dataesr/scientific-paragraphs-categorization huggingface dataset:
To create an automated animal monitoring system, pig detection in an image is the most important step. In fact, the detection performance of a model greatly influences the performance of subsequent computer vision tasks such as tracking individual animals and classifying behaviours (standing, lying, feeding etc. ). To accomplish this task, traditional image processing techniques have been employed by several authors such as Zhu et al. [1]. Despite the good performance of their datasets, these detection algorithms have a poor generalization capability. The use of convolutional neural networks (CNNs) for computer vision tasks for pig monitoring has become increasingly common with recent advances in technology and deep learning  [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]. While this technology has shown satisfactory results for the detection of other animals and humans  [13] , the various constraints mentioned above prevent the development of a robust framework for pig detection under real-world conditions.
The experiments are conducted on two publicly available pig detection datasets: Bergamini et al. [11]  and Psota et al  [6]. Thus, we compare our proposed approach to an anchor-based framework and an anchor-free keypoint-based framework. The main contributions of our work can therefore be summarized as follows: • We propose a new detector known as anchor-free centrebased (AFCB) that is designed for the overlapping and crowded scenario of pig detection. • We propose a combination of novel negative data augmentation during training and test time augmentation to improve the robustness and generalization ability of our AFCB framework. • The proposed method was tested on two pig detection datasets and shown to be more effective than state-of-the-art detectors. The remainder of this paper is structured as follows.
Single-stage detection models have also been used to detect pigs  [4, 8, 9, 11]. This approach does not use region proposals and directly predicts the classification and regression from anchors. In general, two-stage detectors are slower, although they have better performance than single-stage detectors, even though the difference tends to diminish with some datasets  [17]. As these architectures have proven successful in detecting other objects in the literature  [13] , they are also widely applied to the detection of pigs. Indeed, by using a wide variety of locations, sizes, and ratios of the anchors, the model greatly increases the likelihood of locating every object in the image. However, these approaches have several drawbacks. The computation time must be balanced with the number of anchors used.
Due to the wide variations in pig bounding box shapes, we cannot predict a scale as CSP does, but instead we must separately predict width and height. Several anchor-free methods have been proposed lastly to perform well in crowded environments. DETR  [23]  and deformable DETR  [24]  are two recent detectors that directly predict a set of box predictions by combining a CNN and a transformer-based backbone. Sparse R-CNN  [25]  uses a feature pyramid network (FPN) based on ResNet architecture to extract multi-scale feature maps. A dynamic head outputs a set of object proposals. YOLOX  [26]  is an anchor-free version of YOLO series  [27]. These methods achieve good results on the Crowd-Human dataset  [28] , a benchmark for detecting humans in a crowd. Howewer, they have never been used for the challenging task of pig detection.
Nevertheless, the artificial images created are too similar to the original distribution and are therefore insufficient to drastically reduce the prediction failures. In other studies, hard negative mining has been employed to improve the generalization of models  [30]. A pretrained model is used to detect false positives and add them as hard negative samples to the training set; the model is then retrained. However, these methods require another diversified dataset. Shrivastava et al. [31]  suggest performing hard negative mining during training by employing an initial prediction and then only using training examples that cause high loss. The samples still originate from the same training base, although they are not sufficient to guarantee the performance of the model under uncontrolled usage conditions.
During training Data augmentation techniques are used to train our model. The input image is randomly flipped left or right, rotated from 0 • to 360 • , zoomed in or out, and shifted XY from the ranges [0 ∶ W 2 ] and [0 ∶ ]. According to the augmentation method applied to the input image, we modify the output targets. These techniques artificially increase the number of images seen during training. Thus, overtraining is delayed, while the generalization ability of the model is increased. Nevertheless, the created artificial images are too close to the original distribution, thus making them insufficient to drastically reduce the prediction failures. We use the ImageNet dataset  [35]  as a source for negatives in our training process. Some data augmentation functions may result in black pixels in the image background during training (such as zooming out, shifting, and rotating).
Bergamini dataset Bergamini et al. [11]  used an anchor-based detector in their study. Their dataset comprises twelve videos of a single pen with eight animals. In each sequence, 600 images (each containing the eight pigs) are labelled in a bounding box format by the authors. We likewise used seven training sequences and five for testing. Bergamini et al. did not focus on the detection technique but rather on the statistical application of these techniques to a large dataset. Thus, the model is trained and evaluated using images of the same pen.
Training details The proposed method is implemented using Keras. We optimize the losses with a GTX 2080 TI GPU with 11 GB of RAM. images of a mini-batch are randomly flipped, rotated, zoomed in or out and shifted. If NTDA is enabled and the data augmentation functions create black pixels, they are replaced with a random image from the ImageNet database. To compare them, we trained a model with and without NTDA. We use the Ima-geNet validation set containing 50,000 images as negatives for models that are trained with NTDA. The toolbox MMDetection  [39]  is used to train and evaluate state-of-the-art methods commonly used in crowded scenarios. It contains many object detection methods and includes training and inference codes. Models are also trained for 33. 6K iterations and evaluated at their optimal capacity iteration.
Training losses Figure  6  shows the loss curves for every 1. 6K training iteration for 33. 6K iterations on both datasets. In this illustration, the red and blue curves indicate the training and validation losses, respectively. We show losses for the AFCB models and for those trained with NTDA. It is noteworthy that despite the randomness of the negative images, the NTDA does not have a significant effect on the behaviour of the training loss curves on either datasets. Thus, the negative augmentation does not affect the stability of the iterative gradient descent algorithm. However, for each dataset, NTDA delays the overfitting and reduces the generalization gap between training and validation loss at the optimal capacity iteration. We demonstrate that NTDA leads to a higher level of generalization by learning more robust features.
To perform experiments, we use each model at their optimal capacity iteration before they are overfit. The images in the Bergamini test dataset are similar to those in the training dataset. Thus, their method is already known to achieve a high performance of 97. 98% with F measure. With our AFCB model, we obtain a small improvement of 0. 06 and 0. 07 when combined with TTA. With our AFCB model trained with NTDA, we achieve an improvement of 0. 10 and 0. 22 when combined with TTA.
Results for the Bergamini dataset Several examples of false negatives produced by anchorbased detectors are presented by the authors. The failures are indicated by red arrows in Figure  7. One of the pigs in the top left image is not visible, because it is occluded by another. This resulted in a classification failure, because the anchor containing the pig provides insufficient features. A bounding box contains two pigs in the top right and bottom left images. We are unable to determine whether this results from a classification or regression failure. Indeed, two pigs in an anchor could be classified as positive, or otherwise a pig in an anchor could be regressed to contain two pigs. Furthermore, by removing all neighbouring bounding boxes, the post-processing NMS prevents us from detecting the two animals separately.
This may lead to incorrect classifications or regressions. By scanning for interesting pixels, our model is less affected by noisy neighbouring pixels. and our own study using their test:seen and test:unseen sets. We also present results from state-of-the-art methods commonly used in crowded scenarios: DETR  [23] , deformable DETR  [24] , sparse R-CNN  [25] , and YOLOX  [26]. Based on their sharp drop in performance between test:seen and test:unseen sets, these methods fail to generalize as much as our method. As with the Bergamini dataset and the Psota test:seen set, our AFCB model achieves only a small improvement of 0. 11 in F measure and 0. 12 when combined with TTA. Using our AFCB model trained with NTDA, we obtain an improvement of 0. 12 and 0. 13 when combined with TTA. These evaluation data have the same distribution as the training data, which results in a high performance.
CONCLUSION In this study, we propose an anchor-free centre-based detector called AFCB which locates the central point of each pig and regresses its width and height. This work differs from state-ofthe-art works which use NMS or parts association algorithms. We demonstrate that our method is robust in extreme overlapping and crowded scenarios. However, as with many CNN frameworks, our AFCB model suffers from performance degradation under uncontrolled conditions. To tackle these problems, we propose a combination of a novel negative training data augmentation and test time augmentation. We conduct experiments on two pig detection datasets available online and find that our network surpasses state-of-the-art results on both datasets. Moreover, our real-time system achieves an improvement of 10% in F measure when performing in unknown conditions.
Acknowledgements This work is part of the collective scientific expertise (ESCo) on the 'Quality of animal-derived foods according to animal production and processing conditions' that was carried out by  INRAE  at the request of the  French ministry responsible for Agriculture and Food , in cooperation with the agency FranceAgriMer. The authors are grateful to the other experts from ESCo panel for their fruitful discussions. The authors acknowledge  Sophie Prache  ( INRAE, France ) for her advices on the structure and the content of the manuscript.
Acknowledgements Members of the  Subcommittee on Antifungal Susceptibility Testing (AFST)  of the  ESCMID European Committee for Antimicrobial Susceptibility Testing (EUCAST) M. C. Arendrup (Chairman, Denmark) ,  S. Arikan-Akdagli  ( Steering Committee, Turkey ),  F. Barchiesi  (Italy),  Jochem Buil  (the Netherlands),  M. Castanheira   (USA), E. Chryssanthou (Sweden) ,  N. Friberg (Finland) ,  J. Guinea  ( Scientific Secretary, Spain ),  P. Hamal  ( Czech Republic ),  Ingibjorg Hilmarsdottir (Iceland), N. Klimko (Russia), O. Kurzai (Germany), K. Lagrou (Belgium), C. Lass-Flörl (Austria) ,  T. Matos  (Slovenia),  J. Meletiadis  (Scientific Data Coordinator, Greece),  C. Moore (UK) ,  K. Muehlethaler  ( Steering Committee, Switzerland ),  T. R. Rogers  (Ireland). ThermoFisher and  F2G , and non-financial support from OLM and IMMY, outside the submitted work.
Long-Term Follow Up for Reversibility Assessment and Back-to-Normal Reproductive Function Evaluation In order to evaluate both the reversibility of the implant effects and a return-to-normal reproductive function overtime, additional data on clinical reproductive parameters (heat cycles and pregnancies) were collected every 6 months after the first heat, and for a maximum period of 24 months. Various parameters were qualitatively evaluated regarding either the functional reproductive system (frequency of heats, number of puppies by litter, prolonged oestrous or galactorrhoea occurrence, and follicular resorption), or other parameters related to the effectiveness of the reproductive function (normality of the delivery, stillborn/alive puppies ratio, malformation, or abortion occurrences).
Study Population A total of 83 female dogs were enrolled. Seventy-seven animals were followed-up until the first oestrus apparition. Four female dogs were then lost to follow-up, which led to a total of seventy-three animals, which could be followed up after the first oestrus. Six animals were excluded from the study for reasons defined by the study protocol (Table  S1 ): confirmation of sexual maturity within the 3 weeks following treatment (n = 3), serious or fatal adverse events unrelated to the treatment (n = 2, e. g. , run over by a car and parvovirosis) and accidental ingestion of an unrelated sexual hormonal treatment (n = 1). Efficacy data were calculated from eighty bitches, and three of them were considered until the time of their withdrawal (until the ingestion of the owner's pill, death from parvovirosis infection, or death from a car accident).
Safety data were obtained from the 83 enrolled bitches. General characteristics at baseline were calculated from 59 and 21 bitches in the deslorelin and control groups, respectively, and showed similar conditions for both of them. The age at inclusion was 15. 12 ± 1. 79 weeks for the deslorelin group and 14. 62 ± 1. 86 weeks for the placebo group, ranging from 12 to 18 weeks in both groups. The mean bodyweight at inclusion was 8. 2 ± 5. 27 kg [min = 1 kg; max = 27 kg] in the deslorelin group and 8. 5 ± 4. 40 kg [min = 2 kg; max = 18 kg] in the control group. The ratio of mixed breed versus purebred was balanced between groups with a little over 50% of purebred in both groups. The breeds of the recruited female dogs and the proportion of these breeds within the deslorelin and the control group are presented in Table  S2.
Long-Term Follow-Up for Reversibility of Implant Effect Reproductive data were collected on the 73 animals (n = 52 in deslorelin group and n = 21 in placebo group) every 6 months and for a total length of a 24-month period observation, with the time limit set on October 2020. Following the animals for an additional 24-month period allowed for up to 5 consecutive heat cycles to be observed in some of the bitches (Table  1 ). Some variability between individuals was noticeable in both groups, with a wide range of times observed in both groups (Table  1 ). The two groups were comparable regarding the frequency and regularity of the occurrence of heat signs (Table  1 ). Intervals between clinical signs of heat and clinical patterns of oestrus cycles were similar between groups (Table  2 ). One animal from the deslorelin group was reported with an oestrus that lasted 24 days.
Conclusions In this multicentric, randomized placebo-controlled study, 98. 2% of prepubertal female dogs aged from 12 to 18 weeks and receiving a 4. 7 mg deslorelin implant expressed their first oestrus from 6 to 24 months post implantation. The median onset of oestrus was significantly delayed by 160 days when compared to the control group. No safety concerns, including induced-oestrus, were observed in female dogs that were implanted until 16 weeks of age. At sexual maturity, implanted and control female dogs displayed comparable clinical heat signs and sex hormones levels. Reproductive data collected in the long-term (up to two years after the appearance of the first oestrus) support the hypothesis that the deslorelin-implanted female dogs returned to fertility, displayed regular heat cycles, were able to breed, and went on to conceive and to give birth without any major safety concerns.
ACKNOWLEDGMENTS We would like to thank  B. Laurenssen ,  M. Ooms ,  K. Lin ,  J. Arts ,  I. van den Anker ,  R. Koopmanschap ,  I. Reimert ,  I. Minussi ,  G. Binnendijk , and  A. Ipema  for skilful assistance in conducting the experiment. We are also grateful to the animal caretakers and students involved.
Before moving on to selecting appropriate dose levels, the investigator should consider all available information, including (but not limited to): • The plethora of large data bases of in vivo toxicity and computer-based models can be used to obtain an understanding of chemical structures and mechanisms, facilitating focus on the key toxicological endpoints that should be considered. For example, databases that are searchable by chemical structure, such as the US EPA's CompTox Dashboard, can be used along with read across approaches to find acceptable analogs which already have toxicity data, negating the need for testing. In some circumstances, if a specific read-across candidate is read across along with Quantitative Structure Activity Relationships (QSARs) this may highlight data for substances that are partially similar, e.
g. , through the sharing of functional groups, and provide understanding of possible modes of action, target organs and indications of data which may help in selecting dose groups for a target substance. The most easily accessible and widely used tool is the OECD QSAR toolbox (OECD 2021). Use of this software can identify structurally similar substances and identify opportunities for read-across, and the profiling module is also a good early warning system to indicate toxicological mechanisms or endpoints that could be sensitive during in vivo testing. • Results of existing toxicity studies, including known hazardous properties such as extensive irritation which can cause a disruption in the natural barrier of the tissue system at the portal of entry (whether the skin, the gastric lining or the nasal epithelium), with inflammation, hyperkeratosis, ulceration and breakdown of epithelial integrity (cytotoxicity, venous access, scarring etc.
Thus, an R max ≥ 4 can be considered representative of a synergic association. However, the E max model offers more precision than the FIC index to determine the strength of a synergy. Interestingly, most of the molecules tested in the literature in combination with AmB have an antifungal activity even if they are not antifungal drugs. In our study, PEG15HS showed no antifungal activity up to concentrations of 1024 mg/L. This type of molecule, without antifungal activity, should not allow the selection of resistant fungi  [30] , which reinforces the interest in this association. PEG15HS is a non-ionic surfactant, listed as an inactive ingredient in the FDA database. It is used as a solubilizer in marketed oral, parenteral and ophthalmic formulations  [31, 32]. It has low toxicity and excellent biocompatibility due to low hemolytic properties  [33] [34] [35] [36]  and thus represents a potentially interesting synergistic agent with AmB.
Introduction In recent years, gut microbiota has been highlighted as a critical partner of feed and digestive efficiency of pigs, as reviewed by  Gardiner et al. (2020)  and  Patil et al. (2020). Indeed, microbiota composition differs for animals with extreme feed and digestive efficiency values, and some specific intestinal microbes were statistically associated with feed efficiency  (McCormack et al. 2017 (McCormack et al. , 2019;; Yang et al. , 2017; Bergamaschi et al. , 2020b; Aliakbari et al. , 2021)  and digestive efficiency traits  (Niu et al. , 2015; Sciellour et al. , 2018). Moreover, it has been shown in pigs, as well as in many species, that individual host genetic factors influence the gut microbiota composition. Based on small datasets (< 1000 samples), low to moderate heritability estimates were obtained for certain bacterial genera in pigs  (Estelle et al.
, 2020). However, in this latter study the effect of climate, feed, and housing could not be disentangled. To be used in practice, it is critical to estimate the impact of genotype-by-environment (GxE) interactions on the genetic relationships between genera abundances on one hand, and feed and digestive efficiency traits on the other hand. The objective of this study was to evaluate the genetic relationships between gut microbiota composition and efficiency traits with a focus on the effect of diet on genetic parameters. A large dataset of closely related pigs tested in the same farm with two diets of contrasted dietary fiber contents that significantly affected the abundances of 130 out of 231 genera  (Déru et al. , 2021b)  was used. Genetic parameters of microbiota traits (genera abundances and alpha diversity indices) were estimated within and across diets to quantify the magnitude of genetic by diet interactions.
Measurements and sampling For each animal, average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI) were computed between 35 and 115 kg. The ADG was computed as the ratio between body weight gain and number of days on test. The FCR was calculated as the ratio between DFI and ADG, and was expressed in kg/kg. The RFI was determined using a multiple linear regression of DFI on ADG, lean meat percentage, carcass yield and average metabolic body weight considering data from the two diets jointly in the linear regression, as described in  Déru et al. (2020). A spot collection of fecal samples was carried out at 16 weeks of age just before feed transition, for digestive efficiency determination and microbiota composition analyses. For each pig, feces were collected in a piping bag and manually homogenized.
Microbiota DNA preparation and sequencing A total of 1,564 fecal samples were used for ribosomal 16S DNA gene sequencing and analysis, with the protocol described in  Verschuren et al. (2018). The microbial DNA was extracted using the Quick-DNA Fecal Microbe Miniprep Kit (Zymo Research, Freiburg, Germany) and a 15-min bead-beating step at 30 Hz was applied. The V3-V4 region was then amplified from diluted genomic DNA with the primers F343 (CTTTCCCTACACGACGCTCTTCCGATCT-TACGGRAGGCAGCAG) and R784 (GGAGTTCAGAC-GTGTGCTCTTCCGATCTTACCAGGGTATCTAATCCT) using 30 amplification cycles with an annealing temperature of 65 °C  (Zemb et al. , 2020). The ends of each read overlap and can be stitched: the Flash software v1. 2. 6 150  (Magoč and Salzberg, 2011)  was used to assemble each pair-end sequence, with at least a 10-bp overlap between the forward and reverse sequences, allowing 10% mismatch.
Single multiplexing was performed using an in-house 6-bp index, which was added to the R784 primer during a second polymerase chain reaction (PCR) with 12 cycles using forward primer (AATGATACGGCGACCACCGAGATCTACACTCTTTC-CCTACACGAC) and reverse primer (CAAGCAGAAGACG-GCATACGAGAT-index-GTGACTGGAGTTCAGACGTGT). The resulting PCR products were purified and loaded to the Illumina MiSeq cartridge following the manufacturer's instructions. Run quality was internally checked using PhiX (a library used as a control for Illumina sequencing runs), and each pair-end sequence was assigned to its sample using the bcl2fastq Illumina software. The sequences were submitted to the Short-Read Archive with accession number PRJNA741111. Filtering and trimming of sequences of high quality was applied to the reads with the "DADA2" package  (Callahan et al.