Search arXivSearch

arXiv · 2102.01512

Mimicry mechanism model of octopus epidermis pattern by inverse operation of Turing reaction model

Abstract

Many cephalopods such as octopus and squid change their skin color purposefully within a very short time. Furthermore, it is widely known that some octopuses have the ability to change the color and unevenness of the skin and to mimic the surroundings in short time. However, much research has not been done on the entire mimicry mechanism in which the octopus recognizes the surrounding landscape and changes the skin pattern. It seems that there is no hypothetical model to explain the whole mimicry mechanism yet. In this study, the mechanism of octopus skin pattern formation was assumed to be based on the Turing model. Here, the pattern formation by the Turing model was realized by the equivalent filter calculation model using the cellular automaton, instead of directly solving the differential equations. It was shown that this model can create various patterns with two feature parameters. Furthermore, for the eyes recognition part where two features are extracted from the Turing pattern image, our study proposed a method that can be calculated back with small amount of calculation using the characteristics of the cellular Turing pattern model. These two calculations can be expressed in the same mathematical frame based on the cellular automaton model using the convolution filter. As a result, it can be created a model which is capable of extracting features from patterns and reconstructing patterns in a short time, the model is considered to be a basic model for considering the mimicry mechanism of octopus. Also, in terms of application to machine learning, it is considered that it shows the possibility of leading to a model with a small amount of learning calculation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Takeshi Ishida. 2021-09-02. Mimicry mechanism model of octopus epidermis pattern by inverse operation of Turing reaction model. https://doi.org/10.1371/journal.pone.0256025

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

KEEP EXPLORING

Related papers

Triplication: an important component of the modern scientific method

A scientific-study protocol (defined) is designed to deliver results from which inductive inference is allowed. In the nineteenth century, triplication was introduced into the plant sciences and Fisher's p<0.05 rule (1925) incorporated into triple-result protocols designed to counter random/systematic errors which contribute to real-world variability. The aims of the present study were to: (1) classify replication protocols; (2) assess their prevalence in plant-science studies (published during one twenty-first-century year; for defined variable construct); (3) explore triplication rationale. Methods: a plant-sciences protocol-prevalence report was produced; experimental/associational-study proportions analyzed; and real-world-data proxies used to show confidence-interval-width patterns with increasing replicate number. Results: 25% plant-science studies analyzed showed triplication, including 11% triple-result protocols (including greater replicate numbers: 48%;17%, respectively). Theoretical considerations indicated that even if systematic errors predominate, (previously-known) square-root rules sometimes apply, contributing to triplication importance (exemplified by real-world-data proxies). Conclusions: The defined protocols, with minor modifications, should provide the means for assessment of most sciences. Triplication was extensively applied in studies analysed and there are strong methodological reasons why triplication, rather than duplication/quadruplication, is the appropriate standard: triple-result protocols: (a) effectively reduce false positives to acceptable levels; (b) give qualitatively-different information (shape) from duplication; (c) have a large efficiency advantage (concerning confidence-interval widths) over quadruplication. The application of batch replication is not, primarily, a statistical problem and cannot effectively be replaced by simulation.

q-bio.QM

Retracing the Process of Translation: Proteome-wide mapping of stable transcriptomic predictors of protein abundance in cancer cell lines

Understanding the relationship between gene expression and protein abundance is central to molecular and systems biology. While gene expression reflects transcriptional activity, proteins are the functional molecules that determine cellular phenotypes. However, numerous post-transcriptional and translational regulatory layers complicate this relationship, and prior studies have reported only weak to moderate correlations between RNA and protein levels. Predicting protein abundance from transcriptomic data remains challenging, but it is a valuable goal for biological insight, especially when proteomic data is limited or unavailable. In this study, we applied a large-scale, Ridge regression-based feature selection strategy to identify predictive gene expression features for each of 8,423 proteins across 940 cancer cell lines. To our knowledge, this is the first work to perform such comprehensive protein-wise feature selection at this scale. Our analysis revealed both globally predictive and context-specific gene features. These included biologically meaningful modules such as immune-related genes, HOX transcription factor targets, and cytoskeletal components. The models identified stable candidate gene-protein associations that remained interpretable at the level of individual proteins and recurrent transcriptomic predictor patterns. Our approach enables interpretable modeling of protein expression from transcriptomic data and provides insight into transcriptomic features associated with protein abundance. This framework may support hypothesis generation, protein imputation in incomplete datasets, and deeper understanding of post-transcriptional regulation in cancer biology.

q-bio.QM

Surf_2_Volume: a workflow for converting CIFTI parcellations to NIfTI volume space

Many neuroimaging programs require volumetric NIfTI files and cannot directly use parcellations stored in CIFTI format. We developed Surf_2_Volume, a workflow that uses Connectome Workbench, FreeSurfer, AFNI/SUMA, neuromaps, and Python to convert categorical CIFTI parcellations into NIfTI volumes. Cortical labels are transferred through fsaverage to a surface representation of the MNI152 template and assigned to voxels within a smoothed cortical ribbon mask. A threshold controls how much of this mask is included. We tested twelve thresholds with the Schaefer2018 atlas with 100 parcels and 7 networks, which is available as both an fsLR representation and a published FSL MNI152 1 mm volume. We compared each converted volume with the published reference and with standard Workbench ribbon and nearest-vertex mappings. At threshold 0.05, Surf_2_Volume had higher scores on the two measures of parcel overlap, Dice and Jaccard, than the best Workbench setting (0.729 vs. 0.709 and 0.580 vs. 0.556, respectively). It also left fewer reference voxels unlabeled (11.88% vs. 20.57%) and assigned fewer labels outside the reference (9.43% vs. 11.22%). Among the tested Surf_2_Volume thresholds, 0.05 also produced the lowest average parcel volume distortion. Surf_2_Volume provides a tunable and reproducible workflow for generating volumetric atlases from CIFTI parcellations when volume-based analyses are required.

q-bio.QM