Search for: All records

The intelligence of the human biological system is enabled by the highly distributed sensing receptors on soft skin that can distinguish various stimulations or environmental cues, thus establishing the fundamental logic of sensing and physiological regulation or response. To replicate biological perception, two approaches have emerged: artificial nervous systems that utilize soft electronics as biomimetic receptors to convert external stimuli into frequency-encoded signals, and biohybrid solutions that integrate living cells, plants, or even live animals with electronic components to decode environmental cues for life-like sensations. However, most current biohybrid approaches for artificial sensation are based on eukaryotic cells, which suffer from slow growth, stringent culture conditions, environmental susceptibility, and short lifespans, thus limiting their integration into practical wearables or robotic sensory skins. Here, we introduce fungi-based printable “Mycoelectronics”, which are created by additive bioprinting of living fungal mycelium networks onto stretchable electronics, as a practical living thermo-responsive sensory platform. This Mycoelectronics approach leverages fungi’s capacity for rapid biological responsiveness, cultivability with exponential growth, stability and self-healing in ambient conditions, bioprintability for scalable manufacturing, and mechanical flexibility for seamless integration with soft electronics. Critically, we discovered that the thermal responsiveness of the fungal network arises from intrinsic cellular processes—specifically, heat-induced vacuole remodeling and fusion, which modulate ionic transport and thus the electrical conductivity of the mycelial cells and networks, enabling a rapid temperature response. By bridging the gap between cell biology and soft electronics, the Mycoelectronics device with a living mycelium network functions as a thermal sensation system with rapid response and intrinsic self-healing properties, autonomously restoring sensing capabilities after damage or autonomously establishing sensor pathways in hard-to-reach locations. Furthermore, by integrating fungal thermal sensing with electronic circuits, we established a hybrid bioelectronic reflex arc that can actuate muscles and initiate diverse actions, suggesting promising applications in future neurorobotics and neuroprosthetics. 

We present the first ambient mechanosynthesis of 16 flexible covalent organic frameworks (COFs) within an hour. Notably, one representative COF exhibited a high iodine uptake capacity of ∼4.3 g g⁻¹from aqueous solutions and 5.97 g g⁻¹from vapor. 

ABSTRACT This study explores how suppressing asexual development inAspergillus nidulansenhances the mechanical properties of mycelial materials. Using four aconidial mutants(ΔbrlA, ΔflbA, ΔfluG, andfadA^G42R) that lack asexual development and a control strain (A28) that undergoes typical asexual development, we found that the absence of asexual development significantly improves mechanical strength. All mutants exhibited higher ultimate tensile strength (UTS) than the control, with ΔfluGand ΔbrlA(fluffy nonsporulating, FNS phenotype) showing the highest UTS. Additionally,fadA^G42Rand ΔflbA(fluffy autolytic dominant, FAD phenotype) demonstrated significantly higher strain at failure (SF), linked to increased autolysis and lower dry cell mass compared to the control and FNS mutants. Solid-state NMR analysis revealed that autolysis in FAD mutants disrupts galactofuranose-related metabolic processes, altering cell wall composition and contributing to higher elasticity. These findings suggest that suppressing asexual development enhances mycelial material strength, while autolysis mechanisms influence elasticity. This research highlights the potential for genetic manipulation in fungi to engineer advanced mycelial-based materials with tailored mechanical properties. 

Abstract Antifungal echinocandins inhibit the biosynthesis of β−1,3-glucan, a major and essential polysaccharide component of the fungal cell wall. However, the efficacy of echinocandins against the pathogenAspergillus fumigatusis limited. Here, we use solid-state nuclear magnetic resonance (ssNMR) and other techniques to show that echinocandins induce dynamic changes in the assembly of mobile and rigid polymers within theA. fumigatuscell wall. The reduction of β−1,3-glucan induced by echinocandins is accompanied by a concurrent increase in levels of chitin, chitosan, and highly polymorphic α−1,3-glucans, whose physical association with chitin maintains cell wall integrity and modulates water permeability. The rearrangement of the macromolecular network is dynamic and controls the permeability and circulation of the drug throughout the cell wall. Thus, our results indicate that echinocandin treatment triggers compensatory rearrangements in the cell wall that may helpA. fumigatusto tolerate the drugs’ antifungal effects. 

In multi‐season clinical trials with a randomize‐once strategy, patients enrolled from previous seasons who stay alive and remain in the study will be treated according to the initial randomization in subsequent seasons. To address the potentially selective attrition from earlier seasons for the non‐randomized cohorts, we develop an inverse probability of treatment weighting method using season‐specific propensity scores to produce unbiased estimates of survival functions or hazard ratios. Bootstrap variance estimators are used to account for the randomness in the estimated weights and the potential correlations in repeated events within each patient from season to season. Simulation studies show that the weighting procedure and bootstrap variance estimator provide unbiased estimates and valid inferences in Kaplan‐Meier estimates and Cox proportional hazard models. Finally, data from the INVESTED trial are analyzed to illustrate the proposed method. 

Solid‐state nuclear magnetic resonance (ssNMR) measurements of intact cell walls and cellular samples often generate spectra that are difficult to interpret due to the presence of many coexisting glycans and the structural polymorphism observed in native conditions. To overcome this analytical challenge, we present a statistical approach for analyzing carbohydrate signals using high‐resolution ssNMR data indexed in a carbohydrate database. We generate simulated spectra to demonstrate the chemical shift dispersion and compare this with experimental data to facilitate the identification of important fungal and plant polysaccharides, such as chitin and glucans in fungi and cellulose, hemicellulose, and pectic polymers in plants. We also demonstrate that chemically distinct carbohydrates from different organisms may produce almost identical signals, highlighting the need for high‐resolution spectra and validation of resonance assignments. Our study provides a means to differentiate the characteristic signals of major carbohydrates and allows us to summarize currently undetected polysaccharides in plants and fungi, which may inspire future investigations.