The Open Field Test as a Tool for Behaviour Analysis in Pigs: Recommendations for Set-Up Standardization - A Systematic Review.

INTRODUCTION: The open field test (OFT) is a common tool to assess anxiety and behavioural changes in rodents. It has been adapted to pigs with no systematic investigation of how environmental changes may alter the performance of pigs. Currently, the number of published studies including the OFT in domestic pig models is increasing without standardization. METHODS: Our review aimed to investigate the open field (OF) set-ups in published studies and the similarities between performance and published parameters. RESULTS: Following the PRISMA guidelines for reviews, we selected 69 studies for inclusion in this systematic review. We determined the specific set-up conditions such as dimensions, duration, and time of day for most of the included studies; we found high variability across studies with respect to these test specifics. DISCUSSION: Our results indicate the inconsistent implementation of the set-up, including dimensions, timing, parameters, and additional combined tests (e.g., new object tests). Based on our findings, we have made recommendations for the performance of the OFT, according to the current literature.

Postoperative Severity Assessment in Sheep.

INTRODUCTION: Sheep are frequently used in translational surgical orthopedic studies. Naturally, a good pain management is mandatory for animal welfare, although it is also important with regard to data quality. However, methods for adequate severity assessment, especially considering pain, are rather rare regarding large animal models. Therefore, in the present study, accompanying a surgical pilot study, telemetry and the Sheep Grimace Scale (SGS) were used in addition to clinical scoring for severity assessment after surgical interventions in sheep. METHODS: Telemetric devices were implanted in a first surgery subcutaneously into four German black-headed mutton ewes (4-5 years, 77-115 kg). After 3-4 weeks of recovery, sheep underwent tendon ablation of the left M. infraspinatus. Clinical scoring and video recordings for SGS analysis were performed after both surgeries, and the heart rate (HR) and general activity were monitored by telemetry. RESULTS: Immediately after surgery, clinical score and HR were slightly increased, and activity was decreased in individual sheep after both surgeries. The SGS mildly elevated directly after transmitter implantation but increased to higher levels after tendon ablation immediately after surgery and on the following day. CONCLUSION: In summary, SGS- and telemetry-derived data were suitable to detect postoperative pain in sheep with the potential to improve individual pain recognition and postoperative management, which consequently contributes to refinement.

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings.

We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.

An unsupervised learning approach for tracking mice in an enclosed area.

BACKGROUND: In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis. RESULTS: We present an unsupervised learning procedure that is basically built as a two-stage process to track mice in an enclosed area using shape matching and deformable segmentation models. The system is validated by comparing the tracking results with previously manually labeled landmarks in three setups with different environment, contrast and lighting conditions. Furthermore, we demonstrate that the system is able to automatically detect non-social and social behavior of interacting mice. The system demonstrates a high level of tracking accuracy and clearly outperforms the MiceProfiler, a recently proposed tracking software, which serves as benchmark for our experiments. CONCLUSIONS: The proposed method shows promising potential to automate behavioral screening of mice and other animals. Therefore, it could substantially increase the experimental throughput in behavioral assessment automation.

A model-specific simplification of the Mouse Grimace Scale based on the pain response of intraperitoneal CCl4 injections.

Despite its long establishment and applicability in mice pain detection, the Mouse Grimace Scale still seems to be underused in acute pain detection during chronic experiments. However, broadening its applicability can identify possible refinement approaches such as cumulative severity and habituation to painful stimuli. Therefore, this study focuses on two main aspects: First, five composite MGS criteria were evaluated with two independent methods (the MoBPs algorithm and a penalized least squares regression) and ranked for their relative importance. The most important variable was used in a second analysis to specifically evaluate the context of pain after an i.p. injection (intervention) in two treatment groups (CCl4 and oil (control)) at fixed times throughout four weeks in 24 male C57BL/6 N mice. One hour before and after each intervention, video recordings were taken, and the MGS assessment was performed. In this study, the results indicate orbital tightening as the most important criterion. In this experimental setup, a highly significant difference after treatment between week 0 and 1 was found in the CCl4 group, resulting in a medium-sized effect (W = 62.5, p value < 0.0001, rCCl4 = 0.64). The oil group showed no significant difference (week 0 vs 1, W = 291.5, p value = 0.7875, rcontrol = 0.04). Therefore, the study showed that the pain caused by i.p. injections was only dependent on the applied substance, and no significant cumulation or habituation occurred due to the intervention. Further, the results indicated that the MGS system can be simplified.

Improvement of the Mouse Grimace Scale set-up for implementing a semi-automated Mouse Grimace Scale scoring (Part 1).

The Mouse Grimace Scale (MGS) has been widely used for the noninvasive examination of distress/pain in mice. The aim of this study was to further improve its performance to generate repeatable, faster, blinded and reliable results for developing automated and standardized pictures for MGS scoring and simultaneous evaluation of up to four animals. Videos of seven C57BL/6N mice were generated in an experiment to assess pain and stress induced by repeated intraperitoneal injection of carbon tetrachloride (CCl4). MGS scores were taken 1 h before and after the injection. Videotaping was performed for 10 min in special observation boxes. For manual selection, pictures of each mouse were randomly chosen for quality analysis and scored according six quality selection criteria (0 = no, 1 = moderate, 2 = full accordance); the maximum possible score was 12. Overall, 609 pictures from six videos were evaluated for MGS scoring quality; evaluation was performed by using the picture selection tool or by manual scoring. With manual scoring, 288 pictures (48.3% of all randomly generated pictures) were deemed scorable using MGS (mean score = 22.15 ± SD 6.3). To evaluate the algorithm, ratings from different rater groups (beginner, medium-level trained, professional) were compared with the automated image generated. These differences were not significant (p = 0.1091). This study demonstrates an improved set-up and a picture selection tool that can generate repeatable, not-observer biased and standardized pictures for MGS scoring.

Semi-automated generation of pictures for the Mouse Grimace Scale: A multi-laboratory analysis (Part 2).

The Mouse Grimace Scale (MGS) is an established method for estimating pain in mice during animal studies. Recently, an improved and standardized MGS set-up and an algorithm for automated and blinded output of images for MGS evaluation were introduced. The present study evaluated the application of this standardized set-up and the robustness of the associated algorithm at four facilities in different locations and as part of varied experimental projects. Experiments using the MGS performed at four facilities (F1-F4) were included in the study; 200 pictures per facility (100 pictures each rated as positive and negative by the algorithm) were evaluated by three raters for image quality and reliability of the algorithm. In three of the four facilities, sufficient image quality and consistency were demonstrated. Intraclass correlation coefficient, calculated to demonstrate the correlation among raters at the three facilities (F1-F3), showed excellent correlation. The specificity and sensitivity of the results obtained by different raters and the algorithm were analysed using Fisher's exact test (p < 0.05). The analysis indicated a sensitivity of 77% and a specificity of 64%. The results of our study showed that the algorithm demonstrated robust performance at facilities in different locations in accordance with the strict application of our MGS setup.

Assessment of Laboratory Mouse Activity in Video Recordings Using Deep Learning Methods.

Analysis of laboratory animal behavior allows assessment of animal wellbeing. We present a method for the classification of different activities of laboratory mice by analyzing video clips using three deep learning methods. Animals placed in observation cages are filmed and short video clips are labelled as belonging to one of five defined behaviors. Subsequently, three different methods based on convolutional neural networks (CNNS) are applied to classify the clips. The best performing method - a two-stream network that analyzes individual frames as well as the video's optical flow - achieves an accuracy of 86.4%, including detection of important behavioral patterns such as self-grooming. These results show that the presented analysis protocol allows automated assessment of animal behavior by algorithmic analysis of videos of mice on observation boxes.