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Mycorrhizal fungi are vital to terrestrial ecosystem functioning. Yet monitoring their biodiversity at landscape scales is often unfeasible due to time and cost constraints. Current predictions suggest that 90\% of mycorrhizal diversity hotspots remain unprotected, opening questions of how to broadly and effectively map underground fungal communities. Here, we show that self-supervised learning (SSL) applied to satellite imagery can predict below-ground ectomycorrhizal fungal richness across diverse environments. Our models explain over half the variance in species richness across ~12,000 field samples spanning Europe and Asia. SSL-derived features prove to be the single most informative predictor, subsuming the majority of information contained in climate, soil, and land cover datasets. Using this approach, we achieve a 10,000-fold increase in spatial resolution over existing techniques, moving from 1km landscape averages to 10m habitat-scale observations with nearly no systematic bias. As satellite observations are dynamic rather than static, this enables temporal monitoring of below-ground biodiversity at landscape scales for the first time. We analyze multi-year trends in predi

Background: Fungal keratitis is a serious blinding eye disease. Traditional drugs used to treat fungal keratitis commonly have the disadvantages of low bioavailability, poor dispersion, and limited permeability. Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability. Purpose: To develop a new method for the treatment of fungal keratitis with improved bioavailability, dispersion, and permeability. Methods: Zeolitic Imidazolate Framework-8 (ZIF-8) was formed by zinc ions and 2-methylimidazole linked by coordination bonds and characterized by Scanning electron microscopy (SEM), X-ray diffraction (XRD), and Zeta potential. The safety of ZIF-8 on HCECs and RAW 264.7 cells was detected by Cell Counting Kit-8 (CCK-8). The anti-inflammatory effects of ZIF-8 on RAW 246.7 cells were evaluated by Quantitative Real-Time PCR Experiments (qPCR) and Enzyme-linked immunosorbent assay (ELISA). Clinical score, Colony-Forming Units (CFU). In vivo, treatment with ZIF-8 reduced corneal fungal load and mitigated neutrophil infiltration in fungal keratitis, which effectively reduced the severity of keratitis in mice and alleviate

Fungal automata are a nature-inspired computational model, where a rule is alternatively applied verticaly and horizontaly. In this work we study the computational complexity of predicting the dynamics of all fungal freezing totalistic one-dimentional rules of radius $1$, exhibiting various behaviors. Despite efficiently predictable in most cases (with non-deterministic logspace algorithms), a non-linear rule is left open to characterize. We further explore the freezing majority rule (which is totalistic), and prove that at radius $1.5$ it becomes $\mathbf{P}$-complete to predict.

Fungal simulation and control are considered crucial techniques in Bio-Art creation. However, coding algorithms for reliable fungal simulations have posed significant challenges for artists. This study equates fungal morphology simulation to a two-dimensional graphic time-series generation problem. We propose a zero-coding, neural network-driven cellular automaton. Fungal spread patterns are learned through an image segmentation model and a time-series prediction model, which then supervise the training of neural network cells, enabling them to replicate real-world spreading behaviors. We further implemented dynamic containment of fungal boundaries with lasers. Synchronized with the automaton, the fungus successfully spreads into pre-designed complex shapes in reality.

The sandpile automata of Bak, Tang, and Wiesenfeld (Phys. Rev. Lett., 1987) are a simple model for the diffusion of particles in space. A fundamental problem related to the complexity of the model is predicting its evolution in the parallel setting. Despite decades of effort, a classification of this problem for two-dimensional sandpile automata remains outstanding. Fungal automata were recently proposed by Goles et al. (Phys. Lett. A, 2020) as a spin-off of the model in which diffusion occurs either in horizontal $(H)$ or vertical $(V)$ directions according to a so-called update scheme. Goles et al. proved that the prediction problem for this model with the update scheme $H^4V^4$ is $\textbf{P}$-complete. This result was subsequently improved by Modanese and Worsch (Algorithmica, 2024), who showed the problem is $\textbf{P}$-complete also for the simpler updatenscheme $HV$. In this work, we fill in the gaps and prove that the prediction problem is $\textbf{P}$-complete for any update scheme that contains both $H$ and $V$ at least once.

Fungal infections, such as Coccidioidomycosis, Aspergillosis, and Histoplasmosis, represent a growing public health concern in the United States. The rising incidence of these mycoses is linked to climate shifts, demographic changes, and social determinants of health. However, the actual burden of these infections is often underestimated by traditional surveillance methods. Therefore, this study aims to characterize these infections within the All of Us Research Program and evaluate the quality of clinical and health data related to fungal infections. We constructed three fungi cohorts of Coccidioidomycosis (n=1,173), Aspergillosis (n=687), and Histoplasmosis (n=345) among over 400,000 participants using electronic health record data. We analyzed geographic and sociodemographic distributions and performed a data quality assessment on ten key laboratory biomarkers to evaluate data completeness, unit conformance, and measurement concordance within a 90-day window of diagnosis. Our analysis confirmed known epidemiological patterns, including the geographic distributions of Coccidioidomycosis in the Southwest and Histoplasmosis in the Midwest. Fungal infections disproportionately affec

Accurate taxonomic classification from DNA barcodes is a cornerstone of global biodiversity monitoring, yet fungi present extreme challenges due to sparse labelling and long-tailed taxa distributions. Conventional supervised learning methods often falter in this domain, struggling to generalize to unseen species and to capture the hierarchical nature of the data. To address these limitations, we introduce BarcodeMamba+, a foundation model for fungal barcode classification built on a powerful and efficient state-space model architecture. We employ a pretrain and fine-tune paradigm, which utilizes partially labelled data and we demonstrate this is substantially more effective than traditional fully-supervised methods in this data-sparse environment. During fine-tuning, we systematically integrate and evaluate a suite of enhancements--including hierarchical label smoothing, a weighted loss function, and a multi-head output layer from MycoAI--to specifically tackle the challenges of fungal taxonomy. Our experiments show that each of these components yields significant performance gains. On a challenging fungal classification benchmark with distinct taxonomic distribution shifts from th

Modern security, infrastructure, and safety-critical systems increasingly operate in environments characterised by disruption, uncertainty, physical damage, and degraded communications. Conventional digital technologies -- centralised sensors, software-defined control, and energy-intensive monitoring -- often struggle under such conditions. We propose fungi, and in particular living mycelial networks, as a novel class of biohybride systems for security, resilience, and protection in extreme environments. We discuss how fungi can function as distributed sensing substrates, self-healing materials, and low-observability anomaly-detection layers. We map fungal properties -- such as decentralised control, embodied memory, and autonomous repair -- to applications in infrastructure protection, environmental monitoring, tamper evidence, and long-duration resilience.

Fungal keratitis is a severe vision-threatening corneal infection with a prognosis influenced by fungal virulence and the host's immune defense mechanisms. The immune system, through its regulation of the inflammatory response, ensures cells and tissues can effectively activate defense mechanisms in response to infection and injury. However, there is still a lack of effective drugs that attenuate fungal virulence while relieving the inflammatory response caused by fungal keratitis. Therefore, finding effective treatments to solve these problems is particularly important. We synthesized ZIF-90 by water-based synthesis and characterized by SEM, XRD etc. In vitro experiments included CCK-8 and ELISA. These evaluations verified the disruptive effects of ZIF-90 on Aspergillus. fumigatus spore adhesion, morphology, cell membrane, and the effect of ZIF-90 on apoptosis. In addition, to investigate whether the metal-ligand zinc and the organic ligand imidazole act as essential factors in ZIF-90, we investigated the in vitro antimicrobial and anti-inflammatory effects of ZIF-8, ZIF-67, and MOF-74 (Zn) by MIC and ELISA experiments. ZIF-90 has therapeutic effects on fungal keratitis, which cou

The effectiveness of zero-shot classification in large vision-language models (VLMs), such as Contrastive Language-Image Pre-training (CLIP), depends on access to extensive, well-aligned text-image datasets. In this work, we introduce two complementary data sources, one generated by large language models (LLMs) to describe the stages of fungal growth and another comprising a diverse set of synthetic fungi images. These datasets are designed to enhance CLIPs zero-shot classification capabilities for fungi-related tasks. To ensure effective alignment between text and image data, we project them into CLIPs shared representation space, focusing on different fungal growth stages. We generate text using LLaMA3.2 to bridge modality gaps and synthetically create fungi images. Furthermore, we investigate knowledge transfer by comparing text outputs from different LLM techniques to refine classification across growth stages.

As the impending consequences of climate change loom over the Earth, it has become vital for researchers to understand the role microorganisms play in this process. In this paper, we examine how environmental factors, including moisture levels and temperature, affect the expression of certain fungal characteristics on a microscale, and how these in turn affect fungal biodiversity and ecosystem decomposition rates over time. We first present a differential equation model to understand how the distribution of different fungal isolates depends on regional moisture levels. We introduce both slow and sudden variations into the environment in order to represent the various ways climate change will impact fungal ecosystems. This model demonstrates that increased variability in moisture (both short-term and long-term) increases biodiversity and that fungal populations will shift towards more stress-tolerant fungi as aridity increases. The model further suggests the lack of any direct link between biodiversity and decomposition rates. To better describe fungal competition with respect to space, we develop a local agent-based model (ABM). Unlike the previous model, our ABM focuses on individ

Cells in a fungal hyphae are separated by internal walls (septa). The septa have tiny pores that allow cytoplasm flowing between cells. Cells can close their septa blocking the flow if they are injured, preventing fluid loss from the rest of filament. This action is achieved by special organelles called Woronin bodies. Using the controllable pores as an inspiration we advance one and two-dimensional cellular automata into Elementary fungal cellular automata (EFCA) and Majority fungal automata (MFA) by adding a concept of Woronin bodies to the cell state transition rules. EFCA is a cellular automaton where the communications between neighboring cells can be blocked by the activation of the Woronin bodies (Wb), allowing or blocking the flow of information (represented by a cytoplasm and chemical elements it carries) between them. We explore a novel version of the fungal automata where the evolution of the system is only affected by the activation of the Wb. We explore two case studies: the Elementary Fungal Cellular Automata (EFCA), which is a direct application of this variant for elementary cellular automata rules, and the Majority Fungal Automata (MFA), which correspond to an appl

Light interacting with plant leaves undergoes reflection, transmission, scattering, and absorption, which together determine leaf optical properties. Changes in leaf architecture disrupt internal light scattering dynamics and consequently affect photosynthetic performance. Previous studies on internal leaf light scattering have primarily relied on ray-tracing approaches (e.g., Raytran) or radiative-transfer models (e.g., PROSPECT). However, these high-frequency approximations cannot capture diffraction and coherent multiple scattering in wavelength-scale leaf tissues, unlike full-wave electromagnetic simulations. Here, we employ GPU-accelerated Finite-Difference Time-Domain (FDTD) simulations to model internal light scattering dynamics using segmented cross-section image geometries of representative dicot and monocot leaves with wavelength-dependent complex refractive indices. The simulations accurately reproduce the reflectance and transmittance characteristics of healthy leaves, showing strong agreement with the PROSPECT model, with average Lin's concordance values of 0.8962 for dicot leaves and 0.7849 for monocot leaves. We further simulate early-stage necrotrophic fungal infect

This paper studied the relationship between the decomposition rate of fungi and temperature, humidity, fungus elongation, moisture tolerance and fungus density in a given volume in the presence of a variety of fungi, and established a series of models to describe the decomposition of fungi in different states. Since the volume of soil was given in this case, the latter two characteristics could be attributed to the influence of the number of fungal population on the decomposition rate. Based on the Logistic model, the relationship between the number of population and time was established, and finally the number of fungi in the steady state was obtained The interaction between different species of fungi was analyzed by Lotka-Volterra model, and the decomposition rate of various fungal combinations in different environments was obtained. After studying the one and two cases, we can extrapher from one to the other, and the community consisting of n fungal populations will be similar to the community consisting of n+1 fungal populations. After the study, we substituted the collected data into the model and found that the fungal community composed of two kinds of fungi had a lower decom

The use of biodegradation as a method for cleaning up soil that has been contaminated by spilt petroleum can be an effective strategy. So, this study investigated the existence of the wild microorganism in soil contaminated with oil and study their ability to degrade petroleum in vitro. Nineteen samples were collected from various locations near Taq Taq (TTOPCO) natural seeps in the Kurdistan Region of Iraq. Morphological, cultural, biochemical tests and molecular identification were used to identify the microbial communities, in addition, spore texture and the colour of the fungal isolates were investigated on the fungal isolates. Out of the19 samples, 17 indigenous bacterial strains and 5 fungal strains were successfully isolated. From the absorption spectrophotometry, Bacillus anthracis, Bacillus cereus, Achromobacter sp. and Pseudomonas aeruginosa for the bacterial isolates grew well on a minimal salt medium supplemented with 1% crude oil. Results showed that these isolates mentioned above had a strong ability to degrade crude oil by reducing the colour of 2,6-dichlorophenol indophenol (DCPIP) from deep blue to colourless. However, for the fractions of hydrocarbon, the bacteria

We study a cellular automaton (CA) model of information dynamics on a single hypha of a fungal mycelium. Such a filament is divided in compartments (here also called cells) by septa. These septa are invaginations of the cell wall and their pores allow for flow of cytoplasm between compartments and hyphae. The septal pores of the fungal phylum of the Ascomycota can be closed by organelles called Woronin bodies. Septal closure is increased when the septa become older and when exposed to stress conditions. Thus, Woronin bodies act as informational flow valves. The one dimensional fungal automata is a binary state ternary neighbourhood CA, where every compartment follows one of the elementary cellular automata (ECA) rules if its pores are open and either remains in state `0' (first species of fungal automata) or its previous state (second species of fungal automata) if its pores are closed. The Woronin bodies closing the pores are also governed by ECA rules. We analyse a structure of the composition space of cell-state transition and pore-state transitions rules, complexity of fungal automata with just few Woronin bodies, and exemplify several important local events in the automaton dy

Hyphae within the mycelia of the ascomycetous fungi are compartmentalised by septa. Each septum has a pore that allows for inter-compartmental and inter-hyphal streaming of cytosol and even organelles. The compartments, however, have special organelles, Woronin bodies, that can plug the pores. When the pores are blocked, no flow of cytoplasm takes place. Inspired by the controllable compartmentalisation within the mycelium of the ascomycetous fungi we designed two-dimensional fungal automata. A fungal automaton is a cellular automaton where communication between neighbouring cells can be blocked on demand. We demonstrate computational universality of the fungal automata by implementing sandpile cellular automata circuits there. We reduce the Monotone Circuit Value Problem to the Fungal Automaton Prediction Problem. We construct families of wires, cross-overs and gates to prove that the fungal automata are P-complete.