Background: Rodent ultrasonic vocalizations (USVs) are crucial to their social communication and a widely used translational tool for linking gene mutations to behavior. To maximize the causal interpretation of experimental treatments, we need to understand how neural control affects USV production. However, both the aerodynamics of USV production and its neural control remain poorly understood. Results: Here, we test three intralaryngeal whistle mechanisms—the wall and alar edge impingement, and shallow cavity tone—by combining in vitro larynx physiology and individual-based 3D airway reconstructions with fluid dynamics simulations. Our results show that in the mouse and rat larynx, USVs are produced by a glottal jet impinging on the thyroid inner wall. Furthermore, we implemented an empirically based motor control model that predicts motor gesture trajectories of USV call types. Conclusions: Our results identify wall impingement as the aerodynamic mechanism of USV production in rats and mice. Furthermore, our empirically based motor control model shows that both neural and anatomical components contribute to USV production, which suggests that changes in strain specific USVs or USV changes in disease models can result from both altered motor programs and laryngeal geometry. Our work provides a quantitative neuromechanical framework to evaluate the contributions of brain and body in shaping USVs and a first step in linking descending motor control to USV production.

In this study, the unsteady flow and acoustic characteristics of a three-dimensional (3D) flapping wing model of a Tibicen linnei cicada in forward-flight are numerically investigated. A single cicada wing is modelled as a membrane with a prescribed motion reconstructed from high-speed videos of a live insect. The numerical solution takes a hydrodynamic/acoustic splitting approach: the flow field is solved with an incompressible Navier-Stokes flow solver based on an immersed boundary method, and the acoustic field is solved with linearized perturbed compressible equations. The 3D simulation allows for the examination of both the directivity and frequency compositions of the flapping wing sound in a full space. Along with the flexible wing model, a rigid wing model that is extracted from real motion is also simulated to investigate the effects of wing flexibility. The simulation results show that the flapping sound is directional; the dominant frequency varies around the wing. The first and second frequency harmonics show different radiation patterns in the rigid and flexible wing cases, which are demonstrated to be highly associated with wing kinematics and loadings. Furthermore, the rotation and deformation in the flexible wing is found to help lower the sound strength in all directions.

Seals are able to use their uniquely shaped whiskers to track hydrodynamic trails generated 30 s ago and detect hydrodynamic velocities as low as 245 [Formula: see text]m s-1. The high sensibility has long thought to be related to the wavy shape of the whiskers. This work revisited the hydrodynamics of a seal whisker model in a uniform flow, and discovered a new mechanism of seal whiskers in reducing self-induced noises, which is different from the long thought-of effect of the wavy shape. It was reported that the major and minor axes of the elliptical cross-sections of seal whisker are out of phase by approximately 180 degrees. Three-dimensional numerical simulations of laminar flow (Reynolds number range: 150-500) around seal-whisker-like cylinders were performed to examine the effect of the phase-difference on hydrodynamic forces and wake structures. It was found that the phase-difference induced hairpin vortices in the wake over a wide range of geometric and flow parameters (wavelength, wavy amplitude and Reynolds number), therefore substantially reducing lift-oscillations and self-induced noises. The formation mechanism of the hairpin vortices was analyzed and is discussed in details. The results provide valuable insights into an innovative vibration reduction and hydrodynamic sensing mechanism.

Tissue dynamics play critical roles in many physiological functions and provide important metrics for clinical diagnosis. Capturing real-time high-resolution 3D images of tissue dynamics, however, remains a challenge. This study presents a hybrid physics-informed neural network algorithm that infers 3D flow-induced tissue dynamics and other physical quantities from sparse 2D images. The algorithm combines a recurrent neural network model of soft tissue with a differentiable fluid solver, leveraging prior knowledge in solid mechanics to project the governing equation on a discrete eigen space. The algorithm uses a Long-short-term memory-based recurrent encoder-decoder connected with a fully connected neural network to capture the temporal dependence of flow-structure-interaction. The effectiveness and merit of the proposed algorithm is demonstrated on synthetic data from a canine vocal fold model and experimental data from excised pigeon syringes. The results showed that the algorithm accurately reconstructs 3D vocal dynamics, aerodynamics, and acoustics from sparse 2D vibration profiles.

In this work, a high-fidelity three-dimensional continuum model of the canine laryngeal framework was developed for simulating laryngeal posturing. By building each muscle and cartilage from magnetic resonance imaging (MRI), the model is highly realistic in anatomy. The muscle mechanics is modeled using the finite-element method. The model was tested by simulating vocal fold postures under systematic activations of individual as well as groups of laryngeal muscles, and it accurately predicted vocal fold posturing parameters reported from in vivo canine larynges. As a demonstration of its application, the model was then used to investigate muscle controls of arytenoid movements, medial surface morphology, and vocal fold abduction. The results show that the traditionally categorized adductor and abductor muscles can have opposite effects on vocal fold posturing, making highly complex laryngeal adjustments in speech and singing possible. These results demonstrate that a realistic comprehensive larynx model is feasible, which is a critical step toward a causal physics-based model of voice production.