Low-rank network decomposition reveals structural characteristics of small-world networks

Abnormal suppression of thermal transport by long-range interactions in networks

Heat and electricity are two fundamental forms of energy widely utilized in our daily lives. Recently, in the study of complex networks, there is growing evidence that they behave significantly different at the micro-nanoscale. Here, we use a small-world network model to investigate the effects of reconnection probability p and decay exponent α on thermal and electrical transport within the network. Our results demonstrate that the electrical transport efficiency increases by nearly one order of magnitude, while the thermal transport efficiency falls off a cliff by three to four orders of magnitude, breaking the traditional rule that shortcuts enhance energy transport in small-world networks. Furthermore, we elucidate that phonon localization is a crucial factor in the weakening of thermal transport efficiency in small-world networks by characterizing the density of states, phonon participation ratio, and nearest-neighbor spacing distribution. These insights will pave new ways for designing thermoelectric materials with high electrical conductance and low thermal conductance.

Increased precipitation alters the effects of nitrogen deposition on soil bacterial and fungal communities in a temperate forest

Anthropogenic nitrogen (N) deposition and increased precipitation are known to alter soil microbial communities. However, the combined effects of elevated N deposition and increased precipitation on soil microbial community dynamics and co-occurrence networks in temperate forests remain elusive. In this study, we conducted a field manipulation experiment by applying N solution and water to the forest canopy to simulate natural N deposition and increased precipitation in a temperate forest. We collected samples in the litter layer, organic soil layer, and mineral soil layer in 2018-2019 after 6-7 years of N and water treatments, and explored how elevated N deposition and increased precipitation regulate soil microbial diversity, community composition, and co-occurrence networks in different soil layers and at different sampling times. We found that the effects of N deposition and increased precipitation on soil microbial communities varied with soil layers and sampling times. Compared to the ambient environment, single canopy N addition (CN) or single canopy water addition (CW) did not affect bacterial Shannon diversity in the mineral soil layer in 2018, but the combined canopy N and water additions (CNW) decreased it in this layer at this time. CN increased fungal OTU richness in the organic and mineral soil layers in 2018; however, CW and CNW did not have an effect on it in the same layer at the same time. CW and CNW, but not CN, significantly affected bacterial and fungal community compositions in the litter layer in 2018 and in the organic soil layer in 2019. In contrast, CN, but not CW or CNW, significantly affected fungal community composition in the litter layer in 2019. CNW exhibited higher complexities of bacterial and fungal co-occurrence networks than CN and the ambient environment, indicating increased precipitation can strengthen the effect of N deposition on the complexity of bacterial and fungal co-occurrence networks. Our findings suggest that increased precipitation alters the effects of atmospheric N deposition on soil bacterial and fungal communities in this temperate forest, depending on soil layer and sampling time. Moreover, both bacterial and fungal community compositions are sensitive to increased precipitation, but the bacterial community composition is more sensitive to N deposition than the fungal community composition in the organic and mineral soil layers in this forest.

Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks

Reconstructing the recurrent structural connectivity of neuronal networks is a challenge crucial to address in characterizing neuronal computations. While directly measuring the detailed connectivity structure is generally prohibitive for large networks, we develop a novel framework for reverse-engineering large-scale recurrent network connectivity matrices from neuronal dynamics by utilizing the widespread sparsity of neuronal connections. We derive a linear input-output mapping that underlies the irregular dynamics of a model network composed of both excitatory and inhibitory integrate-and-fire neurons with pulse coupling, thereby relating network inputs to evoked neuronal activity. Using this embedded mapping and experimentally feasible measurements of the firing rate as well as voltage dynamics in response to a relatively small ensemble of random input stimuli, we efficiently reconstruct the recurrent network connectivity via compressive sensing techniques. Through analogous analysis, we then recover high dimensional natural stimuli from evoked neuronal network dynamics over a short time horizon. This work provides a generalizable methodology for rapidly recovering sparse neuronal network data and underlines the natural role of sparsity in facilitating the efficient encoding of network data in neuronal dynamics.

Bacterial community succession and influencing factors for Imperata cylindrica litter decomposition in a copper tailings area of China

Litter decomposition is a critical component of the ecological nutritional transformation process. In a copper mining area, the litter from Imperata cylindrica is the major indicator for restoring heavy metal-polluted copper mining lands. Large amounts of litter are generated at the end of the plant growing season during the process of vegetation restoration in copper mining areas, and the microbial dynamics play an important role in soil nutrient turnover during the decomposition of litter. Investigating the characteristics and interactions of bacterial communities during litter decomposition will clarify the driving mechanisms of organic matter and nutrient cycling in copper mining areas that harbor contaminated soils. Here, we report the results of an in situ decomposition experiment that lasted for a total of 460 days from three of the 16 copper mining subdams with heavy metal pollution and different phytoremediation histories (e.g., 50, 22 and 5 years) to explore the bacterial communities as the driving factors of litter decomposition. The total carbon contents of the litter decreased by 62.6% and 71.5% in the decomposition process at those sites with phytoremediation histories of 50 and 22 years (S516 and S536), respectively, but decreased by only 25.8% at the site with a phytoremediation history of 5 years (S560). The optimal C/N ratios in the three different restoration stages varied and were 65.5, 86.7 and 39.3 in S516, S536, S560, respectively. Litter decomposition enriched the heavy metal contents such as cadmium, copper (Cu), lead and zinc (P < 0.05) in litter. Proteobacteria and Actinobacteriota were the dominant bacterial phyla during the different litter decomposition stages, which accounted for 91.66% of the relative abundances in the bacterial communities. Moreover, the role of Friedmanniella, which had the highest betweenness centrality (BC) value, was critical in sustaining both the structure and function of the bacterial communities during the early decomposition stage. However, Quadrisphaera, with the maximum BC value (1074.8), became the dominant genus as litter decomposition progressed. The most crucial factors that affected the litter bacterial communities were the litter pH and copper contents. The obtained results will be helpful to provide a further understanding of litter decomposition mechanisms and will provide a scientific basis for improving the effectiveness of material circulation and nutrient transformation in degraded copper mining ecosystems.

Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making

The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual units that send both positive and negative feedback signals, we investigate the functional implications of network model dynamics that violate Dale's law by allowing each neuron to send out both excitatory and inhibitory signals to its neighbors. We show how balanced network dynamics, in which large excitatory and inhibitory inputs are dynamically adjusted such that input fluctuations produce irregular firing events, are theoretically preserved for a single population of neurons violating Dale's law. We further leverage this single-population network model in the context of two competing pools of neurons to demonstrate that effective decision-making dynamics are also produced, agreeing with experimental observations from honeybee dynamics in selecting a food source and artificial neural networks trained in optimal selection. Through direct comparison with the classical two-population balanced neuronal network, we argue that the one-population network demonstrates more robust balanced activity for systems with less computational units, such as honeybee colonies, whereas the two-population network exhibits a more rapid response to temporal variations in network inputs, as required by the brain. We expect this study will shed light on the role of neurons violating Dale's law found in experiment as well as shared design principles across biological systems that perform complex computations.

Searching for small-world and scale-free behaviour in long-term historical data of a real-world power grid

Since the introduction of small-world and scale-free properties, there is an ongoing discussion on how certain real-world networks fit into these network science categories. While the electrical power grid was among the most discussed examples of these real-word networks, published results are controversial, and studies usually fail to take the aspects of network evolution into consideration. Consequently, while there is a broad agreement that power grids are small-world networks and might show scale-free behaviour; although very few attempts have been made to find how these characteristics of the network are related to grid infrastructure development or other underlying phenomena. In this paper the authors use the 70-year-long historical dataset (1949-2019) of the Hungarian power grid to perform complex network analysis, which is the first attempt to evaluate small-world and scale-free properties on long-term real-world data. The results of the analysis suggest that power grids show small-world behaviour only after the introduction of multiple voltage levels. It is also demonstrated that the node distribution of the examined power grid does not show scale-free behaviour and that the scaling is stabilised around certain values after the initial phase of grid evolution.

Plant litter decomposition in wetlands is closely associated with phyllospheric fungi as revealed by microbial community dynamics and co-occurrence network

Phyllospheric microbes play a crucial role in the biological decomposition of plant litter in wetland ecosystems. Previous studies have mainly focused on single stages of decomposition process, and to date there have been no reports on dynamic changes in the composition of phyllospheric microbes during the multiple stages of decomposition from living plant to death. Here we investigated fungal and bacterial community succession in the leaf litter of Schoenoplectus tabernaemontani, a wetland plant species using sequencing of the both fungal ITS and bacterial 16S genes. Our results revealed that, over the whole period of decomposition, the fungal communities underwent more distinct succession than did the bacterial communities. Proteobacteria dominated throughout the entire period, while, across different decomposition stages, the Ascomycete fungi were gradually replaced by the Ciliophora and Rozellomycota as the dominant fungi. Network analysis revealed higher degrees of species segregation and shorter average path lengths between species of fungi compared with species of bacteria. This suggests that fungal communities may harbor more niches and functional diversity and are potentially more susceptible to external interference than are bacterial communities. During decomposition, the contents of leaf cellulose, hemicellulose and lignin in the litter were significantly (p < 0.01) correlated with the fungal communities, and abiotic factors accounted for 89.8% of the total variation in the fungal communities. In contract, abiotic factors only explained 6.10% of the total variation in bacterial communities, suggesting external environments as drivers of fungal community succession. Overall, we provide evidence that the complex litter decay in wetlands is the result of a dynamic cross-kingdom succession, and this process is accompanied by distinct phyllospheric fungal community dynamics.

Network structure and input integration in competing firing rate models for decision-making

Making a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characterizing decision making in the face of a large set of alternatives remains challenging. We explore this issue by formulating a scalable mechanistic network model for decision making and analyzing the dynamics evoked given various potential network structures. In the case of a fully-connected network, we provide an analytical characterization of the model fixed points and their stability with respect to winner-take-all behavior for fair tasks. We compare several means of input integration, demonstrating a more gradual sigmoidal transfer function is likely evolutionarily advantageous relative to binary gain commonly utilized in engineered systems. We show via asymptotic analysis and numerical simulation that sigmoidal transfer functions with smaller steepness yield faster response times but depreciation in accuracy. However, in the presence of noise or degradation of connections, a sigmoidal transfer function garners significantly more robust and accurate decision-making dynamics. For fair tasks and sigmoidal gain, our model network also exhibits a stable parameter regime that produces high accuracy and persists across tasks with diverse numbers of alternatives and difficulties, satisfying physiological energetic constraints. In the case of more sparse and structured network topologies, including random, regular, and small-world connectivity, we show the high-accuracy parameter regime persists for biologically realistic connection densities. Our work shows how neural system architecture is potentially optimal in making economic, reliable, and advantageous decisions across tasks.

Synchronization of conservative parallel discrete event simulations on a small-world network

We examine the question of the influence of sparse long-range communications on the synchronization in parallel discrete event simulations. We build a model of the evolution of local virtual times in a conservative algorithm including several choices of local links. All network realizations belong to the small-world network class. We find that synchronization depends on the average shortest path of the network. The time profile dynamics are similar to the surface profile growth, which helps to analyze synchronization effects using a statistical physics approach. Without long-range links of the nodes, the model belongs to the universality class of the Kardar-Parisi-Zhang equation for surface growth. We find that the critical exponents depend logarithmically on the fraction of long-range links. We present the results of simulations and discuss our observations.