Network Clustering Research Articles

Evolution of Resilience Spatiotemporal Patterns and Spatial Correlation Networks in African Regional Economies

This paper comprehensively utilizes the entropy-TOPSIS method, Lyapunov index, and kernel density estimation to measure the spatiotemporal evolution characteristics of regional economic resilience in 52 African countries (regions) from 2008 to 2019. It also examines the spatial network characteristics of regional economic resilience in each country (region) through gravity models and social network analysis. The findings reveal that: (1) Although the resilience of African regional economies fluctuates, it generally shows an improving trend. Traditional economic powers and regional giants such as Libya, Nigeria, South Africa, Egypt, Morocco, and Tunisia demonstrate outstanding performance in economic resilience. (2) In terms of scale resilience, the countries along the North African Mediterranean coast exhibit particularly prominent advantages. However, the overall performance of Africa in fiscal resilience and openness resilience tends to be weak. Industrial resilience is influenced by colonial legacies and tends to stabilize. (3) The differences in economic resilience values and the fluctuation trajectories of economic resilience levels converge. North African economies exhibit resilience far higher than the mean and other regions, while East, West, and Central Africa consistently perform below the mean in the long term. Southern Africa’s gap from the mean is relatively small, leading to a stalemate. The fluctuation amplitude of differences within each region varies. (4) The overall level of resilience in African regional economies has steadily improved, displaying a trend of polarization. There is evident spatial polarization in West Africa, with Southern Africa demonstrating a trend of multipolarity transitioning towards bipolarity. Conversely, North Africa strengthens its features of bipolar differentiation, while East and Central Africa exhibit tendencies towards multipolarity. (5) Despite some fluctuations in the spatial network of regional economic resilience around 2016, connections among African countries have become increasingly tight, gradually forming three major spatial correlation network clusters: the North African Mediterranean coast, the West–Central African Pan-Gulf of Guinea region, and the East–South African Rift Valley region. Nigeria holds a prominent position as a regional core. Zambia, Cameroon, and the Central African Republic have played certain regional core roles at different times. Nigeria and South Africa also demonstrate significant intermediary roles, while Zambia, Cameroon, and Burkina Faso act as bridges in different periods of network connections. Based on the characteristics of spatial correlation networks, African regions gradually form four major cohesive subgroups and eight sub-subgroups.

SADCL-Net: Sparse-driven Attention with Dual-Consistency Learning Network for Incomplete Multi-view Clustering

SADCL-Net: Sparse-driven Attention with Dual-Consistency Learning Network for Incomplete Multi-view Clustering

Quantifying Western Discourses about Technology

In this article, we explore the social impact of technology in contemporary societies using a dataset of multilingual newspapers in English, French, Spanish, Italian, and German. Our observational time covers twenty years (1999-2018). We filter documents using four technological key terms: nuclear, oil, internet, and automation, that we consider as proxies reflecting Smil 2006 taxonomy of technology. Our methodology is formed by a five-step pipeline: Topic modelling (Pachinko Allocation), word embeddings, Ward’s hierarchical cluster analysis, network analysis, and sentiment analysis. We analyze information stability (which we define as low levels of semantic diversity in our data outcomes over time at the individual key term level) and information homogeneity (which we understand as low levels of semantic diversity in our data outcomes across our selection of key terms). We seek to observe to what extent our selection of key terms permeates into press discourses similarly or differently, and whether those discourses fall into semantic categories that could be considered as essential elements of the fabric of contemporary societies (such as finance, education, or politics). Results show, firstly, a consistent overlap of content across newspapers’ semantic fields that could be considered as pillars of society. Secondly, we notice a progressive simplification of information historically, reflecting less polarizing views across countries and, therefore, demonstrating an increasing agreement on technologically related discourses. We interpret these results as an indicator of the rising intrusion of technology in Western, industrialized countries.

Microstructure evolution and mechanical property of B4C/Ti65 composites via laser directed energy deposition

Application of laser additive manufacture technique to fabricate the discontinuous reinforced high-temperature titanium matrix composites found its great potential in the complex components of aerospace industry. This work prepared the B4C/Ti65 composites by laser directed energy deposition (LDED) with special attention paid to the relationship between microstructure and mechanical property of composites. The microstructure of B4C/Ti65 composites under different laser power and B4C content were investigated, based on which the tensile strength and ductility evolution and the underlying strengthening mechanism of the B4C/Ti65 composites were discussed. The submicron TiBw were randomly distributed in the composites with additions of 0.1 wt% and 0.2 wt% B4C. The TiBw and TiCp in the composites with addition of 0.3 wt% B4C exhibited a uniform distribution rather than distributed along the prior β grain boundaries at high laser power. There existed the network structure of TiBw and TiCp in the 0.5 wt% B4C/Ti65 composites. TiCp always adhered to TiBw, and both TiBw and TiCp acted as the nucleation sites for equiaxed α grain, which is responsible for the finer α colonies that benefit to strength with higher B4C content. The tensile tests showed that the 0.3 wt% B4C/Ti65 composite shows a good combination of ultimate tensile strength (1201 MPa) and elongation (5.54 %). The grain refinement strengthening played the dominant role in the strength increment of the B4C/Ti65 composites. In addition, the formation of clustered submicron-TiBw and network structure of TiBw and TiCp in the 0.5 wt% B4C/Ti65 composites greatly upgraded the yield strength.

Stochastic Block Smooth Graphon Model

In this article, we propose combining the stochastic blockmodel and the smooth graphon model, two of the most prominent modeling approaches in statistical network analysis. Stochastic blockmodels are generally used for clustering networks into groups of actors with homogeneous connectivity behavior. Smooth graphon models, on the other hand, assume that all nodes can be arranged on a one-dimensional scale such that closeness implies a similar behavior in connectivity. Both frameworks belong to the class of node-specific latent variable models, entailing a natural relationship. While these two modeling concepts have developed independently, this article proposes their generalization toward stochastic block smooth graphon models. This combined approach enables to exploit the advantages of both worlds. Employing concepts of the EM-type algorithm allows to develop an appropriate estimation routine, where MCMC techniques are used to accomplish the E-step. Simulations and real-world applications support the practicability of our novel method and demonstrate its advantages. The article is accompanied by supplementary material covering details about computation and implementation.

An efficient network clustering approach using graph-boosting and nonnegative matrix factorization

Network clustering is a critical task in data analysis, aimed at uncovering the underlying structure and patterns within complex networks. Traditional clustering methods often struggle with large-scale and noisy data, leading to suboptimal results. Also, the efficiency of positive samples in network clustering depends on the carefully constructed data augmentation, and the pre-training process of the model deals with large-scale data. To address these issues, in this paper, we introduce an efficient network clustering approach that leverages Graph-Boosting and Nonnegative Matrix Factorization to enhance clustering performance (GBNMF). Our algorithm addresses the limitations of traditional clustering techniques by incorporating the strengths of graph-boosting, which iteratively improves the quality of clusters, and Nonnegative Matrix Factorization (NMF), which effectively captures latent structures within the data. We validate our algorithm through extensive experiments on various benchmark network datasets, demonstrating significant improvements in clustering accuracy and robustness. The proposed algorithm not only achieves superior clustering results but also exhibits remarkable computational efficiency, making it a valuable tool for large-scale network analysis applications.

Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks

Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks.

A versatile framework for attributed network clustering via K-nearest neighbor augmentation

AbstractAttributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order interactions and multiplex graphs with separate layers. An important graph mining task is node clustering, aiming to partition the nodes of an attributed network into k disjoint clusters such that intra-cluster nodes are closely connected and share similar attributes, while inter-cluster nodes are far apart and dissimilar. It is highly challenging to capture multi-hop connections via nodes or attributes for effective clustering on multiple types of attributed networks. In this paper, we first present as an efficient approach to attributed hypergraph clustering (AHC). includes a carefully-crafted K-nearest neighbor augmentation strategy for the optimized exploitation of attribute information on hypergraphs, a joint hypergraph random walk model to devise an effective AHC objective, and an efficient solver with speedup techniques for the objective optimization. The proposed techniques are extensible to various types of attributed networks, and thus, we develop as a versatile attributed network clustering framework, capable of attributed graph clustering, attributed multiplex graph clustering, and AHC. Moreover, we devise with algorithmic designs tailored for GPU acceleration to boost efficiency. We have conducted extensive experiments to compare our methods with 19 competitors on 8 attributed hypergraphs, 16 competitors on 6 attributed graphs, and 16 competitors on 3 attributed multiplex graphs, all demonstrating the superb clustering quality and efficiency of our methods.

Abstract B050: Convergent signaling via C/EBPβ regulates MDSC-intrinsic NLRP3 inflammasomes to drive inflammatory CAF polarization

Abstract OBJECTIVE: Pivotal contributors to therapeutic resistance in pancreatic ductal adenocarcinoma (PDAC) are myeloid cell infiltration and signaling, and interleukin-1 (IL1)-mediated inflammatory polarization of cancer-associated fibroblasts (iCAF). We have shown that granulocytic myeloid- derived suppressor cell (gMDSC) promote iCAF polarization via NLRP3 inflammasome-derived IL-1β. Here, we sought to dissect the mechanisms regulating NLRP3 inflammasome induction in gMDSCs that drive iCAF polarization in PDAC. METHODS: Single-cell RNA sequencing (scRNA-seq) was performed in PKT mice treated with NLRP3i SB-NL02 vs vehicle. Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis identified putative regulons binding upstream to Nlrp3 Transcription Start Site (TSS) in gMDSC subclusters in scRNA-seq data. CRISPR/cas9 silencing of Nlrp3 and Cebpb was performed in gMDSC-like J774M cells in vitro. These edited gMDSCs and J774M cells treated with/without SB-NL02 were co-cultured with Il6/Acta2 dual-reporter CAFs to examine iCAF polarization. RESULTS: PKT mice treated with NLRP3i SB-NL02 showed reduced tumor burden, gMDSC-intrinsic ASC oligomerization, and iCAF polarization in vivo. In silico analysis of intercellular single-cell communication showed striking attenuation in gMDSC-Nlrp3/Il1b:CAF-Il1r1 ligand-receptor crosstalk following NLRP3i treatment. To dissect mechanisms underlying NLRP3 regulation in gMDSCs that drive iCAF skewness, lineage trajectory inference revealed strong co-expression of Tlr4, Cxcr2, Nlrp3 and Il1b genes. Mechanistic studies in J774M gMDSCs showed activation of NLRP3 inflammasomes and IL-1βsecretion not only via canonical TLR4-MyD88 signaling, but also via CXCR2-mediated p38 MAPK induction. This was corroborated in vivo where ASC speck formation in intratumoral Ly6G+ gMDSCs was diminished in mice treated with CXCR2i AZD5069 and p38i peximetinib. To dissect how TLR4 and CXCR2-p38 signaling converge to regulate NLRP3, SCENIC analysis in single-cell gMDSCs transcriptomes revealed Cebpb as the top predicted regulon at the Nlrp3 TSS. In vitro studies using peximetinib and TLR4i (and combinations) revealed that p38 MAPK and TLR4-Myd88 signaling cooperatively regulate C/EBPβ, which in turn dictates Nlrp3 transcription. Cebpb silencing in J774M gMDSCs abolished Nlrp3 transcription, ASC assembly, and IL-1β secretion. ChIP-seq analysis showed strong occupancy of Cebpb on promoter regions of Nlrp3 upon inflammasome induction in gMDSCs. Finally, gMDSC-mediated CAF-Il6 induction in dual-reporter CAFs was significantly disrupted by co-culture with J774M-Cebpb KO cells, recapitulating the effect on iCAF mitigation demonstrated by co-cultures with J774M-Nlrp3 KO or gMDSCs pre-treated with SB-NL02. CONCLUSIONS: CXCR2-p38 and TLR4 signaling converge on C/EBPβ to regulate gMDSC-intrinsic NLRP3 inflammasomes, which drives iCAF polarization in PDAC. The role of C/EBPβ activation as a central node in imparting myeloid checkpoint function in gMDSCs warrants further exploration. Citation Format: Karthik Rajkumar, Andrew Adams, Haleh Amirian, Manan Patel, Erin Dickey, Harper Margaret Marsh, Siddharth Mehra, Nagaraj Nagathihalli, Nipun Merchant, Anna Bianchi, Jashodeep Datta. Convergent signaling via C/EBPβ regulates MDSC-intrinsic NLRP3 inflammasomes to drive inflammatory CAF polarization [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B050.

Single-cell RNA sequencing reveals the differentiation and regulation of endplate cells in human intervertebral disc degeneration

Low back pain (LBP) is largely attributed to intervertebral disc degeneration (IVDD), of which the endplate changes are an important component. However, the alterations in cell fate and properties within the endplates during degeneration remain unknown. Here, we firstly performed the single-cell RNA-sequencing analysis (scRNA-seq) of the cells focusing on degenerative human endplates. By unsupervised clustering of the 8,534 single-cell based on the gene expression, we identified nine distinct cell types. We employed Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and the single-cell regulatory network inference and clustering (SCENIC) to determine the enriched pathways and transcriptional activities across seven chondrocyte subpopulations. Furthermore, two cell fates of chondrocyte differentiation were found by trajectory analysis, one was enriched in inflammation-related genes, and the other was related to extracellular matrix (ECM). Additionally, the intercellular interactions of macrophages (MA) and chondrocytes, T cells/natural killer cells (T/NK) and chondrocytes were examined by ligand-receptor pairs analysis, showing the important regulative function of FN1 from MA and CD74 from T/NK during endplate degeneration. Overall, our findings provide novel perspectives on the endplate degeneration at the single-cell level and a whole-transcriptome size.

Multimedia Cognitive Wireless Sensor Network Cluster Routing based on Intelligent Robot Edge Computing and Collection

Multimedia Cognitive Wireless Sensor Network Cluster Routing based on Intelligent Robot Edge Computing and Collection

Comparative genomic analysis of thermophilic fungi reveals convergent evolutionary adaptations and gene losses

Thermophily is a trait scattered across the fungal tree of life, with its highest prevalence within three fungal families (Chaetomiaceae, Thermoascaceae, and Trichocomaceae), as well as some members of the phylum Mucoromycota. We examined 37 thermophilic and thermotolerant species and 42 mesophilic species for this study and identified thermophily as the ancestral state of all three prominent families of thermophilic fungi. Thermophilic fungal genomes were found to encode various thermostable enzymes, including carbohydrate-active enzymes such as endoxylanases, which are useful for many industrial applications. At the same time, the overall gene counts, especially in gene families responsible for microbial defense such as secondary metabolism, are reduced in thermophiles compared to mesophiles. We also found a reduction in the core genome size of thermophiles in both the Chaetomiaceae family and the Eurotiomycetes class. The Gene Ontology terms lost in thermophilic fungi include primary metabolism, transporters, UV response, and O-methyltransferases. Comparative genomics analysis also revealed higher GC content in the third base of codons (GC3) and a lower effective number of codons in fungal thermophiles than in both thermotolerant and mesophilic fungi. Furthermore, using the Support Vector Machine classifier, we identified several Pfam domains capable of discriminating between genomes of thermophiles and mesophiles with 94% accuracy. Using AlphaFold2 to predict protein structures of endoxylanases (GH10), we built a similarity network based on the structures. We found that the number of disulfide bonds appears important for protein structure, and the network clusters based on protein structures correlate with the optimal activity temperature. Thus, comparative genomics offers new insights into the biology, adaptation, and evolutionary history of thermophilic fungi while providing a parts list for bioengineering applications.

Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks.

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

Recovering nested structures in networks: an evaluation of hierarchical clustering techniques

Abstract In this article, we present various algorithms to partition the nodes of a network into groups that show the property of nestedness. Since perfect nestedness is a rare phenomenon, we consider the task from a data mining perspective, and we search for groups having high-level of nestedness. We utilize both agglomerative and divisive hierarchical clustering procedures and compare them on several benchmark and real-life networks. Furthermore, we propose different metrics derived from the results of our algorithms. We show that average-linkage and complete-linkage clustering can recover the largest fully nested clusters, and that the cluster size-weighted mean nestedness was a more stable metric for measuring clustering performance. Our proposed algorithms allow us to create multiple resolution views of nestedness-based clustering of networks, extending the field of graph-based data mining.

An efficient cluster head selection in WSNs using transient search optimization (TSO) algorithm

SummaryIn this manuscript, a nature‐inspired optimization method, named transient search optimization (TSO), is proposed. Energy‐based monetary custom is a serious issue on the wireless sensor network (WSN). Here, the network clustering is an effectual technique to reduce node energy depletion and increased network lifetime. The proposed method aims to improve the efficiency of sensor nodes (SNs) by reducing their detachment, minimizing energy transmission, and protecting excessive energy stored in the nodes. This approach helps decrease delays, reduce traffic flow, and optimize network performance. The execution is implemented on the NS2 software. The experimental outcomes exhibit that the proposed system performs better based on two wireless sensor architectures, such as 50 nodes and 100 nodes. The parameter produces 52.24%, 54.38%, and 56.37% better network lifetime; 44.71%, 46.24%, and 49.45% higher alive node; and 39.26%, 36.26%, and 28.65% lesser dead SNs compared with existing techniques like multi‐objective cluster head (CH)–based energy‐aware optimized routing approach in wireless sensor network (MOCH‐ORR‐WSN), energy effective CH selection with improved sparrow search algorithm in WSN (ECH‐ISS‐WSN), and energy effectual cluster basis routing protocol under butterfly optimization along ant colony optimization algorithms for WSN (EEC‐BOA‐ACO‐WSN).

RDSC: Range-Based Device Spatial Clustering for IoT Networks.

The growth of the Internet of Things (IoT) has become a crucial area of modern research. While the increasing number of IoT devices has driven significant advancements, it has also introduced several challenges, such as data storage, data privacy, communication protocols, complex network topologies, and IoT device management. In essence, the management of IoT devices is becoming more and more challenging, especially with the limited capacity and power of the IoT devices. The devices, having limited capacities, cannot store the information of the entire environment at once. In addition, device power consumption can affect network performance and stability. The devices' sensing areas with device grouping and management can simplify further networking tasks and improve response quality with data aggregation and correction techniques. In fact, most research papers are looking forward to expanding network lifetimes by relying on devices with high power capabilities. This paper proposes a device spatial clustering technique that covers crucial challenges in IoT. Our approach groups the dispersed devices to create clusters of connected devices while considering their coverage, their storage capacities, and their power. A new clustering protocol alongside a new clustering algorithm is introduced, resolving the aforementioned challenges. Moreover, a technique for non-sensed area extraction is presented. The efficiency of the proposed approach has been evaluated with extensive experiments that gave notable results. Our technique was also compared with other clustering algorithms, showing the different results of these algorithms.

C60 Functionalized Single‐Walled Carbon Nanotube‐based Phototransistor for Efficient Detection of Visible Light Under Appropriate Gate Electrostatics

AbstractThe current study concerns highly reliable visible light sensing by C60 fullerene functionalized single‐walled carbon nanotube (SWCNT) based phototransistor. The absorbance of visible light (532 nm) increases significantly due to the formation of an interlink between the SWCNT network and C60 clusters. Photogenerated excess electrons in SWCNT are trapped by the C60 clusters and increase the effective hole concentrations in the SWCNT channel, which eventually improves the photoconductive gain. C60‐SWCNT channel is further integrated into the back gated field effect transistor (FET) structure in which 90 nm SiO2 dielectric thickness is used. The responsivity toward visible light is further enlarged with appropriate negative gate electrostatic. In order to ensure the morphological and structural behavior of C60‐SWCNT, various microscopic and spectroscopic characterizations are performed. The C60‐SWCNT phototransistor exhibits responsivity of 1.219 A W−1 at Vgs = – 21 V. The detectivity and rise/fall time of C60‐SWCNT came around to be 2.34 × 1010 Jones, 86.42 ms/3.35 ms at the same Vgs. The maximum external quantum efficiency (EQE) of the C60‐SWCNT phototransistor is very high, ≈274%. In the overall study, a comprehensive discussion is introduced on the effect of variable gate potential on the performance of the C60‐SWCNT phototransistor.

Key microbial taxa play essential roles in maintaining soil muti-nutrient cycling following an extreme drought event in ecological buffer zones along the Yangtze River.

Climatic extremes, especially extreme droughts, are occurring more frequently and profoundly impacting biogeochemical processes. However, the relative importance of microbial communities on soil nutrient cycling and community maintenance under natural extreme drought events remains elusive. During a record-breaking drought in the Yangtze River Basin (YRB) in the summer of 2022, we collected ambient soils and drought-affected bare and vegetated soils in ecological buffer zones from two sites with similar soil and vegetation characteristics along the YRB, and examined the relative contribution of soil bacterial communities in supporting multi-nutrient cycling index (MNCI) involving carbon-, nitrate- and phosphorus-cycling and their associations with microbial network. Extreme drought decreased (p < 0.05) bacterial α-diversity but increased MNCI in vegetated soils at both sites, while both remained unchanged (p > 0.05) in bare soils, possibly as a result of vegetation releasing rhizodeposits under drought which selectively recruited bacterial communities. Bacterial community compositions were shifted (p < 0.05) only in vegetated soils, and they exerted more influence than α-diversity on soil MNCI. Notably, the Anaerolineae, identified as a biomarker enriched in vegetated soils, had close associations with enzyme activities and soil MNCI at both sites, suggesting their potential recruitment by vegetation to withstand drought. Furthermore, key ecological clusters (Module 1) in bacterial co-occurrence networks at both sites supported (p < 0.05) higher MNCI, despite no substantial variation in network structure due to drought. Specifically, the most important taxa within Module 1 for predicting soil MNCI revealed by random forest modeling analysis (R2=0.44 - 0.63, p < 0.001), such as B1-7BS, SBR1031 and Nocardioides, could be deeply involved in soil nitrogen-cycling, suggesting an essential role of specialized interactions of bacterial communities in maintaining soil multifunctionality. Overall, this study demonstrates that changes in biomarkers and functional taxa under extreme drought may better reflect the biological mechanisms involved in microbial communities impacting ecosystem function, which may aid in forecasting the ecological consequences of ongoing climate change in the ecological buffer zones along the YRB.

Integrative microbiome and metabolome profiles reveal the impacts of periodontitis via oral-gut axis in first-trimester pregnant women

BackgroundPeriodontitis results from host-microbe dysbiosis and the resultant dysregulated immunoinflammatory response. Importantly, it closely links to numerous systemic comorbidities, and perplexingly contributes to adverse pregnancy outcomes (APOs). Currently, there are limited studies on the distal consequences of periodontitis via oral-gut axis in pregnant women. This study investigated the integrative microbiome-metabolome profiles through multi-omics approaches in first-trimester pregnant women and explored the translational potentials.MethodsWe collected samples of subgingival plaques, saliva, sera and stool from 54 Chinese pregnant women at the first trimester, including 31 maternal periodontitis (Perio) subjects and 23 Non-Perio controls. By integrating 16S rRNA sequencing, untargeted metabolomics and clinical traits, we explored the oral-gut microbial and metabolic connection resulting from periodontitis among early pregnant women.ResultsWe demonstrated a novel bacterial distinguisher Coprococcus from feces of periodontitis subjects in association with subgingival periodontopathogens, being different from other fecal genera in Lachnospiraceae family. The ratio of fecal Coprococcus to Lachnoclostridium could discriminate between Perio and Non-Perio groups as the ratio of subgingival Porphyromonas to Rothia did. Furthermore, there were differentially abundant fecal metabolic features pivotally enriched in periodontitis subjects like L-urobilin and kynurenic acid. We revealed a periodontitis-oriented integrative network cluster, which was centered with fecal Coprococcus and L-urobilin as well as serum triglyceride.ConclusionsThe current findings about the notable influence of periodontitis on fecal microbiota and metabolites in first-trimester pregnant women via oral-gut axis signify the importance and translational implications of preconceptional oral/periodontal healthcare for enhancing maternal wellbeing.

Clust&amp;See3.0 : clustering, module exploration and annotation

Background Cytoscape is an open-source software to visualize and analyze networks. However, large networks, such as protein interaction networks, are still difficult to analyze as a whole. Methods Here, we propose Clust&amp;See3.0, a novel version of a Cytoscape app that has been developed to identify, visualize and manipulate network clusters and modules. It is now enriched with functionalities allowing custom annotations of nodes and computation of their statistical enrichments. Results As the wealth of multi-omics data is growing, such functionalities are highly valuable for a better understanding of biological module composition, as illustrated by the presented use case. Conclusions In summary, the originality of Clust&amp;See3.0 lies in providing users with a complete tool for network clusters analyses: from cluster identification, visualization, node and cluster annotations to annotation statistical analyses.