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Artificial intelligence (AI) in animal behavior and welfare research is on the rise. AI can detect behaviors and localize animals in video recordings, thus it is a valuable tool for studying social dynamics. However, maintaining the identity of individuals over time, especially in homogeneous poultry flocks, remains challenging for algorithms. We propose using differentially colored "backpack" tags (black, gray, white, orange, red, purple, and green) detectable with computer vision (eg. YOLO) from top-view video recordings of pens. These tags can also accommodate sensors, such as accelerometers. In separate experiments, we aim to: (i) evaluate avian visual perception of the different colored tags; (ii) assess the potential impact of tag colors on social behavior; and (iii) test the ability of the YOLO model to accurately distinguish between different colored tags on Japanese quail in social group settings. First, the reflectance spectra of tags and feathers were measured. An avian visual model was applied to calculate the quantum catches for each spectrum. Green and purple tags showed significant chromatic contrast to the feather. Mostly tags presented greater luminance receptor stimulation than feathers. Birds wearing white, gray, purple, and green tags pecked significantly more at their own tags than those with black (control) tags. Additionally, fewer aggressive interactions were observed in groups with orange tags compared to groups with other colors, except for red. Next, heterogeneous groups of 5 birds with different color tags were videorecorded for 1 h. The precision and accuracy of YOLO to detect each color tag were assessed, yielding values of 95.9% and 97.3%, respectively, with most errors stemming from misclassifications between black and gray tags. Lastly using the YOLO output, we estimated each bird's average social distance, locomotion speed, and the percentage of time spent moving. No behavioral differences associated with tag color were detected. In conclusion, carefully selected colored backpack tags can be identified using AI models and can also hold other sensors, making them powerful tools for behavioral and welfare studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The metabolic effects induced by antipsychotics in vitro depend on their action on the trafficking and biosynthesis of sterols and lipids. Previous research showed that antipsychotics with different adverse effects in patients cause similar alterations in vitro, suggesting the low clinical usefulness of cellular studies. Moreover, the inhibition of peripheral AMPK was suggested as potential aetiopathogenic mechanisms of olanzapine, and different effects on autophagy were reported for several antipsychotics. We thus assessed, in clinically-relevant culture conditions, the aetiopathogenic mechanisms of olanzapine, risperidone and ziprasidone, antipsychotics with respectively high, medium, low metabolic risk in patients, finding relevant differences among them. We highlighted that: olanzapine impairs lysosomal function affecting autophagy and autophagosome clearance, and increasing intracellular lipids and sterols; ziprasidone activates AMPK increasing the autophagic flux and reducing intracellular lipids; risperidone increases lipid accumulation, while it does not affect lysosomal function. These in vitro differences align with their different impact on patients. We also provided evidence that metformin add-on improved autophagy in olanzapine-treated cells and reduced lipid accumulation induced by both risperidone and olanzapine in an AMPK-dependent way; metformin also increased the production of bile acids to eliminate cholesterol accumulations caused by olanzapine. These results have different clinical implications. We demonstrated that antipsychotics with different metabolic impacts on patients actually have different mechanisms of action, thus supporting the possibility of a personalised antipsychotic treatment. Moreover, we found that metformin can fully revert the phenotype caused by risperidone but not the one caused by olanzapine, that still activates SREBP2., (© 2024. The Author(s).)