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Camellia oleifera (C. oleifera) is one of the four main, woody, edible oil tree species in the world, while C. oleifera anthracnose is mainly caused by the fungus Colletotrichum fructicola (C. fructicola), which severely affects the yield of C. oleifera and the quality of tea oil. Bacillus velezensis (B. velezensis) CSUFT-BV4 is an antagonistic endophytic bacterium isolated from healthy C. oleifera leaves. This study aimed to investigate the biocontrol potential of strain CSUFT-BV4 against C. oleifera anthracnose and its possible functional mechanism, and to determine its growth-promoting characteristics in host plants. In vitro, CSUFT-BV4 was shown to have efficient biofilm formation ability, as well as significant functions in the synthesis of metabolic substances and the secretion of probiotic substances. In addition, the CSUFT-BV4 fermentation broth also presented efficient antagonistic activities against five major C. oleifera anthracnose pathogens, including C. fructicola, C. gloeosporioides, C. siamense, C. camelliae, and C. kahawae, and the inhibition rate was up to 73.2%. In vivo, it demonstrated that the growth of C. oleifera treated with CSUFT-BV4 fermentation broth was increased in terms of stem width, plant height, and maximum leaf area, while the activities of various defense enzymes, e.g., superoxide dismutase (SOD), phenylalanine aminotransferase (PAL), and polyphenol oxidase (PPO), were effectively increased. The remarkable antagonistic activities against C. oleifera anthracnose, the growth-promoting characteristics, and the induction of host defense responses indicate that endophytic bacterium CSUFT-BV4 can be effectively used in the biological control of C. oleifera anthracnose in the future, which will have a positive impact on the development of the C. oleifera industry.

Phytopathogens pose a devastating threat to the productivity and yield of crops by causing destructive plant diseases in natural and agricultural environments. Hemibiotrophic pathogens have a variable-length biotrophic phase before turning to necrosis and are among the most invasive plant pathogens. Plant resistance to hemibiotrophic pathogens relies mainly on the activation of innate immune responses. These responses are typically initiated after the plant plasma membrane and various plant immune receptors detect immunogenic signals associated with pathogen infection. Hemibiotrophic pathogens evade pathogen-triggered immunity by masking themselves in an arms race while also enhancing or manipulating other receptors to promote virulence. However, our understanding of plant immune defenses against hemibiotrophic pathogens is highly limited due to the intricate infection mechanisms. In this review, we summarize the strategies that different hemibiotrophic pathogens interact with host immune receptors to activate plant immunity. We also discuss the significant role of the plasma membrane in plant immune responses, as well as the current obstacles and potential future research directions in this field. This will enable a more comprehensive understanding of the pathogenicity of hemibiotrophic pathogens and how distinct plant immune receptors oppose them, delivering valuable data for the prevention and management of plant diseases.

Phage-encoded homologous recombination (PEHR) is an efficient tool for bacterial genome editing. We previously developed and utilized a Pseudomonas-specific PEHR system. However, when using the PEHR system for Pseudomonas genome editing, false positives can be a problem. In this study, we combined a compact Cascade-Cas3 system from P. aeruginosa (PaeCas3c) with a Pseudomonas-specific PEHR system, and the results of our recombineering assay showed that this compact Cascade-Cas3 system can significantly improve PEHR recombineering accuracy.

Weaning of piglets could increase the risk of infecting with Gram-negative pathogens, which can further bring about a wide array of virulence factors including the endotoxin lipopolysaccharide (LPS). It is in common practice that the use of antibiotics has been restricted in animal husbandry. Alkaline phosphatase (AKP) plays an important role in the detoxification and anti-inflammatory effects of LPS. This study investigated the protective effects of AKP on intestinal epithelial cells during inflammation. Site-directed mutagenesis was performed to modulate the AKP activity. The enzyme activity tests showed that the activity of the DelSigD153G-D330N mutants in B. subtilis was nearly 1,600 times higher than that of the wild-type AKP. In this study, an in vitro LPS-induced inflammation model using IPEC-J2 cells was established. The mRNA expression of interleukin-(IL-) 6, IL-8, and tumor necrosis factor-α (TNF-α) were extremely significantly downregulated, and that of ASC amino acid transporter 2 (ASCT-2), zonula occludens protein-1 (ZO-1), and occludin-3 (CLDN-3) were significantly upregulated by the DelSigD153G-D330N mutant compared with LPS treatment. This concludes the anti-inflammatory role of AKP on epithelial membrane, and we are hopeful that this research could achieve a sustainable development for the pig industry.

Endophytes represent a ubiquitous and magical world in plants. Almost all plant species studied by different researchers have been found to harbor one or more endophytes, which protect host plants from pathogen invasion and from adverse environmental conditions. They produce various metabolites that can directly inhibit the growth of pathogens and even promote the growth and development of the host plants. In this review, we focus on the biological control of plant diseases, aiming to elucidate the contribution and key roles of endophytes and their metabolites in this field with the latest research information. Metabolites synthesized by endophytes are part of plant disease management, and the application of endophyte metabolites to induce plant resistance is very promising. Furthermore, multi-omics should be more fully utilized in plant–microbe research, especially in mining novel bioactive metabolites. We believe that the utilization of endophytes and their metabolites for plant disease management is a meaningful and promising research direction that can lead to new breakthroughs in the development of more effective and ecosystem-friendly insecticides and fungicides in modern agriculture.

Abstract Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.

Here, the authors present a theoretical framework based on community ecology and network science to investigate the efficacy of fecal microbiota transplantation in conditions associated with a disrupted gut microbiota, using the recurrent Clostridioides difficile infection as a prototype disease.

A relatively simple and local interaction between individuals produces coordinated and ordered collective behaviors that are widespread at all levels of biological groups. Group chase and escape is an important aspect in the field of collective behavior, particularly in regard to predation events in species interactions. Compared with other aspects of collective behavior, less research has been performed on this aspect, and the existing models are constructed only from the phenomenological perspective. We present an individual-based model named Visual Perception-Decision-Propulsion to explore the group chase and escape of biological groups and define several evaluation indicators to assess different aspects of this problem. Within this model, 2 types of self-propulsion individuals, i.e., predators and prey, are considered, and we consider the alignment and repulsion term between homogeneous individuals. Chase and escape are described as the escape (or chase) term between heterogeneous individuals. Based on the model, we identify and distinguish between 2 capture patterns, i.e., cooperative capture and separative capture. Then, we control the internal parameters to analyze the condition of these 2 patterns for production, and the external empirical parameters are adjusted to explore their effect on these 2 patterns. Hence, this paper provides a novel model for group chase and escape based on biological vision to compensate for the shortcomings of classical models and help apply the characteristics of biological groups to human-made swarm systems in the case of confrontation.

Single-cell RNA sequencing (scRNA-seq) provides the expression profiles of individual cells, and it is expected to provide higher cellular differential resolution than traditional bulk RNA sequencing. In scRNA-seq analysis, clustering is crucial for identifying cell types, and can be potentially exploited to understand high-level biological processes. Recently, autoencoder has been successfully applied in scRNA-seq clustering problem and achieved promising results. Most existing works focus on characterizing the sparsity of data, and directly utilize the bottleneck feature of the autoencoder for clustering might not be optimal. In this paper, a novel framework named Adversarial AutoEncoder ScRNA-seq Clustering (AAE-SC) is proposed to bring an additional constraint on the bottleneck feature. Specifically, AAE-SC adds an AAE module on top of the bottleneck layer, and constrains the bottleneck feature distribution to be aligned with a consistent distribution. Also, the AAE and the reconstructed modules are jointly optimized to generate a highly discriminative and consistent feature, which is further proceeded for clustering. We find that by using AAE-SC to impose certain constraints on the features of the hidden layer, the performance of clustering can be improved. Experimental results on three real-world datasets demonstrate that the proposed AAE-SC framework outperformed state-of-the-art methods by 2% at least and 5% at most. And AAE-SC shows more robustness than the baseline model for downsampled and unbalanced cluster size datasets.

Group coordination is embedded in social networks, which aims to reach a consensus or solve conflicts among social individuals. Recently, improving performance has become a challenging task and drawn considerable interest in this field. To eliminate barriers of group coordination, we abstract the problem into a networked color coordination game and introduce learning agents encoded with Q-learning to collect local information and learn local individual behaviors. We first show that learning agents can effectively improve the group coordination performance. By properly selecting parameters, we find that learning agents acting with low greedy parameter levels and placed in central locations can drastically accelerate group coordination. Greedy parameters and positions are vital to the problem. Finally, we indicate that learning agents have a direct effect on neighbor individuals and an indirect effect on non-neighbor individuals to act on the coordination network. Moreover, we propose a conflict relationship index which is the average rounds required for solving conflicts and indicate learning agents solve conflicts that cannot be solved by an individual. Hence, learning agents create further benefits to group coordination in these complex social networks. This paper provides a detailed analysis of the learning agents in a networked color coordination game and shows that artificial intelligence provides a solution to the group coordination problem.

Understanding ecological interactions in microbial communities is limited by lack of informative longitudinal abundance data necessary for reliable inference. Here, Xiao et al. develop a method to infer the interactions between microbes based on their abundances in steady-state samples.

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain "inappropriate" edge directions. However, the existence of multiple sets of "inappropriate" edge directions suggests that different edges have different effects on optimal controllability-that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions-utilizing only local information-which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.