Biology subjects

Sergounioti, A.

Publications and source records attributed to Sergounioti, A..

3 recordsLinked to original sources

bioRxiv2025

Uncovering Symbolic Convergence in Human Sperm Motility: A Data-Driven Analysis of Monotonic Trajectory Clusters

BackgroundUnderstanding heterogeneity in sperm motility requires tools capable of capturing both dynamic patterns and interpretable structure. Symbolic encoding methods offer a novel way to represent motion trajectories through discrete motifs. ObjectiveThis study explores whether symbolic time-series representations can uncover latent structure in sperm motility data, focusing on patterns of symbolic monotony and entropy. MethodsWe applied Symbolic Aggregate approXimation (SAX) to 1176 sperm trajectories and computed motif entropy and dominance across multiple parameterizations (a = 3-7, k = 2-4). Trajectories were embedded using UMAP and evaluated for symbolic convergence. ResultsA compact trajectory cluster (82/1176, 7%) consistently emerged across SAX configurations, characterized by very low entropy (median: 0.00) and high motif dominance (median: 0.97), with >85% of motifs consisting of the "DDD" triplet. The cluster exhibited a markedly negative VSL slope (median: -0.00021), in contrast to non-clustered trajectories (median: +0.00002). No external labels were available to determine functional significance. ConclusionsSymbolic encoding revealed a highly consistent pattern of motion monotony. While these findings may reflect constrained or declining motility, their biological interpretation remains uncertain. Symbolic representations may serve as useful hypothesis-generating tools in the discovery of emergent sperm motility phenotypes.

bioinformatics↗

bioRxiv2025

Phase-Space Dynamics Reveal Structured and Chaotic Motility in Human Sperm via DTW Clustering

BackgroundTraditional sperm motility metrics often fail to reflect the dynamic complexity of motion patterns. Here, we present an unsupervised framework combining dynamic time warping (DTW) clustering with phase-space and fatigue-sensitive descriptors to uncover latent motility phenotypes. MethodsWe analyzed 1,176 sperm tracks from the VISEM dataset using DTW distance matrices applied to velocity time series, followed by agglomerative hierarchical clustering (n = 2). After cluster assignment, we extracted phase-space features--recurrence rate, spectral entropy, fractal index, and Lyapunov approximation--and computed fatigue metrics such as VSL slope. ResultsDTW clustering revealed two well-separated motility phenotypes with a mean silhouette score of 0.861. Chaotic-like tracks exhibited higher spectral entropy (4.45 vs. 2.58), elevated fractal index (0.079 vs. 0.434), and increased local instability as reflected by the Lyapunov approximation (0.131 vs. 0.009; all p < 0.001). Recurrence rate showed no significant difference. VSL slope was markedly more negative in Chaotic-like tracks, indicating a stronger fatigue component. ConclusionsOur pipeline stratifies sperm motility into biologically interpretable dynamic classes using raw temporal profiles--without relying on predefined scalar indices. This approach may enhance phenotypic analysis in reproductive diagnostics by capturing structural and fatigue-driven variability in sperm motion.

biophysics↗

bioRxiv2025

Novel Fatigue Profiling Approach Highlights Temporal Dynamics of Human Sperm Motility

BackgroundAccurate characterization of human sperm motility is crucial for understanding male fertility potential. Traditional motility assessment methods primarily focus on static velocity parameters, often overlooking temporal declines in motility during the sperm trajectory. ObjectiveWe aimed to develop and validate a novel fatigue-based profiling approach to assess intra-trajectory motility decline in human spermatozoa. MethodsUsing computer-assisted sperm analysis (CASA)-derived motion tracking data from 1,118 sperm trajectories, we introduced the Fatigue Index, a log-fold metric quantifying the decline in forward progression (VSL) over time. Fatigue status was classified using complementary strategies, including fixed and percentile-based thresholds, z-score normalization, and unsupervised clustering. Descriptive and feature-level analyses were performed to characterize motility patterns associated with fatigue. ResultsFatigued spermatozoa exhibited significantly lower straight-line velocity (VSL: 18.4 vs 42.7 m/s) and steeper VSL slopes (-0.34 vs -0.08 m/s/frame) compared to non-fatigued counterparts. The Fatigue Index reliably identified subpopulations of sperm with time-dependent motility deterioration across multiple classification schemes. ConclusionsFatigue-based temporal profiling offers a new dimension for understanding sperm motility, highlighting the dynamic nature of forward progression and identifying subtle impairments that may be overlooked by conventional assessment methods. While preliminary, this approach provides a biologically grounded framework for dynamic sperm quality evaluation.

cell biology↗