Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signalling networks.

Many genes and signalling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signalling networks that must faithfully complete a developmental programme while also defending against parasites, and trait signalling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.

Fuzzy Logic System Assisted Sensing Resource Allocation for Optical Fiber Sensing and Communication Integrated Network.

With the development of information transmission, there is an increasing demand for state monitoring of fiber-optic communication networks to improve the security and self-healing ability of the network. Distributed optical fiber sensing is one of the most attractive methods because it can achieve real-time detection of the whole network without additional sensing heads. However, when the sensing network is introduced into the communication network, the failure probability should be efficiently suppressed with limited sensing resources. In this paper, the fuzzy logic system is used to evaluate the impact of different sensing resource allocation on optical cable network quality. The link failure probability and path failure probability under the condition of uniform and non-uniform sensing resource allocation are simulated and analyzed, respectively. As shown in the analysis results, the failure probability under non-uniform allocation is significantly lower than under uniform allocation. In this paper, we discussed and addressed the allocation of the optical fiber sensing and communication integrated (OFSCI) network with the limited sensing resource for the first time. The results are helpful to develop an allocation strategy for optical fiber sensing and a communication integrated network with a higher robustness.

Clarifying How Degree Entropies and Degree-Degree Correlations Relate to Network Robustness.

It is often claimed that the entropy of a network's degree distribution is a proxy for its robustness. Here, we clarify the link between degree distribution entropy and giant component robustness to node removal by showing that the former merely sets a lower bound to the latter for randomly configured networks when no other network characteristics are specified. Furthermore, we show that, for networks of fixed expected degree that follow degree distributions of the same form, the degree distribution entropy is not indicative of robustness. By contrast, we show that the remaining degree entropy and robustness have a positive monotonic relationship and give an analytic expression for the remaining degree entropy of the log-normal distribution. We also show that degree-degree correlations are not by themselves indicative of a network's robustness for real networks. We propose an adjustment to how mutual information is measured which better encapsulates structural properties related to robustness.

Spatiotemporal variation of the worldwide air transportation network induced by COVID-19 pandemic in 2020.

This paper studies the spatiotemporal variation of the worldwide air transportation network (WATN) induced by the COVID-19 pandemic in 2020. The variations are captured from four perspectives: passenger throughput, network connectivity, airport centrality, and international connections. Further, this work also considers both global and local connectivity-based metrics for the network analysis. Supported by real-world data, we show that the performance of the WATN has experienced a dynamic pattern of decline and recovery in 2020. Interestingly, the network metrics undergo tremendous variations in a very short period after the World Health Organization declared COVID-19 as a pandemic, with the number of flights and connections dropping by more than 40% within only the first four weeks. Intuitively, the passenger throughput's changing rate is highly correlated to confirmed cases' growth rate during the early period of the COVID-19 outbreak. However, the air transport response to the pandemic condition is very diverse among different countries. The major airports in the WATN fluctuate gradually in different pandemic stages, which is further influenced by the domestic pandemic situation that restricts airport operations. Also, the restoration speed of local connectivity is faster than that of global connectivity because the recovery of international aviation is geographically dependent on different policies of travel restriction, conditional openings, and the number of COVID-19 cases. The analysis deepens our understanding to formulate bilateral policies for pandemic-induced ATN design and management.

Robustness of Internet of Battlefield Things (IoBT): A Directed Network Perspective.

Through the combination of various intelligent devices and the Internet to form a large-scale network, the Internet of Things (IoT) realizes real-time information exchange and communication between devices. IoT technology is expected to play an essential role in improving the combat effectiveness and situation awareness ability of armies. The interconnection between combat equipment and other battlefield resources is referred to as the Internet of Battlefield Things (IoBT). Battlefield real-time data sharing and the cooperative decision-making among commanders are highly dependent on the connectivity between different combat units in the network. However, due to the wireless characteristics of communication, a large number of communication links are directly exposed in the complex battlefield environment, and various cyber or physical attacks threaten network connectivity. Therefore, the ability to maintain network connectivity under adversary attacks is a critical property for the IoBT. In this work, we propose a directed network model and connectivity measurement of the IoBT network. Then, we develop an optimal attack strategy optimization model to simulate the optimal attack behavior of the enemy. By comparing with the disintegration effect of some benchmark strategies, we verify the optimality of the model solution and find that the robustness of the IoBT network decreases rapidly with an increase of the unidirectional communication links in the network. The results show that the adversary will change the attack mode according to the parameter settings of attack resources and network communication link density. In order to enhance the network robustness, we need to adjust the defense strategy in time to deal with this change. Finally, we validated the model and theoretical analysis proposed in this paper through experiments on a real military network.

Loss of functional connectivity in migration networks induces population decline in migratory birds.

Migratory birds rely on a habitat network along their migration routes by temporarily occupying stopover sites between breeding and non-breeding grounds. Removal or degradation of stopover sites in a network might impede movement and thereby reduce migration success and survival. The extent to which the breakdown of migration networks, due to changes in land use, impacts the population sizes of migratory birds is poorly understood. We measured the functional connectivity of migration networks of waterfowl species that migrate over the East Asian-Australasian Flyway from 1992 to 2015. We analysed the relationship between changes in non-breeding population sizes and changes in functional connectivity, while taking into account other commonly considered species traits, using a phylogenetic linear mixed model. We found that population sizes significantly declined with a reduction in the functional connectivity of migration networks; no other variables were important. We conclude that the current decrease in functional connectivity, due to habitat loss and degradation in migration networks, can negatively and crucially impact population sizes of migratory birds. Our findings provide new insights into the underlying mechanisms that affect population trends of migratory birds under environmental changes. Establishment of international agreements leading to the creation of systematic conservation networks associated with migratory species' distributions and stopover sites may safeguard migratory bird populations.

A simple stochastic model for complex coextinctions in mutualistic networks: robustness decreases with connectance.

Understanding and predicting species extinctions and coextinctions is a major goal of ecological research in the face of a biodiversity crisis. Typically, models based on network topology are used to simulate coextinctions in mutualistic networks. However, such topological models neglect two key biological features of species interactions: variation in the intrinsic dependence of species on the mutualism, and variation in the relative importance of each interacting partner. By incorporating both types of variation, we developed a stochastic coextinction model capable of simulating extinction cascades far more complex than those observed in previous topological models. Using a set of empirical mutualistic networks, we show that the traditional topological model may either underestimate or overestimate the number and likelihood of coextinctions, depending on the intrinsic dependence of species on the mutualism. More importantly, contrary to topological models, our stochastic model predicts extinction cascades to be more likely in highly connected mutualistic communities.

Changes in the land-use landscape pattern and ecological network of Xuzhou planning area.

Ongoing rapid urbanization has triggered significant changes in land use, rendering landscape patterns adversely impacted and certain habitat patches degraded. Ecological networks have consequently contracted overall. As such, an investigation into how land-use landscape patterns and ecological networks change over time and space is of major significance for ecological restoration and regional sustainability. Taking Xuzhou Planning Area as a case study, we examined spatiotemporal changes and features of the landscape pattern by employing the land-use change degree, the land-use transition matrix, and quantified landscape pattern indices. An ecological network analysis, which studies the changes in network connectivity and robustness, as well as their causes and contributors, was undertaken to probe into the features and trends of spatiotemporal changes in the land-use landscape pattern and ecological network amid expeditious urbanization. Analysis results unveiled the following: (1) From 1985 to 2020, there was a decline in the area of farmland, forest, and grassland, accompanied by an increase in land for construction, water bodies, and unused land. The southwestern research area witnessed farmland substantially give way to land for construction for this period, and the most dramatic change in land use occurred between 2000 and 2010. (2) The area of dominant patches in the research area shrank, along with more fragmented, complex landscapes. The land for construction was emerging as the dominant landscape by area, whereas patches of farmland, forest, grassland, and water bodies became less connected. (3) The ecological network was densely linked in the northeast, with sparser connections in the southwest. Spatial shrinkage was observed in the research area's southwestern and central ecological corridors. Overall, the number of ecological sources and corridors rose and subsequently dropped before a rebound. (4) The ecological network grew more connected and robust from 1985 through 1990, as portions of farmland were converted into water bodies, which led to an increase in ecological sources. Given a reduction in ecological sources and corridors in the southwestern and central regions between 1990 and 2010, network connectivity and robustness declined, which was reversed from 2010 onward with the addition of two ecological sources-Pan'an Lake and Dugong Lake. With an optimal ecological network in 1990, however, it deteriorated significantly by 2010. The research area saw the minimum value of its network connectivity indices of network stability index (α), evenness index (ß), and connectivity index (γ), in 2010, when its ecological network was highly fragmented and vulnerable, attributing to a strong contrast between the maximal connected subgraph's relative size and connectivity robustness. The research findings can lay scientific groundwork for addressing ecological issues, restoring landscape patterns, and developing ecological networks amid urbanization.

Generic properties of the oligodendrocyte - Axon network.

The role of oligodendrocytes (OLs) extends beyond saltatory conduction to a modulatory role in neural information processing. Given this exalted role, we take first steps to frame the OL - axon interaction as a network of cells. We find that the OL - axon network has a natural encoding as a bipartite network, allowing us to determine key network properties, estimate the number of OLs or axons in various brain regions and determine the robustness of the network to random removal of cell nodes.

Quantification of the robustness of functional neural networks: application to the characterization of Alzheimer's disease continuum.

Objective.Brain connectivity networks are usually characterized in terms of properties coming from the complex network theory. Using new measures to summarize the attributes of functional connectivity networks can be an important step for their better understanding and characterization, as well as to comprehend the alterations associated with neuropsychiatric and neurodegenerative disorders. In this context, the main objective of this study was to introduce a novel methodology to evaluate network robustness, which was subsequently applied to characterize the brain activity in the Alzheimer's disease (AD) continuum.Approach.Functional connectivity networks were built using 478 electroencephalographic and magnetoencephalographic resting-state recordings from three different databases. These functional connectivity networks computed in the conventional frequency bands were modified simulating an iterative attack procedure using six different strategies. The network changes caused by these attacks were evaluated by means of Spearman's correlation. The obtained results at the conventional frequency bands were aggregated in a correlation surface, which was characterized in terms of four gradient distribution properties: mean, variance, skewness, and kurtosis.Main results.The new proposed methodology was able to consistently quantify network robustness. Our results showed statistically significant differences in the inherent ability of the network to deal with attacks (i.e. differences in network robustness) between controls, mild cognitive impairment subjects, and AD patients for the three different databases. In addition, we found a significant correlation between mini-mental state examination scores and the changes in network robustness.Significance.To the best of our knowledge, this is the first study which assesses the robustness of the functional connectivity network in the AD continuum. Our findings consistently evidence the loss of network robustness as the AD progresses for the three databases. Furthermore, the changes in this complex network property may be related with the progressive deterioration in brain functioning due to AD.

Modules in robust but low-efficiency phyllosphere fungal networks drive saponin accumulation in leaves of different Panax species.

BACKGROUND: The phyllosphere mycobiome plays a crucial role in plant fitness and ecosystem functions. The complex microbial ecological networks (MEN) formed by these fungi remain poorly understood, particularly with regard to their organization strategy and their contributions to plant secondary metabolites such as saponin. RESULTS: In this study, we constructed six MENs from leaf epiphytic and endophytic mycobiomes of three Panax species distributed in the northeast and southwest ends of mainland China. Hub nodes were absent in these MENs, which were significantly more complex, robust, and less efficient compared to random graphs (P < 0.05), indicating a hub-independent high-robustness strategy to maintain structural homeostasis. The important roles of specific MEN modules in shaping leaf saponin profiles of each Panax species were proved by multiple machine learning algorithms. Positive regulation modules (PRMs) of total saponin content were further identified, which exhibited more deterministic ecological assembly and comprised of highly connected nodes as well as higher proportion of plant-associated fungal guilds compared to other network members, indicating their tight links with host plant. The significant and direct effects (P < 0.05) of PRMs on total saponin accumulation were validated by well-fitted structural equation models (χ2 < 0.3, P > 0.5). Taxonomic analysis revealed that Pleosporales and Chaetothyriales were significantly overrepresented by positive regulation taxa (PRT) of total saponin content (FDR < 0.05). Across PRT identified in three Panax species, Epicoccum and Coniothyrium were conservatively present, while species-specific taxa such as Agaricales were also found, indicating the conservatism and specificity of plant-fungi interactions associated with leaf saponin accumulation in Panax genus. CONCLUSIONS: These findings provide a foundation for understanding mechanisms maintaining the steady state of phyllosphere mycobiome in healthy plant, and offer clues for engineering phyllosphere mycobiome to improve the accumulation of bioactive secondary metabolites on the basis of network modules.

Seasonal and elevational changes of plant-pollinator interaction networks in East African mountains.

Across an elevation gradient, several biotic and abiotic factors influence community assemblages of interacting species leading to a shift in species distribution, functioning, and ultimately topologies of species interaction networks. However, empirical studies of climate-driven seasonal and elevational changes in plant-pollinator networks are rare, particularly in tropical ecosystems. Eastern Afromontane Biodiversity Hotspots in Kenya, East Africa. We recorded plant-bee interactions at 50 study sites between 515 and 2600 m asl for a full year, following all four major seasons in this region. We analysed elevational and seasonal network patterns using generalised additive models (GAMs) and quantified the influence of climate, floral resource availability, and bee diversity on network structures using a multimodel inference framework. We recorded 16,741 interactions among 186 bee and 314 plant species of which a majority involved interactions with honeybees. We found that nestedness and bee species specialisation of plant-bee interaction networks increased with elevation and that the relationships were consistent in the cold-dry and warm-wet seasons respectively. Link rewiring increased in the warm-wet season with elevation but remained indifferent in the cold-dry seasons. Conversely, network modularity and plant species were more specialised at lower elevations during both the cold-dry and warm-wet seasons, with higher values observed during the warm-wet seasons. We found flower and bee species diversity and abundance rather than direct effects of climate variables to best predict modularity, specialisation, and link rewiring in plant-bee-interaction networks. This study highlights changes in network architectures with elevation suggesting a potential sensitivity of plant-bee interactions with climate warming and changes in rainfall patterns along the elevation gradients of the Eastern Afromontane Biodiversity Hotspot.

Link importance assessment strategy based on improved k-core decomposition in complex networks.

Improving the effectiveness of target link importance assessment strategy has become an important research direction within the field of complex networks today. The reasearch shows that the link importance assessment strategy based on betweenness centrality is the current optimal solution, but its high computational complexity makes it difficult to meet the application requirements of large-scale networks. The k-core decomposition method, as a theoretical tool that can effectively analyze and characterize the topological properties of complex networks and systems, has been introduced to facilitate the generation of link importance assessment strategy and, based on this, a link importance assessment indicator link shell has been developed. The strategy achieves better results in numerical simulations. In this study, we incorporated topological overlap theory to further optimize the attack effect and propose a new link importance assessment indicator link topological shell called t-shell. Simulations using real world networks and scale-free networks show that t-shell based target link importance assessment strategies perform better than shell based strategies without increasing the computational complexity; this can provide new ideas for the study of large-scale network destruction strategies.

Diet drives the gut microbiome composition and assembly processes in winter migratory birds in the Poyang Lake wetland, China.

The complex gut bacterial communities may facilitate the function, distribution, and diversity of birds. For migratory birds, long-distance traveling poses selection pressures on their gut microbiota, ultimately affecting the birds' health, fitness, ecology, and evolution. However, our understanding of mechanisms that underlie the assembly of the gut microbiome of migratory birds is limited. In this study, the gut microbiota of winter migratory birds in the Poyang Lake wetland was characterized using MiSeq sequencing of 16S rRNA genes. The sampled bird included herbivorous, carnivorous, and omnivorous birds from a total of 17 species of 8 families. Our results showed that the gut microbiota of migratory birds was dominated by four major bacterial phyla: Firmicutes (47.8%), Proteobacteria (18.2%), Fusobacteria (12.6%), and Bacteroidetes (9.1%). Dietary specialization outweighed the phylogeny of birds as an important factor governing the gut microbiome, mainly through regulating the deterministic processes of homogeneous selection and stochastic processes of homogeneous dispersal balance. Moreover, the omnivorous had more bacterial diversity than the herbivorous and carnivorous. Microbial networks for the gut microbiome of the herbivorous and carnivorous were less integrated, i.e., had lower average node degree and greater decreased network stability upon node attack removal than those of the omnivorous birds. Our findings advance the understanding of host-microbiota interactions and the evolution of migratory bird dietary flexibility and diversification.

Age-related alterations of brain metabolic network based on [18F]FDG-PET of rats.

Using animal models to study the underlying mechanisms of aging will create a critical foundation from which to develop new interventions for aging-related brain disorders. Aging-related reorganization of the brain network has been described for the human brain based on functional, metabolic and structural connectivity. However, alterations in the brain metabolic network of aging rats remain unknown. Here, we submitted young and aged rats to [18F]fluorodeoxyglucose with positron emission tomography (18F-FDG PET) and constructed brain metabolic networks. The topological properties were detected, and the network robustness against random failures and targeted attacks was analyzed for age-group comparison. Compared with young rats, aged rats showed reduced betweenness centrality (BC) in the superior colliculus and a decreased degree (D) in the parietal association cortex. With regard to network robustness, the brain metabolic networks of aged rats were more vulnerable to simulated damage, which showed significantly lower local efficiency and clustering coefficients than those of the young rats against targeted attacks and random failures. The findings support the idea that aged rats have similar aging-related changes in the brain metabolic network to the human brain and can therefore be used as a model for aging studies to provide targets for potential therapies that promote healthy aging.

Grazing lowers soil multifunctionality but boosts soil microbial network complexity and stability in a subtropical grassland of China.

Introduction: Long-term grazing profoundly affects grassland ecosystems, whereas how the soil microbiome and multiple soil ecosystem functions alter in response to two-decades of grazing, especially how soil microbiome (diversity, composition, network complexity, and stability) forms soil multifunctionality is rarely addressed. Methods: We used a long-term buffalo grazing grassland to measure the responses of soil physicochemical attributes, stoichiometry, enzyme activities, soil microbial niche width, structure, functions, and networks to grazing in a subtropical grassland of Guizhou Plateau, China. Results: The evidence from this work suggested that grazing elevated the soil hardness, available calcium content, and available magnesium content by 6.5, 1.9, and 1.9 times (p = 0.00015-0.0160) and acid phosphatase activity, bulk density, pH by 59, 8, and 0.5 unit (p = 0.0014-0.0370), but decreased the soil water content, available phosphorus content, and multifunctionality by 47, 73, and 9-21% (p = 0.0250-0.0460), respectively. Grazing intensified the soil microbial carbon limitation (+78%, p = 0.0260) as indicated by the increased investment in the soil ß-glucosidase activity (+90%, p = 0.0120). Grazing enhanced the complexity and stability of the bacterial and fungal networks but reduced the bacterial Simpson diversity (p < 0.05). The bacterial diversity, network complexity, and stability had positive effects, while bacterial and fungal compositions had negative effects on multifunctionality. Discussions: This work is an original attempt to show that grazing lowered multifunctionality via the reduced bacterial diversity and shifted soil bacterial and fungal compositions rather than the enhanced bacterial and fungal network complexities and stability by grazing. Protecting the bacterial diversity from decreasing, optimizing the composition of bacteria and fungi, and enhancing the complexity and stability of bacterial network may be conducive to improving the soil multifunction of grazing grassland, on a subtropical grassland.

The type III secretion system effector network hypothesis.

Type III secretion system (T3SS) effectors are key virulence factors that underpin the infection strategy of many clinically important Gram-negative pathogens, including Salmonella enterica, Shigella spp., enteropathogenic and enterohemorrhagic Escherichia coli and their murine equivalent, Citrobacter rodentium. The cellular processes or proteins targeted by the effectors can be common to multiple pathogens or pathogen-specific. The main approach to understanding T3SS-mediated pathogenesis has been to determine the contribution of one effector at a time, with the aim of piecing together individual functions and unveiling infection mechanisms. However, in contrast to this prevailing approach, simultaneous deletion of multiple effectors revealed that they function as an interconnected network in vivo, uncovering effector codependency and context-dependent effector essentiality. This paradigm shift in T3SS biology is at the heart of this opinion article.

Herbivore camping reshapes the taxonomy, function and network of pasture soil microbial communities.

Although the effects of herbivore camping on soil physicochemical properties have been studied, whether the effects alter the soil microbial communities (e.g., composition, functions, taxonomic and functional diversities, network) remain unknown, especially below the surface. Here, using paired subsoil samples from half month-camping and non-camping, we showed for the first time that camping significantly changed the relative abundance of 21 bacterial phylotypes and five fungal phylotypes. Specifically, we observed significant increases in the relative abundance of putative chitinase and terpenes vanillin-decomposition genes, nitrite reduction function (nirB, nasA), decreases in the relative abundance of putative carbon fixation genes (ackA, PGK, and Pak), starch-decomposition gene (dexB), gene coding nitrogenase (anfG), and tetracycline resistance gene (tetB) for bacterial communities, and significant decreases in the relative abundance of animal endosymbiont and increases in the relative abundance of litter saprotroph and endophyte for fungal communities. However, camping did not significantly impact the taxonomic and functional diversity. The niche restriction was the main driving force of bacterial and fungal community assembly. Compared to no camping, camping increased the stability of bacterial networks but decreased the stability of fungal networks. Camping exerted a positive effect on the network by compressing the niche width and reduced the change in the network by reducing the niche overlap. Our results suggest that camping restructures the soil microbial composition, function, and network, and provides a novel insight into the effect of animal camping on soil microbial communities in grassland.

Exploration of the Specific Pathology of HXMM Tablet Against Retinal Injury Based on Drug Attack Model to Network Robustness.

Retinal degenerative diseases are related to retinal injury because of the activation of the complement cascade, oxidative stress-induced cell death mechanisms, dysfunctional mitochondria, chronic neuroinflammation, and production of the vascular endothelial growth factor. Anti-VEGF therapy demonstrates remarkable clinical effects and benefits in retinal degenerative disease patients. Hence, new drug development is necessary to treat patients with severe visual loss. He xue ming mu (HXMM) tablet is a CFDA-approved traditional Chinese medicine (TCM) for retinal degenerative diseases, which can alleviate the symptoms of age-related macular degeneration (AMD) and diabetic retinopathy (DR) alone or in combination with anti-VEGF agents. To elucidate the mechanisms of HXMM, a quantitative evaluation algorithm for the prediction of the effect of multi-target drugs on the disturbance of the disease network has been used for exploring the specific pathology of HXMM and TCM precision positioning. Compared with anti-VEGF agents, the drug disturbance of HXMM on the functional subnetwork shows that HXMM reduces the network robustness on the oxidative stress subnetwork and inflammatory subnetwork to exhibit the anti-oxidation and anti-inflammation activity. HXMM provides better protection to ARPE-19 cells against retinal injury after H2O2 treatment. HXMM can elevate GSH and reduce LDH levels to exhibit antioxidant activity and suppress the expression of IL-6 and TNF-α for anti-inflammatory activity, which is different from the anti-VEGF agent with strong anti-VEGF activity. The experimental result confirmed the accuracy of the computational prediction. The combination of bioinformatics prediction based on the drug attack on network robustness and experimental validation provides a new strategy for precision application of TCM.

Probabilistic robustness estimates for feed-forward neural networks.

Robustness of deep neural networks is a critical issue in practical applications. In the general case of feed-forward neural networks (including convolutional deep neural network architectures), under random noise attacks, we propose to study the probability that the output of the network deviates from its nominal value by a given threshold. We derive a simple concentration inequality for the propagation of the input uncertainty through the network using the Cramer-Chernoff method and estimates of the local variation of the neural network mapping computed at the training points. We further discuss and exploit the resulting condition on the network to regularize the loss function during training. Finally, we assess the proposed tail probability estimates empirically on various public datasets and show that the observed robustness is very well estimated by the proposed method.