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Biohybrid is a newly emerging and promising approach to construct soft robotics and soft machines with novel functions, high energy efficiency, great adaptivity and intelligence. Despite many unique advantages of biohybrid systems, it is well known that most biohybrid systems have a relatively short lifetime, require complex fabrication process, and only remain functional with careful maintenance. Herein, we introduce a simple method to create a highly robust and power-free soft biohybrid mechanoluminescence, by encapsulating dinoflagellates, bioluminescent unicellular marine algae, into soft elastomeric chambers. The dinoflagellates retain their intrinsic bioluminescence, which is a near-instantaneous light response to mechanical forces. We demonstrate the robustness of various geometries of biohybrid mechanoluminescent devices, as well as potential applications such as visualizing external mechanical perturbations, deformation-induced illumination, and optical signaling in a dark environment. Our biohybrid mechanoluminescent devices are ultra-sensitive with fast response time and can maintain their light emission capability for weeks without special maintenance. Despite the advantages of biohybrid systems for soft robotics, most systems have short lifetime, require complex fabrication, and only remain functional with careful maintenance. Here, the authors report biohybrid mechanoluminescence in soft elastomer-encapsulated bioluminescent dinoflagellates.

Our investigation delves into the scaling law governing planetary cratering impacts. We meticulously analyze the interplay between dimensionless parameters driving crater growth and the morphological transition of craters and construct the scaling analysis between the scale model tests and the prototype tests by numerical simulation. With practical engineering applications in mind, we design scale model tests based on the experimental setups of geotechnical centrifuges, ensuring the robust validity of test designs. This meticulous approach is integral to achieving fidelity between simulations and experimental scenarios. Validation of our scale model tests is conducted through a numerical modeling framework, coupling the finite element-smoothed particle hydrodynamics adaptive method (FE-SPH). This validation procedure serves to bolster the reliability and credibility of our methodology, facilitating an accurate depiction of the cratering mechanism. Of particular interest is the investigation into the depth-to-diameter ratio of impact craters, wherein we explore its intricate relationship with projectile diameter and gravity. Through rigorous analysis, we delineate the transition diameter at which terrestrial impact craters manifest a transition from simple to complex morphologies, thereby shedding light on the underlying dynamics of crater formation. Moreover, our study meticulously scrutinizes the relationship of crater formation time between the scaling model tests and the prototype tests. Our research underscores the consistency of the crater depth–diameter ratio in the scale model tests and the prototype tests and affirms applicability in replicating prototype tests by scale model tests. Notably, our findings reveal compelling correlations between the depth-to-diameter ratio of impact craters and gravity, as well as projectile diameter, providing valuable insights into the governing dynamics of impact crater formation. These insights not only advance our fundamental understanding of planetary cratering processes but also hold implications for practical applications in planetary science and engineering.

The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification. In contrast, the aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD. First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer. Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold crossvalidation strategy. It was found that the classification accuracy of combined method achieved 98.869%, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

Background Tapping panel dryness (TPD) in rubber tree has become the most severe restricting factor of natural rubber production. To date, there is no effective measures to prevent and control TPD. Previous studies primarily focused on analyzing the molecular mechanism underlying TPD occurrence. However, there is no research on the molecular mechanism of TPD recovery. Results In this study, the TPD trees were recovered by treatment with TPD rehabilitation nutrient agents that could promote the recovery of latex flow on the tapping panel of TPD trees. The genes and proteins involved in TPD recovery were first identified by employing integrated transcriptomics and proteomics analyses. In total, 2029 differentially expressed genes (DEGs) and 951 differentially expressed proteins (DEPs) were detected in the bark of recovery trees compared to that of TPD trees. Among them, 19 DEPs and 11 DEGs were found to be involved in the starch and sucrose metabolism pathway, suggesting their important roles in regulating the syntheses of sucrose and D-glucose, which were the key precursors of natural rubber biosynthesis. Furthermore, 16 DEPs and 15 DEGs were identified in the rubber biosynthesis pathway. Interestingly, almost all the DEPs and DEGs related to rubber biosynthesis exhibited significantly up-regulated expressions in the recovery trees, indicating that latex biosynthesis were probably markedly enhanced during TPD recovery. Conclusions These results provide new insights into the molecular mechanisms underlying TPD recovery, as well as excellent supplements to the mechanisms of TPD occurrence, which will contribute to the development of more effective agents for the prevention and treatment of TPD in the future.

The debris cloud generated by the hypervelocity impact (HVI) of orbiting space debris directly threatens the spacecraft. A full understanding of the damage mechanism of rear plate is useful for the optimal design of protective structures. In this study, the hypervelocity yaw impact of a cylindrical aluminum projectile on a double-layer aluminum plate is simulated by the FE-SPH adaptive method, and the damage process of the rear plate under the impact of the debris cloud is analyzed based on the debris cloud structure. The damage process can be divided into the main impact stage of the debris cloud and the structural response of the rear plate. The main impact stage lasts a short time and is the basis of the rear plate damage. In the stage of structure response, the continuous deformation and inertial motion of the rear plate dominate the perforation of the rear plate. We further analyze the damage mechanism and damage distribution characteristics of the rear plate in detail. Moreover, the connection between velocity space and position space of the debris cloud is established, which promotes the general analysis of the damage law of debris cloud. Based on the relationship, the features of typical damage areas are identified by the localized fine analysis. Both the cumulative effect and structural response cause the perforation of rear plate; in the non-perforated area, cratering by the impact of hazardous fragments is the main damage mode of the rear plate.

Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients. This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition. First, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective. Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy. These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

Powdery mildew fungi may develop diverse strategies to disturb salicylic acid (SA) signaling in plants, which plays an important role in activating immunity, and little is known about these strategies. Our results suggest that the Erysiphe quercicola effector protein EqCmu can be translocated into host cells and inhibit host SA levels during the infection stage; however, it is targeted by the plant ubiquitin–proteasome system (UPS) and ubiquitinated, which induces EqCmu degradation. To evade the UPS, EqCmu interacts with EqPdt, another E. quercicola effector protein, to prevent that ubiquitination. EqPdt also inhibits host SA biosynthesis through its prephenate dehydratase activity. Taken together, these two powdery mildew effector proteins cause a synergistic effect in disturbing host SA signaling. Our study also suggests that enhancing SA signaling is required for boosting immunity against powdery mildew fungus.

In phytopathogenic fungi, the Hog1 MAPK pathway plays roles in osmoregulation, oxidation responses, fungicide sensitivity, among others. However, how fungi depend on the HOG MAPK pathway to regulate their functions is poorly understood. To further elucidate the molecular mechanisms of the HOG MAPK pathway, a yeast two-hybrid (Y2H) system was adopted to screen interacting proteins of two key components (CsPbs2 and CsHog1) of the HOG MAPK pathway in a cDNA library of Colletotrichum siamense. The results showed that 19 proteins may interact with CsPbs2, including cytochrome P450 4F5, ceramide very-long-chain fatty acid hydroxylase SCS7, the oxidative stress response two-component system protein SSK1, and other proteins. Twenty-one proteins may interact with CsHog1, including the cell wall protein PhiA, acriflavine sensitivity control protein acr-2, arachidonate 15-lipoxygenase, respiratory supercomplex factor 2-like protein, and others. Five proteins interact with both CsHog1 and CsPbs2: antigenic thaumatin-like protein, aspirochlorine biosynthesis protein N, secreted beta-glucosidase sun 1, alcohol dehydrogenase 1, and hypothetical protein GQ607_005106. Furthermore, the interaction of CsSCS7 and CsCytP450 4F5 with CsPbs2 was verified by His pull-down. qPCR revealed significantly upregulated CsPbs2, CsHog1, CsSCS7 and CsCytP450 4F5 expression in Colletotrichum treated with 50 μg/mL fludioxonil for 2 h. This research identified candidate CsHog1- and CsPbs2-interacting proteins via a Y2H system and confirmed the interactions of CsSCS7 and CsCytP450 4F5 with CsPbs2. The findings lay the foundation for further studies of HOG MAPK pathway function and regulatory mechanisms, especially regarding fungicide sensitivity.

An asteroid impact can potentially destroy life on this planet. Therefore, asteroids should be prevented from impacting the Earth to impede severe disasters. Nuclear explosions are currently the only option to prevent an incoming asteroid impact when the asteroid is large or the warning time is short. However, asteroids exist in an absolute vacuum, where the explosion energy propagation mechanism differs from that in an air environment. It is difficult to describe this process using standard numerical simulation methods. In this study, we used the single-material arbitrary Lagrangian–Eulerian (ALE) method and the finite element-smoothed particle hydrodynamics (FE-SPH) adaptive method to simulate the process of deflecting hazardous asteroids using penetrating explosions. The single-material ALE method can demonstrate the expansion process of explosion products and energy coupling in absolute vacuum. The FE-SPH adaptive method can transform failed elements into SPH particles during the simulation, avoiding system mass loss, energy loss, and element distortion. We analyzed the shock initiation and explosion damage process and obtained an effective simulation of the damage evolution, stress propagation, and fragment distribution of the asteroid. In addition, we decoupled the penetrating explosion into two processes: kinetic impact and static explosion at the impact crater. The corresponding asteroid damage modes, velocity changes, and fragmentation degrees were simulated and compared. Finally, the high efficiency of the nuclear explosion was confirmed by comparing the contribution rates of the kinetic impact and nuclear explosion in the penetrating explosion scheme.

Material phase-transition represents a significant phenomenon and mechanism in the context of hypervelocity protection. This study presents a thorough analysis of the phase-transition phenomena induced by shock pressure as the shock wave propagates initially to the rear of the projectile. The shock wave that induces a phase-transition is commonly referred to as a macroscopic phase-transition wave, whereas the interface that separates the distinct phases is referred to as macroscopic phase-boundary. The contact interface between the spherical projectile and the thin plate, characterized by its curved surface, plays a significant role in the nonlinear propagation and evolution of wave systems. The pressure distribution along the central axis of a spherical projectile is derived in accordance with the linear decay law observed for axial pressure. On this basis, a quadratic function is employed to characterize the trend of changes in wave front pressure, thereby facilitating the establishment of a model for wave front pressure distribution. Using the phase-transition pressure criterion for materials, the wave front phase evolution process is derived, and the macroscopic phase-boundary is determined. Based on the geometric propagation model (GPM) and the pressure distribution of the wave front, a phase geometric propagation model (PGPM) is proposed. The phase distribution of a spherical projectile impacting a thin plate is obtained by theoretical methods. The accuracy of the PGPM is subsequently validated through a comparison of its results with those obtained from numerical simulations.