all the articles were collected, only those fulfilling the following criteria were selected: a) in-vivo swine studies including a control group and a reduced level of protein group in post-weaning pigs, b) published in English and c) reporting at least one of the parameters included in Table  1.
For all the articles included, the diet composition of the different experimental groups was recalculated using EvaPig software, a widely used calculator of nutritional values of ingredients and diets for piglets and sows  (Noblet et al. 2008). This step was performed because several studies did not report the analysis of the nutrient composition of the diet and therefore to homogenise the error in the estimation of the nutrient composition of the diet all the formulations of the studies were recalculated with respect to a single database. EvaPig includes a database of the chemical composition and nutritive values of approximately 100 ingredients taken from the INRA-AFZ Tables  (Sauvant et al. 2002 (Sauvant et al. , 2004)). The experimental groups in the articles were then defined as follows: control group (CO), namely the group with the highest CP level within the study, and the reduced CP group (RD), namely, the groups in which the dietary CP was reduced within a study (some studies had more than one type of diet).
The reduction of dietary CP (DIFF CP) was expressed as the reduction of crude protein in percentage points (pts%) between the CO and the RD groups within each study. A minimum of 2 pts% between the CO and the RD groups was considered to be the cut-off for including the study in the analysis. The ratio between digestible lysine (dLys) and percentage of dietary CP (dLys/CP) was calculated for each diet as an indicator of the protein quality as Lys is considered to be the first limiting AA in pigs. The average daily gain (ADG) was calculated for the studies when information regarding body weight and duration of the experiment was available. Data regarding the faecal score were standardised among the studies according to the following 1 to 4 scale: 1, normal faeces; 2, soft faeces; 3, mild diarrhoea and 4, severe diarrhoea  (Pedersen and Strunz, 2013).
Short-chain fatty acids Short-chain fatty acids are molecules with seven carbon atoms or less which are mainly produced by the fermentation of indigestible carbohydrates and proteins by the gut microbiota. The present meta-analysis collected 8 studies which reported data regarding total SCFAs, namely 11 for acetic acid and propionic acids, 6 for isobutyric acid, 10 for butyric acid, 6 for isovaleric acid and 7 for valeric acid. The results obtained showed that the reduction of dietary CP significantly reduced the quantity of total SCFAs in the intestinal tract of weaned piglets (p ¼. 027; coefficient ¼ À1. 72) and, in particular, the quantity of acetic acid (p ¼. 004; coefficient ¼ À3. 25) (Table  3 ). The production of SCFAs from dietary protein sources derives from the metabolisation of the carbon skeleton which, in turn, is derived from the microbial deamination of the AAs; it is known that, of the different SCFAs, the microbial utilisation of AAs strongly contributes to the production of acetate  (Friend et al.
The effect of the dietary protein level on the intestinal mucosa In the present meta-analysis, some parameters associated with intestinal mucosal homeostasis were included. Morphological parameters of the intestine, including villous height, crypt depth and their ratio have been widely used as markers of the gut health of weaned piglets  (Lall es et al. 2007). A higher villous height has been associated with an increase in absorptive area while deeper crypts have been associated with rapid epithelial turnover as undifferentiated cells migrate along the crypt-villus axes  (Yang et al. 2013). A total of 11 studies reporting data for crypt depth and villus height were included in the meta-analysis; the results showed that the intestinal morphology parameters were not affected by the dietary CP content.
2009; Dai et al. 2010; Dai et al. 2012) ; thus, what was observed was the overall effect of the microbial protein formation and AA utilisation occurring in the guts of piglets. In addition, some data regarding the expression of the genes related to epithelial inflammation and the tight junction, including interleukins (ILs) 1, 6 and 10, Tumour Necrosis Factor-Alpha (TNF -a), Interferon gamma (IFNc), Toll-like receptor 4 (TLR-4), claudins 1 and 3, and occluding and zonulae occludes were recorded. However, very few studies which included these data were found; thus, statistical analysis was carried out only on the level of TLR-4 for which the data were reported in 4 studies. The results showed that the intestinal (in the ileum and the colon) expression of TLR-4 was significantly reduced with the reduction of dietary CP (p <.
Discussion Literature review A narrative review of the literature (Medline and Web of science databases) about paracetamol overdose related to dental pain found 10 articles (published between 2002 and 2019) representing 331 patients  [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]. The reasons given during anamnesis in supratherapeutic doses were all causes of dental pain (pulp necrosis, acute pulpitis, alveolitis or cellulitis with an apical lesion  [4, 5, 12]. Patients were seen in dental or general emergency departments within 1 to 2 weeks of the appearance of symptoms  [5, 13]. These patients were classified as relative emergencies, and therefore did not require prompt treatment, which increases the risk of overdose  [10]. The main clinical manifestations of overdose are digestive and neurological: abdominal pain, vomiting, or tinnitus  [5] [6] [7].
Data collection This study was conducted at the Centre de Diagnostic et de Traitement de la Lèpre et de l'Ulcère de Buruli (CDTLUB; the Leprosy and Buruli Ulcer Diagnosis and Treatment Center) in Pobè, Benin, which has one of the largest databases of patients with PCR-confirmed Buruli ulcer  [11]. Between 2009 and 2018, 1,186 PCR-positive Buruli ulcer patients were diagnosed and treated at this center. Biological confirmation was performed at the molecular laboratory of the CDTLUB Pobè, on swabs or fine-needle aspiration (FNA) samples. Quantitative PCR (qPCR) targeting M. ulcerans DNA was routinely performed, and a culture was set up to isolate M. ulcerans strains  [16]. Briefly, swabs were rehydrated in 2ml of sterile water. To isolate M. ulcerans strains, 400μl of swab specimens were decontaminated by the Kubica method, whereas FNA specimens were not because the sampling procedure is considered as sterile.
DNA capture One sample sequenced in this study corresponded to the DNA extracted from a swab. No isolate was obtained. To sequence this DNA sample, we applied a method of DNA capture before performing a whole genone sequencing. A custom targeted sequence capture array for M. ulcerans was generated using the Roche NimbleGen SeqCap method (Madison, USA), biotinylated DNA probes were designed in silico to cover 100% of the genome M. ulcerans reference (GenBank: GCA_000013925. 2, 1 chromosome and 1 plasmid). The probes that hybridized to human genome HG38 were removed. Libraries were prepared according to the SeqCap EZ Hyper Cap Workflow by Roche. In resume, 100ng gDNA were fragmented using a Covaris E200 targeting 180-220bp fragment size. We then proceed to prepare libraries using the Kapa Hyper Prep kit as recommended by Roche.
Sequencing and NGS analysis DNA was extracted from pure cultures, and libraries were prepared and sequenced, as previously described  [17]. Details of the clinical isolates are shown in S1 Table. Reads from the DNA capture data mapping with the GRCh38 human genome were removed using Bowtie 2. 2. 7  [18]. We used snippy 4. 6. 0 [19]  to detect SNPs and we kept only SNPs with sufficient coverage (10) on all our 177 strains. We used Phyml 3. 3  [20]  and R 4. 1. 1 [21]  with the ggtree package  [22]  to visualize the phylogenetic tree, and a map was generated with an OpenStreet-Map layer. The model used in the analysis is the HKY85 model. We submitted generated reads to the National Center for Biotechnology Information Sequence (Bioproject ID PRJNA743744) and previous genome data used in this study are available under the Bioproject ID PRJNA499075.