Identifying highly connected sites for risk-based surveillance and control of cucurbit downy mildew in the eastern United States.

Objective: Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread. Methods: Static networks were constructed based on the distance between fields, while dynamic networks were constructed based on the distance between fields and wind speed and direction, using disease data collected from 2008 to 2016. Three strategies were used to identify highly connected field sites. First, the probability of pathogen transmission between nodes and the probability of node infection were modeled over a discrete weekly time step within an epidemic year. Second, nodes identified as important were selectively removed from networks and the probability of node infection was recalculated in each epidemic year. Third, the recurring patterns of node infection were analyzed across epidemic years. Results: Static networks exhibited scale-free properties where the node degree followed a power-law distribution. Betweenness centrality was the most useful metric for identifying important nodes within the networks that were associated with disease transmission and prediction. Based on betweenness centrality, field sites in Maryland, North Carolina, Ohio, South Carolina and Virginia were the most central in the disease network across epidemic years. Removing field sites identified as important limited the predicted risk of disease spread based on the dynamic network model. Conclusions: Combining the dynamic network model and centrality metrics facilitated the identification of highly connected fields in the southeastern United States and the mid-Atlantic region. These highly connected sites may be used to inform surveillance and strategies for controlling cucurbit downy mildew in the eastern United States.

Sampling unknown large networks restricted by low sampling rates.

Graph sampling plays an important role in data mining for large networks. Specifically, larger networks often correspond to lower sampling rates. Under the situation, traditional traversal-based samplings for large networks usually have an excessive preference for densely-connected network core nodes. Aim at this issue, this paper proposes a sampling method for unknown networks at low sampling rates, called SLSR, which first adopts a random node sampling to evaluate a degree threshold, utilized to distinguish the core from periphery, and the average degree in unknown networks, and then runs a double-layer sampling strategy on the core and periphery. SLSR is simple that results in a high time efficiency, but experiments verify that the proposed method can accurately preserve many critical structures of unknown large scale-free networks with low sampling rates and low variances.

A scale-free model of acute and ventilator-induced lung injury: a network theory approach inspired by seismology.

Introduction: Acute respiratory distress syndrome (ARDS) presents a significant clinical challenge, with ventilator-induced lung injury (VILI) being a critical complication arising from life-saving mechanical ventilation. Understanding the spatial and temporal dynamics of VILI can inform therapeutic strategies to mitigate lung damage and improve outcomes. Methods: Histological sections from initially healthy mice and pulmonary lavage-injured mice subjected to a second hit of VILI were segmented with Ilastik to define regions of lung injury. A scale-free network approach was applied to assess the correlation between injury regions, with regions of injury represented as 'nodes' in the network and 'edges' quantifying the degree of correlation between nodes. A simulated time series analysis was conducted to emulate the temporal sequence of injury events. Results: Automated segmentation identified different lung regions in good agreement with manual scoring, achieving a sensitivity of 78% and a specificity of 85% across 'injury' pixels. Overall accuracy across 'injury', 'air', and 'other' pixels was 81%. The size of injured regions followed a power-law distribution, suggesting a 'rich-get-richer' phenomenon in the distribution of lung injury. Network analysis revealed a scale-free distribution of injury correlations, highlighting hubs of injury that could serve as focal points for therapeutic intervention. Simulated time series analysis further supported the concept of secondary injury events following an initial insult, with patterns resembling those observed in seismological studies of aftershocks. Conclusion: The size distribution of injured regions underscores the spatially heterogeneous nature of acute and ventilator-induced lung injury. The application of network theory demonstrates the emergence of injury 'hubs' that are consistent with a 'rich-get-richer' dynamic. Simulated time series analysis demonstrates that the progression of injury events in the lung could follow spatiotemporal patterns similar to the progression of aftershocks in seismology, providing new insights into the mechanisms of injury distribution and propagation. Both phenomena suggest a potential for interventions targeting these injury 'hubs' to reduce the impact of VILI in ARDS management.

State-of-art review of information diffusion models and their impact on social network vulnerabilities.

With the development of information society and network technology, people increasingly depend on information found on the Internet. At the same time, the models of information diffusion on the Internet are changing as well. However, these models experience the problem due to the fast development of network technologies. There is no thorough research in regards to the latest models and their applications and advantages. As a result, it is essential to have a comprehensive study of information diffusion models. The primary goal of this research is to provide a comparative study on the existing models such as the Ising model, Sznajd model, SIR model, SICR model, Game theory and social networking services models. We discuss several of their applications with the existing limitations and further categorizations. Vulnerabilities and privacy challenges of information diffusion models are extensively explored. Furthermore, categorization including strengths and weaknesses are discussed. Finally, limitations and recommendations are suggested with diverse solutions for the improvement of the information diffusion models and envisioned future research directions.

Study of dynamic characteristics of scale-free spiking neural networks based on synaptic plasticity / 生物医学工程学杂志

Biological neural networks have dual properties of small-world attributes and scale-free attributes. Most of the current researches on neural networks are based on small-world networks or scale-free networks with lower clustering coefficient, however, the real brain network is a scale-free network with small-world attributes. In this paper, a scale-free spiking neural network with high clustering coefficient and small-world attribute was constructed. The dynamic evolution process was analyzed from three aspects: synaptic regulation process, firing characteristics and complex network characteristics. The experimental results show that, as time goes by, the synaptic strength gradually decreases and tends to be stable. As a result, the connection strength of the network decreases and tends to be stable; the firing rate of neurons gradually decreases and tends to be stable, and the synchronization becomes worse; the local information transmission efficiency is stable, the global information transmission efficiency is reduced and tends to be stable, and the small-world attributes are relatively stable. The dynamic characteristics vary with time and interact with each other. The regulation of synapses is based on the firing time of neurons, and the regulation of synapses will affect the firing of neurons and complex characteristics of networks. In this paper, a scale-free spiking neural network was constructed, which has biological authenticity. It lays a foundation for the research of artificial neural network and its engineering application.

Preliminary Study on Traditional Chinese Medicine Treatment Characteristics of Intestinal Cancer based on Scale-free Network Analysis Method / 世界科学技术-中医药现代化

This article was aimed to study the characteristics of intestinal cancer by traditional Chinese medicine (TCM) based on the scale-free network analysis method. History information of 145 hospitalized cases from 2008 to 2011 was collected from the Oncology Department, Guang A nmen Hospital, in the clinical collection system. The rule of medication in the treatment of intestinal cancer by TCM was explored by scale-free network analysis method from several aspects, such as effects, classification and compatibility relations. The analysis results of 145 intestinal cancer cases showed that strengthening the body resistance was the main treatment principle. Si-Jun-Zi (SJZ) decoc-tion was used with the highest frequency. In the prescription design, qi-supplementing herbs were the most. The heat-clearing, dampness-eliminating, qi-regulating, blood-activating and stasis-removing, food stagnation removing and phlegm-removing herbs were also used frequently. The TCM treatment and syndrome differentiation will be changed due to the treatment and disease stages. It was concluded that scale-free network analysis method is able to relatively show the TCM treatment rule of intestinal cancer quantificationally and intuitively.

Study on Traditional Chinese Medicine Medication Rules in the Treatment of Gastric Cancer based on Scale-free Network Analysis / 世界科学技术-中医药现代化

This article was aimed to study medication rules of traditional Chinese medicine (TCM) in the treatment of gastric cancer. The history information of 111 hospitalized cases from 2008 to 2011 was collected from the Department of Oncology of Guang'anmen Hospital with the clinical collection system. The medication rules in the treatment of gastric cancer by TCM were explored by the scale-free network analysis from several aspects, such as effects, classifications and compatibility relations of Chinese medicine. The analysis result of 111 gastric cancer cases showed that strengthening the body resistance was the main treatment principle. Liujunzi decoction was the most frequently used prescription. In TCM compatibility, deficiency-nourishing herbs were the main ingredients, especiallyqi-supplementing herbs which take the first place in the prescription. And the heat-reducing herbs, dampness-eliminating herbs, blood-activating and stasis-eliminating herbs, food stagnation resolving herbs and qi regulation herbs were also used in the combination. Core herbs in the prescription for the treatment of gastric cancer were Tuckahoe, Codonopsis pilosula, Astragalus root, Pericarpium Citri Reticulatae, Fructus Aurantii, Nidus Vespae and etc. TCM treatment, syndrome differentiation and medication are varied according to different therapeutic stages and disease stages. The result showed that scale-free network analysis had certain significance in the understanding of medication rules of TCM treatment of gastric cancer.

Parsing KEGG XML Files to Find Shared and Duplicate Compounds Contained in Metabolic Pathway Maps: A Graph-Theoretical Perspective

The basic graph layout technique, one of many visualization techniques, deals with the problem of positioning vertices in a way to maximize some measure of desirability in a graph. The technique is becoming critically important for further development of the field of systems biology. However, applying the appropriate automatic graph layout techniques to the genomic scale flow of metabolism requires an understanding of the characteristics and patterns of duplicate and shared vertices, which is crucial for bioinformatics software developers. In this paper, we provide the results of parsing KEGG XML files from a graph-theoretical perspective, for future research in the area of automatic layout techniques in biological pathway domains.

J2dpathway: A Global Metabolic Pathway Viewer with Node-Abstracting Features

The static approach of representing metabolic pathway diagrams offers no flexibility. Thus, many systems adopt automatic graph layout techniques to visualize the topological architecture of pathways. There are weaknesses, however, because automatically drawn figures are generally difficult to understand. The problem becomes even more serious when we attempt to visualize all of the information in a single, big picture, which usually results in a confusing diagram. To provide a partial solution to this thorny issue, we propose J2dpathway, a metabolic pathway atlas viewer that has node-abstracting features.

A Metabolic Pathway Drawing Algorithm for Reducing the Number of Edge Crossings

For the direct understanding of flow, pathway data are usually represented as directed graphs in biological journals and texts. Databases of metabolic pathways or signal transduction pathways inevitably contain these kinds of graphs to show the flow. KEGG, one of the representative pathway databases, uses the manually drawn figure which can not be easily maintained. Graph layout algorithms are applied for visualizing metabolic pathways in some databases, such as EcoCyc. Although these can express any changes of data in the real time, it exponentially increases the edge crossings according to the increase of nodes. For the understanding of genome scale flow of metabolism, it is very important to reduce the unnecessary edge crossings which exist in the automatic graph layout. We propose a metabolic pathway drawing algorithm for reducing the number of edge crossings by considering the fact that metabolic pathway graph is scale-free network. The experimental results show that the number of edge crossings is reduced about 37~40% by the consideration of scale-free network in contrast with non-considering scale-free network. And also we found that the increase of nodes do not always mean that there is an increase of edge crossings.