Biology subjects

Cazzola, D.

Publications and source records attributed to Cazzola, D..

4 recordsLinked to original sources

bioRxiv2026

Walking on the Moon: Hypogravity drives the emergence of a proprioception-dependent locomotor state

Animals must adapt locomotion to changing environmental conditions, but how the nervous system flexibly controls movement remains unclear. Gravity provides a powerful perturbation because it alters body loading, limb dynamics, and proprioceptive feedback. Apollo astronauts often skipped on the Moon, adopting an asymmetric gait rarely used on Earth, yet the sensorimotor basis of this behaviour is unknown. Here, by studying hypogravity locomotion in humans and mice, we show that reduced gravity reveals a conserved proprioception-dependent mechanism for locomotor state expression. In humans, skipping in simulated lunar gravity is generated through flexible temporal reconfiguration of existing motor modules rather than construction of a new motor architecture. In mice, simulated lunar gravity elicited a skipping-like locomotor state, and genetic elimination of muscle proprioceptors abolished this response. Thus, hypogravity reveals how proprioceptive feedback enables flexible expression of locomotor states.

neuroscience↗

bioRxiv2023

pyRBDome: A comprehensive computational platform for enhancing and interpreting RNA-binding proteome data

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet the extent of noise, including false-positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that while UV cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.

systems biology↗

bioRxiv2022

Hyperflexion is unlikely to be the primary cervical spine injury mechanism in accidental head-on rugby tackling

In Rugby a high proportion of catastrophic cervical spine injuries occur during tackling. In the injury prevention literature, there is still an open debate on the injury mechanisms related to such injuries, with hyperflexion and buckling being under scrutiny. The aims of this study were to determine the primary cervical spine injury mechanism during head-on rugby tackling, and evaluate the effect of tackling technique on cervical spine intervertebral loading. We conducted an in silico study to examine the dynamic response of the cervical spine under loading conditions representative of accidental head-on rugby tackles by using a subject-specific musculoskeletal model of a rugby player. The computer simulations were driven by experimental in vivo data of an academy rugby player tackling a punchbag, and in vitro data of head-first impacts using a dummy head. Results showed that: i) the earlier generation of high compression and anterior shear loads with low values of flexion moments provides evidence that hyperflexion is unlikely to be the primary injury mechanism in the sub-axial cervical spine (C3-C7) during central and posterior head impact locations; ii) a higher degree of neck flexion at impact poses the cervical spine in a more hazardous position. These findings provide objective evidence to inform injury prevention strategies or rugby law changes, with the final view of improving the safety of the game of rugby.

bioengineering↗

bioRxiv2021

Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running

The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [-10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [-10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [-15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNets source code publicly available for step event detection in treadmill running when force data are not available.

bioinformatics↗