Biology subjects

Mielke, F.

Publications and source records attributed to Mielke, F..

3 recordsLinked to original sources

bioRxiv2026

Coordinated beak-tongue mechanics enable dexterous seed manipulation in songbirds

Dexterous manipulation of objects relies on precise coordination between anatomical elements. In seed-eating birds, seeds are manipulated and dehusked using both the beak and tongue, but the functional roles and coordination of these structures remain unresolved. Here, we quantified the 3D movements of the upper beak, lower beak, tongue, and seed in a hard-biting and a weak-biting songbird species using X-ray Reconstruction of Moving Morphology (XROMM) and measured contractile properties of their primary jaw muscles. We show that the tongue serves as the main tool for seed rotation, transport, and stabilization. Multi-dimensional, high-frequency movements of the upper and lower beaks reveal that efficient seed processing depends on high mobility of the kinetic avian skull. Strong and weak biters differ in feeding kinematics and jaw muscle speeds, suggesting ecological specialization of cranial mechanics. The complexity, precision, and tight coordination of beak and tongue motions show that the avian cranium rivals the dexterity of the primate hand despite limited degrees of freedom.

ecology↗

bioRxiv2024

Joint and tissue mechanics in post-traumatic osteoarthritis: insights from the rat model

ObjectiveAltered mechanical loading is a known risk factor for osteoarthritis. Destabilization of the medial meniscus (DMM) is a preclinical gold standard model for post-traumatic osteoarthritis and is thought to induce instability and locally increased loading. However, the joint- and tissue-level mechanical environment underlying cartilage degeneration remains poorly documented. DesignUsing a custom multiscale modeling approach, we assessed joint and tissue biomechanics in rats undergoing sham surgery and DMM. High-fidelity experimental gait data were collected in a setup combining biplanar fluoroscopy and a ground reaction force plate. Knee poses and joint-level loading were estimated through musculoskeletal modeling, using bony landmarks, semi-automatically tracked via deep learning on fluoroscopic images, and ground reaction forces. A musculoskeletal model of the rat hindlimb was adapted to represent knee flexion-extension, valgus-varus, and internal-external rotation. The tissue-level cartilage mechanical environment was then spatially estimated, using the musculoskeletal modeling parameters as inputs into a dedicated finite element (FE) model of the rat knee, comprising cartilage and meniscal tissues. Experimental gait data and modeling workflows, including musculoskeletal models and FE meshes, are openly shared through a data repository. ResultsIn rats with DMM, the frontal plane knee pose was altered, yet there was no indication of joint-level overloading. Tissue-level mechanical cues typically linked with cartilage degeneration were not increased in the medial tibial cartilage, despite evidence of tissue structural changes. ConclusionDMM did not increase joint and tissue mechanical responses in the knee medial compartment, suggesting that mechanical loading alone does not explain the observed osteoarthritis-like structural changes.

bioengineering↗

bioRxiv2022

Probabilistic Modeling Confirms Delayed, But Non-Deficient Locomotor Development in Low Birth Weight Piglets

1Locomotor kinematics have been challenging inputs for automated diagnostic screening of livestock. Locomotion is a highly variable behavior, and influenced by subject characteristics (e.g. body mass, size, age, disease). We assemble a set of methods from different scientific disciplines, composing an automatic, high through-put workflow which can disentangle behavioral complexity and generate precise individual indicators of non-normal behavior for application in diagnostics and research. For this study, piglets (Sus domesticus) were filmed from lateral perspective during their first ten hours of life, an age at which maturation is quick and body mass and size have major consequences for survival. We then apply deep learning methods for point digitization, calculate joint angle profiles, and apply information-preserving transformations to retrieve a multivariate kinematic data set. We train probabilistic models to infer subject characteristics from kinematics. Model accuracy is validated for strides from piglets of normal birth weight (i.e. the category it was trained on), but the models infer the body mass and size of low birth weight piglets (which were left out of training, out-of-sample inference) to be "normal". The age of some (but not all) low birth weight individuals is underestimated, indicating developmental delay. Such individuals could be identified automatically, inspected, and treated accordingly. This workflow has potential for automatic, precise screening in livestock management.

zoology↗