Immobilization is required for the examination and manipulation of honey bees (Apis mellifera), and many techniques have been developed to render honey bees immobile until this date. Among them, two methods, which are carbon dioxide (CO2) and low-temperature narcosis, have been commonly used to induce anesthesia on the bees. Although CO2-induced anesthesia changes the behavior and physiology of honey bees, the anesthesia induced by cold is considered commonly as non-damaging for the bees. There are a very limited number of studies on the effects of different chilling protocols in honey bees. Therefore, the purpose of this study was to investigate the effect of different chilling procedures on the survival rate and depth of anesthesia in honey bees. A total of 4 groups were formed, one of which was the control group. Three temperatures (+4 degrees C, -20 degrees C and -80 degrees C) were applied to three different experimental groups. Furthermore, four different exposure times were implemented on each experimental group (n=20 worker bees in each trial). The depth of anesthesia was assessed and scored based on the movements and anesthesia recovery time in honeybees. There was a statistically significant relationship between duration and survival/death rate in the applications at +4 degrees C, -20 degrees C and -80 degrees C (P<0.001). The method that was applied for 5 minutes at -20 degrees C was the most ideal chilling method (P<0.03) and showed the lowest mortality in addition to the high depth of anesthesia. This study may assist in selecting the safest and deepest anesthesia method required in any study on honey bees.

Colic is a leading cause of mortality in horses, demanding precise and timely interventions. This study integrates machine learning and explainable artificial intelligence (XAI) to predict survival outcomes in horses with colic, using clinical, procedural, and diagnostic data. Random forest and XGBoost emerged as top-performing models, achieving F1 scores of 85.9% and 86.1%, respectively. SHAP (Shapley additive explanations) was employed to provide interpretable insights, offering both global and local explanations for model predictions. The analysis revealed that key features, such as pulse rate, lesion type, and total protein levels, significantly influenced survival likelihood. Local interpretations highlighted the unique contribution of clinical factors to individual cases, enabling personalized insights that guide targeted treatment strategies. These tailored predictions empower veterinarians to prioritize interventions based on the specific conditions of each horse, moving beyond generalized care protocols. By combining predictive accuracy with interpretability, this study advances precision veterinary medicine, enhancing outcomes for equine colic cases and setting a benchmark for future applications of AI in animal health.

Animal emotion detection has emerged as a critical domain in understanding animal welfare and enhancing human–animal interactions. This study evaluates the performance of GPT-4, a generative AI model, in recognizing and classifying pet emotions from images, with a particular focus on dogs. The research was conducted in two phases: a general pet emotion classification across multiple species and a dog-specific classification. In Phase 1, GPT-4 achieved an overall accuracy of 50.2%, reflecting its baseline capability in handling diverse animal images. In Phase 2, accuracy significantly improved to 76.7% due to refined prompts and the use of a targeted dataset. Sentiment analysis of the model’s textual justifications revealed alignment with visual cues in correct predictions and highlighted challenges in ambiguous cases. These findings demonstrate the potential of generative AI in animal emotion detection and emphasize the importance of targeted datasets and advanced prompt engineering. This study contributes to bridging the gap between AI capabilities and practical applications in animal welfare and behavioral research.