Complexity Of Networks Research Articles

The Comprehensive Root Metabolite–Rhizomicrobiota Response Patterns of Rhododendron delavayi (R. delavayi) to Waterlogging Stress and Post–Waterlogging Recovery

Waterlogging is a critical abiotic stressor that significantly impacts plant growth. Plants under waterlogging stress release metabolic signals that recruit rhizosphere microorganisms and enhance stress resistance. However, the mechanisms through which the non-adaptive species R. delavayi responds to waterlogging stress via the synergistic interaction between root metabolites and rhizosphere microbiota remain poorly elucidated. Here, we employed pot experiments to characterize the responses of the root metabolite–microbiota complex in R. delavayi during waterlogging stress and subsequent recovery. Our results revealed that waterlogging altered the root morphology, the root metabolite profile, rhizosphere microbial diversity and network complexity, and these effects persisted during recovery. A significant correlation between root metabolites and the rhizosphere microbial community structure during waterlogging stress and recovery. Importantly, some differentially accumulated metabolites had significant effects on the assembly of rhizosphere microbes. Most of the core microbes in the rhizosphere microbial community under waterlogging and post–waterlogging recovery treatment were likely beneficial bacteria. Based on these findings, we propose a model for how root metabolites and rhizosphere microbes interact to help R. delavayi cope with waterlogging and recover. Based on these findings, we propose a possible response pattern of root metabolites and rhizosphere microbiota complex in R. delavayi under waterlogging stress and recovery. This work provides new insights into the synergistic mechanisms enhancing plant waterlogging tolerance and highlights the potential of harnessing rhizosphere microbiota to improve resilience in rhododendrons.

Toll-like receptor-mediated immune imbalance in asthma: controversies, breakthroughs, and future directions

As a chronic inflammatory illness of the respiratory system, asthma occurs due to various factors and is characterized by a T helper 2 (Th2)-skewed immune response, airway hyperresponsiveness, and reversible airflow obstruction. Toll-like receptors (TLRs) perform a “double-edged sword” function in asthma-related immunological dysregulation by recognizing damage-associated molecular patterns and pathogen-associated molecular patterns. In turn, the activation of some TLRs stimulates epithelial cells to release inflammatory cytokines, exacerbating Th2-driven inflammation and contributing to airway remodeling. Certain TLR signals help inhibit allergic responses by inducing type I interferon or regulatory T cells. The TLR family comprises 10 members, each responsible for recognizing the distinct molecular structure of multiple microbial sources. Variations in environmental microbial exposure duration and host genetic background contribute to the complexity of the TLR signaling network during asthma development. In recent years, therapeutic strategies targeting TLRs have shown potential for asthma treatment. However, a comprehensive review of TLRs in asthma is lacking. Therefore, this review sought to examine the functional mechanisms of TLRs and associated signaling cascades in asthma, and explore novel prevention and treatment approaches centered on TLRs modulation.

Application of the Pipeline Metabolic Model in Food Synthetic Biology.

Food synthetic biology aims to improve the production of functional food molecules such as carbohydrates, proteins, and fats through genetically manipulating cellular metabolism. However, these projects often face challenges due to the inherent complexity of metabolic networks and a lack of effective design frameworks. To address this, we propose a conceptual and operational framework called the pipeline metabolic model. This model abstracts metabolism into a network of pipes (metabolic pathways) and valves (key enzymes), enabling more intuitive visualization and modular engineering. Compared with traditional modular approaches, the pipeline model separates enzyme-level control from pathway-level optimization, allowing for simplified design logic. We illustrate the utility of this model using a case study of lacto-N-tetraose (LNT) synthesis and explore its broader implications for food-specific challenges. This approach ultimately aims to reduce the barriers to synthetic biology research and foster innovation in sustainable food production.

Coexistence networks of soil methanogens are closely tied to methane generation in wetlands on the northeastern of the Qinghai–Tibet Plateau

Wetlands are the largest natural sources of methane (CH4) emissions worldwide, with methanogenic archaea serving as the primary drivers of CH4 production. Nevertheless, the influences of biotic factors (e.g., methanogen abundance, community diversity and composition) and abiotic factors (e.g., soil properties) on potential CH4 production rates remain insufficiently understood in Qinghai-Tibet Plateau. In this study, we examined soil properties, potential methane production rates (PMPRs), methanogenic archaeal abundance, diversity, community structure, and co-occurrence networks across four wetlands (two desert wetlands and two peatlands) with contrasting soil conditions on the northeastern edge of the Qinghai-Tibet Plateau. We found no significant differences in methanogen abundance and PMPRs among the four wetlands, but the two were significantly positively correlated. The structure of methanogenic communities varied markedly among wetlands and was mainly shaped by soil pH. The complexity of co-occurrence networks was positively correlated with both methanogen diversity and PMPRs. Further analysis using partial least squares path modeling (PLS-PM) revealed that PMPRs were closely associated with soil nutrition (soil total organic carbon and total nitrogen; standardized path coefficient = 0.307), methanogenic abundance (0.570) and network complexity (0.238). It indicated that biotic factors may exert a greater influence than abiotic factors on soil PMPRs in wetland ecosystems. Additionally, complex microbial interaction networks may play a more crucial role in regulating PMPRs than methanogenic diversity and community structure. Our study highlights a strong link between methanogenic network complexity and methane-producing potential, offering a novel perspective on the relationship between community interactions and ecosystem function.

Vegetation succession enhances microbial diversity, network complexity, and stability in coastal wetlands, underscoring the pivotal role of soil salinity and key microbial species.

Vegetation succession enhances microbial diversity, network complexity, and stability in coastal wetlands, underscoring the pivotal role of soil salinity and key microbial species.

Effects of substrate materials on microbial diversity and network dynamics: Comparing microplastics and silica-based materials.

Effects of substrate materials on microbial diversity and network dynamics: Comparing microplastics and silica-based materials.

Community stability of free-living and particle-attached prokaryotes in coastal waters across four seasons: insights from 9.5years of weekly sampling.

Community stability of free-living and particle-attached prokaryotes in coastal waters across four seasons: insights from 9.5years of weekly sampling.

Identification and expression analysis of miRNA-mRNA regulatory modules related to lipid accumulation in Camellia drupifera.

Identification and expression analysis of miRNA-mRNA regulatory modules related to lipid accumulation in Camellia drupifera.

Metagenomic analysis reveals soil microbiome responses to microplastics and ZnO nanoparticles in an agricultural soil.

Metagenomic analysis reveals soil microbiome responses to microplastics and ZnO nanoparticles in an agricultural soil.

Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction.

Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction.

Role of microRNAs in lung oncogenesis: Diagnostic implications, resistance mechanisms, and therapeutic strategies.

Role of microRNAs in lung oncogenesis: Diagnostic implications, resistance mechanisms, and therapeutic strategies.

Introducing intercropping into rotation system altered the structure, function and network complexity of soil microbial communities in farmlands of the North China Plain

Introducing intercropping into rotation system altered the structure, function and network complexity of soil microbial communities in farmlands of the North China Plain

Vegetation degradation dominates over elevation in structuring fungal communities in alpine meadows

IntroductionAlpine meadows provide a critical natural laboratory for investigating interactions between ecosystem degradation and biogeochemical processes across elevational gradients.MethodsThis study examines how degradation states and elevation (3,700 m vs. 4,300 m) influence soil fungal community composition, diversity, and network architecture in Qinghai-Tibetan Plateau grasslands. Through comparative analysis of degraded and intact meadows, we reveal fundamental shifts in belowground ecology driven by environmental change.ResultsKey environmental parameters showed differential responses: soil organic matter (SOM) decreased significantly with degradation, while soil water content exhibited elevation-dependent patterns (p < 0.05). High-throughput sequencing identified Ascomycota, Mortierellomycota, and Basidiomycota as dominant phyla across all samples. Redundancy analysis (RDA) analysis demonstrated that edaphic factors explained 71.3% of fungal community variation, with SOM emerging as the principal driver (p = 0.001). Interestingly, meadow degradation led to an increase in fungal species diversity, thereby simplifying network complexity. Fungal communities show greater sensitivity to degradation than elevational gradients.DiscussionOur results provide a mechanistic framework for predicting fungal community responses to environmental change, with implications for alpine ecosystem management. Future restoration efforts should prioritize SOM conservation and monitor network properties as early warning indicators of ecosystem degradation.

Compartment-specific dynamics of soil microbiota along a Pinus armandii plantation chronosequence in karst mountain ecosystems

Soil microbiomes play pivotal roles in mediating plant diversity maintenance by regulating multifunctional ecosystem services during plant development. However, how different stand age of plants influence soil microbial communities in various soil compartments remains poorly understood. Through Illumina-based 16S rRNA and ITS amplicon sequencing, we systematically investigated the successional trajectories of soil microbiome in Pinus armandii plantations spanning various developmental phases. Key findings revealed that stand age exerted a stronger influence on microbial restructuring than soil compartment, significantly altering community composition in both soil types. Alpha diversity (Shannon and Chao1 indices) exhibited a U-shaped trajectory with stand age, except for fungal Chao1 in bulk soil. While dominant bacterial and fungal phyla remained relatively stable, community composition displayed significant stage-dependent variations. Co-occurrence network analysis demonstrated lower fungal network complexity compared to bacterial networks, with rhizosphere soils harboring more intricate interactions compared to bulk soils. Community assembly mechanisms diverged: deterministic processes dominated bacterial assembly, whereas stochasticity governed fungal communities. Soil properties exerted significant influences on microbial composition and diversity: bacterial composition correlated strongly with pH and stoichiometric ratios (C/N, C/P, N/P), while fungal composition showed stronger associations with TN, TP, and AN. Our results demonstrate that P. armandii plantations maintain core phylum-level microbial populations while developing stage-specific diversity patterns. Crucially, bacteria and fungi exhibit divergent responses to stand development, highlighting their divergent ecological strategies in adapting to nutrient-limited karst ecosystems.

AMF and biochar reshape the bacterial network in rhizosphere soil of Ricinus communis under chromium (Cr) stress and improve soil quality.

AMF and biochar reshape the bacterial network in rhizosphere soil of Ricinus communis under chromium (Cr) stress and improve soil quality.

Enhanced dechlorination and degradation of Aroclor 1260 by resuscitation-promoting factor under alternating anaerobic-aerobic conditions: Superior performance and associated microbial populations.

Enhanced dechlorination and degradation of Aroclor 1260 by resuscitation-promoting factor under alternating anaerobic-aerobic conditions: Superior performance and associated microbial populations.

Sheep dung addition and reseeding promote ecosystem multifunctionality by mediating soil microbial network complexity in a subtropical grassland

Sheep dung addition and reseeding promote ecosystem multifunctionality by mediating soil microbial network complexity in a subtropical grassland

Automotive Ethernet: Trends, Protocols, and Challenges with Reference to IEEE 1722

The rapid evolution of in-vehicle electronics and the growing demand for high-bandwidth, real-time data exchange have positioned Automotive Ethernet as a cornerstone of next-generation vehicle architectures. Although earlier advancements relied heavily on legacy protocols like CAN, LIN, and FlexRay, they increasingly fall short in supporting the scalability, flexibility, and performance required by modern ADAS, infotainment, and zonal systems. Automotive Ethernet, fortified by IEEE Time-Sensitive Networking (TSN) standards, now enables determin-istic, low-latency communication across multiple vehicular do-mains, fostering convergence of heterogeneous systems onto a unified backbone. The development, important protocols, and current trends in automotive Ethernet deployment are all thoroughly examined in this review. The paper systematically examines critical technology gaps—including challenges in scalability, interoperability, real-time determinism, network complexity, and security—that impact the effectiveness and future readiness of in-vehicle networks. Drawing from current research and standardization efforts, the review proposes robust solutions and strategic directions—such as unified network architectures, advanced TSN scheduling, modular design, and integrated security frameworks—to address these systemic challenges. Specific protocols like IEEE 1722 are discussed as illustrative examples within the broader context. Ultimately, this work offers a holistic perspective on enabling scalable, interoperable, and secure Automotive Ethernet for next-generation vehicles.

DROPc-Dynamic Resource Optimization for Convolution Layer

The computational complexity of convolutional neural networks (CNNs) becomes challenging for resource-constrained hardware devices. The convolution layer is predominant in the overall CNN architecture, performing the expensive multiplication and accumulation operation. Therefore, designing a hardware-efficient convolution layer will effectively improve the overall performance of a CNN. In this research, we propose a dynamic resource optimization (DROP) approach to improve the power and delay of the convolution layer. The proposed approach controls the computational path in accordance to the interrupts which are dependent on a non-zero-bit pattern. With a single interrupt, our solution provides 42.5% power and 36.7% delay efficiency compared to the standard bit-serial-parallel approach. Moreover, the power consumed by eight parallel functioning blocks is 27.7% less than the traditional bit-parallel approach.

The Hybrid Modern Network Model: A multi-technique framework for comprehensive network analysis

This research addresses the limitations of traditional network models in capturing the complexity and dynamics of real-world social networks. Motivated by the need for a more comprehensive and flexible framework, the study introduces the Hybrid Modern Network Model (HMNM). The HMNM integrates foundational models like the Stochastic Block Model (SBM) and Preferential Attachment with advanced machine learning techniques, including Graph Neural Networks (GNNs), Reinforcement Learning (RL), Hierarchical Random Graphs (HRGs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). The methods employed involve constructing initial network structures using SBM, simulating network growth through preferential Attachment, learning node embeddings with GNNs, dynamically optimizing network properties using RL, capturing hierarchical community structures with HRGs, controlling degree distributions using GANs, and uncovering latent patterns with VAEs. The empirical illustration of HMNM highlights its effectiveness in providing a more realistic, scalable, and comprehensive analysis of social networks compared to traditional models. Integrating diverse methodologies allows for accurately modeling of network structures, dynamic processes, and latent patterns. In conclusion, the HMNM offers significant advancements in network modeling, providing a robust and flexible framework for analyzing social networks. This model overcomes the limitations of traditional models and delivers deeper insights into the complexities and dynamics of social structures. Future research will optimize the HMNM and explore its applications across various domains. The R programming code used for the network simulations and visualizations is conceptual and demonstrates the HMNM framework. The results and metrics are illustrative placeholders, emphasizing the methodology rather than empirical validation.