Community structure in social and biological networks

Prescribed fire increases plant-pollinator network robustness to losses of rare native forbs

Restoration efforts often focus on changing the composition and structure of invaded plant communities, with two implicit assumptions: (1) functional interactions with species of other trophic levels, such as pollinators, will reassemble automatically when native plant diversity is restored and (2) restored communities will be more resilient to future stressors. However, the impact of restoration activities on pollinator richness, plant-pollinator interaction network structure, and network robustness is incompletely understood. Leveraging a restoration chronosequence in Pacific Northwest prairies, we examined the effects of restoration-focused prescribed fire and native forb replanting on floral resources, pollinator visitation, and plant-pollinator network structure. We then simulated the effects of plant species loss/removal scenarios on secondary extinction cascades in the networks. Specifically, we explored three management-relevant plant loss scenarios (removal of an abundant exotic forb, removal of an abundant forb designated a noxious weed, and loss of the rarest native forb) and compared them to control scenarios. Pyrodiversity and proportion of area recently burned increased the abundance and diversity of floral resources, with concomitant increases in pollinator visitation and diversity. Pyrodiversity also decreased network connectance and nestedness, increased modularity, and buffered networks against secondary extinction cascades. Rare forbs contributed disproportionately to network robustness in less restored prairies, while removal of typical "problem" plants like exotic and noxious species had relatively small impacts on network robustness, particularly in prairies with a long history of restoration activities. Restoration actions aimed mainly at improving the diversity and abundance of pollinator-provisioning plants may also produce plant-pollinator networks with increased resilience to plant species losses.

The Spread of Beliefs in Partially Modularized Communities

Many life-influencing social networks are characterized by considerable informational isolation. People within a community are far more likely to share beliefs than people who are part of different communities. The spread of useful information across communities is impeded by echo chambers (far greater connectivity within than between communities) and filter bubbles (more influence of beliefs by connected neighbors within than between communities). We apply the tools of network analysis to organize our understanding of the spread of beliefs across modularized communities and to predict the effect of individual and group parameters on the dynamics and distribution of beliefs. In our Spread of Beliefs in Modularized Communities (SBMC) framework, a stochastic block model generates social networks with variable degrees of modularity, beliefs have different observable utilities, individuals change their beliefs on the basis of summed or average evidence (or intermediate decision rules), and parameterized stochasticity introduces randomness into decisions. SBMC simulations show surprising patterns; for example, increasing out-group connectivity does not always improve group performance, adding randomness to decisions can promote performance, and decision rules that sum rather than average evidence can improve group performance, as measured by the average utility of beliefs that the agents adopt. Overall, the results suggest that intermediate degrees of belief exploration are beneficial for the spread of useful beliefs in a community, and so parameters that pull in opposite directions on an explore-exploit continuum are usefully paired.

Community detection with Greedy Modularity disassembly strategy

Community detection recognizes groups of densely connected nodes across networks, one of the fundamental procedures in network analysis. This research boosts the standard but locally optimized Greedy Modularity algorithm for community detection. We introduce innovative exploration techniques that include a variety of node and community disassembly strategies. These strategies include methods like non-triad creating, feeble, random as well as inadequate embeddedness for nodes, as well as low internal edge density, low triad participation ratio, weak, low conductance as well as random tactics for communities. We present a methodology that showcases the improvement in modularity across the wide variety of real-world and synthetic networks over the standard approaches. A detailed comparison against other well-known community detection algorithms further illustrates the better performance of our improved method. This study not only optimizes the process of community detection but also broadens the scope for a more nuanced and effective network analysis that may pave the way for more insights as to the dynamism and structures of its functioning by effectively addressing and overcoming the limitations that are inherently attached with the existing community detection algorithms.

Allostery in Protein Tyrosine Phosphatases is Enabled by Divergent Dynamics

Dynamics-driven allostery provides important insights into the working mechanics of proteins, especially enzymes. In this study, we employ this paradigm to answer a basic question: in enzyme superfamilies, where the catalytic mechanism, active sites, and protein fold are conserved, what accounts for the difference in the catalytic prowess of the individual members? We show that when subtle changes in sequence do not translate to changes in structure, they do translate to changes in dynamics. We use sequentially diverse PTP1B, TbPTP1, and YopH as representatives of the conserved protein tyrosine phosphatase (PTP) superfamily. Using amino acid network analysis of group behavior (community analysis) and influential node dominance on networks (eigenvector centrality), we explain the dynamic basis of the catalytic variations seen between the three proteins. Importantly, we explain how a dynamics-based blueprint makes PTP1B amenable to allosteric control and how the same is abstracted in TbPTP1 and YopH.

Emergent invariance and scaling properties in the collective return dynamics of a stock market

A key metric to determine the performance of a stock in a market is its return over different investment horizons (τ). Several works have observed heavy-tailed behavior in the distributions of returns in different markets, which are observable indicators of underlying complex dynamics. Such prior works study return distributions that are marginalized across the individual stocks in the market, and do not track statistics about the joint distributions of returns conditioned on different stocks, which would be useful for optimizing inter-stock asset allocation strategies. As a step towards this goal, we study emergent phenomena in the distributions of returns as captured by their pairwise correlations. In particular, we consider the pairwise (between stocks i, j) partial correlations of returns with respect to the market mode, ci,j(τ), (thus, correcting for the baseline return behavior of the market), over different time horizons (τ), and discover two novel emergent phenomena: (i) the standardized distributions of the ci,j(τ)'s are observed to be invariant of τ ranging from from 1000min (2.5 days) to 30000min (2.5 months); (ii) the scaling of the standard deviation of ci,j(τ)'s with τ admits good fits to simple model classes such as a power-law τ-λ or stretched exponential function [Formula: see text] (λ, β > 0). Moreover, the parameters governing these fits provide a summary view of market health: for instance, in years marked by unprecedented financial crises-for example 2008 and 2020-values of λ (scaling exponent) are substantially lower. Finally, we demonstrate that the observed emergent behavior cannot be adequately supported by existing generative frameworks such as single- and multi-factor models. We introduce a promising agent-based Vicsek model that closes this gap.

Design thinking for engaged learning in animal science: lessons from five semesters of a senior capstone course

This study presents a design-based research approach involving five iterations (semester) of implementing design thinking for engaged learning (DTEL) in an animal science capstone course. DTEL scaffolds design thinking into 10 stages for collaborative project-based learning to foster skills like problem solving and teamwork. Across five semesters (spring 2021 to spring 2023), student reflections (n = 276) were analyzed to identify aspects that worked well or were challenging. Network analysis visualized relationships (P < 0.05; Q > 0.4) between codes representing strengths, struggles, and alignment with principles from learning theories. Utilizing the relationships between strengths and theory-based principles to address struggles, resulted in changes to the design of the capstone course each iteration (time that the course was taught). The complexity of maps increased over iterations. Initially, struggles were prominent but decreased as responsive design refinements were made. Alignment of student experiences with principles from learning theories grew substantially from the first iteration to the last (theory-related nodes representing 11.4% vs. 24.4% in each network map, respectively), with learning theories also occupying more central positions in the last map (iteration five) compared to earlier ones (iterations one through four). These changes suggest student experiences increasingly aligned with principles of cognitive constructivism, social constructivism, constructionism, situated learning, and transformative learning. Design principles derived from the five-iteration study include: (1) allocating most time to hands-on lab work vs. lecture, (2) designating a coordinator faculty, (3) scaffolding for instructors unfamiliar with DTEL, (4) emphasizing consistency in processes over grades, and (5) intentionally developing teamwork skills. The study demonstrates the value of design-based research for iteratively refining and studying learning experiences to foster critical skills for undergraduate students in animal science.

Quantifying Disorder in a Protein by Mapping its Locally Correlated Structure and Kinetics

Proteins under physiological conditions are inherently mobile and sample a vast array of structures. Consequently, the need arises, on the one hand, at a local level to determine the independent moving parts and their associated conformations and kinetics, and on the other hand, at a global level, to quantify the disorder in the full protein molecule. We present an approach that provides these quantities in the form of local kinetic network models, which are constructed by analyzing the molecular dynamics (MD) trajectories of the protein molecule. Entropies of independent parts of the molecule are quantified. The method outlined here, using the Trp-cage miniprotein prototype, offers a new tool to understand the dynamic structural changes that ultimately govern the functioning of a protein. The method is particularly suited to problems where there are subtle changes in the structure or dynamics at local levels, for example, due to ligand binding.

DeepSLICEM: Clustering CryoEM particles using deep image and similarity graph representations

Finding the 3D structure of proteins and their complexes has several applications, such as developing vaccines that target viral proteins effectively. Methods such as cryogenic electron microscopy (cryo-EM) have improved in their ability to capture high-resolution images, and when applied to a purified sample containing copies of a macromolecule, they can be used to produce a high-quality snapshot of different 2D orientations of the macromolecule, which can be combined to reconstruct its 3D structure. Instead of purifying a sample so that it contains only one macromolecule, a process that can be difficult, time-consuming, and expensive, a cell sample containing multiple particles can be photographed directly and separated into its constituent particles using computational methods. Previous work, SLICEM, has separated 2D projection images of different particles into their respective groups using 2 methods, clustering a graph with edges weighted by pairwise similarities of common lines of the 2D projections. In this work, we develop DeepSLICEM, a pipeline that clusters rich representations of 2D projections, obtained by combining graphical features from a similarity graph based on common lines, with additional image features extracted from a convolutional neural network. DeepSLICEM explores 6 pretrained convolutional neural networks and one supervised Siamese CNN for image representation, 10 pretrained deep graph neural networks for similarity graph node representations, and 4 methods for clustering, along with 8 methods for directly clustering the similarity graph. On 6 synthetic and experimental datasets, the DeepSLICEM pipeline finds 92 method combinations achieving better clustering accuracy than previous methods from SLICEM. Thus, in this paper, we demonstrate that deep neural networks have great potential for accurately separating mixtures of 2D projections of different macromolecules computationally.

Single-molecule FRET probes allosteric effects on protein-translocating pore loops of a AAA+ machine

AAA+ proteins (ATPases associated with various cellular activities) comprise a family of powerful ring-shaped ATP-dependent translocases that carry out numerous vital substrate-remodeling functions. ClpB is a AAA+ protein disaggregation machine that forms a two-tiered hexameric ring, with flexible pore loops protruding into its center and binding to substrate proteins. It remains unknown whether these pore loops contribute only passively to substrate-protein threading or have a more active role. Recently, we have applied single-molecule FRET spectroscopy to directly measure the dynamics of substrate-binding pore loops in ClpB. We have reported that the three pore loops of ClpB (PL1-3) undergo large-scale fluctuations on the microsecond timescale that are likely to be mechanistically important for disaggregation. Here, using single-molecule FRET, we study the allosteric coupling between the pore loops and the two nucleotide-binding domains of ClpB (NBD1-2). By mutating the conserved Walker B motifs within the NBDs to abolish ATP hydrolysis, we demonstrate how the nucleotide state of each NBD tunes pore-loop dynamics. This effect is surprisingly long-ranged; in particular, PL2 and PL3 respond differentially to a Walker B mutation in either NBD1 or NBD2, as well as to mutations in both. We characterize the conformational dynamics of pore loops and the allosteric paths connecting NBDs to pore loops by molecular dynamics simulations and find that both principal motions and allosteric paths can be altered by changing the ATPase state of ClpB. Remarkably, PL3, which is highly conserved in AAA+ machines, is found to favor an upward conformation when only NBD1 undergoes ATP hydrolysis but a downward conformation when NBD2 is active. These results explicitly demonstrate a significant long-range allosteric effect of ATP hydrolysis sites on pore-loop dynamics. Pore loops are therefore established as active participants that undergo ATP-dependent conformational changes to translocate substrate proteins through the central pores of AAA+ machines.

Decoupling the dynamic mechanism revealed by FGFR2 mutation-induced population shift

The fibroblast growth factor receptor 2 (FGFR2) is a key component in cellular signaling networks, and its dysfunctional activation has been implicated in various diseases including cancer and developmental disorders. Mutations at the activation loop (A-loop) have been suggested to trigger an increased basal kinase activity. However, the molecular mechanism underlying this highly dynamic process has not been fully understood due to the limitation of static structural information. Here, we conducted multiple, large-scale Gaussian accelerated molecular dynamics simulations of five (K659E, K659N, K659M, K659Q, and K659T) FGFR2 mutants at the A-loop, and comprehensively analyzed the dynamic molecular basis of FGFR2 activation. The results quantified the population shift of each system, revealing that all mutants had a higher proportion of active-like states. Using Markov state models, we extracted the representative structure of different conformational states and identified key residues related to the increased kinase activity. Furthermore, community network analysis showed enhanced information connections in the mutants, highlighting the long-range allosteric communication between the A-loop and the hinge region. Our findings may provide insights into the dynamic mechanism for FGFR2 dysfunctional activation and allosteric drug discovery.Communicated by Ramaswamy H. Sarma.

Uncovering allostery and regulation in SORCIN through molecular dynamics simulations

Soluble resistance-related calcium-binding protein or Sorcin is an allosteric, calcium-binding Penta-EF hand (PEF) family protein implicated in multi-drug resistant cancers. Sorcin is known to bind chemotherapeutic molecules such as Doxorubicin. This study uses in-silico molecular dynamics simulations to explore the dynamics and allosteric behavior of Sorcin in the context of Ca2+ uptake and Doxorubicin binding. The results show that Ca2+ binding induces large, but reversible conformational changes in the Sorcin structure which manifest as rigid body reorientations that preserve the local secondary structure. A reciprocal allosteric handshake centered around the EF5 hand is found to be key in Sorcin dimer formation and stabilization. Binding of Doxorubicin results in rearrangement of allosteric communities which disrupts long-range allosteric information transfer from the N-terminal domain to the middle lobe. However, this binding does not result in secondary structure destabilization. Sorcin does not appear to have a distinct Ca2+ activated mode of Doxorubicin binding.Communicated by Ramaswamy H. Sarma.

Optimizing parameter search for community detection in time-evolving networks of complex systems

Network representations have been effectively employed to analyze complex systems across various areas and applications, leading to the development of network science as a core tool to study systems with multiple components and complex interactions. There is a growing interest in understanding the temporal dynamics of complex networks to decode the underlying dynamic processes through the temporal changes in network structures. Community detection algorithms, which are specialized clustering algorithms, have been instrumental in studying these temporal changes. They work by grouping nodes into communities based on the structure and intensity of network connections over time, aiming to maximize the modularity of the network partition. However, the performance of these algorithms is highly influenced by the selection of resolution parameters of the modularity function used, which dictate the scale of the represented network, in both size of communities and the temporal resolution of the dynamic structure. The selection of these parameters has often been subjective and reliant on the characteristics of the data used to create the network. Here, we introduce a method to objectively determine the values of the resolution parameters based on the elements of self-organization and scale-invariance. We propose two key approaches: (1) minimization of biases in spatial scale network characterization and (2) maximization of scale-freeness in temporal network reconfigurations. We demonstrate the effectiveness of these approaches using benchmark network structures as well as real-world datasets. To implement our method, we also provide an automated parameter selection software package that can be applied to a wide range of complex systems.

Analysis of the spatial correlation pattern of logistics carbon emission efficiency and its influencing factors: the case of China

As logistics carbon emission efficiency is an essential industry linking regions, investigating this issue from a spatial correlation perspective is practically significant. Utilizing data from 282 prefecture-level cities spanning 2006 to 2019, we used a super slacks-based measure model, a modified gravity model, motif analysis, the Infomap algorithm, and an exponential random graph model to analyze the spatial correlation patterns and influencing factors of logistics carbon emission efficiency. The following conclusions were drawn. (1) The spatial correlation of logistics carbon emission efficiency during the study period exhibited a core-edge pattern, with the central region emerging as a high-correlation hub. (2) The scale of the spatial association network community of carbon emission efficiency in the logistics industry changed constantly, and the stability of the network community structure gradually increased. From a microstructural perspective, the dispersed-mode structure was a pivotal element in the formation of the spatial correlation network of logistics carbon emission efficiency. (3) Node interaction tendencies were a critical force driving network formation. Financial investment, government concern, international openness, population density, and innovation ability were conducive to the formation of spatial correlations of logistics carbon emission efficiency.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Discord in the voter model for complex networks

Online social networks have become primary means of communication. As they often exhibit undesirable effects such as hostility, polarization, or echo chambers, it is crucial to develop analytical tools that help us better understand them. In this paper we are interested in the evolution of discord in social networks. Formally, we introduce a method to calculate the probability of discord between any two agents in the multistate voter model with and without zealots. Our work applies to any directed, weighted graph with any finite number of possible opinions, allows for various update rates across agents, and does not imply any approximation. Under certain topological conditions, the opinions are independent and the joint distribution can be decoupled. Otherwise, the evolution of discord probabilities is described by a linear system of ordinary differential equations. We prove the existence of a unique equilibrium solution, which can be computed via an iterative algorithm. The classical definition of active links density is generalized to take into account long-range, weighted interactions. We illustrate our findings on real-life and synthetic networks. In particular, we investigate the impact of clustering on discord and uncover a rich landscape of varied behaviors in polarized networks. This sheds lights on the evolution of discord between, and within, antagonistic communities.

Extending beyond individual caves: a graph theory approach broadening conservation priorities in Amazon iron ore caves

The Amazon is renowned worldwide for its biological significance, but it also harbors substantial mineral reserves. Among these, the ferruginous geosystems of the region are critical for iron ore extraction, accounting for 10% of Brazil's export revenue. Additionally, this region holds a significant speleological heritage with more than 1,000 caves. However, cave conservation efforts are often in conflict with land use, necessitating mediation through environmental regulations. While conservation decisions typically consider only the caves' characteristics, such an approach fails to account for the interactions among cave communities and their surrounding landscape. This poses a challenge to reserve design for cave conservation purposes. To address this issue, we assessed the predictors that influence the similarity among cave communities, suggesting the use of this parameter as a proxy for subterranean connectivity. Applying graph theory, we proposed a tool to aid in the selection of priority caves for conservation purposes. Our study involved the sampling of invertebrates in 69 iron ore caves and analyzing 28 environmental variables related to these subterranean habitats and adjacent landscape. Our analysis revealed that landscape and habitat characteristics are more important than geographical distance in determining patterns of similarity among caves. Our graph approach highlighted densely interconnected clusters based on similarity. However, specific caves stood out for harboring exclusive fauna and/or exhibiting habitat specificity, making them unique in the study area. Thus, we recommend prioritizing cave clusters for conservation, assembling both singular caves and others that influence them. It is crucial to note that protocols for the protection of subterranean biodiversity must consider measures that encompass both the caves and the surrounding landscape. Our methodology provides insights into the connectivity among caves, identifies existing groups, highlights singular (or unique) cavities that require preservation, and recognizes those influencing these unique habitats. This methodological advancement is crucial for the development of better conservation policies for the speleological heritage in areas under constant economic pressure.

The shortest path method (SPM) webserver for computational enzyme design

SPMweb is the online webserver of the Shortest Path Map (SPM) tool for identifying the key conformationally-relevant positions of a given enzyme structure and dynamics. The server is built on top of the DynaComm.py code and enables the calculation and visualization of the SPM pathways. SPMweb is easy-to-use as it only requires three input files: the three-dimensional structure of the protein of interest, and the two matrices (distance and correlation) previously computed from a Molecular Dynamics simulation. We provide in this publication information on how to generate the files for SPM construction even for non-expert users and discuss the most relevant parameters that can be modified. The tool is extremely fast (it takes less than one minute per job), thus allowing the rapid identification of distal positions connected to the active site pocket of the enzyme. SPM applications expand from computational enzyme design, especially if combined with other tools to identify the preferred substitution at the identified position, but also to rationalizing allosteric regulation, and even cryptic pocket identification for drug discovery. The simple user interface and setup make the SPM tool accessible to the whole scientific community. SPMweb is freely available for academia at http://spmosuna.com/.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Diet and monensin influence the temporal dynamics of the rumen microbiome in stocker and finishing cattle

BACKGROUND

Stocker cattle diet and management influence beef cattle performance during the finishing stage, but knowledge of the dynamics of the rumen microbiome associated with the host are lacking. A longitudinal study was conducted to determine how the feeding strategy from the stocker to the finishing stages of production affects the temporal dynamics of rumen microbiota. During the stocker phase, either dry hay or wheat pasture were provided, and three levels of monensin were administrated. All calves were then transported to a feedlot and received similar finishing diets with or without monensin. Rumen microbial samples were collected on d 0, 28, 85 during the stocker stage (S0, S28 and S85) and d 0, 14, 28, 56, 30 d before slaughter and the end of the trial during the finishing stage (F0, F14, F28, F56, Pre-Ba, and Final). The V4 region of the bacterial 16S rRNA gene of 263 rumen samples was sequenced.

RESULTS

Higher alpha diversity, including the number of observed bacterial features and the Shannon index, was observed in the stocker phase compared to the finishing phase. The bacterial amplicon sequence variants (ASVs) differentiating different sampling time points were identified. Dietary treatments during the stocker stage temporally impact the dynamics of rumen microbiota. For example, shared bacteria, including Bacteroidales (ASV19) and Streptococcus infantarius (ASV94), were significantly higher in hay rumen on S28, S85, and F0, while Bacteroidaceae (ASV11) and Limivicinus (ASV15) were more abundant in wheat. Monensin affected rumen microbial composition at a specific time. Transportation to feedlot significantly influenced microbiome structure and diversity in hay-fed calves. Bacterial taxa associated with body weight were classified, and core microbiotas interacted with each other during the trial.

CONCLUSIONS

In summary, the temporal dynamics of the rumen microbiome in cattle at the stocker and finishing stage are influenced by multiple factors of the feeding strategy. Diet at the stocker phase may temporarily affect the microbial composition during this stage. Modulating the rumen microbiome in the steers at the stocker stage affects the microbial interactions and performance in the finishing stage.

Connectome-Based Neurosurgery in Primary Intra-Axial Neoplasms: Beyond the Traditional Modular Conception of Brain Architecture for the Preservation of Major Neurological Domains and Higher-Order Cognitive Functions

Despite the therapeutical advancements in the surgical treatment of primary intra-axial neoplasms, which determined both a significative improvement in OS and QoL and a reduction in the incidence of surgery-induced major neurological deficits, nowadays patients continue to manifest subtle post-operative neurocognitive impairments, preventing them from a full reintegration back into social life and into the workforce. The birth of connectomics paved the way for a profound reappraisal of the traditional conception of brain architecture, in favour of a model based on large-scale structural and functional interactions of a complex mosaic of cortical areas organized in a fluid network interconnected by subcortical bundles. Thanks to these advancements, neurosurgery is facing a new era of connectome-based resections, in which the core principle is still represented by the achievement of an ideal onco-functional balance, but with a closer eye on whole-brain circuitry, which constitutes the foundations of both major neurological functions, to be intended as motricity; language and visuospatial function; and higher-order cognitive functions such as cognition, conation, emotion and adaptive behaviour. Indeed, the achievement of an ideal balance between the radicality of tumoral resection and the preservation, as far as possible, of the integrity of local and global brain networks stands as a mandatory goal to be fulfilled to allow patients to resume their previous life and to make neurosurgery tailored and gentler to their individual needs.

Network physics of attractive colloidal gels: Resilience, rigidity, and phase diagram

Colloidal gels exhibit solid-like behavior at vanishingly small fractions of solids, owing to ramified space-spanning networks that form due to particle-particle interactions. These networks give the gel its rigidity, and with stronger attractions the elasticity grows as well. The emergence of rigidity can be described through a mean field approach; nonetheless, fundamental understanding of how rigidity varies in gels of different attractions is lacking. Moreover, recovering an accurate gelation phase diagram based on the system's variables has been an extremely challenging task. Understanding the nature of colloidal clusters, and how rigidity emerges from their connections is key to controlling and designing gels with desirable properties. Here, we employ network analysis tools to interrogate and characterize the colloidal structures. We construct a particle-level network, having all the spatial coordinates of colloids with different attraction levels, and also identify polydisperse rigid fractal clusters using a Gaussian mixture model, to form a coarse-grained cluster network that distinctly shows main physical features of the colloidal gels. A simple mass-spring model then is used to recover quantitatively the elasticity of colloidal gels from these cluster networks. Interrogating the resilience of these gel networks shows that the elasticity of a gel (a dynamic property) is directly correlated to its cluster network's resilience (a static measure). Finally, we use the resilience investigations to devise [and experimentally validate] a fully resolved phase diagram for colloidal gelation, with a clear solid-liquid phase boundary using a single volume fraction of particles well beyond this phase boundary.

Gauging Dynamics-driven Allostery Using a New Computational Tool: A CAP Case Study

In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAP's entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.

GSLAlign: community detection and local PPI network alignment

High throughput protein-protein interaction (PPI) profiling and computational techniques have resulted in generating a large amount of PPI network data. The study of PPI networks helps in understanding the biological processes of the proteins. The comparative study of the PPI networks helps in identifying the conserved interactions across the species. This article presents a novel local PPI network aligner 'GSLAlign' that consists of two stages. It first detects the communities from the PPI networks by applying the GraphSAGE algorithm using gene expression data. In the second stage, the detected communities are aligned using a community aligner that is based on protein sequence similarity. The community detection algorithm produces more separable and biologically accurate communities as compared to previous community detection algorithms. Moreover, the proposed community alignment algorithm achieves 3-8% better results in terms of semantic similarity as compared to previous local aligners. The average connectivity and coverage of the proposed algorithm are also better than the existing aligners.Communicated by Ramaswamy H. Sarma.

Exploring Alzheimer's disease: a comprehensive brain connectome-based survey

Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it is undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.

Dietary β-hydroxybutyric acid improves the growth performance of young ruminants based on rumen microbiota and volatile fatty acid biosynthesis

Introduction

The ketone body β-hydroxybutyric acid (BHB) plays critical roles in cellular proliferation and metabolic fuel utilization; however, its effects on the rumen microbiota remain unknown.

Methods

Here, three doses of BHB (low, medium, and high) were supplemented to early-weaned goat kids.

Results

Compared with controls, the beneficial effects of BHB on growth and rumen development were observed in goats at 90 days of age (d). The low dose of dietary BHB increased the concentration of rumen acetate, propionate, and butyrate on d90. The sequencing results of the rumen microbiota revealed marked shifts in rumen microbial community structure after early-weaned goat kids consumed BHB for 2 months. The signature bacterial ASVs for each treatment were identified and were the main drivers contributing to microbial interactions in the rumen. The bacteria associated with rumen weight were also correlated with body weight. Some classified bacterial signatures, including Prevotella, Olsenella umbonate, and Roseburia faecis, were related to rumen volatile fatty acids and host development.

Conclusion

Overall, dietary BHB altered rumen microbiota and environments in young goats, which contributed to rumen development and growth.

The Pivotal Distinction between Antagonists' and Agonists' Binding into Dopamine D4 Receptor-MD and FMO/PIEDA Studies

The dopamine D4 receptor (D4R) is a promising therapeutic target in widespread diseases, and the search for novel agonists and antagonists appears to be clinically relevant. The mechanism of binding to the receptor (R) for antagonists and agonists varies. In the present study, we conducted an in-depth computational study, teasing out key similarities and differences in binding modes, complex dynamics, and binding energies for D4R agonists and antagonists. The dynamic network method was applied to investigate the communication paths between the ligand (L) and G-protein binding site (GBS) of human D4R. Finally, the fragment molecular orbitals with pair interaction energy decomposition analysis (FMO/PIEDA) scheme was used to estimate the binding energies of L-R complexes. We found that a strong salt bridge with D3.32 initiates the inhibition of the dopamine D4 receptor. This interaction also occurs in the binding of agonists, but the change in the receptor conformation to the active state starts with interaction with cysteine C3.36. Such a mechanism may arise in the case of agonists unable to form a hydrogen bond with the serine S5.46, considered, so far, to be crucial in the activation of GPCRs. The energy calculations using the FMO/PIEDA method indicate that antagonists show higher residue occupancy of the receptor binding site than agonists, suggesting they could form relatively more stable complexes. Additionally, antagonists were characterized by repulsive interactions with S5.46 distinguishing them from agonists.

A computational method for small molecule-RNA binding sites identification by utilizing position specificity and complex network information

Some computational methods have been given for small molecule-RNA binding site identification due to that it plays a significant role in revealing biology function researches. However, it is still challenging to design an accurate model, especially for MCC. We designed a feature extraction technology from two aspects (position specificity and complex network information). Specifically, complex network was employed to express the space topological structure and sequence position information for improving prediction effect. Then, the features fused position specificity and complex network information were input into random forest classifier for model construction. The AUC of 88.22%, 77.92% and 81.46% were obtained on three independent datasets (RB19, CS71, RB78). Compared with the existing method, the best MCC were obtained on three datasets, which were 8.19%, 0.59% and 4.35% higher than the state-of-the-art prediction methods, respectively. The outstanding performances show that our method is a powerful tool to identify RNA binding sites, helping to the design RNA-targeting small molecule drugs. The data and resource codes are available at https://github.com/Kangxiaoneuq/PCN_RNAsite.

Thrombin - A Molecular Dynamics Perspective

Thrombin is a crucial enzyme involved in blood coagulation, essential for maintaining circulatory system integrity and preventing excessive bleeding. However, thrombin is also implicated in pathological conditions such as thrombosis and cancer. Despite the application of various experimental techniques, including X-ray crystallography, NMR spectroscopy, and HDXMS, none of these methods can precisely detect thrombin's dynamics and conformational ensembles at high spatial and temporal resolution. Fortunately, molecular dynamics (MD) simulation, a computational technique that allows the investigation of molecular functions and dynamics in atomic detail, can be used to explore thrombin behavior. This review summarizes recent MD simulation studies on thrombin and its interactions with other biomolecules. Specifically, the 17 studies discussed here provide insights into thrombin's switch between 'slow' and 'fast' forms, active and inactive forms, the role of Na+ binding, the effects of light chain mutation, and thrombin's interactions with other biomolecules. The findings of these studies have significant implications for developing new therapies for thrombosis and cancer. By understanding thrombin's complex behavior, researchers can design more effective drugs and treatments that target thrombin.

Unveiling local and global conformational changes and allosteric communications in SOD1 systems using molecular dynamics simulation and network analyses

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a serious neurodegenerative disorder affecting nerve cells in the brain and spinal cord that is caused by mutations in the superoxide dismutase 1 (SOD1) enzyme. ALS-related mutations cause misfolding, dimerisation instability, and increased formation of aggregates. The underlying allosteric mechanisms, however, remain obscure as far as details of their fundamental atomistic structure are concerned. Hence, this gap in knowledge limits the development of novel SOD1 inhibitors and the understanding of how disease-associated mutations in distal sites affect enzyme activity.

METHODS

We combined microsecond-scale based unbiased molecular dynamics (MD) simulation with network analysis to elucidate the local and global conformational changes and allosteric communications in SOD1 Apo (unmetallated form), Holo, Apo_CallA (mutant and unmetallated form), and Holo_CallA (mutant form) systems. To identify hotspot residues involved in SOD1 signalling and allosteric communications, we performed network centrality, community network, and path analyses.

RESULTS

Structural analyses showed that unmetallated SOD1 systems and cysteine mutations displayed large structural variations in the catalytic sites, affecting structural stability. Inter- and intra H-bond analyses identified several important residues crucial for maintaining interfacial stability, structural stability, and enzyme catalysis. Dynamic motion analysis demonstrated more balanced atomic displacement and highly correlated motions in the Holo system. The rationale for structural disparity observed in the disulfide bond formation and R143 configuration in Apo and Holo systems were elucidated using distance and dihedral probability distribution analyses.

CONCLUSION

Our study highlights the efficiency of combining extensive MD simulations with network analyses to unravel the features of protein allostery.

Fundamental patterns of signal propagation in complex networks

Various disasters stem from minor perturbations, such as the spread of infectious diseases and cascading failure in power grids. Analyzing perturbations is crucial for both theoretical and application fields. Previous researchers have proposed basic propagation patterns for perturbation and explored the impact of basic network motifs on the collective response to these perturbations. However, the current framework is limited in its ability to decouple interactions and, therefore, cannot analyze more complex structures. In this article, we establish an effective, robust, and powerful propagation framework under a general dynamic model. This framework reveals classical and dense network motifs that exert critical acceleration on signal propagation, often reducing orders of magnitude compared with conclusions generated by previous work. Moreover, our framework provides a new approach to understand the fundamental principles of complex systems and the negative feedback mechanism, which is of great significance for researching system controlling and network resilience.

The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations

Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can be attributed to population-specific traits? To answer this question, the mutation data must be linked with functional outcomes. A new "edgotype" concept has been proposed, which emphasizes the interaction-specific, "edgetic", perturbations caused by mutations in the interacting proteins. In this work, we performed systematic in silico edgetic profiling of ~50,000 non-synonymous SNVs (nsSNVs) from the 1000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated the functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in silico edgetic study, and reveals insights into the orchestrated play of population-specific mutations in the human interactome.

A network approach to zooarchaeological datasets and human-centered ecosystems in southwestern Florida

Zooarchaeological datasets are often large, complex, and difficult to visualize and communicate. Many visual aids and summaries often limit the patterns that can be identified and mask interpretations of relationships between contexts, species, and environmental information. The most commonly used of these often include bar charts, pie charts, and other such graphs that aid in categorizing data and highlighting the differences or similarities between categories. While such simplification is often necessary for effective communication, it can also obscure the full range of complexity of zooarchaeological datasets and the human-environment dynamics they reflect. In this paper, we demonstrate the utility of formal network graphs to capturing the complexity of zooarchaeological datasets and to effectively highlighting the kinds of relationships between contexts, time, and faunal assemblages in which zooarchaeologists are primarily interested. Using a case study from southwestern Florida (USA), we argue that network graphs provide a quick solution to visualizing the structure of zooarchaeological datasets and serve as a useful aid in interpreting patterns that represent fundamental reflections of human-centered ecosystems.

Spatial clustering and spillover pathways analysis of O3, NO2, and CO in eastern China during 2017-2021

The eastern China presented the most serious O3 pollution and increasingly prominent regional characteristics. To understand the transport characteristics of O3 and its precursors and identify their potential relationships are of great guiding significance for interregional joint prevention and control. In this study, the annual and seasonal transport networks of O3 and its precursors (NO2 and CO) during 2017-2021 were constructed by applying the complex network method to air quality observations. And the key spatial clusters, the spillover paths and the potential links among pollutants were comprehensively analyzed based on the topological characteristic analysis of the established air pollutant transport networks. As the results showed, O3 pollution in the eastern China was affected by active regional transports of O3 and its precursors. Regional transports of O3, NO2, and CO were more prominent in autumn and showed high synchronization. The regional transport of precursors, especially NOx, was an important cause of regional O3 pollution. Air pollutant transport characteristics varied with seasons and regions, which demonstrating the importance of regulating seasonal and regional differentiated joint prevention and control strategies, especially for NOx. The results of this study can provide science-based guidance for the regional cooperative control of O3 pollution in the eastern China, and the application of complex networks can also provide a new methodological perspective for the study of air pollution transmission.

Network pharmacology on mechanistic role of Thymus linearis Benth. against gastrointestinal and neurological diseases

BACKGROUND

Thymus linearis Benth. (TL) is native to the Himalayas and has been used traditionally to cure various diseases. Thymus is a well-known aromatic perennial herb commonly known as Van ajwain or Himalayan Thyme. The main components of the TL essential oil are thymol, terpinene, and p-cymene. There are many biological properties that TL has been reported to exhibit, including antioxidant, analgesic, anti-inflammatory, antipyretic, antibacterial, anticancerogenic, and neuroprotective effects.

PURPOSE

In this study, the network pharmacology and molecular docking were used to explore the potential compounds of TL and their interaction mechanism toward gastrointestinal complications and neurological diseases.

METHODS

Network pharmacology was used to analyze the active compounds and protein targets of TL on gastrointestinal and neurological related diseases. Protein-protein interaction and Kyoto Encyclopaedia of Genes and Genomes analysis were used to enrich and evaluate key pathways of target proteins. To ensure the reliability of the network pharmacology prediction estimates, molecular docking was used to confirm the relationships between the core components and targets of TL.

RESULT

77 physiologically active compounds and their 717 predicted protein targets with high association to the neuroactive ligand-receptor interaction pathway were derived from TL. Beta-citronellol, piperitol, p-cymen-8-ol, and alpha-humulene were found to have a role in gastrointestinal diseases associated with neurological diseases. These compounds showed significant levels of multitargeting cluster regulatory activity. The molecular docking results showed regulatory activity of highlighted multi-targeting compounds and the highest docking energy was reported in piperitol.

CONCLUSION

The study shows that Thymus linearis Benth., a medicinal plant with traditional use, possesses valuable biologically active compounds. It emphasizes the potential of Thymus in treating gastrointestinal and neurological diseases by regulating oxidative stress pathways. This research opens up possibilities for discovering antioxidant molecules for future drug development. It is an interesting study with promising implications for further research.

Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space

Hyperbolic spaces have been quite popular in the recent past for representing hierarchically organized data. Further, several classification algorithms for data in these spaces have been proposed in the literature. These algorithms mainly use either hyperplanes or geodesics for decision boundaries in a large margin classifiers setting leading to a non-convex optimization problem. In this paper, we propose a novel large margin classifier based on horospherical decision boundaries that leads to a geodesically convex optimization problem that can be optimized using any Riemannian gradient descent technique guaranteeing a globally optimal solution. We present several experiments depicting the competitive performance of our classifier in comparison to SOTA.

A data-driven approach shows that individuals' characteristics are more important than their networks in predicting fertility preferences

People's networks are considered key in explaining fertility outcomes-whether people want and have children. Existing research on social influences on fertility is limited because data often come from small networks or highly selective samples, only few network variables are considered, and the strength of network effects is not properly assessed. We use data from a representative sample of Dutch women reporting on over 18 000 relationships. A data-driven approach including many network characteristics accounted for 0 to 40% of the out-of-sample variation in different outcomes related to fertility preferences. Individual characteristics were more important for all outcomes than network variables. Network composition was also important, particularly those people in the network desiring children or those choosing to be childfree. Structural network characteristics, which feature prominently in social influence theories and are based on the relations between people in the networks, hardly mattered. We discuss to what extent our results provide support for different mechanisms of social influence, and the advantages and disadvantages of our data-driven approach in comparison to traditional approaches.

PanDelos-frags: A methodology for discovering pangenomic content of incomplete microbial assemblies

Pangenomics was originally defined as the problem of comparing the composition of genes into gene families within a set of bacterial isolates belonging to the same species. The problem requires the calculation of sequence homology among such genes. When combined with metagenomics, namely for human microbiome composition analysis, gene-oriented pangenome detection becomes a promising method to decipher ecosystem functions and population-level evolution. Established computational tools are able to investigate the genetic content of isolates for which a complete genomic sequence is available. However, there is a plethora of incomplete genomes that are available on public resources, which only a few tools may analyze. Incomplete means that the process for reconstructing their genomic sequence is not complete, and only fragments of their sequence are currently available. However, the information contained in these fragments may play an essential role in the analyses. Here, we present PanDelos-frags, a computational tool which exploits and extends previous results in analyzing complete genomes. It provides a new methodology for inferring missing genetic information and thus for managing incomplete genomes. PanDelos-frags outperforms state-of-the-art approaches in reconstructing gene families in synthetic benchmarks and in a real use case of metagenomics. PanDelos-frags is publicly available at https://github.com/InfOmics/PanDelos-frags.

Insight into the conserved structural dynamics of the C-terminus of mammal PrPC identifies structural core and possible structural role of pharmacological chaperones

Misfolding of the prion protein is central to prion disease aetiology. Although understanding the dynamics of the native fold helps to decipher the conformational conversion mechanism, a complete depiction of distal but coupled prion protein sites common across species is lacking. To fill this gap, we used normal mode analysis and network analysis to examine a collection of prion protein structures deposited on the protein data bank. Our study identified a core of conserved residues that sustains the connectivity across the C-terminus of the prion protein. We propose how a well-characterized pharmacological chaperone may stabilize the fold. Also, we provide insight into the effect on the native fold of initial misfolding pathways identified by others using kinetics studies.

Gut microbiota-derived 12-ketolithocholic acid suppresses the IL-17A secretion from colonic group 3 innate lymphoid cells to prevent the acute exacerbation of ulcerative colitis

Intestinal microbiota dysbiosis and metabolic disruption are well-known as the primary triggers of ulcerative colitis (UC). However, their role in regulating the group 3 innate lymphoid cells (ILC3s), which are essential for intestinal health, remains unexplored during the development of disease severity. Here, our results showed that the microbiota structure of patients with severe UC (SUCs) differed from those with mild UC (MiUCs), moderate UC (MoUCs), and healthy controls (HCs). Microbes producing secondary bile acids (SBAs) and SBAs decreased with the aggravation of UC, and a strong positive correlation existed between them. Next, fecal microbiota transfer was used to reproduce the human-derived microbiota in mice and decipher the microbiota-mediated inflammatory modulation during an increase in disease severity. Mice receiving SUC-derived microbiota exhibited enhancive inflammation, a lowered percentage of ILC3s, and the down-regulated expressions of bile acid receptors, including vitamin D receptor (VDR) and pregnane X receptor (PXR), in the colon. Similar to clinical results, SBA-producing microbes, deoxycholic acids (DCA), and 12-ketolithocholic acids (12-KLCA) were diminished in the intestine of these recipients. Finally, we compared the therapeutic potential of DCA and 12-KLCA in preventing colitis and the regulatory mechanisms mediated by ILC3s. 12-KLCA but not DCA represented a strong anti-inflammatory effect associated with the higher expression of VDR and the lower secretion of IL-17A from colonic ILC3s. Collectively, these findings provide new signatures for monitoring the acute deterioration of UC by targeting gut microbiota and bile acid metabolism and demonstrate the therapeutic and preventive potential of a novel microbiota-derived metabolite, 12-KLCA.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks

BACKGROUND

As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters.

RESULTS

Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment.

CONCLUSIONS

BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .

Microbial biogeography of the eastern Yucatán carbonate aquifer

IMPORTANCE

The extensive Yucatán carbonate aquifer, located primarily in southeastern Mexico, is pockmarked by numerous sinkholes (cenotes) that lead to a complex web of underwater caves. The aquifer hosts a diverse yet understudied microbiome throughout its highly stratified water column, which is marked by a meteoric lens floating on intruding seawater owing to the coastal proximity and high permeability of the Yucatán carbonate platform. Here, we present a biogeographic survey of bacterial and archaeal communities from the eastern Yucatán aquifer. We apply a novel network analysis software that models ecological niche space from microbial taxonomic abundance data. Our analysis reveals that the aquifer community is composed of several distinct niches that follow broader regional and hydrological patterns. This work lays the groundwork for future investigations to characterize the biogeochemical potential of the entire aquifer with other systems biology approaches.

A network community structure similarity index for weighted networks

Identification of communities in complex systems is an essential part of network analysis. Accordingly, measuring similarities between communities is a fundamental part of analysing community structure in different, yet related, networks. Commonly used methods for quantifying network community similarity fail to consider the effects of edge weights. Existing methods remain limited when the two networks being compared have different numbers of nodes. In this study, we address these issues by proposing a novel network community structure similarity index (NCSSI) based on the edit distance concept. NCSSI is proposed as a similarity index for comparing network communities. The NCSSI incorporates both community labels and edge weights. The NCSSI can also be employed to assess the similarity between two communities with varying numbers of nodes. We test the proposed method using simulated data and case-study analysis of New York Yellow Taxi flows and compare the results with that of other commonly used methods (i.e., mutual information and the Jaccard index). Our results highlight how NCSSI effectively captures the impact of both label and edge weight changes and their impacts on community structure, which are not captured in existing approaches. In conclusion, NCSSI offers a new approach that incorporates both label and weight variations for community similarity measurement in complex networks.

Minority-group incubators and majority-group reservoirs support the diffusion of climate change adaptations

Successful climate change adaptation depends on the spread and maintenance of adaptive behaviours. Current theory suggests that the heterogeneity of metapopulation structure can help adaptations diffuse throughout a population. In this paper, we develop an agent-based model of the spread of adaptations in populations with minority-majority metapopulation structure, where subpopulations learn more or less frequently from their own group compared to the other group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for an adaptation, while majority groups act as reservoirs for an adaptation once it has spread widely. This means that adaptations diffuse throughout populations better when minority groups start out knowing an adaptation, as Indigenous populations often do, while cohesion among majority groups further promotes adaptation diffusion. Our work advances the goal of this theme issue by developing new theoretical insights and demonstrating the utility of cultural evolutionary theory and methods as important tools in the nascent science of culture that climate change adaptation needs. This article is part of the theme issue 'Climate change adaptation needs a science of culture'.

A community partitioning algorithm for cyberspace

Community partitioning is an effective technique for cyberspace mapping. However, existing community partitioning algorithm only uses the topological structure of the network to divide the community and disregards factors such as real hierarchy, overlap, and directionality of information transmission between communities in cyberspace. Consequently, the traditional community division algorithm is not suitable for dividing cyberspace resources effectively. Based on cyberspace community structure characteristics, this study introduces an algorithm that combines an improved local fitness maximization (LFM) algorithm with the PageRank (PR) algorithm for community partitioning on cyberspace resources, called PR-LFM. First, seed nodes are determined using degree centrality, followed by local community expansion. Nodes belonging to multiple communities undergo further partitioning so that they are retained in the community where they are most important, thus preserving the community's original structure. The experimental data demonstrate good results in the resource division of cyberspace.

Community transmission of SARS-CoV-2 during the Delta wave in New York City

BACKGROUND

Understanding community transmission of SARS-CoV-2 variants of concern (VOCs) is critical for disease control in the post pandemic era. The Delta variant (B.1.617.2) emerged in late 2020 and became the dominant VOC globally in the summer of 2021. While the epidemiological features of the Delta variant have been extensively studied, how those characteristics shaped community transmission in urban settings remains poorly understood.

METHODS

Using high-resolution contact tracing data and testing records, we analyze the transmission of SARS-CoV-2 during the Delta wave within New York City (NYC) from May 2021 to October 2021. We reconstruct transmission networks at the individual level and across 177 ZIP code areas, examine network structure and spatial spread patterns, and use statistical analysis to estimate the effects of factors associated with COVID-19 spread.

RESULTS

We find considerable individual variations in reported contacts and secondary infections, consistent with the pre-Delta period. Compared with earlier waves, Delta-period has more frequent long-range transmission events across ZIP codes. Using socioeconomic, mobility and COVID-19 surveillance data at the ZIP code level, we find that a larger number of cumulative cases in a ZIP code area is associated with reduced within- and cross-ZIP code transmission and the number of visitors to each ZIP code is positively associated with the number of non-household infections identified through contact tracing and testing.

CONCLUSIONS

The Delta variant produced greater long-range spatial transmission across NYC ZIP code areas, likely caused by its increased transmissibility and elevated human mobility during the study period. Our findings highlight the potential role of population immunity in reducing transmission of VOCs. Quantifying variability of immunity is critical for identifying subpopulations susceptible to future VOCs. In addition, non-pharmaceutical interventions limiting human mobility likely reduced SARS-CoV-2 spread over successive pandemic waves and should be encouraged for reducing transmission of future VOCs.

Robustness of community structure under edge addition

Communities often represent key structural and functional clusters in networks. To preserve such communities, it is important to understand their robustness under network perturbations. Previous work in community robustness analysis has focused on studying changes in the community structure as a response of edge rewiring and node or edge removal. However, the impact of increasing connectivity on the robustness of communities in networked systems is relatively unexplored. Studying the limits of community robustness under edge addition is crucial to better understanding the cases in which density expands or false edges erroneously appear. In this paper, we analyze the effect of edge addition on community robustness in synthetic and empirical temporal networks. We study two scenarios of edge addition: random and targeted. We use four community detection algorithms, Infomap, Label Propagation, Leiden, and Louvain, and demonstrate the results in community similarity metrics. The experiments on synthetic networks show that communities are more robust when the initial partition is stronger or the edge addition is random, and the experiments on empirical data also indicate that robustness performance can be affected by the community similarity metric. Overall, our results suggest that the communities identified by the different types of community detection algorithms exhibit different levels of robustness, and so the robustness of communities depends strongly on the choice of detection method.

Global efficiency and network structure of urban traffic flows: A percolation-based empirical analysis

Making the connection between the function and structure of networked systems is one of the fundamental issues in complex systems and network science. Urban traffic flows are related to various problems in cities and can be represented as a network of local traffic flows. To identify an empirical relation between the function and network structure of urban traffic flows, we construct a time-varying traffic flow network of a megacity, Seoul, and analyze its global efficiency with a percolation-based approach. Comparing the real-world traffic flow network with its corresponding null-model network having a randomized structure, we show that the real-world network is less efficient than its null-model network during rush hour, yet more efficient during non-rush hour. We observe that in the real-world network, links with the highest betweenness tend to have lower quality during rush hour compared to links with lower betweenness, but higher quality during non-rush hour. Since the top betweenness links tend to be the bridges that connect the network together, their congestion has a stronger impact on the network's global efficiency. Our results suggest that the spatial structure of traffic flow networks is important to understand their function.

Top-heavy trophic structure within benthic viral dark matter

A better understanding of system-specific viral ecology in diverse environments is needed to predict patterns of virus-host trophic structure in the Anthropocene. This study characterised viral-host trophic structure within coral reef benthic cyanobacterial mats-a globally proliferating cause and consequence of coral reef degradation. We employed deep longitudinal multi-omic sequencing to characterise the viral assemblage (ssDNA, dsDNA, and dsRNA viruses) and profile lineage-specific host-virus interactions within benthic cyanobacterial mats sampled from Bonaire, Caribbean Netherlands. We recovered 11,012 unique viral populations spanning at least 10 viral families across the orders Caudovirales, Petitvirales, and Mindivirales. Gene-sharing network analyses provided evidence for extensive genomic novelty of mat viruses from reference and environmental viral sequences. Analysis of coverage ratios of viral sequences and computationally predicted hosts spanning 15 phyla and 21 classes revealed virus-host abundance (from DNA) and activity (from RNA) ratios consistently exceeding 1:1, suggesting a top-heavy intra-mat trophic structure with respect to virus-host interactions. Overall, our article contributes a curated database of viral sequences found in Caribbean coral reef benthic cyanobacterial mats (vMAT database) and provides multiple lines of field-based evidence demonstrating that viruses are active members of mat communities, with broader implications for mat functional ecology and demography.

Generative models for two-ground-truth partitions in networks

A myriad of approaches have been proposed to characterize the mesoscale structure of networks most often as a partition based on patterns variously called communities, blocks, or clusters. Clearly, distinct methods designed to detect different types of patterns may provide a variety of answers' to the networks mesoscale structure. Yet even multiple runs of a given method can sometimes yield diverse and conflicting results, producing entire landscapes of partitions which potentially include multiple (locally optimal) mesoscale explanations of the network. Such ambiguity motivates a closer look at the ability of these methods to find multiple qualitatively different "ground truth" partitions in a network. Here we propose the stochastic cross-block model (SCBM), a generative model which allows for two distinct partitions to be built into the mesoscale structure of a single benchmark network. We demonstrate a use case of the benchmark model by appraising the power of stochastic block models (SBMs) to detect implicitly planted coexisting bicommunity and core-periphery structures of different strengths. Given our model design and experimental setup, we find that the ability to detect the two partitions individually varies by SBM variant and that coexistence of both partitions is recovered only in a very limited number of cases. Our findings suggest that in most instances only one-in some way dominating-structure can be detected, even in the presence of other partitions. They underline the need for considering entire landscapes of partitions when different competing explanations exist and motivate future research to advance partition coexistence detection methods. Our model also contributes to the field of benchmark networks more generally by enabling further exploration of the ability of new and existing methods to detect ambiguity in the mesoscale structure of networks.

Molecular dynamics simulation study on Bacillus subtilis EngA: the presence of Mg2+ at the active-sites promotes the functionally important conformation

EngA, a GTPase contains two GTP binding domains [GD1, GD2], and the C-terminal KH domain shown to be involved in the later stages of ribosome maturation. Association of EngA to the ribosomal subunit in the intermediate stage of maturation is essential for complete ribosome maturation. However, this association was shown to be dependent on the nucleotide bound combinations. This nucleotide dependent association tendency is attributed to the conformational changes that occur among different nucleotide bound combinations. Therefore, to explore the conformational changes, all-atom molecular dynamics simulations for Bacillus subtilis EngA in different nucleotide bound combinations along with the presence or absence of Mg2+ in the active-sites were carried out. The presence of Mg2+ along with the bound nucleotide at the GD2 active-site dictates the GD2-Sw-II mobility, but the GD1-Sw-II mobility has not shown any nucleotide or Mg2+ dependent movement. However, the GD1-Sw-II secondary conformations are shown to be influenced by the GD2 nucleotide bound state. This allosteric connection between the GD2 active-site and the GD1-Sw-II is also observed through the dynamic network analysis. Further, the exploration of the GD1-KH interface interactions exhibited a more attractive tendency when GD1 is bound to GTP-Mg2+. In addition, the presence of Mg2+ stabilizes active-site water and also increases the distances between the α- and γ- phosphates of the bound GTP. Curiously, three water molecules in the GD1 active-site and only one water molecule in the GD2 active-site are stabilized. This indicates that the probability of GTP hydrolysis is more in GD1 compared to GD2.Communicated by Ramaswamy H. Sarma.