Biology subjects

Cross, C.

Publications and source records attributed to Cross, C..

3 recordsLinked to original sources

bioRxiv2025

Attuning to song duels facilitates song-matching in nightingales

Complex vocal communication requires precise, flexible adjustments to vocal-motor commands for the production of coherent responses to auditory inputs. The mechanisms underlying how such sensorimotor integration controls vocal production is poorly understood. Here, we investigate the dynamics of territorial vocal duels and song-matching in common nightingales (Luscinia megarhynchos). Using a combination of field observations, playback experiments and accelerometry, we find that wild nightingales readily modified the acoustic features of their song, matched the rivals song and engaged more vigorously against vocal opponents with a shared song repertoire. Furthermore, we demonstrate that neurons in the sensorimotor nucleus HVC are tuned to specific song stimuli, potentially enabling context-specific integration of auditory information to facilitate matched song production. These findings highlight the crucial role of song recognition in allowing nightingales to adjust their vocalizations in response to specific social situations, demonstrating the complexity and adaptability of their communication system. Moreover, this study provides a new framework and model system for investigating online sensorimotor processing during dynamic vocal interactions.

neuroscience↗

bioRxiv2024

Distinct Type 1 Immune Networks Underlie the Severity of Restrictive Lung Disease after COVID-19

The variable etiology of persistent breathlessness after COVID-19 have confounded efforts to decipher the immunopathology of lung sequelae. Here, we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed by their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+ CD8+ T-cell perturbations in mild-to-moderate lung disease, but attenuated T-cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators, and autoantibodies distinguished each restrictive phenotype, and differed from those of patients without significant lung involvement. These differences were reflected in divergent T-cell-based type 1 networks according to severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.

immunology↗

bioRxiv2019

NeuroGPU, software for NEURON modeling in GPU-based hardware

The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur exponential increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of graphics processing unit (GPU) to accelerate neuronal simulation. NeuroGPU can simulate most of biologically detailed models 800x faster than traditional simulators when using multiple GPU cores, and even 10-200 times faster when implemented on relatively inexpensive GPU systems. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. Thus, NeuroGPU enables the rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.

neuroscience↗