The structure of bacteria-fungi bipartite networks along elevational gradients in contrasting climates.

Climate change is altering species distribution and modifying interactions in microbial communities. Understanding microbial community structure and their interactions is crucial to interpreting ecosystem responses to climate change. Here, we examined the assemblages of stream bacteria and fungi, and the associations between the two groups along elevational gradients in two regions with contrasting precipitation and temperature, that is the Galong and Qilian mountains of the Tibetan Plateau. In the wetter and warmer region, the species richness significantly increased and decreased with elevation for bacteria and fungi, respectively, while were nonsignificant in the drier and colder region. Their bipartite network structure was also different by showing significant increases in connectance and nestedness towards higher elevations only in the wetter and warmer region. In addition, these correlation network structure generally exhibited similar positive association with species richness in the wetter and warmer region and the drier and colder region. In the wetter and warmer region, climatic change along elevation was more important in determining connectance and nestedness, whereas microbial species richness exerted a stronger influence on network structure and robustness in the drier and colder region. These findings indicate substantial forthcoming changes in microbial diversity and network structure in warming climates, especially in wetter and warmer regions on Earth, advancing the understanding of microbial bipartite interactions' response to climate change.

More complex than you think: Taxonomic and temporal patterns of plant-pollinator networks of caraway (Carum carvi L.).

Caraway (Carum carvi L.) is a crop species that is gaining in importance in Europe, especially as a condiment and medicinal plant. Here, we present the plant-pollinator network of caraway in a central European agricultural landscape, focusing on two diverse potential pollinator taxa, Diptera: Brachycera (= true flies) and Hymenoptera (sawflies, bees, and wasps). We specifically studied qualitative differences in interactions between the two insect taxa as well as the intraday and intraseasonal variability of the network. Insect and pollen plant species determination was done via morphological identification and DNA (meta)barcoding. In total, 121 species representing 33 families of Hymenoptera and Brachycera were found to carry caraway pollen. These taxa included many nonhoneybee and nonhoverfly species, showing a wide taxonomic breadth of potential pollinators and a higher network complexity than previously anticipated. There are distinct qualitative differences between Brachycera and Hymenoptera networks, suggesting complementary roles of both taxa in the pollination of native and crop plants. Strong intraday differences in potential pollinator diversity make it necessary to collect insects and pollen at different times of the day to compile complete plant-pollinator networks. Intraseasonal analyses of the plant-pollinator network of caraway show the potential of caraway as an important food source for insect species with an activity peak in late summer.

Temperature-mediated microbial carbon utilization in China's lakes.

Microbes play an important role in aquatic carbon cycling but we have a limited understanding of their functional responses to changes in temperature across large geographic areas. Here, we explored how microbial communities utilized different carbon substrates and the underlying ecological mechanisms along a space-for-time substitution temperature gradient of future climate change. The gradient included 47 lakes from five major lake regions in China spanning a difference of nearly 15°C in mean annual temperatures (MAT). Our results indicated that lakes from warmer regions generally had lower values of variables related to carbon concentrations and greater carbon utilization than those from colder regions. The greater utilization of carbon substrates under higher temperatures could be attributed to changes in bacterial community composition, with a greater abundance of Cyanobacteria and Actinobacteriota and less Proteobacteria in warmer lake regions. We also found that the core species in microbial networks changed with increasing temperature, from Hydrogenophaga and Rhodobacteraceae, which inhibited the utilization of amino acids and carbohydrates, to the CL500-29-marine-group, which promoted the utilization of all almost carbon substrates. Overall, our findings suggest that temperature can mediate aquatic carbon utilization by changing the interactions between bacteria and individual carbon substrates, and the discovery of core species that affect carbon utilization provides insight into potential carbon sequestration within inland water bodies under future climate warming.

How network size strongly determines trophic specialisation: A technical comment on Luna et al. (2022).

Luna et al. (2022) concluded that the environment contributes to explaining specialisation in open plant-pollinator networks. When reproducing their study, we instead found that network size alone largely explained the variation in their specialisation metrics. Thus, we question whether empirical network specialisation is driven by the environment.

Many bee species, including rare species, are important for function of entire plant-pollinator networks.

It is important to understand how biodiversity, including that of rare species, affects ecosystem function. Here, we consider this question with regard to pollination. Studies of pollination function have typically focused on pollination of single plant species, or average pollination across plants, and typically find that pollination depends on a few common species. Here, we used data from 11 plant-bee visitation networks in New Jersey, USA, to ask whether the number of functionally important bee species changes as we consider function separately for each plant species in increasingly diverse plant communities. Using rarefaction analysis, we found the number of important bee species increased with the number of plant species. Overall, 2.5 to 7.6 times more bee species were important at the community scale, relative to the average plant species in the same community. This effect did not asymptote in any of our datasets, suggesting that even greater bee biodiversity is needed in real-world systems. Lastly, on average across plant communities, 25% of bee species that were important at the community scale were also numerically rare within their network, making this study one of the strongest empirical demonstrations to date of the functional importance of rare species.

Deciphering Eurasian otter (Lutra lutra L.) and seal (Phoca vitulina L.; Halichoerus grypus F.) diet: Metabarcoding tailored for fresh and saltwater fish species.

Long-lived top predators shape biodiversity structure in their ecosystems and predator-prey interactions are critical in decoding how communities function. Studies on the foraging ecology of seals and Eurasian otters in Western Europe are outdated and most studies solely performed traditional hard part analysis. Molecular metabarcoding can be used as an innovative noninvasive diet analysis tool, which has proven efficient and complementary to hard part analysis, however, lacking application in the wider North Sea area. In this study, DNA from digesta, collected between 2014-2020, were used to identify fish species in the diet of 47 Eurasian otters, 54 harbour seals and 21 grey seals by applying a next-generation metabarcoding approach. A newly designed 16S rRNA primer, providing the best coverage of >130 local marine and freshwater fish species, was used to amplify prey DNA from seal scats and otter gut content sampled from the North Sea and regional freshwater bodies. Frequent fish species included tench, ninespine stickleback and white bream in otters; hooknose and common roach in grey seals and Pleuronectidae and sand gobies in harbour seals. Bipartite network analysis showed a strong overlap of harbour and grey seal diets. Otter diet intersected with both seal species in terms of freshwater species. This study provides new knowledge about dietary composition and community assemblage of fish prey in otters and seals in the North Sea and regional freshwaters, and a new molecular tool to elucidate predator-prey interactions and interspecies competition in complex and changing ecosystems under pressure from anthropogenic activities.

Social Network Analytics for Supervised Fraud Detection in Insurance.

Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. Next, we establish fraud as a social phenomenon in the network and use the BiRank algorithm with a fraud-specific query vector to compute a fraud score for each claim. From the network, we extract features related to the fraud scores as well as the claims' neighborhood structure. Finally, we combine these network features with the claim-specific features and build a supervised model with fraud in motor insurance as the target variable. Although we build a model for only motor insurance, the network includes claims from all available lines of business. Our results show that models with features derived from the network perform well when detecting fraud and even outperform the models using only the classical claim-specific features. Combining network and claim-specific features further improves the performance of supervised learning models to detect fraud. The resulting model flags highly suspicions claims that need to be further investigated. Our approach provides a guided and intelligent selection of claims and contributes to a more effective fraud investigation process.

Species-habitat networks elucidate landscape effects on habitat specialisation of natural enemies and pollinators.

Measuring habitat specialisation is pivotal for predicting species extinctions and for understanding consequences on ecosystem functioning. Here, we sampled pollinator and natural enemy communities in all major habitat types occurring across multiple agricultural landscapes and used species-habitat networks to determine how habitat specialisation changed along gradients in landscape composition and configuration. Although it is well known that landscape simplification often causes the replacement of specialists with generalists, our study provided evidence for intraspecific variation in habitat specialisation, highlighting how a large number of arthropod species adapted their way of selecting habitat resources depending on the landscape structure. Groups with higher diet specialisation and limited foraging flexibility appeared to have a reduced ability to respond to landscape changes, indicating that some arthropod taxa are better able than others to adapt to an increasingly broad set of resources and persist in highly impacted landscapes.

Disease dynamics and mean field models for clustered networks.

Social networks are clustered networks with short mean path length. In this work we analyze the disease dynamics in a class of this type of small-world networks composed of set of households and a set of workplaces. Individuals from each household are randomly assigned to workplaces. In both environments we assumed complete mixing and therefore we obtain highly clustered networks with short mean path lengths. Basic reproduction numbers were computed numerically and we show that at endemic equilibrium the average susceptible proportion is different from the inverse of the basic reproduction number (R0-1). Therefore exist an exponent p≠1 for which p=R0-1. Using this exponent we developed a mean field model which closely capture the disease dynamics in the network. Finally we outline how this model could be use to model vector-borne diseases in social networks.

Cancer-mutation network and the number and specificity of driver mutations.

Cancer genomics has produced extensive information on cancer-associated genes, but the number and specificity of cancer-driver mutations remains a matter of debate. We constructed a bipartite network in which 7,665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer genes. We show that about 27% of the tumors can be assigned to statistically supported modules, most of which encompass one or two cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene specificity of driver mutations. Linear regression of the mutational loads in cancer genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers in known cancer genes is approximately two, with a range of one to five. Cancers that are associated with modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

Exploring regulation in tissues with eQTL networks.

Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA-disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA-disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses.

BACKGROUND: Recent studies have shown that epigenetic differences can increase the risk of spontaneous preterm birth (PTB). However, little is known about heterogeneity underlying such epigenetic differences, which could lead to hypotheses for biological pathways in specific patient subgroups, and corresponding targeted interventions critical for precision medicine. Using bipartite network analysis of fetal DNA methylation data we demonstrate a novel method for classification of PTB. METHODS: The data consisted of DNA methylation across the genome (HumanMethylation450 BeadChip) in cord blood from 50 African-American subjects consisting of 22 cases of early spontaneous PTB (24-34 weeks of gestation) and 28 controls (>39 weeks of gestation). These data were analyzed using a combination of (1) a supervised method to select the top 10 significant methylation sites, (2) unsupervised "subject-variable" bipartite networks to visualize and quantitatively analyze how those 10 methylation sites co-occurred across all the subjects, and across only the cases with the goal of analyzing subgroups and their underlying pathways, and (3) a simple linear regression to test whether there was an association between the total methylation in the cases, and gestational age. RESULTS: The bipartite network analysis of all subjects and significant methylation sites revealed statistically significant clustering consisting of an inverse symmetrical relationship in the methylation profiles between a case-enriched subgroup and a control-enriched subgroup: the former was predominantly hypermethylated across seven methylation sites, and hypomethylated across three methylation sites, whereas the latter was predominantly hypomethylated across the above seven methylation sites and hypermethylated across the three methylation sites. Furthermore, the analysis of only cases revealed one subgroup that was predominantly hypomethylated across seven methylation sites, and another subgroup that was hypomethylated across all methylation sites suggesting the presence of heterogeneity in PTB pathophysiology. Finally, the analysis found a strong inverse linear relationship between total methylation and gestational age suggesting that methylation differences could be used as predictive markers for gestational length. CONCLUSIONS: The results demonstrate that unsupervised bipartite networks helped to identify a complex but comprehensible data-driven hypotheses related to patient subgroups and inferences about their underlying pathways, and therefore were an effective complement to supervised approaches currently used.

Link Prediction in Bipartite Nested Networks.

Real networks typically studied in various research fields-ecology and economic complexity, for example-often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.

A New and Stable Estimation Method of Country Economic Fitness and Product Complexity.

We present a new metric estimating fitness of countries and complexity of products by exploiting a non-linear non-homogeneous map applied to the publicly available information on the goods exported by a country. The non homogeneous terms guarantee both convergence and stability. After a suitable rescaling of the relevant quantities, the non homogeneous terms are eventually set to zero so that this new metric is parameter free. This new map almost reproduces the results of the original homogeneous metrics already defined in literature and allows for an approximate analytic solution in case of actual binarized matrices based on the Revealed Comparative Advantage (RCA) indicator. This solution is connected with a new quantity describing the neighborhood of nodes in bipartite graphs, representing in this work the relations between countries and exported products. Moreover, we define the new indicator of country net-efficiency quantifying how a country efficiently invests in capabilities able to generate innovative complex high quality products. Eventually, we demonstrate analytically the local convergence of the algorithm involved.

Network structure and local adaptation in co-evolving bacteria-phage interactions.

Numerous theoretical and experimental studies have investigated antagonistic co-evolution between parasites and their hosts. Although experimental tests of theory from a range of biological systems are largely concordant regarding the influence of several driving processes, we know little as to how mechanisms acting at the smallest scales (individual molecular and phenotypic changes) may result in the emergence of structures at larger scales, such as co-evolutionary dynamics and local adaptation. We capitalized on methods commonly employed in community ecology to quantify how the structure of community interaction matrices, so-called bipartite networks, reflected observed co-evolutionary dynamics, and how phages from these communities may or may not have adapted locally to their bacterial hosts. We found a consistent nested network structure for two phage types, one previously demonstrated to exhibit arms race co-evolutionary dynamics and the other fluctuating co-evolutionary dynamics. Both phages increased their host ranges through evolutionary time, but we found no evidence for a trade-off with impact on bacteria. Finally, only bacteria from the arms race phage showed local adaptation, and we provide preliminary evidence that these bacteria underwent (sometimes different) molecular changes in the wzy gene associated with the LPS receptor, while bacteria co-evolving with the fluctuating selection phage did not show local adaptation and had partial deletions of the pilF gene associated with type IV pili. We conclude that the structure of phage-bacteria interaction networks is not necessarily specific to co-evolutionary dynamics, and discuss hypotheses for why only one of the two phages was, nevertheless, locally adapted.

Ecological, historical and evolutionary determinants of modularity in weighted seed-dispersal networks.

Modularity is a recurrent and important property of bipartite ecological networks. Although well-resolved ecological networks describe interaction frequencies between species pairs, modularity of bipartite networks has been analysed only on the basis of binary presence-absence data. We employ a new algorithm to detect modularity in weighted bipartite networks in a global analysis of avian seed-dispersal networks. We define roles of species, such as connector values, for weighted and binary networks and associate them with avian species traits and phylogeny. The weighted, but not binary, analysis identified a positive relationship between climatic seasonality and modularity, whereas past climate stability and phylogenetic signal were only weakly related to modularity. Connector values were associated with foraging behaviour and were phylogenetically conserved. The weighted modularity analysis demonstrates the dominating impact of ecological factors on the structure of seed-dispersal networks, but also underscores the relevance of evolutionary history in shaping species roles in ecological communities.

Complex Traits Heritability is Highly Clustered in the eQTL Bipartite Network.

Single Nucleotide Polymorphisms (SNPs) associated with traits typically explain a small part of the trait genetic heritability-with the remainder thought to be distributed throughout the genome. Such SNPs are likely to alter expression levels of biologically relevant genes. Expression Quantitative Trait Locus (eQTL) networks analysis has helped to functionally characterize such variants. We systematically analyze the distribution of SNP heritability for ten traits across 29 tissue-specific eQTL networks. We find that heritability is clustered in a small number or tissue-specific, functionally relevant SNP-gene modules and that the greatest occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. Together, these results define a conceptual framework for understanding complex trait architecture and identifying key mutations carrying most of the heritability.

Research on systemic risk of China's bank-asset bipartite network.

Systemic risk caused by banks due to common asset holdings serve as a significant contagion channel. In this study, we use empirical data from Chinese banks to construct a bank-asset bipartite network, employ the DebtRank algorithm for risk measurement, and incorporate asset price correlation into the DebtRank algorithm. Then we show the changes of the systemic risk in the Chinese banking system from 2018 to 2021. Furthermore, we analyze the systemic risk triggered by different types of banks and different industry assets and quantify the impact of each asset under different stress scenarios. We also conduct a validity analysis of asset price correlation, finding that the systemic risk considering asset price correlation is higher than that without considering asset price correlation. This study of financial systemic risk under the bank-asset bipartite network provides a new perspective for the regulation of systemic risk and is of significant importance for the prevention of systemic risk.

Mechanisms of multi-strain coexistence in host-phage systems with nested infection networks.

Bacteria and their viruses (bacteriophages) coexist in natural environments forming complex infection networks. Recent empirical findings suggest that phage-bacteria infection networks often possess a nested structure such that there is a hierarchical relationship among who can infect whom. Here we consider how nested infection networks may affect phage and bacteria dynamics using a multi-type Lotka-Volterra framework with cross-infection. Analysis of similar models has, in the past, assumed simpler interaction structures as a first step towards tractability. We solve the proposed model, finding trade-off conditions on the life-history traits of both bacteria and viruses that allow coexistence in communities with nested infection networks. First, we find that bacterial growth rate should decrease with increasing defense against infection. Second, we find that the efficiency of viral infection should decrease with host range. Next, we establish a relationship between relative densities and the curvature of life history trade-offs. We compare and contrast the current findings to the "Kill-the-Winner" model of multi-species phage-bacteria communities. Finally, we discuss a suite of testable hypotheses stemming from the current model concerning relationships between infection range, life history traits and coexistence in complex phage-bacteria communities.