Dataset Information

Feature-preserving synthesis of termite-mimetic spinodal nest morphology.

ABSTRACT: Termite-built topology is complex due to group interactions and environmental feedback. Being interlinked with material characteristics and related to functionality, an accurate synthesis of termite mound topology has never been achieved. We scanned inner termite mound pieces via high-resolution micro-computed tomography. A wavelet scattering transform followed by optimization extracts features that are fed into a Gaussian Random Fields (GRFs) approach to synthesize termite-mimetic spinodal topology. Compared to natural structures the GRF topology is more regular. Irregularity is related to anisotropy, indicative of directionality caused by porous network connectivity of chambers and corridors. Since GRFs are related to diffusion, we assume that deterministic behavioral traits play a significant role in the development of these local differences. We pioneer a framework to reliably mimic termite mound spinodal features. Engineering termite-inspired structures will allow to inspect aspects of termite architectures and their behavior to manufacture novel material concepts with imprinted multi-functionality.

SUBMITTER: Oberst S

PROVIDER: S-EPMC10825051 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Feature-preserving synthesis of termite-mimetic spinodal nest morphology.

Oberst Sebastian S Martin Richard R

iScience 20231214 1

Termite-built topology is complex due to group interactions and environmental feedback. Being interlinked with material characteristics and related to functionality, an accurate synthesis of termite mound topology has never been achieved. We scanned inner termite mound pieces via high-resolution micro-computed tomography. A wavelet scattering transform followed by optimization extracts features that are fed into a Gaussian Random Fields (GRFs) approach to synthesize termite-mimetic spinodal topo ...[more]

PMID: 38292166

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Project description:The discovery of subtypes is pivotal for disease diagnosis and targeted therapy, considering the diverse responses of different cells or patients to specific treatments. Exploring the heterogeneity within disease or cell states provides insights into disease progression mechanisms and cell differentiation. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, at large scales. While presenting opportunities for subtype discovery, these datasets pose challenges in finding relevant signatures due to their high dimensionality. Feature selection, a crucial step in the analysis pipeline, involves choosing signatures that reduce the feature size for more efficient downstream computational analysis. Numerous existing methods focus on selecting signatures that differentiate known diseases or cell states, yet they often fall short in identifying features that preserve heterogeneity and reveal subtypes. To identify features that can capture the diversity within each class while also maintaining the discrimination of known disease states, we employed deep metric learning-based feature embedding to conduct a detailed exploration of the statistical properties of features essential in preserving heterogeneity. Our analysis revealed that features with a significant difference in interquartile range (IQR) between classes possess crucial subtype information. Guided by this insight, we developed a robust statistical method, termed PHet (Preserving Heterogeneity) that performs iterative subsampling differential analysis of IQR and Fisher's method between classes, identifying a minimal set of heterogeneity-preserving discriminative features to optimize subtype clustering quality. Validation using public single-cell RNA-seq and microarray datasets showcased PHet's effectiveness in preserving sample heterogeneity while maintaining discrimination of known disease/cell states, surpassing the performance of previous outlier-based methods. Furthermore, analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells revealed, through PHet-based features, the presence of two distinct basal cell subtypes undergoing differentiation toward a luminal secretory phenotype. Notably, one of these subtypes exhibited high expression of BPIFA1. Interestingly, previous studies have linked BPIFA1 secretion to the emergence of secretory cells during mucociliary differentiation of airway epithelial cells. PHet successfully pinpointed the basal cell subtype associated with this phenomenon, a distinction that pre-annotated markers and dispersion-based features failed to make due to their admixed feature expression profiles. These findings underscore the potential of our method to deepen our understanding of the mechanisms underlying diseases and cell differentiation and contribute significantly to personalized medicine.

Project description:BackgroundLarge and complex mounds built by termites of the genus Macrotermes characterize many dry African landscapes, including the savannas, bushlands, and dry forests of the Tsavo Ecosystem in southern Kenya. The termites live in obligate symbiosis with filamentous fungi of the genus Termitomyces. The insects collect dead plant material from their environment and deposit it into their nests where indigestible cell wall compounds are effectively decomposed by the fungus. Above-ground mounds are built to enhance nest ventilation and to maintain nest interior microclimates favorable for fungal growth.ObjectivesIn Tsavo Ecosystem two Macrotermes species associate with three different Termitomyces symbionts, always with a monoculture of one fungal species within each termite nest. As mound architecture differs considerably both between and within termite species we explored potential relationships between nest thermoregulatory strategies and species identity of fungal symbionts.MethodsExternal dimensions were measured from 164 Macrotermes mounds and the cultivated Termitomyces species were identified by sequencing internal transcribed spacer (ITS) region of ribosomal DNA. We also recorded the annual temperature regimes of several termite mounds to determine relations between mound architecture and nest temperatures during different seasons.ResultsMound architecture had a major effect on nest temperatures. Relatively cool temperatures were always recorded from large mounds with open ventilation systems, while the internal temperatures of mounds with closed ventilation systems and small mounds with open ventilation systems were consistently higher. The distribution of the three fungal symbionts in different mounds was not random, with one fungal species confined to "hot nests."ConclusionsOur results indicate that different Termitomyces species have different temperature requirements, and that one of the cultivated species is relatively intolerant of low temperatures. The dominant Macrotermes species in our study area can clearly modify its mound architecture to meet the thermal requirements of several different symbionts. However, a treacherous balance seems to exist between symbiont identity and mound architecture, as the maintenance of the thermophilic fungal species obviously requires reduced mound architecture that, in turn, leads to inadequate gas exchange. Hence, our study concludes that while the limited ventilation capacity of small mounds sets strict limits to insect colony growth, in this case, improving nest ventilation would invariable lead to excessively low nest temperatures, with negative consequences to the symbiotic fungus.

Dataset Information

Feature-preserving synthesis of termite-mimetic spinodal nest morphology.

Publications

Feature-preserving synthesis of termite-mimetic spinodal nest morphology.

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