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Miyamae, J. A.

Publications and source records attributed to Miyamae, J. A..

2 recordsLinked to original sources

Specializations in Tail Anatomy of the Lesser Egyptian Jerboa (Jaculus jaculus) Compared with the Mouse and Rat

Mammal tails have long been recognized for their diversity of morphological form and function, however, there remains a substantial gap between the motivation to understand and emulate the various performance functions of the tail and what is known about tail anatomy. In this study, we were motivated to discover the anatomical foundations of the fast, whipping motions of the tail of the lesser Egyptian jerboa (Jaculus jaculus), which may aid in the quick changes of direction as the animal escapes from predators using ricochetal bipedal hopping. We employed microCT scans, dissections, and museum data to describe the musculoskeletal anatomy of the jerboa in comparison with the laboratory mouse (Mus musculus) and rat (Rattus norvegicus). While many aspects of tail anatomy are conserved across these species, the jerboa does possess unique characteristics such as an extremely long tail arising from caudal vertebral elongation, development of extensive dorsal musculature differentiated into lateral and medial components to increase points of skeletal attachment, and a novel anatomical feature - the bi-lobed cranial transverse process - which serves as a supernumerary dorsal tendon attachment site and possible brace to protect the ventral tendons and intrinsic muscles for a section of caudal vertebrae which likely experiences high mechanical stress.

zoology↗

Computer Vision for Lesser Egyptian Jerboa (Jaculus jaculus) Behavioral Analysis and Animal Care Refinement

We validated the use of an open-source computer vision toolkit to analyze high-quality behavioral data and evaluate welfare in the Lesser Egyptian Jerboa (Jaculus jaculus). Movements of these small, nocturnal rodents are rapid and difficult to observe, potentially obscuring behavioral assessment. However, assessment became warranted when alopecia and jumping were noted. We compared trained human observers to machine learning trained computer vision algorithms, evaluating accuracy and precision in behavioral classification. Human observers categorized behaviors with an overall accuracy of 0.71 + 0.11 and an intraclass correlation coefficient (ICC) of 0.92 + 0.07, with greater odds of misidentifying behaviors lasting less than one second. Computer vision classifiers successfully met human-grade accuracy and ICC, with significantly less sensitivity to behavioral duration. As 34% of manually classified behaviors lasted less than 0.5 seconds, we used computer vision to annotate activity budgets of captive jerboas before and after adding novel enrichment. Alopecia was significantly associated with grooming, and while grooming was negatively associated with terrarium height and with opaque dividers between terraria, conventional rodent enrichment had no significant effect on behavior. Inflammatory causes of alopecia were not found with cytologic, molecular, or histopathologic analysis. These results suggest captive jerboa may demonstrate psychogenic alopecia. Furthermore, computer vision automation allows for fast, accurate analysis of large volumes of behavioral data that can be used to tailor species-specific husbandry practices and improve animal welfare.

animal behavior and cognition↗