Multiplex dependence analysis of China's interprovincial virtual water based on an ecological network

The interprovincial circulation of goods and services has formed virtual water flows between regions, which can redistribute water resources. Based on previous virtual water trade research, this study further explored the multiple dependencies of virtual water, i.e., direct, indirect, and complete dependence. This study examined the direct, indirect, and complete dependence of virtual water between provinces in China by constructing multilayer dependence networks and identified the key regions and paths of virtual water trade network. The results showed direct dependence was the densest and had the largest overall dependence degree, but indirect dependence was the most stable and orderly. Second, the dominant provinces were Guangxi, Hunan, Sichuan, Xinjiang, and Anhui, referred to as "core‒five‒region," and the flow relevant to them accounted for approximately 30% of the virtual water. The seven provinces of Shanxi, Zhejiang, Shandong, Hubei, Guangdong, Shaanxi, and Gansu depend both directly and indirectly on the "core‒five‒region." Shanxi and Zhejiang have close direct and indirect dependence, with more than one of the "core‒five‒region." Guangdong was the province with the most direct and indirect input of virtual water from the "core‒five‒region." The study provides a scientific basis for multiregional identification for the collaborative management of water resources in China from the perspective of dependence.

Industrial and textile waste trade: Multilayer network and environmental policy effects

Waste management is an international enterprise, and it is important to understand global flows of recyclable materials. The Pollution Haven Hypothesis (PHH) suggests that waste moves from high income nations with stringent environmental policy to low income nations with less environmentally stringent policy, by exploiting low labor and regulatory costs. This paper assesses the PHH thesis for slag/dross and textiles (SDT) wastes in PHH through novel integration of the multilayer network and gravity models. The multilayer network model generates network effect that quantifies interlayer connections of multiple waste trade networks. Instead of North-South movement of waste, North-North, South-South, and even South-North are shown. Results from the gravity model indicate that stringent waste management policies reduce both waste exports and imports. PHH is not found for slags/dross where high income countries are importing the waste, contradicting PHH. On the other hand, PHH is more evident between highly connected hubs and havens in SDT waste trade networks.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

BACKGROUND

Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank.

RESULTS

We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics.

CONCLUSION

Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

Multilayer network analysis of dynamic network reconfiguration in patients with moderate-to-severe obstructive sleep apnea and its association with neurocognitive function

BACKGROUND

Brain functional network disruption and neurocognitive dysfunction have been reported in obstructive sleep apnea (OSA) patients. Nevertheless, most research studies static networks, while brain evolution continues dynamically.

PURPOSE

To investigate the characteristics of dynamical networks in moderate-to-severe OSA patients using multilayer network analysis of dynamic networks and compare their association with neurocognitive function.

METHODS

Twenty-seven moderate-to-severe OSA patients and twenty-five matched healthy controls (HCs) who completed the examination of the Epworth sleepiness scale (ESS), neurocognitive function, polysomnography, and functional magnetic resonance imaging (fMRI) were prospectively included. The dynamic variations of resting-state functional networks in both groups were described via network switching rate. Switching rates and their correlation with clinical parameters were analyzed.

RESULTS

At the global level, network switching rates were notably lower in the OSA group than in the HCs group (p = 0.002). More specifically, the differences include the default mode network (DMN), auditory network, and ventral attention network at the subnetwork level, and the right rolandic operculum, left middle temporal gyrus, and right precentral gyrus at the nodal level. Furthermore, these altered switching rates have a close correlation with ESS, sleep parameters, and neurocognitive function.

CONCLUSION

Patients with moderate-to-severe OSA showed lower network switching rates, especially in the DMN, auditory network, and ventral attention network. The disruption of dynamic functional networks may be a potentially crucial mechanism of neurocognitive dysfunction in moderate-to-severe OSA patients.

Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach

Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information.

BRANEnet: embedding multilayer networks for omics data integration

Background

Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.

Results

We test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04955-w.

The impact of spatial and social structure on an SIR epidemic on a weighted multilayer network

A key factor in the transmission of infectious diseases is the structure of disease transmitting contacts. In the context of the current COVID-19 pandemic and with some data based on the Hungarian population we develop a theoretical epidemic model (susceptible-infected-removed, SIR) on a multilayer network. The layers include the Hungarian household structure, with population divided into children, adults and elderly, as well as schools and workplaces, some spatial embedding and community transmission due to sharing communal spaces, service and public spaces. We investigate the sensitivity of the model (via the time evolution and final size of the epidemic) to the different contact layers and we map out the relation between peak prevalence and final epidemic size. When compared to the classic compartmental model and for the same final epidemic size, we find that epidemics on multilayer network lead to higher peak prevalence meaning that the risk of overwhelming the health care system is higher. Based on our model we found that keeping cliques/bubbles in school as isolated as possible has a major effect while closing workplaces had a mild effect as long as workplaces are of relatively small size.

A multilayer network-enabled ultrasonic image series analysis approach for online cancer drug delivery monitoring

The objective of this study is to develop an effective data-driven methodology for the online monitoring of cancer drug delivery guided by the ultrasonic images. To achieve this goal, effective image quantification and accurate feature extraction play a critical role on image-guided drug delivery (IGDD) monitoring. However, the existing image-guided approaches in such area are mainly focused on the analysis for individual images rather than the image series. In fact, the temporal patterns between consecutive images may contain critical information and it is necessary to be considered in the monitoring analysis. In addition, the conventional approaches, such as the pure intensity-based method, also do not sufficiently consider the effects of noise in the ultrasonic images, which also limits the monitoring sensitivity and accuracy. To address the challenges, this paper proposed a novel multilayer network-enabled IGDD (MNE-IGDD) monitoring approach. The contributions of the proposed method can be summarized into three aspects: (1) formulate the sequential ultrasound images to a multilayer network by the proposed spatial-regularized distance; (2) detect drug delivery area based on community detection algorithm of multilayer network; and (3) quantify the drug delivery progress by incorporating the image intensity-based features with the detected community. Both the detected communities and feature increment percentages are applied as the evaluation metric for validation. A simulation study was conducted and this method was also applied to a real-world mouse colon tumor treatment case study under three temperature conditions. Both simulation and the real-world case studies demonstrated that the proposed method is promising to achieve satisfactory monitoring performance in clinical trials.

Identifying the essential nodes in network pharmacology based on multilayer network combined with random walk algorithm

Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions.

Prediction of drug response in multilayer networks based on fusion of multiomics data

Predicting the response of each individual patient to a drug is a key issue assailing personalized medicine. Our study predicted drug response based on the fusion of multiomics data with low-dimensional feature vector representation on a multilayer network model. We named this new method DREMO (Drug Response prEdiction based on MultiOmics data fusion). DREMO fuses similarities between cell lines and similarities between drugs, thereby improving the ability to predict the response of cancer cell lines to therapeutic agents. First, a multilayer similarity network related to cell lines and drugs was constructed based on gene expression profiles, somatic mutation, copy number variation (CNV), drug chemical structures, and drug targets. Next, low-dimensional feature vector representation was used to fuse the biological information in the multilayer network. Then, a machine learning model was applied to predict new drug-cell line associations. Finally, our results were validated using the well-established GDSC/CCLE databases, literature, and the functional pathway database. Furthermore, a comparison was made between DREMO and other methods. Results of the comparison showed that DREMO improves predictive capabilities significantly.

Ten principles to integrate the water-energy-land nexus with climate services for co-producing local and regional integrated assessments

The water-energy-land nexus requires long-sighted approaches that help avoid maladaptive pathways to ensure its promise to deliver insights and tools that improve policy-making. Climate services can form the foundation to avoid myopia in nexus studies by providing information about how climate change will alter the balance of nexus resources and the nature of their interactions. Nexus studies can help climate services by providing information about the implications of climate-informed decisions for other economic sectors across nexus resources. First-of-its-kind guidance is provided to combine nexus studies and climate services. The guidance consists of ten principles and a visual guide, which are discussed together with questions to compare diverse case studies and with examples to support the application of the principles.

A multiplex financial network approach to policy evaluation: the case of euro area Quantitative Easing

Over the last decades, both advanced and emerging economies have experienced a striking increase in the intra-financial activity across different asset classes and increasingly complex contract types, leading to a far more complex financial system. Until the 2007-2008 crisis, the increased financial intensity and complexity was believed beneficial in making the financial system more resilient and less vulnerable to shocks. However, in 2007-2008, the advanced economies suffered the biggest financial crisis since the 1930s, followed by a severe post-crisis recession, questioning the adequacy of traditional tools in predicting, explaining, and responding to periods of financial distress. In particular, the effect of complex interconnections among financial actors on financial stability has been widely acknowledged. A recent debate focused on the effects of unconventional policies aimed at achieving both price and financial stability. Among these unconventional policies, Quantitative Easing (QE, i.e., the large-scale asset purchase programme conducted by a central bank upon the creation of new money) has been recently implemented by the European Central Bank (ECB). In this context, two questions deserve more attention in the literature. First, to what extent, the resources provided to the banking system through QE are transmitted to the real economy. Second, to what extent, the QE may also alter the pattern of intra-financial exposures and what are the implications in terms of financial stability. Here, we address these two questions by developing a methodology to map the multilayer macro-network of financial exposures among institutional sectors across financial instruments (i.e., loans and deposits, debt securities, and equity), and we illustrate our approach on recently available data. We then test the effect of the implementation of ECB's QE on the time evolution of the financial linkages in the multilayer macro-network of the euro area, as well as the effect on macroeconomic variables, such as consumption, investment, unemployment, growth, and inflation.

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural oscillations and anatomical-connectivity maps, as derived from functional and diffusion MRI, in a multilayer-graph framework that captures transient networks of spatio-temporal connectivity. These networks group anatomically wired and temporary synchronized brain regions and encode the propagation of functional activity on the structural connectome. In a group of 71 healthy subjects, we find that these transient networks demonstrate power-law spatial and temporal size, globally organize into well-known functional systems and describe wave-like trajectories of activation across anatomically connected regions. Within the transient networks, activity propagates through polysynaptic paths that include selective ensembles of structural connections and differ from the structural shortest paths. In the light of the communication-through-coherence principle, the identified spatio-temporal networks could encode communication channels' selection and neural assemblies, which deserves further attention. This work contributes to the understanding of brain structure-function relationships by considering the time-varying nature of resting-state interactions on the axonal scaffold, and it offers a convenient framework to study large-scale communication mechanisms and functional dynamics.