Zamora, R. | Search bioRxiv

bioRxiv2024

Evolution of vocal production learning in parrots

Vocal production learning (VPL), the capacity to imitate sounds, is a crucial, but not exclusive component of human language. VPL is rare in animals but common in birds, where it evolved independently in songbirds, hummingbirds, and parrots. Parrots (Psittaciformes) learn new vocalizations throughout their lives and exhibit astonishing vocal flexibility and imitation capacity. They can copy allospecific sounds, e.g., human words and learn their associated meanings. Parrots, therefore, present an intriguing model to shed light on how VPL evolved and how it may relate to other language-relevant traits. How widely VPL is distributed across Psittaciformes and to what extent (qualitative) species differences exist, remains unknown. Here, we provide the first comprehensive overview of the phylogenetic distribution of (allospecific) VPL in this clade by conducting surveys of publicly available video footage. Out of the 398 currently recognized extant species, we found videos for 163, of which 136 showed evidence of VPL. Phylogenetic analyses suggest secondary losses and reacquisitions of VPL covarying with socioecological parameters (gregariousness), life-history (longevity), and morphological (body size) traits. This study provides the first insights into interspecific variation in vocal learning across all parrot species and reveals potential socio-ecological drivers of its evolution. SignificanceLittle is known about the selective forces that favor the evolution of vocal production learning (VPL), a rare trait in animals and a prerequisite for the evolution of human language. We provide the first insights into interspecific variation in VPL in the evolutionary history of parrots and uncover candidate evolutionary drivers. The current data suggest that the evolution of VPL within parrots has been highly dynamic, with multiple secondary losses and reacquisitions. Our model showed that VPL most likely was the ancestral state. Sociality, longevity and body size explain variation in VPL together with a highly uncertain effect of brain size. The findings may motivate comparative work in other taxa and contribute to research into the evolutionary origins of human language.

evolutionary biology↗

bioRxiv2024

The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss

Volumetric Muscle Loss (VML) injuries are characterized by significant loss of muscle mass, usually due to trauma or surgical resection, often with a residual open wound in clinical settings and subsequent loss of limb function due to the replacement of the lost muscle mass with non-functional scar. Being able to regrow functional muscle in VML injuries is a complex control problem that needs to override robust, evolutionarily conserved healing processes aimed at rapidly closing the defect in lieu of restoration of function. We propose that discovering and implementing this complex control can be accomplished by the development of a Medical Digital Twin of VML. Digital Twins (DTs) are the subject of a recent report from the National Academies of Science, Engineering and Medicine (NASEM), which provides guidance as to the definition, capabilities and research challenges associated with the development and implementation of DTs. Specifically, DTs are defined as dynamic computational models that can be personalized to an individual real world "twin" and are connected to that twin via an ongoing data link. DTs can be used to provide control on the real-world twin that is, by the ongoing data connection, adaptive. We have developed an anatomic scale cell-level agent-based model of VML termed the Wound Environment Agent Based Model (WEABM) that can serve as the computational specification for a DT of VML. Simulations of the WEABM provided fundamental insights into the biology of VML, and we used the WEABM in our previously developed pipeline for simulation-based Deep Reinforcement Learning (DRL) to train an artificial intelligence (AI) to implement a robust generalizable control policy aimed at increasing the healing of VML with functional muscle. The insights into VML obtained include: 1) a competition between fibrosis and myogenesis due to spatial constraints on available edges of intact myofibrils to initiate the myoblast differentiation process, 2) the need to biologically "close" the wound from atmospheric/environmental exposure, which represents an ongoing inflammatory stimulus that promotes fibrosis and 3) that selective, multimodal and adaptive local mediator-level control can shift the trajectory of healing away from a highly evolutionarily beneficial imperative to close the wound via fibrosis. Control discovery with the WEABM identified the following design principles: 1) multimodal adaptive tissue-level mediator control to mitigate pro-inflammation as well as the pro-fibrotic aspects of compensatory anti-inflammation, 2) tissue-level mediator manipulation to promote myogenesis, 3) the use of an engineered extracellular matrix (ECM) to functionally close the wound and 4) the administration of an anti-fibrotic agent focused on the collagen-producing function of fibroblasts and myofibroblasts. The WEABM-trained DRL AI integrates these control modalities and provides design specifications for a potential device that can implement the required wound sensing and intervention delivery capabilities needed. The proposed cyber-physical system integrates the control AI with a physical sense-and-actuate device that meets the tenets of DTs put forth in the NASEM report and can serve as an example schema for the future development of Medical DTs.

bioengineering↗