Biology subjects

Rodero, C.

Publications and source records attributed to Rodero, C..

3 recordsLinked to original sources

bioRxiv2026

BioMARathons as a seasonal engagement model for marine citizen science: adapting BioBlitzes to challenging coastal environments

BioBlitzes are widely used citizen science events that combine biodiversity monitoring, public participation, and environmental awareness through short and intensive observation campaigns. However, applying this model to marine environments presents additional challenges related to safety, access, weather dependency, specialised equipment, species identification, and sustained participation. This paper presents the BioMARathon model as a case study of how BioBlitz-inspired events can be adapted to marine citizen science contexts. The BioMARathon extends the conventional BioBlitz format into a longer, seasonal, and distributed engagement model designed specifically for marine and coastal environments. The paper describes the conceptual foundations of the model in the Janus Engagement Framework, which informed both the design of the BioMARathon and the adaptation of the MINKA citizen science observatory to better support participation, validation, feedback, and continuity over time. BioMARato Catalunya, launched in 2021, is presented as the founding implementation of the model and as the basis for later replication in Portugal. Between 2021 and 2025, BioMARato Catalunya showed continued growth in participation, observations, and taxonomic coverage, while also contributing to the detection of non-indigenous species, first regional records, and climate-related ecological impacts. Beyond biodiversity outcomes, the case suggests that extending participation across a season, distributing activities through local mobilising organisations, and combining expert validation with visible feedback mechanisms can support recurrent participation, retention, and community reactivation in marine citizen science. Rather than offering a formal causal evaluation, this article contributes practical lessons for the design of citizen science initiatives in challenging environments.

scientific communication and education↗

bioRxiv2026

A Cohort-Based Global Sensitivity Benchmark of MRI-Derived Whole-Heart Electromechanical Models in Healthy Hearts

Patient-specific four-chamber electromechanical models provide a physics-constrained framework for investigating whole-heart cardiac physiology and disease mechanisms. Identifying which model parameters impact whole-heart function is important for understanding cellular-, tissue-, and organ-scale determinants of cardiac performance and for calibrating patient-specific models. However, previous global sensitivity analyses of cardiac electromechanical models have typically been performed on a single heart, and systematic evaluation of how parameter influence compares across anatomically different subjects remains limited. We created four-chamber electromechanical models using cardiac MRI from five healthy subjects (n = 5). The models simulated atrial and ventricular cellular electrophysiology, calcium dynamics, and active contraction, with heterogeneous fibre orientation, transversely isotropic tissue mechanics, pericardial constraint, and a closed-loop cardiovascular system providing physiological boundary conditions. In total, 46 parameters described the integrated model. Using Gaussian process emulators, we performed multi-scale global sensitivity analysis to evaluate the relative contribution of model parameters to left and right atrial and ventricular function. Across all anatomies, the most influential parameters were systemic and pulmonary resistances, ventricular end-diastolic pressures, and the venous reference pressure, highlighting the dominant role of haemodynamic loading conditions in governing pressure- and volume-based outputs. A chamber-level analysis of atrioventricular coupling revealed a phase-dependent pattern. Atrial pressures were predominantly governed by global haemodynamic parameters (> 90% of total sensitivity), atrial filling volumes showed substantial ventricular influence ({approx}40-55% across anatomies), and atrial end-systolic volumes were primarily determined by intrinsic atrial parameters ({approx}60-65%). These patterns were consistent across subjects despite differences in anatomy. We show that, in healthy male subjects, inter-individual anatomical variation does not substantially change the ranking of dominant parameters. This work provides a repeatable modelling and sensitivity analysis framework and establishes a benchmark reference for whole-heart electromechanical modelling in healthy hearts. Author summaryComputational models of the heart can simulate cardiac physiology in unprecedented detail, but these models contain many parameters whose influence on predicted function is not fully understood. We built patient-specific four-chamber heart models from MRI scans of five healthy subjects and used statistical methods to systematically test how 46 model parameters affect simulated cardiac performance. Across all five subjects, we found that the haemodynamic loading parameters, including systemic and pulmonary vascular resistance, ventricular filling pressures, and the venous reference pressure, consistently had the greatest influence on the model outputs, regardless of differences in individual heart anatomy. This finding suggests that in healthy resting conditions, the boundary conditions of the cardiovascular system, rather than individual differences in heart geometry or electrical properties, are the primary drivers of whole-heart function. We also found a structured coupling pattern between the upper and lower heart chambers, where global haemodynamic parameters dominate atrial pressure regulation, ventricular mechanics shape atrial filling, and intrinsic atrial properties control atrial emptying. This work provides a benchmark dataset of five anatomically detailed heart models and a sensitivity analysis framework to guide calibration of future cardiac digital twin models.

systems biology↗

bioRxiv2023

Advancing GABA-edited MRS Research through a Reconstruction Challenge

PurposeTo create a benchmark for the comparison of machine learning-based Gamma-Aminobutyric Acid (GABA)-edited Magnetic Resonance Spectroscopy (MRS) reconstruction models using one quarter of the transients typically acquired during a complete scan. MethodsThe Edited-MRS reconstruction challenge had three tracks with the purpose of evaluating machine learning models trained to reconstruct simulated (Track 1), homogeneous in vivo (Track 2), and heterogeneous in vivo (Track 3) GABA-edited MRS data. Four quantitative metrics were used to evaluate the results: mean squared error (MSE), signal-to-noise ratio (SNR), linewidth, and a shape score metric that we proposed. Challenge participants were given three months to create, train and submit their models. Challenge organizers provided open access to a baseline U-NET model for initial comparison, as well as simulated data, in vivo data, and tutorials and guides for adding synthetic noise to the simulations. ResultsThe most successful approach for Track 1 simulated data was a covariance matrix convolutional neural network model, while for Track 2 and Track 3 in vivo data, a vision transformer model operating on a spectrogram representation of the data achieved the most success. Deep learning (DL) based reconstructions with reduced transients achieved equivalent or better SNR, linewidth and fit error as conventional reconstructions with the full amount of transients. However, some DL models also showed the ability to optimize the linewidth and SNR values without actually improving overall spectral quality, pointing to the need for more robust metrics. ConclusionThe edited-MRS reconstruction challenge showed that the top performing DL based edited-MRS reconstruction pipelines can obtain with a reduced number of transients equivalent metrics to conventional reconstruction pipelines using the full amount of transients. The proposed metric shape score was positively correlated with challenge track outcome indicating that it is well-suited to evaluate spectral quality.

bioengineering↗