Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.05.02.722422

Box-Counting Fractal Dimensions of Cranial Suture: Effects of Measurement Conditions and Model-Based Reproduction of Fractal-Like Patterns

Abstract

Cranial sutures are important structures associated with skull growth, and it is widely believed that cranial sutures exhibit fractal structure. However, measurement conditions and analytical procedures have varied among studies, making direct comparison and interpretation difficult. In this study, by reviewing previous work, we established a standardized box-counting protocol for quantifying the fractal dimension (FD) of cranial sutures. Using this protocol, we quantified FD in 45 digitized images of human lambdoid sutures and in eight structure-formation model variants designed to generate fractal-like patterns via distinct kernel designs (step, Gaussian, Mexican-hat, and time-dependent/dual-stage), spatially inhomogeneous inhibition (Fbase), low-frequency noise, and time-dependent change of differentiation. We tested whether each model can generate structures with FD values at least as high as those of real sutures using one-sided Welch t-tests with Bonferroni correction for eight comparisons (adj = 0.00625), and found that six of eight model variants satisfied this criterion. Analysis of scale-dependent FD further revealed that FD is close to 1 at fine scales and approaches 2 at coarse scales in both real and model-generated patterns, indicating that cranial sutures are better described as finite-range fractal-like structures than as strict fractals, and that the box-counting FD is a scale-conditioned descriptor sensitive to preprocessing choices.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Haishi, K., Miura, T.. 2026-05-06. Box-Counting Fractal Dimensions of Cranial Suture: Effects of Measurement Conditions and Model-Based Reproduction of Fractal-Like Patterns. https://doi.org/10.64898/2026.05.02.722422

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions

Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.

systems biology↗

Comprehensive in silico analysis reveals candidate regulatory mechanisms underlying selective cerebellar vulnerability in pontocerebellar hypoplasia

Pontocerebellar hypoplasia (PCH) is a group of ultrarare, neurodegenerative disorders characterized by cerebellar and pontine hypoplasia. Genetic analysis over the last two decades has revealed an increasing number of pathogenic variants in a wide range of broadly expressed genes functioning in RNA processing, tRNA metabolism, and translation. However, the mechanisms linking these ubiquitous processes to brain region-specific vulnerability are unknown. Here, we established a multi-level variant-to-function in silico framework to predict the molecular consequences of PCH-associated variants in TSEN complex genes. These variants were predicted to have heterogeneous effects on diverse protein properties, including stability, subcellular localization, and degradation, supporting variant-specific rather than uniform disease mechanisms. Complementary transcriptomic analyses showed that PCH-associated genes were not globally enriched in the prenatal cerebellum. Instead, their expression was coordinated in a stage- and cell type-specific manner during cerebellar development. We therefore hypothesize that multiple PCH-associated genes are regulated by a common set of transcription factors, providing an explanation of the selective vulnerability of the cerebellum and to the phenotypic convergence of genetically diverse PCH subtypes. In summary, this study prioritizes candidate variants for biochemical, cellular, and in vivo validation, and identifies regulatory programs, cell lineages, and developmental windows for targeted, mechanistically informed disease modelling.

systems biology↗

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗