Development of a Facial Expression Scale Using Farrowing as a Model of Pain in Sows

Effects of free farrowing system on the productive performance and welfare of sows and piglets

Regardless of international animal welfare regulations, most sows in production currently spend most of their lives and the peripartum period in caged housing systems. Although this type of management is intended to reduce neonatal mortality in piglets, several studies consider that there has been no significant reduction in its incidence over the last 30 years. On the contrary, cage housing has promoted the appearance of alterations during the farrowing process such as dystocia, as well as in maternal behavior and health, promoting stereotypes, skin lesions and other alterations, which not only impact the sows but also their progeny during the lactational period. Therefore, the aim of the present review is to compare the productive performance and welfare of sows and their piglets within the farrowing, lactation, and post-weaning period in both traditional and free-farrowing systems, highlighting the differences in these indicators in each of them.

Simplified assessment of castration-induced pain in pigs using lower complexity algorithms

Pigs are raised on a global scale for commercial or research purposes and often experience pain as a by product of management practices and procedures performed. Therefore, ensuring pain can be effectively identified and monitored in these settings is critical to ensure appropriate pig welfare. The Unesp-Botucatu Pig Composite Acute Pain Scale (UPAPS) was validated to diagnose pain in pre-weaned and weaned pigs using a combination of six behavioral items. To date, statistical weighting of supervised and unsupervised algorithms was not compared in ranking pain-altered behaviors in swine has not been performed. Therefore, the aim of this study was to verify if supervised and unsupervised algorithms with different levels of complexity can improve UPAPS pain diagnosis in pigs undergoing castration. The predictive capacity of the algorithms was evaluated by the area under the curve (AUC). Lower complexity algorithms containing fewer pain-altered behaviors had similar AUC (90.1-90.6) than algorithms containing five (89.18-91.24) and UPAPS (90.58). In conclusion, utilizing a short version of the UPAPS did not influence the predictive capacity of the scale, and therefore it may be easier to apply and be implemented consistently to monitor pain in commercial and experimental settings.

Effects of assessment method (real-time versus video-recorded) on a validated pain-altered behavior scale used in castrated piglets

We aimed to compare two assessment methodologies (real-time vs. video-recorded) using the Unesp-Botucatu Pig Composite Acute Pain Scale (UPAPS) in piglets before and after castration. Twenty-nine male piglets were castrated. Four observers scored the UPAPS over three perioperative timepoints of castration following two assessment methodologies. In real-time assessments, the observers were in-person observing the piglets in front of the pen. After two weeks, the observers did video-recorded assessments randomizing piglets and timepoints. Modeling was conducted to compare the UPAPS and each pain-altered behavior between methodologies. Intraclass correlation coefficient (ICC), Bland-Altman, and Lin's concordance correlation coefficient (CCC) were conducted to investigate agreement between methodologies. UPAPS was statistically equivalent between methodologies (P = 0.4371). The ICC for each method was very good (0.85 to 0.91). The agreement of the UPAPS assessed between methodologies had minimal bias (- 0.04), no proportion bias, and 53% of the assessments presented a perfect agreement. However, CCC of the UPAPS was moderate (0.65), and only one pain-altered behavior ("presents difficulty in overcoming obstacles or other animals") occurred more in real-time assessments (P = 0.0444). In conclusion, piglet pain assessment by UPAPS can be conducted in real-time based on a suitable agreement between the real-time and video-recorded assessment methods.

Effects of a Multi-Component Mycotoxin-Detoxifying Agent on Oxidative Stress, Health and Performance of Sows

This in vivo study aimed to investigate the effects of a multi-component mycotoxin-detoxifying agent, containing clays (bentonite, sepiolite), phytogenic feed additives (curcumin, silymarin) and postbiotics (yeast cell wall, hydrolyzed yeast) on the antioxidant capacity, health and reproductive performance of pregnant and lactating sows challenged by mycotoxins. Eighty (80) primiparous sows (mean age 366 ± 3 days) per each of the two trial farms were divided into two groups in each farm: a) T1 (control group): 40 sows received the contaminated feed and b) T2 group (experimental group): 40 sows received the contaminated feed plus the mycotoxin-detoxifying agent, one month before farrowing until the end of the lactation period. Thiobarbituric acid reactive substances (TBARS), protein carbonyls (CARBS) and total antioxidant capacity (TAC) were evaluated as biomarkers of oxidative stress. Clinical and reproductive parameters were recorded. Our results indicate that the administration of a multi-component mycotoxin-detoxifying agent's administration in sow feed has beneficial effects on oxidative stress biomarkers and can improve sows' health and performance.

The impact of stress and anesthesia on animal models of infectious disease

Stress and general anesthesia have an impact on the functional response of the organism due to the detrimental effects on cardiovascular, immunological, and metabolic function, which could limit the organism's response to an infectious event. Animal studies have formed an essential step in understanding and mitigating infectious diseases, as the complexities of physiology and immunity cannot yet be replicated in vivo. Using animals in research continues to come under increasing societal scrutiny, and it is therefore crucial that the welfare of animals used in disease research is optimized to meet both societal expectations and improve scientific outcomes. Everyday management and procedures in animal studies are known to cause stress, which can not only cause poorer welfare outcomes, but also introduces variables in disease studies. Whilst general anesthesia is necessary at times to reduce stress and enhance animal welfare in disease research, evidence of physiological and immunological disruption caused by general anesthesia is increasing. To better understand and quantify the effects of stress and anesthesia on disease study and welfare outcomes, utilizing the most appropriate animal monitoring strategies is imperative. This article aims to analyze recent scientific evidence about the impact of stress and anesthesia as uncontrolled variables, as well as reviewing monitoring strategies and technologies in animal models during infectious diseases.

"Do not look at me like that": Is the facial expression score reliable and accurate to evaluate pain in large domestic animals? A systematic review

Introduction

Facial expression scoring has proven to be useful for pain evaluation in humans. In the last decade, equivalent scales have been developed for various animal species, including large domestic animals. The research question of this systematic review was as follows: is facial expression scoring (intervention) a valid method to evaluate pain (the outcome) in large domestic animals (population)?

Method

We searched two databases for relevant articles using the search string: "grimace scale" OR "facial expression" AND animal OR "farm animal" NOT "mouse" NOT "rat" NOT "laboratory animal." The risk of bias was estimated by adapting the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist.

Results

The search strategy extracted 30 articles, with the major share on equids and a considerable number on cows, pigs, and sheep. Most studies evaluated facial action units (FAUs), including the eye region, the orbital region, the cheek or the chewing muscles, the lips, the mouth, and the position of the ears. Interobserver reliability was tested in 21 studies. Overall FAU reliability was substantial, but there were differences for individual FAUs. The position of the ear had almost perfect interobserver reliability (interclass coefficient (ICC): 0.73-0.97). Validity was tested in five studies with the reported accuracy values ranging from 68.2 to 80.0%.

Discussion

This systematic review revealed that facial expression scores provide an easy method for learning and reliable test results to identify whether an animal is in pain or distress. Many studies lack a reference standard and a true control group. Further research is warranted to evaluate the test accuracy of facial expression scoring as a live pen side test.

Improving the Reliability of Scale-Free Image Morphometrics in Applications with Minimally Restrained Livestock Using Projective Geometry and Unsupervised Machine Learning

Advances in neural networks have garnered growing interest in applications of machine vision in livestock management, but simpler landmark-based approaches suitable for small, early stage exploratory studies still represent a critical stepping stone towards these more sophisticated analyses. While such approaches are well-validated for calibrated images, the practical limitations of such imaging systems restrict their applicability in working farm environments. The aim of this study was to validate novel algorithmic approaches to improving the reliability of scale-free image biometrics acquired from uncalibrated images of minimally restrained livestock. Using a database of 551 facial images acquired from 108 dairy cows, we demonstrate that, using a simple geometric projection-based approach to metric extraction, a priori knowledge may be leveraged to produce more intuitive and reliable morphometric measurements than conventional informationally complete Euclidean distance matrix analysis. Where uncontrolled variations in image annotation, camera position, and animal pose could not be fully controlled through the design of morphometrics, we further demonstrate how modern unsupervised machine learning tools may be used to leverage the systematic error structures created by such lurking variables in order to generate bias correction terms that may subsequently be used to improve the reliability of downstream statistical analyses and dimension reduction.

Facial Expressions of Horses Using Weighted Multivariate Statistics for Assessment of Subtle Local Pain Induced by Polylactide-Based Polymers Implanted Subcutaneously

Simple Summary

Facial expression (FE) has been used for pain diagnosis in horses. The current study aimed to identify pain in horses undergoing under-skin polylactide-based polymer implantation. Five statistical methods for analyzing FE were used, including conventional and new approaches. First, we scored the seven FEs separately. Subsequently, the scores of the seven FEs were added (SUM). Subsequently, principal component analysis (PCoA) was performed using the scores of the seven FEs obtained using the first method. Afterwards, weights were created for each FE based on each variable’s contribution variability obtained from the PCoA (SUM.W). Finally, we applied a general score to the animal’s face (GFS). The horses were filmed before and 24 and 48 h after implantation. The tissue sensitivity to mechanical stimulation and skin temperature of the horses were assessed at the same time points. The results show no changes in the FEs analyzed separately or jointly. The horses with incision and suture but no polymer implant displayed a higher pain-related FE 48 h after implantation, while the horses implanted with polymers displayed more apparent alterations in the mechanical skin sensitivity and temperature. Our findings show that the five statistical methods used to analyze the faces of the horses were not able to detect low-grade inflammatory pain.

Abstract

Facial-expression-based analysis has been widely applied as a pain coding system in horses. Herein, we aimed to identify pain in horses undergoing subcutaneously polylactide-based polymer implantation. The sham group was submitted only to surgical incision. The horses were filmed before and 24 and 48 h after implantation. Five statistical methods for evaluating their facial expressions (FEs) were tested. Primarily, three levels of scores (0, 1, and 2) were applied to the seven FEs (ear movements, eyebrow tension, orbicularis tension, dilated nostrils, eye opening, muzzle tension, and masticatory muscles tension). Subsequently, the scores of the seven FEs were added (SUM). Afterwards, principal component analysis (PCoA) was performed using the scores of the seven FEs obtained using the first method. Subsequently, weights were created for each FE, based on each variable’s contribution variability obtained from the PCoA (SUM.W). Lastly, we applied a general score (GFS) to the animal’s face (0 = without pain; 1 = moderate pain; 2 = severe pain). The mechanical nociceptive threshold (MNT) and cutaneous temperature (CT) values were collected at the same moments. The results show no intra- or intergroup differences, when evaluating each FE separately or in the GFS. In the intragroup comparison and 48 h after implantation, the control group showed higher values for SUM, PCoA, and SUM.W, although the horses implanted with polymers displayed more obvious alterations in the CT and MNT. Our findings show that the five statistical strategies used to analyze the faces of the horses were not able to detect low-grade inflammatory pain.

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Simple Summary

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.

Abstract

Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Incidence of dystocia at piglet level in cloprostenol-induced farrowings and associated risk factors

Few studies have investigated risk factors for dystocia in swine, although this birthing abnormality can compromise welfare of both sows and piglets by increasing stillbirth rate and decreasing sow productivity. This study aimed to determine risk factors associated with dystocia at piglet level in cloprostenol-induced farrowings. A dystocia event was recorded when a birth interval exceeded 45 min or when manual extraction was applied. Data were collected from 898 piglets born from 77 Landrace × Yorkshire crossbred sows, which were induced for farrowing on day 114 of gestation. Generalized linear mixed models (GLMMs) were used to evaluate the association between dystocia and parity, gestation length, litter size, relative birth order (RBO (%) = 100 ⋅ birth order/litter size), birth weight, crown rump length, body mass index, ponderal index, piglet's sex, use of oxytocin, and stillbirth. Sows nested in farrowing batches were fitted as random factors in GLMMs. Incidence of dystocia at piglet and farrowing levels was 11.0 % and 75.3 %, respectively. The final multivariate model explained 20.1 % variation of dystocia. RBO had a quadratic effect on dystocia in which incidence of dystocia decreased from RBO ≤ 40 % to RBO = 60 %–70 %, and then increased to the end of parturition. Piglets with birth weight > 1700 g and stillborn piglets had higher odds of dystocia in comparison with piglets with a birth weight of 900–1700 g (OR = 2.63; 95 % CI = 1.66–4.18) and live-born piglets (OR = 2.62; 95 % CI = 1.12–6.15), respectively. This study indicates that dystocia is very common in cloprostenol-induced farrowings and suggests that the last one-third of parturitions is the most important stage to be supervised, and selection for homogenous litters and moderate high birth weight may reduce the rate of dystocia.

A systematic review of porcine models in translational pain research

Translating basic pain research from rodents to humans has proven to be a challenging task. Efforts have been made to develop preclinical large animal models of pain, such as the pig. However, no consistent overview and comparison of pig models of pain are currently available. Therefore, in this review, our primary aim was to identify the available pig models in pain research and compare these models in terms of intensity and duration. First, we systematically searched Proquest, Scopus and Web of Science and compared the duration for which the pigs were significantly sensitized as well as the intensity of mechanical sensitization. We searched models within the specific field of pain and adjacent fields in which pain induction or assessment is relevant, such as pig production. Second, we compared assessment methodologies in surrogate pain models in humans and pigs to identify areas of overlap and possible improvement. Based on the literature search, 23 types of porcine pain models were identified; 13 of which could be compared quantitatively. The induced sensitization lasted from hours to months and intensities ranged from insignificant to the maximum attainable. We also found a near to complete overlap of assessment methodologies between human and pig models within the area of peripheral neurophysiology, which allows for direct comparison of results obtained in the two species. In spite of this overlap, further development of pain assessment methodologies is still needed. We suggest that central nervous system electrophysiology, such as electroencephalography, electrocorticography or intracortical recordings, may pave the way for future objective pain assessment.

Meloxicam and Dexamethasone Administration as Anti-Inflammatory Compounds to Sows Prior to Farrowing Does Not Improve Lactation Performance

Simple Summary

Sows may experience pain and discomfort whilst giving birth. Additionally, the birthing process is accompanied by an inflammation response. Administering anti-inflammatory compounds prior to birth may provide an opportunity to improve piglet survival and growth. The aim of this experiment was to assess the efficacy of both a non-steroidal (NSAID; meloxicam) and steroidal (SAID; dexamethasone) anti-inflammatory drug for improving farrowing house performance of sows. In younger sows, there was no impact of treatment; however, older sows from the NSAID treatment gave birth to fewer live piglets. Postnatal mortality was unaffected by treatment and no improvement in piglet growth was observed. Feed intake of both NSAID and SAID sows was improved when compared with the control group; however, there was a tendency for a delayed oestrus in the NSAID group. Administering NSAID to sows prior to farrowing is not recommended as it reduces piglet survival and subsequent reproduction.

Abstract

The aim of this experiment was to determine whether administration of an anti-inflammatory compound to sows prior to farrowing would, via reduced pain and inflammation, increase piglet survival and growth. At day 114 of gestation, multiparous sows were randomly allocated to one of the following treatments: Control (n = 43), which received 10 mL saline, NSAID (n = 55) which received 0.4 mg/kg meloxicam and SAID (n = 54) which received 0.1 mg/kg dexamethasone. Treatments were applied again on day 116 if farrowing had not occurred. There was no treatment effect on piglets born alive or dead from parity two to four sows but in those of parity five and older, NSAID administration reduced the number of piglets born alive and increased the number of piglets born dead (p < 0.05). Sow rectal temperature and incidence of mastitis were unaffected by treatment (p > 0.05). Lactation day two plasma concentrations of cortisol, prostaglandin F2 alpha metabolite and haptoglobin did not differ among treatments (p > 0.05). Treatment effects were not observed in liveborn piglet mortality at any age, or litter weight at day 21 (p > 0.05). Average feed intake during lactation was increased by both NSAID and SAID treatments (p = 0.001). The use of meloxicam prior to farrowing should be avoided as it reduced the number of piglets born alive and did not improve piglet survival and growth.

Happy Cow or Thinking Pig? WUR Wolf—Facial Coding Platform for Measuring Emotions in Farm Animals

Emotions play an indicative and informative role in the investigation of farm animal behaviors. Systems that respond and can measure emotions provide a natural user interface in enabling the digitalization of animal welfare platforms. The faces of farm animals can be one of the richest channels for expressing emotions. WUR Wolf (Wageningen University & Research: Wolf Mascot), a real-time facial recognition platform that can automatically code the emotions of farm animals, is presented in this study. The developed Python-based algorithms detect and track the facial features of cows and pigs, analyze the appearance, ear postures, and eye white regions, and correlate these with the mental/emotional states of the farm animals. The system is trained on a dataset of facial features of images of farm animals collected in over six farms and has been optimized to operate with an average accuracy of 85%. From these, the emotional states of animals in real time are determined. The software detects 13 facial actions and an inferred nine emotional states, including whether the animal is aggressive, calm, or neutral. A real-time emotion recognition system based on YoloV3, a Faster YoloV4-based facial detection platform and an ensemble Convolutional Neural Networks (RCNN) is presented. Detecting facial features of farm animals simultaneously in real time enables many new interfaces for automated decision-making tools for livestock farmers. Emotion sensing offers a vast potential for improving animal welfare and animal–human interactions.

Pain Management in Farm Animals: Focus on Cattle, Sheep and Pigs

Simple Summary

Pain causes behavioral, autonomic and neuroendocrine changes and is a common cause of animal welfare compromise in farm animals. These recommendations focus on cattle, sheep, and pigs, and present the implications of unmanaged pain in terms of animal welfare and ethical perspectives, and its challenges and misconceptions. We provide an overview of pain management including assessment and treatment applied to the most common husbandry procedures, and recommendations to improve animal welfare in these species.

Abstract

Pain causes behavioral, autonomic, and neuroendocrine changes and is a common cause of animal welfare compromise in farm animals. Current societal and ethical concerns demand better agricultural practices and improved welfare for food animals. These guidelines focus on cattle, sheep, and pigs, and present the implications of pain in terms of animal welfare and ethical perspectives, and its challenges and misconceptions. We provide an overview of pain management including assessment and treatment applied to the most common husbandry procedures, and recommendations to improve animal welfare in these species. A cost-benefit analysis of pain mitigation is discussed for food animals as well as the use of pain scoring systems for pain assessment in these species. Several recommendations are provided related to husbandry practices that could mitigate pain and improve farm animal welfare. This includes pain assessment as one of the indicators of animal welfare, the use of artificial intelligence for automated methods and research, and the need for better/appropriate legislation, regulations, and recommendations for pain relief during routine and husbandry procedures.