Elsa Raynal

Jacques Raynal, Pierre Slangen, Elsa Raynal et al.

Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct organizations, and configurations that appear comparable at a given time may follow different longitudinal trajectories. This limitation motivates a methodological framework for moving beyond performance-based interpretation without assuming a complete mechanistic model in advance. This article proposes a bootstrap framework for latent-space representation learning in adaptive biological systems. Here, bootstrap is used in a methodological and epistemological sense: new analytical levels are introduced when the preceding representation becomes insufficient to account for observed adaptive dynamics. The framework is organized around five levels: observable performance, dynamic organization, latent organization, longitudinal viability, and internal predictive approximation. The framework is illustrated by three previously reported gait--occlusion studies, used here only as a methodological case sequence and not as new experimental evidence. The article formalizes how performance analysis led to latent organization, how static latent organization led to longitudinal viability, and how observed viability led to internal predictive approximation. The contribution is not a new learning algorithm, clinical protocol, or dataset, but a bootstrap framework for latent-space representation learning describing how increasingly informative representations can emerge from observational insufficiencies in adaptive biological data.

Jacques Raynal, Pierre Slangen, Elsa Raynal et al.

Adaptive biomechanical systems may show similar observable gait performance while differing in latent organization and longitudinal behavior. This study examines whether an observed longitudinal transformation of gait organization can be approximated within a predictive latent-space framework, without claiming clinical prediction or causal occlusal effects. Using an exploratory single-subject design in a Parkinsonian participant, gait was recorded with instrumented insoles during two sessions separated by eleven weeks. Six occlusal observational probes were tested: natural occlusion, open-mouth disengagement, strong clenching, two vertical-dimension increases in centric relation, and one vertical-dimension increase with mandibular protrusion. Principal Component Analysis was used to construct a PC1--PC2 latent representation. A simplified supervised machine-learning model, implemented as a feed-forward neural network, was trained to approximate the observed M1--M2 transformation. The primary analysis focused on the three centric-relation conditions and tested whether the displacement hierarchy could be reproduced. The model preserved the ordering OC3 < ONL < OC2.5. The extended six-probe analysis also preserved the global structure of the exploratory displacement pattern, with OC3 and OC3P closely grouped and the highest displacements associated with OC2.5 and open-mouth disengagement. Held-out M2 and leave-condition-out analyses showed condition-dependent approximation variability. These findings do not establish generalizable prediction, therapeutic superiority, causal occlusal effects, or clinical viability forecasting. They support only the restricted conclusion that observed longitudinal latent transformations can be internally approximated within this single-subject dataset, providing a methodological bridge toward future multi-subject predictive viability models.