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Over the past few decades, formation of Turing patterns in reaction-diffusion systems has been shown to be the underlying process in several examples of biological morphogenesis, confirming Alan Turing's hypothesis, put forward in 1952. However, theoretical studies suggest that Turing patterns formation via classical "short-range activation and long-range inhibition" concept in general can happen within only narrow parameter ranges. This feature seemingly contradicts the accuracy and reproducibility of biological morphogenesis given the stochasticity of biochemical processes and the influence of environmental perturbations. Moreover, it represents a major hurdle to synthetic engineering of Turing patterns. In this work it is shown that this problem can be overcome in some systems under certain sets of interactions between their elements, one of which is immobile and therefore corresponding to a cell-autonomous factor. In such systems Turing patterns formation can be guaranteed by a simple universal control under any values of kinetic parameters and diffusion coefficients of mobile elements. This concept is illustrated by analysis and simulations of a specific three-component system, characterized in absence of diffusion by a presence of codimension two pitchfork-Hopf bifurcation.
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http://dx.doi.org/10.1103/PhysRevE.105.014209 | DOI Listing |
Brain Commun
August 2025
Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford OX3 9DU, UK.
Understanding the cognitive trajectory of a neurological disease can provide important insight on underlying mechanisms and disease progression. Cognitive impairment is now well established as beginning many years before the diagnosis of Alzheimer's disease, but pre-diagnostic profiles are unclear for other neurological conditions that may be associated with cognitive impairment. We analysed data from the prospective UK Biobank cohort with study baseline assessment performed between 2006 and 2010 and participants followed until 2021.
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September 2025
The Swiss Institute for Dryland Environmental and Energy Research, BIDR, Ben-Gurion University of the Negev, Sede Boqer Campus, Midreshet Ben-Gurion, Israel.
Drying trends driven by climate change and the water stress they entail threaten ecosystem functioning and the services they provide to humans. To get a better understanding of an ecosystem response to drying trends, we study a mathematical model of plant communities that compete for water and light. We focus on two major responses to water stress: community shifts to stress-tolerant species and spatial self-organization in periodic vegetation patterns.
View Article and Find Full Text PDFbioRxiv
August 2025
Marine Eco-Evo-Devo Unit, Okinawa Institute of Science and Technology; Okinawa, 904-0495, Japan.
The diverse pigmentation patterns of animals are crucial for predation avoidance and behavioral display, yet mechanisms underlying this diversity remain poorly understood. In zebrafish, Turing models have been proposed to explain stripe patterns, but it is unclear if they apply to other fishes. In anemonefish (, we identified , a gene orthologous to zebrafish and encoding a connexin involved in pigment cell communication, as responsible for the phenotype.
View Article and Find Full Text PDFSci Rep
September 2025
Department of Applied Psychology, School of Humanities and Social Science, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Boulevard, 518172, Shenzhen, China.
Recent advances in large language models (LLMs) have highlighted their potential to predict human decisions. In two studies, we compared predictions by GPT-3.5 and GPT-4 across 51 scenarios (9,600 responses) against published data from 2,104 human participants within an evolutionary-psychology framework.
View Article and Find Full Text PDFbioRxiv
August 2025
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA.
Identifying genomic regions shaped by natural selection is a central goal in evolutionary genomics. Existing machine learning methods for this task are typically trained using simulated genomic data labeled according to specific evolutionary scenarios. While effective in controlled settings, these models are limited by their reliance on explicit class labels.
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