Research on the Influence of Information Iterative Propagation on Complex Network Structure.

Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.

Study on the Stability of Complex Networks in the Stock Markets of Key Industries in China.

Investigating the significant "roles" within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network's resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network.

A Critical Candidate Node-Based Attack Model of Network Controllability.

The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closeness-based, pagerank-based, and hierarchical attacks. Results show that the model outperforms these methods in both disruption effectiveness and computational efficiency. Extensive experiments on both synthetic and real-world networks validate the superior performance of this approach. This study provides valuable insights for identifying key nodes crucial for maintaining network controllability. It also offers a solid framework for enhancing network resilience against malicious attacks.

Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.

The study presents a novel technique for lung auscultation based on graph theory, emphasizing the potential of graph parameters in distinguishing lung sounds and supporting earlier detection of various respiratory pathologies. The frequency spread and the component magnitudes are revealed from the analysis of eighty-five bronchial (BS) and pleural rub (PS) lung sounds employing the power spectral density (PSD) plot and wavelet scalogram. The low-frequency spread, and persistence of the high-intensity frequency components are visible in BS sounds emanating from the uniform cross-sectional area of the trachea. The frictional rub between the pleurae causes a higher frequency spread of low-intensity intermittent frequency components in PS signals. From the complex networks of BS and PS, the extracted graph features are - graph density ([Formula: see text], transitivity ([Formula: see text], degree centrality ([Formula: see text]), betweenness centrality ([Formula: see text], eigenvector centrality ([Formula: see text]), and graph entropy (En). The high values of [Formula: see text] and [Formula: see text] show a strong correlation between distinct segments of the BS signal originating from a consistent cross-sectional tracheal diameter and, hence, the generation of high-intense low-spread frequency components. An intermittent low-intense and a relatively greater frequency spread in PS signal appear as high [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] values. With these complex network parameters as input attributes, the supervised machine learning techniques- discriminant analyses, support vector machines, k-nearest neighbors, and neural network pattern recognition (PRNN)- classify the signals with more than 90% accuracy, with PRNN having 25 neurons in the hidden layer achieving the highest (98.82%).

Tele-connection of embodied carbon emissions from industries in China's trade: A complex network analysis.

Regions can meet their development demands through trade, with the attendant environmental costs being shifted to other regions, and carbon emissions emitted from different industries could be transferred over long distances through the increasingly diversified trade network. However, it remains unclear how regional trade leads to the tele-connection and transfer of embodied carbon emissions form industries, and what is the structure and characteristics of the transfer. Thus, multiregional input‒output models and complex network analysis are employed to reveal the tele-connection of carbon emissions from industries in China. The results show that embodied carbon emissions from trade increased by 869.47 million tons during in five years, with North China being the largest outflow area, while the coastal regions being the inflow areas. Moreover, the secondary industry is the highest source of embodied carbon emissions, accounting for 96.68 % of the volume, and the transfer of carbon emissions mainly occurs in North and East China. In carbon emissions networks, North China holds a controlling position, as analysed by degree and strength. The first 23.3%-30% of nodes carry about 62.6%-72.4% of the entire carbon emissions flow, and the network conforms to scale-free features. Centrality further reveals that northern and coastal areas occupy core positions, with interregional carbon flows dominating the critical pathways in the network. The number of clusters evolved from three to four communities during 2012-2017 in the network, demonstrating that the carbon flow network is developing towards multipolarity and modularity. This study underscores the urgency of mitigating carbon emissions in industrial trade by identifying key nodes and cluster structures in emission networks.

Drivergene.net: A Cytoscape app for the identification of driver nodes of large-scale complex networks and case studies in discovery of drug target genes.

There are no tools to identify driver nodes of large-scale networks in approach of competition-based controllability. This study proposed a novel method for this computation of large-scale networks. It implemented the method in a new Cytoscape plug-in app called Drivergene.net. Experiments of the software on large-scale biomolecular networks have shown outstanding speed and computing power. Interestingly, 86.67% of the top 10 driver nodes found on these networks are anticancer drug target genes that reside mostly at the innermost K-cores of the networks. Finally, compared method with those of five other researchers and confirmed that the proposed method outperforms the other methods on identification of anticancer drug target genes. Taken together, Drivergene.net is a reliable tool that efficiently detects not only drug target genes from biomolecular networks but also driver nodes of large-scale complex networks. Drivergene.net with a user manual and example datasets are available https://github.com/tinhpd/Drivergene.git.

[Modular pharmacology: research progress and development of analysis platform].

Based on the systematic deconstruction of multi-dimensional and multi-target biological networks, modular pharmacology explains the complex mechanism of diseases and the interactions of multi-target drugs. It has made progress in the fields of pathogenesis of disease, biological basis of disease and traditional Chinese medicine(TCM) syndrome, pharmacological mechanism of multi-target herbs, compatibility of formulas, and discovery of new drug of TCM compound. However, the complexity of multi-omics data and biological networks brings challenges to the modular deconstruction and analysis of the drug networks. Here, we constructed the "Computing Platform for Modular Pharmacology" online analysis system, which can implement the function of network construction, module identification, module discriminant analysis, hub-module analysis, intra-module and inter-module relationship analysis, and topological visualization of network based on quantitative expression profiles and protein-protein interaction(PPI) data. This tool provides a powerful tool for the research on complex diseases and multi-target drug mechanisms by means of modular pharmacology. The platform may have broad range of application in disease modular identification and correlation mechanism, interpretation of scientific principles of TCM, analysis of complex mechanisms of TCM and formulas, and discovery of multi-target drugs.

The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau.

Global warming has led to severe land desertification on the Mongolian plateau. It puts great environmental pressure on vegetation communities. This pressure leads to fragmentation of land use and landscape patterns, thus triggering changes in the spatial distribution patterns of vegetation. The spatial distribution pattern of vegetation is crucial for the performance of its ecosystem services. However, there is not enough research on the relationship between large-scale spatial distribution patterns of vegetation and ecosystem services. Therefore, this study is to construct an ecological spatial network on the Mongolian Plateau based on landscape ecology and complex network theory. Combining pattern analysis methods to analyze the network, we obtained the spatial and temporal trends of forest and grass spatial distribution patterns from 2000 to 2100, and explored the relationship between the topological properties of source patches and ecosystem services in different patterns. It was found that there are four basic patterns of spatial distribution of forest and grass in the Mongolian Plateau. The Core-Linked Ring pattern accounts for 40.74 % and exhibits the highest stability. Under the SSP5-RCP8.5 scenario, source patches are reduced by 22.76 % in 2100. Topological indicators of source patches showed significant correlations with ecosystem services. For example, the CUE of grassland patches in the Centralized Star pattern was positively correlated with betweeness centrality. The most significant improvement in WUE after optimization is 19.90 % compared to pre-optimization. The conclusion of the study shows that the spatial distribution pattern of vegetation can be used to enhance the stability of ecological spatial network and improve ecosystem services at a larger scale. It can provide a certain reference for the study of spatial patterns of vegetation distribution in arid and semi-arid areas.

ExGenet, Integrating Design of Experiments and Response Surface Methodology for Cancer Gene Detection in Gene Regulatory Networks.

Objective: Network analysis techniques often require tuning hyperparameters for optimal performance. For instance, the independent cascade model necessitates determining the probability of diffusion. Despite its importance, a consensus on effective parameter adjustment remains elusive. Methods: In this study, we propose a novel approach utilizing experimental design methodologies, specifically 2-Factorial Analysis for Screening, and Response Surface Methodology (RSM) for parameter adjustment. We apply this methodology to the task of detecting cancer driver genes in colorectal cancer. Result: Through experimental validation of colorectal cancer data, we demonstrate the effectiveness of our proposed methodology. Compared with existing methods, our approach offers several advantages, including reduced computational overhead, systematic parameter selection grounded in statistical theory, and improved performance in detecting cancer driver genes. Conclusion: This study presents a significant advancement in the field of network analysis by providing a practical and systematic approach to hyperparameter tuning. By optimizing parameter settings, our methodology offers promising implications for critical biomedical applications such as cancer driver gene detection.

Population interaction network in representative gravitational search algorithms: Logistic distribution leads to worse performance.

In this paper, a novel study on the way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using a scheme known as population interaction networks (PIN). Specifically, three representative MHAs, including the differential evolutionary algorithm (DE), the particle swarm optimization algorithm (PSO), the gravitational search algorithm (GSA), and four classical variations of the gravitational search algorithm, are analyzed in terms of inter-individual information interactions and the differences in the performance of each of the algorithms on IEEE Congress on Evolutionary Computation 2017 benchmark functions. The cumulative distribution function (CDF) of the node degree obtained by the algorithm on the benchmark function is fitted to the seven distribution models by using PIN. The results show that among the seven compared algorithms, the more powerful DE is more skewed towards the Poisson distribution, and the weaker PSO, GSA, and GSA variants are more skewed towards the Logistic distribution. The more deviation from Logistic distribution GSA variants conform, the stronger their performance. From the point of view of the CDF, deviating from the Logistic distribution facilitates the improvement of the GSA. Our findings suggest that the population interaction network is a powerful tool for characterizing and comparing the performance of different MHAs in a more comprehensive and meaningful way.

A network model of social contacts with small-world and scale-free features, tunable connectivity, and geographic restrictions.

Small-world networks and scale-free networks are well-known theoretical models within the realm of complex graphs. These models exhibit "low" average shortest-path length; however, key distinctions are observed in their degree distributions and average clustering coefficients: in small-world networks, the degree distribution is bell-shaped and the clustering is "high"; in scale-free networks, the degree distribution follows a power law and the clustering is "low". Here, a model for generating scale-free graphs with "high" clustering is numerically explored, since these features are concurrently identified in networks representing social interactions. In this model, the values of average degree and exponent of the power-law degree distribution are both adjustable, and spatial limitations in the creation of links are taken into account. Several topological metrics are calculated and compared for computer-generated graphs. Unexpectedly, the numerical experiments show that, by varying the model parameters, a transition from a power-law to a bell-shaped degree distribution can occur. Also, in these graphs, the degree distribution is most accurately characterized by a pure power-law for values of the exponent typically found in real-world networks.

[Quantitative study on the acupoint effects based on complex network analysis: with the examples of the textbook networks formed by Chinese Acupuncture and Moxibustion Therapy and Practical Acupuncture and Moxibustion].

By extracting the acupoint names and their main indications from cases in Chinese Acupuncture and Moxibustion Therapy and Practical Acupuncture and Moxibustion, the acupoints and their main indications are represented in a reduced dimension, establishing an "acupoint-indication" linkage. Using complex network detection results (node degree values), the specificity of acupoints was assessed. The small-world characteristics of the "acupoint-indication" network are utilized to analyze the consistency of acupoint selection in acupuncture prescriptions and strategies to avoid redundant acupoints. The results show that the "acupoint-indication" network formed by both texts exhibited an approximate "long-tail" distribution, with a large number of node degree values concentrated between 0 and 4 000, while a few nodes have degree values exceeding 10 000. There are significant differences in the number and distribution of nodes with degree values> 10 000 between the two texts. Chinese Acupuncture and Moxibustion Therapy includes 11 acupoints with multiple edges across the body, whereas Practical Acupuncture and Moxibustion contains only 2 such acupoints, located in the lower limbs. Clinically, some acupoints have a broad therapeutic effect and appear in numerous prescriptions. The division of acupoints based on node degree values can coarsely evaluate the body region specificity of acupoints' regulatory effects. The "acupoint-indication" network of Chinese Acupuncture and Moxibustion Therapy has a higher number of edges than that of Practical Acupuncture and Moxibustion, which might be related to the different historical contexts of the two texts. In the future, diagnostic and therapeutic patterns with historical continuity can be utilized to optimize acupuncture prescriptions.

Molecular mechanism of the treatment of lung adenocarcinoma by Hedyotis Diffusa: an integrative study with real-world clinical data and experimental validation.

Background: With a variety of active ingredients, Hedyotis Diffusa (H. diffusa) can treat a variety of tumors. The purpose of our study is based on real-world data and experimental level, to double demonstrate the efficacy and possible molecular mechanism of H. diffusa in the treatment of lung adenocarcinom (LUAD). Methods: Phenotype-genotype and herbal-target associations were extracted from the SymMap database. Disease-gene associations were extracted from the MalaCards database. A molecular network-based correlation analysis was further conducted on the collection of genes associated with TCM and the collection of genes associated with diseases and symptoms. Then, the network separation SAB metrics were applied to evaluate the network proximity relationship between TCM and symptoms. Finally, cell apoptosis experiment, Western blot, and Real-time PCR were used for biological experimental level validation analysis. Results: Included in the study were 85,437 electronic medical records (318 patients with LUAD). The proportion of prescriptions containing H. diffusa in the LUAD group was much higher than that in the non-LUAD group (p < 0.005). We counted the symptom relief of patients in the group and the group without the use of H. diffusa: except for symptoms such as fatigue, palpitations, and dizziness, the improvement rate of symptoms in the user group was higher than that in the non-use group. We selected the five most frequently occurring symptoms in the use group, namely, cough, expectoration, fatigue, chest tightness and wheezing. We combined the above five symptom genes into one group. The overlapping genes obtained were CTNNB1, STAT3, CASP8, and APC. The selection of CTNNB1 target for biological experiments showed that the proliferation rate of LUAD A549 cells in the drug intervention group was significantly lower than that in the control group, and it was concentration-dependent. H. diffusa can promote the apoptosis of A549 cells, and the apoptosis rate of the high-concentration drug group is significantly higher than that of the low-concentration drug group. The transcription and expression level of CTNNB1 gene in the drug intervention group were significantly decreased. Conclusion: H. diffusa inhibits the proliferation and promotes apoptosis of LUAD A549 cells, which may be related to the fact that H. diffusa can regulate the expression of CTNNB1.

Does the limelight foster the green transition of listed industrial companies? An empirical analysis from Chinese A-share market.

This paper contributes current research by investigating the extent to which equity linkage networks impact enterprise green transition in sustainable development. By adopting a complex network approach, we constructed a common shareholding network based on the top ten shareholders of listed industrial enterprises in Shanghai and Shenzhen A-shares from 2013 to 2022. The empirical results indicate that enterprises closer to the centre of the equity linkage network tend to have higher degrees of green transition. This impact is facilitated through three mechanism channels of capital flow, information exchange, and knowledge transfer within the network. We find enterprises at the core of the network can effectively reduce their financing constraints on enterprises, mitigate risks associated with external environmental uncertainties and managerial myopia, and promote knowledge exchange and innovation cooperation between enterprises. We have also discovered that the centrality of equity network has a greater impact on promoting transition in state-owned, large-scale enterprises, and enterprises with less heavy pollution and the network effect can be enhanced by strong regional environmental regulations. The above findings not only provide policy makers with policy recommendations to guide the enterprises green transition, but also provide industry practitioners with practical paths and directions, which can help promote the green development process of the whole society.

AAGCN: a graph convolutional neural network with adaptive feature and topology learning.

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep learning have shown remarkable capabilities in processing graph-structured data, such as social networks and traffic networks. As a result, they have garnered significant attention from researchers.However, real-world data often face challenges like data sparsity and missing labels, which can hinder the performance and generalization ability of graph convolutional neural networks. To overcome these challenges, our research aims to effectively extract the hidden features and topological information of graph convolutional neural networks. We propose an innovative model called Adaptive Feature and Topology Graph Convolutional Neural Network (AAGCN). By incorporating an adaptive layer, our model preprocesses the data and integrates the hidden features and topological information with the original data's features and structure. These fused features are then utilized in the convolutional layer for training, significantly enhancing the expressive power of graph convolutional neural networks.To evaluate the effectiveness of the adaptive layer in the AAGCN model, we conducted node classification experiments on real datasets. The results validate its ability to address data sparsity and improve the classification performance of graph convolutional neural networks.In conclusion, our research primarily focuses on addressing data sparsity and missing labels in graph convolutional neural networks. The proposed AAGCN model, which incorporates an adaptive layer, effectively extracts hidden features and topological information, thereby enhancing the expressive power and classification performance of these networks.

[Rules of acupoint selection and pattern-acupoint relationship in treatment with acupuncture and moxibustion for endometriosis based on complex network analysis technology].

OBJECTIVE: To explore the rules of acupoint selection and pattern-acupoint relationship in treatment with acupuncture and moxibustion for endometriosis (EMs) based on complex network analysis technology. METHODS: The articles for clinical trial of EMs treated with acupuncture and moxibustion were searched from CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase and Cochrane Library from the inception of the databases to December 14, 2022. Using Microsoft Excel 2019 software, the database was established to collect the use frequency of acupoint, meridian tropism, location and pattern-acupoint relationship. SPSS Modeler 18.0 Apriori algorithm was adopted to conduct the association rule analysis, Cytoscape3.7.2 software was used to plot the complex co-occurrence network map; and SPSS Statistics 26.0 was adopted to perform hierarchical cluster analysis on high-frequency acupoints and a tree diagram was drawn. RESULTS: A total of 163 articles were included, and 167 core acupoint prescriptions and 74 pattern-associated acupoint prescriptions were extracted, involving 92 acupoints, with a cumulative frequency of 1 223 times. The top five acupoints with the highest use frequency were Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Zigong (EX-CA 1) and Qihai (CV 6). The selected acupoints were mostly distributed in the chest, abdomen and lower limbs; and the involved meridians included the conception vessel, the spleen meridian of foot-taiyin and the stomach meridian of foot-yangming. The acupoint compatibility of high frequency referred to Guanyuan (CV 4) - Sanyinjiao (SP 6), Guanyuan (CV 4) - Zhongji (CV 3), and Guanyuan (CV 4) - Zigong (EX-CA 1). The close association was presented among Guanyuan (CV 4), Sanyinjiao (SP 6), Qihai (CV 6) and Zhongji (CV 3), which had the strongest connection with the other acupoints; among the top 25 acupoints with the highest use frequency, 5 acupoint prescriptions with high frequency were obtained by the cluster analysis. Guanyuan (CV 4), Qihai (CV 6), Sanyinjiao (SP 6), Zigong (EX-CA 1) and Zhongji (CV 3) were selected for cold and blood stagnation; Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Dahe (KI 12) and Taixi (KI 3) for kidney deficiency and blood stagnation; Zhongji (CV 3), Guanyuan (CV 4), Sanyinjiao (SP 6), Xuehai (SP 10) and Diji (SP 8) for qi and blood stagnation; Qihai (CV 6), Guanyuan (CV 4), Zusanli (ST 36), Xuehai (SP 10), and Zigong (EX-CA 1) for qi deficiency and blood stagnation; Sanyinjiao (SP 6), Fenglong (ST 40), Zhongliao (BL 33), Ciliao (BL 32) and Xialiao (BL 34) for interaction of phlegm and stasis; and Daheng (SP 15), Guanyuan (CV 4), Zhongji (CV 3), Qihai (CV 6) and Zhongwan (CV 12) for retention of damp and heat. CONCLUSION: The core acupoints are Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Qihai (CV 6) and Zigong (EX-CA 1) in treatment of endometriosis with acupuncture and moxibustion. Six patterns/syndromes are involved in clinical practice. In terms of the properties, functions and indications, the supplementary acupoints are selected on the basis of the core acupoints for different patterns/sydnromes of the disease.

Adaptive robust structure exploration for complex systems based on model configuration and fusion.

Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.

Analysis of spatial characteristics and influencing factors of the flow network of highly educated talents from national and local perspective.

Based on dynamic monitoring data on China's population, by using complex networks, spatial analysis and mathematical measurement, this study reveals the spatial characteristics and influencing factors of the network of flows of highly educated talents in the Yangtze River Delta region from the national and local perspectives. In the two perspectives, the network has strong isomorphism and certain differences. The in-flow of highly educated talents from cities with high administrative levels and more developed economies to Shanghai constitutes the core of the entire network. From a national perspective, highly educated talents tend to converge to the Yangtze River Delta region. From a local perspective, it was found that these talents cluster towards a limited number of cities in the region. From both perspectives, the flow network has developed into a "core-periphery" progressive hierarchical structure, with Shanghai becoming the sole core city. There is little difference in the influencing factors of talent mobility from both macro and meso perspectives. Highly educated talents would frequently flow between cities with strong economic development levels, and cities with high education level, scientific and technological level, complete infrastructure, and good aesthetics. However, geographical distance still plays a hindering role in the flow of highly educated talents, and factors such as cultural identity, institutional, and social modality differences among regions also have a certain effect on the flow of these talents.

Employing graph attention networks to decode psycho-metabolic interactions in Schizophrenia.

Schizophrenia is a severe mental disorder characterized by intricate and underexplored interactions between psychological symptoms and metabolic health, presenting challenges in understanding the disease mechanisms and designing effective treatment strategies. To delve deeply into the complex interactions between mental and metabolic health in patients with schizophrenia, this study constructed a psycho-metabolic interaction network and optimized the Graph Attention Network (GAT). This approach reveals complex data patterns that traditional statistical analyses fail to capture. The results show that weight management and medication management play a central role in the interplay between psychiatric disorders and metabolic health. Furthermore, additional analysis revealed significant correlations between the history of psychiatric symptoms and physical health indicators, as well as the key roles of biochemical markers(e.g., triglycerides and low-density lipoprotein cholesterol), which have not been sufficiently emphasized in previous studies. This highlights the importance of medication management approaches, weight management, psychological treatment, and biomarker monitoring in comprehensive treatment and underscores the significance of the biopsychosocial model. This study is the first to utilize a GNN to explore the interactions between schizophrenia symptoms and metabolic features, providing new insights into understanding psychiatric disorders and guiding the development of more comprehensive treatment strategies for schizophrenia.

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome.

BACKGROUND: Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking. RESULTS: This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis. CONCLUSIONS: Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.