Towards Machine Recognition of Facial Expressions of Pain in Horses.

Automated recognition of human facial expressions of pain and emotions is to a certain degree a solved problem, using approaches based on computer vision and machine learning. However, the application of such methods to horses has proven difficult. Major barriers are the lack of sufficiently large, annotated databases for horses and difficulties in obtaining correct classifications of pain because horses are non-verbal. This review describes our work to overcome these barriers, using two different approaches. One involves the use of a manual, but relatively objective, classification system for facial activity (Facial Action Coding System), where data are analyzed for pain expressions after coding using machine learning principles. We have devised tools that can aid manual labeling by identifying the faces and facial keypoints of horses. This approach provides promising results in the automated recognition of facial action units from images. The second approach, recurrent neural network end-to-end learning, requires less extraction of features and representations from the video but instead depends on large volumes of video data with ground truth. Our preliminary results suggest clearly that dynamics are important for pain recognition and show that combinations of recurrent neural networks can classify experimental pain in a small number of horses better than human raters.

Effect of transportation and social isolation on facial expressions of healthy horses.

Horses have the ability to generate a remarkable repertoire of facial expressions, some of which have been linked to the affective component of pain. This study describes the facial expressions in healthy horses free of pain before and during transportation and social isolation, which are putatively stressful but ordinary management procedures. Transportation was performed in 28 horses by subjecting them to short-term road transport in a horse trailer. A subgroup (n = 10) of these horses was also subjected to short-term social isolation. During all procedures, a body-mounted, remote-controlled heart rate monitor provided continuous heart rate measurements. The horses' heads were video-recorded during the interventions. An exhaustive dataset was generated from the selected video clips of all possible facial action units and action descriptors, time of emergency, duration, and frequency according to the Equine Facial Action Coding System (EquiFACS). Heart rate increased during both interventions (p<0.01), confirming that they caused disruption in sympato-vagal balance. Using the current method for ascribing certain action units (AUs) to specific emotional states in humans and a novel data-driven co-occurrence method, the following facial traits were observed during both interventions: eye white increase (p<0.001), nostril dilator (p<0.001), upper eyelid raiser (p<0.001), inner brow raiser (p = 0.042), tongue show (p<0.001). Increases in 'ear flicker' (p<0.001) and blink frequency (p<0.001) were also seen. These facial actions were used to train a machine-learning classifier to discriminate between the high-arousal interventions and calm horses, which achieved at most 79% accuracy. Most facial features identified correspond well with previous findings on behaviors of stressed horses, for example flared nostrils, repetitive mouth behaviors, increased eye white, tongue show, and ear movements. Several features identified in this study of pain-free horses, such as dilated nostrils, eye white increase, and inner brow raiser, are used as indicators of pain in some face-based pain assessment tools. In order to increase performance parameters in pain assessment tools, the relations between facial expressions of stress and pain should be studied further.

Equine Facial Action Coding System for determination of pain-related facial responses in videos of horses.

During the last decade, a number of pain assessment tools based on facial expressions have been developed for horses. While all tools focus on moveable facial muscles related to the ears, eyes, nostrils, lips, and chin, results are difficult to compare due to differences in the research conditions, descriptions and methodologies. We used a Facial Action Coding System (FACS) modified for horses (EquiFACS) to code and analyse video recordings of acute short-term experimental pain (n = 6) and clinical cases expected to be in pain or without pain (n = 21). Statistical methods for analyses were a frequency based method adapted from human FACS approaches, and a novel method based on co-occurrence of facial actions in time slots of varying lengths. We describe for the first time changes in facial expressions using EquiFACS in video of horses with pain. The ear rotator (EAD104), nostril dilation (AD38) and lower face behaviours, particularly chin raiser (AU17), were found to be important pain indicators. The inner brow raiser (AU101) and eye white increase (AD1) had less consistent results across experimental and clinical data. Frequency statistics identified AUs, EADs and ADs that corresponded well to anatomical regions and facial expressions identified by previous horse pain research. The co-occurrence based method additionally identified lower face behaviors that were pain specific, but not frequent, and showed better generalization between experimental and clinical data. In particular, chewing (AD81) was found to be indicative of pain. Lastly, we identified increased frequency of half blink (AU47) as a new indicator of pain in the horses of this study.

Characterization of Pyrin Dephosphorylation and Inflammasome Activation in Macrophages as Triggered by the Yersinia Effectors YopE and YopT.

Pathogenic Yersinia species deliver Yop effector proteins through a type III secretion system into host cells. Among these effectors, YopE and YopT are Rho-modifying toxins, which function to modulate host cell physiology and evade immune responses. YopE is a GTPase-activating protein (GAP) while YopT is a protease, and they inhibit RhoA by different modes of action. Modifications to RhoA are sensed by pyrin, which, once activated, assembles a caspase-1 inflammasome, which generates cytokines such as interleukin-1ß (IL-1ß) and cell death by pyroptosis. In Yersinia-infected macrophages, YopE or YopT triggers inflammasome assembly only in the absence of another effector, YopM, which counteracts pyrin by keeping it inactive. The glucosyltransferase TcdB from Clostridium difficile, a well-studied RhoA-inactivating toxin, triggers activation of murine pyrin by dephosphorylation of Ser205 and Ser241. To determine if YopE or YopT triggers pyrin dephosphorylation, we infected lipopolysaccharide (LPS)-primed murine macrophages with ΔyopMYersinia pseudotuberculosis strains expressing wild-type (wt) or YopE mutant variants or YopT. By immunoblotting pyrin after infection, we observed that wt YopE triggered dephosphorylation of Ser205 and inflammasome activation. Pyrin dephosphorylation was reduced if a YopE variant had a defect in stability or RhoA specificity but not membrane localization. We also observed that wt YopT triggered pyrin dephosphorylation but more slowly than YopE, suggesting that YopE is dominant in this process. Our findings provide evidence that RhoA-modifying toxins trigger activation of pyrin by a conserved dephosphorylation mechanism. In addition, by characterization of YopE and YopT, we show that different features of effectors, such as RhoA specificity, affect the efficiency of pyrin dephosphorylation.