Network Interactions Research Articles

Mangrove wetland recovery enhances soil carbon sequestration capacity of soil aggregates and microbial network stability in southeastern China

Mangrove wetlands are highly productive ecosystems in tropical and subtropical coastal zones, play crucial roles in water purification, biodiversity maintenance, and carbon sequestration. Recent years have seen the implementation of pond return initiatives, which have facilitated the gradual recovery of mangrove areas in China. However, the implications of these initiatives for soil aggregate stability, microbial community structure, and network interactions remain unclear. This study assesses the impacts of converting ponds to mangroves—both in natural and artificially restored settings—on soil aggregate stability and microbial networks at typical mangrove restoration sites along China's southeastern coast. Our observations confirmed our hypothesis that pond-to-mangrove conversions resulted in an increase in the proportion of large aggregates (>0.25 mm), improved soil aggregate structural stability, and increased carbon sequestration. However, mangrove recovery led to a decrease in the abundance and diversity of soil fungi communities. In terms of co-occurrence networks, naturally restored mangrove wetlands exhibited more nodes and edges. The naturally recovered mangrove wetlands demonstrated a higher level of community symbiosis compared to those that were manually restored. Conversely, bacterial networks showed a different pattern, with significant shifts in key taxa related to carbon sequestration functions. For instance, the proportion of bacterial Desulfobacterota and fungi Basidiomycota in natural recovery mangrove increased by 15.03 % and 7.82 %, respectively, compared with that in aquaculture ponds. Soil fungi and bacteria communities, as well as carbon sequestration by aggregates, were all positively correlated with soil total carbon content (P < 0.05). Both bacterial and fungal communities contributed to soil aggregate stability. Our study highlights the complex relationships between soil microbial communities, aggregate stability, and the carbon cycle before and after land-use changes. These findings underscore the potential benefits of restoring mangrove wetlands, as such efforts can enhance carbon storage capacity and significantly contribute to climate change mitigation.

Chaotic resonance in hybrid scale-free neural networks

It has been recently shown that weak signal response of a neuron can be amplified with the help of chaotic fluctuations at an optimal intensity. This phenomenon is referred to as chaotic resonance (CR). Here, we numerically investigate the signal detection ability of nervous system via CR using hybrid coupled scale-free networks of Hodgkin-Huxley (H-H) neurons. Firstly, we find that chaotic internal fluctuations are prone to enhance signal response of neuron population, even with a weak connectivity, more than that of single isolated cell's. We show that gap junctions can satisfy the stability against such variability and the quality of perception due to synchronizability. We also show that intense hybrid network interaction with strong chemical coupling can induce the similar degree of stability and decent quality of CR performance. Our results imply that a balanced connectivity may respond to weak signals efficiently in the presence of optimal chaotic fluctuations and exhibit frequency selectivity.

The network interactions between the porcine deltacoronavirus nucleocapsid protein and host cellular proteins

Porcine deltacoronavirus (PDCoV) is an emerging swine coronavirus that can cause diarrhea in pigs of all ages with varying severity. Host–virus protein interactions are critical for intracellular viral replication. Elucidating the interactions between cellular and viral proteins can help us to design antiviral strategies. PDCoV N protein is the most abundant and vital regulator in virus replication. In this study, 604 host proteins were identified to interact with PDCoV N protein by Co-IP combined with LC-MS, of which 243 proteins were specifically bound to N protein. PPI analysis revealed that the N-interacting host proteins are categorized into three groups: ribonucleoprotein complex biogenesis modulation, cellular nitrogen compound metabolism, and nucleic acid binding. GO and KEGG analyses showed that the host proteins are primarily involved in mRNA splicing, stress granule assembly, spliceosomal snRNP assembly. Additionally, four host proteins-TRIM25, HNRNPUL1, RPS27A, and SLC3A2-were selected to validate the interactome data through Co-IP and Confocal assays. This study can help in designing anti-PDCoV strategies and understanding the replication mechanism of PDCoV.

Structure of the human TIP60 complex

Mammalian TIP60 is a multi-functional enzyme with histone acetylation and histone dimer exchange activities. It plays roles in diverse cellular processes including transcription, DNA repair, cell cycle control, and embryonic development. Here we report the cryo-electron microscopy structures of the human TIP60 complex with the core subcomplex and TRRAP module refined to 3.2-Å resolution. The structures show that EP400 acts as a backbone integrating the motor module, the ARP module, and the TRRAP module. The RUVBL1-RUVBL2 hexamer serves as a rigid core for the assembly of EP400 ATPase and YL1 in the motor module. In the ARP module, an ACTL6A-ACTB heterodimer and an extra ACTL6A make hydrophobic contacts with EP400 HSA helix, buttressed by network interactions among DMAP1, EPC1, and EP400. The ARP module stably associates with the motor module but is flexibly tethered to the TRRAP module, exhibiting a unique feature of human TIP60. The architecture of the nucleosome-bound human TIP60 reveals an unengaged nucleosome that is located between the core subcomplex and the TRRAP module. Our work illustrates the molecular architecture of human TIP60 and provides architectural insights into how this complex is bound by the nucleosome.

Federated learning for network attack detection using attention-based graph neural networks

Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security.

Modeling interactions in a dynamic heuristic business network

This article presents a novel model for understanding the structure and dynamics of business networks, emphasizing the role of propensities to connect and cooperate as key drivers. The model incorporates behavioral elements and imperfect information updates, departing from traditional rational actor approaches. Starting from the theoretical background, several propositions are outlined, such as dynamism, connection choices, costs, strategy selection, information update, and the update based on experiences. Through simulations, the study successfully demonstrates that the proposed model effectively captures essential characteristics of business networks, including reciprocity, complexity, adaptation, and cooperation. The findings highlight the significance of propensities to connect and cooperate in shaping network structure, evolution, and stability. Particularly, higher propensities to cooperate and connect lead to denser and more cohesive networks, fostering reciprocity, stability, and network performance. The increase only in connection propensities does not have the same result. The lower cooperation propensities result in scale-free networks and asymmetrical distribution of cumulative payoffs. This highlights a crucial insight: different levels of cooperation lead to distinct network properties. Practical implications, increasingly relevant with the rise of digital platforms and metaverse, suggest targeted interventions to enhance network effectiveness, such as incentivizing cooperation, reducing relationship costs, and promoting a culture of trust and collaboration. While providing valuable insights, certain limitations exist, such as not considering the influence of preexisting economic or social structures, equalizing costs and payoffs among actors, and overlooking specific reasons for network connections. Future research should address these refinements and explore their effects on network structure and process characteristics.

An Integrated Approach Using Network Pharmacology and Experimental Validation to Reveal the Therapeutic Mechanism of Weifuchun in Treating Gastric Cancer.

Gastric cancer (GC) is a prevalent malignancy affecting the gastrointestinal tract. Weifuchun (WFC), a Chinese herbal prescription comprising red ginseng, Isodon amethystoides, and Fructus aurantii, is widely used in China for various chronic stomach disorders. However, its therapeutic role and mechanisms in treating GC remain unexplored. In a randomized, controlled, single-blind trial involving postoperative stages II and III GC patients, we compared adjuvant chemotherapy plus WFC (chemo plus WFC group) to adjuvant chemotherapy alone (chemo group) over 6 months. We assessed recurrence and metastasis rates and used systematic pharmacology to predict WFC's active components, screen target genes, and construct network interaction maps, were validated through in vitro experiments. The combined therapy significantly reduced 2-year recurrence and metastasis rates. We identified 67 active ingredients, 211 drug target proteins, 1539 disease targets, 105 shared targets, and 188 signaling pathways associated with WFC. WFC impacted cell apoptosis, proliferation, and the inflammatory response, with top tumor-related signaling pathways involving 5'-adenosine monophosphate-activated protein kinase (AMPK), mitogen-activated protein kinase, nuclear factor kappa-B (NFKB), and apoptosis. In vitro, WFC inhibited proliferation and migration while inducing apoptosis in GC cells, reduced VEGFA, TNFa, and IL6 expressions. Immunocytochemistry showed increased p-AMPK staining, and molecular analysis revealed decreased NFKB and phosphorylation of extracellular-regulated protein kinase 1/2 (ERK1/2) levels, increased p-AMPK and BAX protein levels in WFC-treated cells, effects reversed by Compound C. WFC's antitumor effects involve AMPK-dependent ERK1/2 and NFKB pathways, regulating proliferation, migration, and apoptosis in GC cells.

Continuous Dictionary of Nodes Model and Bilinear-Diffusion Representation Learning for Brain Disease Analysis.

Brain networks based on functional magnetic resonance imaging (fMRI) provide a crucial perspective for diagnosing brain diseases. Representation learning has recently attracted tremendous attention due to its strong representation capability, which can be naturally applied to brain disease analysis. However, traditional representation learning only considers direct and local node interactions in original brain networks, posing challenges in constructing higher-order brain networks to represent indirect and extensive node interactions. To address this problem, we propose the Continuous Dictionary of Nodes model and Bilinear-Diffusion (CDON-BD) network for brain disease analysis. The CDON model is innovatively used to learn the original brain network, with its encoder weights directly regarded as latent features. To fully integrate latent features, we further utilize Bilinear Pooling to construct higher-order brain networks. The Diffusion Module is designed to capture extensive node interactions in higher-order brain networks. Compared to state-of-the-art methods, CDON-BD demonstrates competitive classification performance on two real datasets. Moreover, the higher-order representations learned by our method reveal brain regions relevant to the diseases, contributing to a better understanding of the pathology of brain diseases.

Path-based reasoning for biomedical knowledge graphs with BioPathNet.

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Trioxane-based MS-cleavable Cross-linking Mass Spectrometry for Profiling Multimeric Interactions of Cellular Networks.

Cross-linking mass spectrometry (XL-MS) is a powerful technology for mapping protein-protein interactions (PPIs) at the systems-level. By covalently connecting pairs of proximal residues, cross-linking reagents provide distance restraints to infer protein conformations and interaction interfaces. While binary cross-links have been remarkably informative, multimeric cross-links can offer enhanced spatial resolution to facilitate the characterization of dynamic and heterogeneous protein complexes. However, the identification of multimeric cross-links remains extremely challenging due to fragmentation complexity and the vast expansion of database search space. Here, we present a novel trioxane-based MS-cleavable homotrifunctional cross-linker TSTO, which can target three proximal lysine residues simultaneously. Owing to its unique structure and MS-cleavability, TSTO enables fast and unambiguous identification of cross-linked peptides using LC-MS n analysis. Importantly, we have demonstrated that the TSTO-based XL-MS platform is effective for mapping PPIs of protein complexes and cellular networks. The trimeric interactions captured by TSTO have uncovered new structural details that cannot be easily revealed by existing reagents, allowing in-depth description of PPIs to facilitate structural modeling. This development not only advances XL-MS technologies for global PPI profiling from living cells, but also offers a new direction for creating multifunctional MS-cleavable cross-linkers to further push structural systems biology forward in the future.

Creation of strong and mildew resistant wood adhesive via high density nano-assemblies based interfacial enhancement strategy

Soybean protein (SP)-based adhesive, as a biomass environmentally friendly adhesive, is an excellent alternative to petroleum derived wood adhesives. However, SP-based adhesives have poor water resistance and are susceptible to mold attack, which limits their application range. In this paper, a synergistic strategy of catechol chemistry and supermolecular host-guest interactions was reported to prepare high strength antibacterial bio-based adhesives. Specifically, the β@SDS with lamellar structure were synthesized by enveloping the hydrophobic cavity of β-cyclodextrin (β-CD) with protein denaturing agent sodium dodecyl sulfate (SDS). The β@SDS was combined with SP and constructed a nano-assemblies based hydrogen bonding network. Meanwhile, plant polyphenol gallic acid (GA) as an adhesive bridge was introduced to the protein system to enhance the cross-linking network and interfacial interactions. Benefiting from the formation of high-density nano-assemblies-based interfacial hydrogen bonding networks, the wet shear strength of SP-GA-β@SDS adhesives reached 0.95 MPa, 163 % higher than that of unmodified SP adhesive. In addition, the polyphenol structure of GA endowed the adhesives with good anti-mold properties. The water resistance and thermal stability of the adhesive were also improved. This novel nano-assemblies based interfacial hydrogen bonding driven strategy provided valuable guidance for producing sustainable high-performance protein adhesives with potential applications in multiple fields.

Psychological characteristics of environmental stakeholders and interactions in their social network

This research explores the roles of (1) the stakeholder categories conservation association, land use association, public authority and research group, (2) Organizational Culture, operationalized by the Organizational Culture Assessment Instrument; (3) Environmental Worldview, operationalized by the New Environmental Paradigm; and (4) Environmental Risk Perception, operationalized by the Myths of Physical Nature, in shaping the interactions in the social network of environmental stakeholders in the city and district of Osnabrück in Northwest Germany. First, the study examines the stakeholder categories (1), regarding the expression of the constructs (2), (3) and (4) among them. Second, all constructs (1), (2), (3) and (4) are examined regarding possible correlations with the presence of collaboration and conflict between stakeholders. Our analysis reveals that (1) Stakeholder Categories are significantly correlated with the presence of collaboration, but not of conflict. (2) A significant correlation between membership of a stakeholder category and the dominant manifestation of a particular organizational culture is present, while no correlation between Organizational Cultures and the presence of collaborations or conflicts could be found. Moreover, the study highlights (3) a generally pro-environmental orientation across stakeholders as captured by the New Environmental Paradigm (NEP), indicating a shared foundation for potential collaborative efforts. (4) Analysis of Environmental Risk Perception, through the Cultural Theory’s myths of physical nature, uncovers a widespread preference for the hierarchical myth, suggesting a common perception of environmental risks and resilience. This study emphasizes the importance of understanding psychological and social dimensions in environmental stakeholder management to enhance collaborative efforts towards sustainable development. It highlights the need for management approaches that acknowledge the complex interplay of diverse psychological constructs in driving effective and sustainable collaborations between stakeholders of regional land use, nature conservation, public authorities, academia and other sectors depending on the context. Through its insights into stakeholder interactions, this research contributes to the broader discourse on achieving sustainable development goals through informed, cooperative stakeholder engagement.

Nucleobase-mediated hydrogen bonding crosslinked highly sulfonated poly(ether ether ketone) electrodialysis membranes for efficient waste acid recovery

Electrodialysis is a promising technology for waste acid recovery with the advantages of low energy consumption and high efficiency. In this work, two nucleobase-modified highly sulfonated poly(ether ether ketone) with adenine (SPEEK-50A) and thymine (SPEEK-50T) were successfully synthesized. Then, a series of blending SPEEK-50Ax/50Ty membranes with varying molar ratios of these two functionalized SPEEKs were fabricated by solution casting. The hydrogen bonding crosslinked structure of ‘A-T base pairs’, as well as the acid-base interactions between the incorporated nucleobases and SO3H groups of the polymer backbone, not only enhanced the mechanical properties and thermal stability of the blending membranes, but more importantly, it successfully addressed the over-swelling issue of highly sulfonated SPEEK membrane. The SPEEK-50Ax/50Ty membranes exhibited tensile strengths between 59.3 and 72.3 MPa, which is 2.2–2.6 times that of the pristine SPEEK membrane. And the water uptake of the blending membranes reduced from 116 % to 14.6–19.2 %, while the swelling ratio decreased from 27.1 % to 5.86–6.60 %. The proton affinity of the multiple basic N atoms in nucleobases, as well as the ‘A-T base pairs’ hydrogen bonding network and acid-base interactions between nucleobases and SO3H groups, all can serve as effective proton transport channels. Additionally, the charge repulsion effect of protonated nucleobases and the crosslinking structure is beneficial to the permselectivity. Therefore, the fabricated blending SPEEK-50Ax/50Ty membranes exhibited superior H+ flux (1.66–1.73 mmol m−2 s−1) and enhanced H+/Fe2+ permselectivity (40.3–43.2). The SPEEK-50A0.5/50T0.5 membrane demonstrated a H+ flux of 1.70 mmol m−2 s−1 and a H+/Fe2+ permselectivity of 43.2, which is 2.8 times that of the pristine SPEEK membrane, surpassing both commercially available and recently reported membranes. Therefore, the nucleobase-modified blending SPEEK-50Ax/50Ty membranes might be potential candidates for the efficient recovery of acid from pickling wastewater.

Apoptosis and Inflammation Involved with Fluoride-Induced Bone Injuries.

Excessive fluoride exposure induces skeletal fluorosis, but the specific mechanism responsible is still unclear. Therefore, this study aimed to identify the pathogenesis of fluoride-induced bone injuries. We systematically searched fluoride-induced bone injury-related genes from five databases. Then, these genes were subjected to enrichment analyses. A TF (transcription factor)-mRNA-miRNA network and protein-protein interaction (PPI) network were constructed using Cytoscape, and the Human Protein Atlas (HPA) database was used to screen the expression of key proteins. The candidate pharmacological targets were predicted using the Drug Signature Database. A total of 85 studies were included in this study, and 112 osteoblast-, 35 osteoclast-, and 41 chondrocyte-related differential expression genes (DEGs) were identified. Functional enrichment analyses showed that the Atf4, Bcl2, Col1a1, Fgf21, Fgfr1 and Il6 genes were significantly enriched in the PI3K-Akt signaling pathway of osteoblasts, Mmp9 and Mmp13 genes were enriched in the IL-17 signaling pathway of osteoclasts, and Bmp2 and Bmp7 genes were enriched in the TGF-beta signaling pathway of chondrocytes. With the use of the TF-mRNA-miRNA network, the Col1a1, Bcl2, Fgfr1, Mmp9, Mmp13, Bmp2, and Bmp7 genes were identified as the key regulatory factors. Selenium methyl cysteine, CGS-27023A, and calcium phosphate were predicted to be the potential drugs for skeletal fluorosis. These results suggested that the PI3K-Akt signaling pathway being involved in the apoptosis of osteoblasts, with the IL-17 and the TGF-beta signaling pathways being involved in the inflammation of osteoclasts and chondrocytes in fluoride-induced bone injuries.

Visual analysis of abdominal aortitis treatment using the CiteSpace bibliometric method.

Abdominal aortitis can induce aneurysms, and tumor rupture can lead to organ ischemia or even sudden death. At present, there is a lack of extensive understanding and identification of key problems in the treatment of abdominal aortitis, which needs to be further analyzed using bibliometric analysis. To discuss the research hotspot and development trend of abdominal aortitis treatment. We searched the English literature (published from January 1, 2000 to March 12, 2024) on the treatment of abdominal aortitis in the Web of Science database. Then, we identified and screened duplicate literature using CiteSpace 6.1R2 software. We conducted an analysis of the number of papers, a co-occurrence analysis of the authors and institutions, and co-occurrence and cluster analyses of the keywords. Then, we drew the author, institution, and keywords of the studies into graphs for visualization. Finally, we expounded on the author, institutional network interactions, and hot keywords of the studies on the treatment of abdominal aortitis. We included 210 English literature articles involving 190 authors; the author cooperation team was mainly represented by Caradu Caroline, Berard Xavier, Lu Guanyi, Harada Kenichi, and Sharma Ashish K. In the keyword analysis, high-frequency keywords include abdominal aortic aneurysm (38), abdominal aorta (24), Takayasu arteritis (22), etc. The three most central keywords were disease (0.69), classification (0.68), and abdominal aortic aneurysm (0.55). The first nine clusters of keywords are case report, abdominal aortic aneurysm, Takayasu arteritis, dyspnea hematuria, aortic elastic, IgG4-related disease, report, mid aortic dysplastic syndrome, and statin. In the keyword emergent analysis, 14 emergent words were obtained. Among them, seven keywords with strong abruptness were Takayasu arteritis, abdominal aortic aneurysm, disease, retroperitoneal fibrosis, expression, management, and large vessel vasculitis. In the past 3 years, the incidences of abdominal aortic aneurysm (intensity: 4.62) and inflammation (intensity: 1.99) were higher. The number of published papers is on the increase, but the cooperation among authors is scattered. The research focus is mainly on the pathogenesis and treatment of abdominal aortitis-related diseases.

Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.

University-Industry Relationships beyond Technology Transfer: the Role of Intangible Knowledge

Abstract This article explores the complexities of intangible knowledge transfer in university- industry collaborations, offering a comprehensive view of the current landscape and its future direction. Using a bibliometric and systematic literature review of 1496 articles and 20 selected studies, the research identifies key trends and patterns. Despite differences between academic and practical domains, the study finds that informal networking and personal interactions facilitate mutually beneficial partnerships, regardless of industry type, firm size, or academic specialization. The paper highlights the crucial role of intangible knowledge transfer platforms in fostering effective collaborations and advocates for streamlined protocols to enhance knowledge exchange between academia and businesses. Introducing the Unity Index, the paper provides a novel tool to analyze key authors and relationships between variables, offering new insights and opportunities for future research. This study emphasizes the critical importance of intangible knowledge transfer in university-industry relationships, addressing gaps often overlooked in traditional analyses.

Distinct ecological niches and community dynamics: understanding free-living and particle-attached bacterial communities in an oligotrophic deep lake.

Oligotrophic deep-water lakes are unique and sensitive ecosystems with limited nutrient availability. Understanding bacterial communities within these lakes is crucial for assessing ecosystem health, biogeochemical cycling, and responses to environmental changes. In this study, we investigated the seasonal and vertical dynamics of both free-living (FL) and particle-attached (PA) bacteria in Lake Fuxian, a typical oligotrophic deep freshwater lake in southeast China. Our findings revealed distinct seasonal and vertical dynamics of FL and PA bacterial communities, driven by similar physiochemical environmental factors. PA bacteria exhibited higher α- and β-diversity and were enriched with Proteobacteria, Cyanobacteria, Firmicutes, Patescibacteria, Planctomycetota, and Verrucomicrobiota, while FL bacteria were enriched with Actinobacteria and Bacteroidota. FL bacteria showed enrichment in putative functions related to chemoheterotrophy and aerobic anoxygenic photosynthesis, whereas the PA fraction was enriched with intracellular parasites (mainly contributed by Rickettsiales, Chlamydiales, and Legionellales) and nitrogen metabolism functions. Deterministic processes predominantly shaped the assembly of both FL and PA bacterial communities, with stochastic processes playing a greater role in the FL fraction. Network analysis revealed extensive species interactions, with a higher proportion of positively correlated edges in the PA network, indicating mutualistic or cooperative interactions. Cyanobium, Comamonadaceae, and Roseomonas were identified as keystone taxa in the PA network, underscoring potential cooperation between autotrophic and heterotrophic bacteria in organic particle microhabitats. Overall, the disparities in bacterial diversity, community composition, putative function, and network characteristics between FL and PA fractions highlight their adaptation to distinct ecological niches within these unique lake ecosystems.IMPORTANCEUnderstanding the diversity of microbial communities, their assembly mechanisms, and their responses to environmental changes is fundamental to the study of aquatic microbial ecology. Oligotrophic deep-water lakes are fragile ecosystems with limited nutrient resources, rendering them highly susceptible to environmental fluctuations. Examining different bacterial types within these lakes offers valuable insights into the intricate mechanisms governing community dynamics and adaptation strategies across various scales. In our investigation of oligotrophic deep freshwater Lake Fuxian in China, we explored the seasonal and vertical dynamics of two bacterial types: free-living (FL) and particle-attached (PA). Our findings unveiled distinct patterns in the diversity, composition, and putative functions of these bacteria, all shaped by environmental factors. Understanding these subtleties provides insight into bacterial interactions, thereby influencing the overall ecosystem functioning. Ultimately, our research illuminates the adaptation and roles of FL and PA bacteria within these unique lake environments, contributing significantly to our broader comprehension of ecosystem stability and health.

Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study.

Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study. Group independent component analysis (GICA) was used to determine target resting-state networks, namely, executive control network (ECN), default mode network (DMN), sensorimotor network (SMN) and dorsal attentional network (DAN). Sliding window and k-means clustering analyses were used to identify dFNC patterns and temporal properties in each subject. We compared sFNC and dFNC differences between the StD and HCs groups. Relationships between changes in FNC strength, temporal properties, and neurophysiological score were evaluated by Spearman's correlation analysis. The sFNC analysis revealed decreased FNC strength in StD individuals, including the DMN-CEN, DMN-SMN, SMN-CEN, and SMN-DAN. In the dFNC analysis, 4 reoccurring FNC patterns were identified. Compared to HCs, individuals with StD had increased mean dwell time and fraction time in a weakly connected state (state 4), which is associated with self-focused thinking status. In addition, the StD group demonstrated decreased dFNC strength between the DMN-DAN in state 2. sFNC strength (DMN-ECN) and temporal properties were correlated with HAMD-17 score in StD individuals (all p < 0.01). Our study provides new evidence on aberrant time-varying brain activity and large-scale network interaction disruptions in StD individuals, which may provide novel insight to better understand the underlying neuropathological mechanisms.

Evolution of green travel behaviour on dynamic social networks

Encouraging residents’ green travel behaviour can reduce carbon emissions. However, existing research focuses more on the individual level than on green travel in the context of group interactions. This study aims to connect individual- and group-level insights by integrating the theory of planned behaviour (TPB) and the norm activation model (NAM). Based on empirical data from a questionnaire survey and web crawler, a simulation of the evolution of green travel behaviour interactions in social networks was conducted. The results reveal that the theory of planned behaviour and the norm activation model (TPB −NAM) can explain and predict green travel behaviour. With individuals’ interactions, social networks present scale-free characteristics, and the state of green travel behaviour tends to be stable. Lowering the opinion-bounded confidence of agents in social networks could promote green travel behaviour. This study extends the literature on the theory of green travel behaviour in terms of model integration and interactive decision-making between individuals and groups. The results support the promotion of residents’ green travel behaviour.