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325 results for “network structure”
Data from: NetView P: a network visualization tool to unravel complex population structure using genome-wide SNPs
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Data from: Ecological divergence among colour morphs mediated by changes in spatial network structure associated with disturbance
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Data from: Contact networks structured by sex underpin sex-specific epidemiology of infection
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Data from: Social network structure in wintering golden-crowned sparrows is not correlated with kinship
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Structural brain network abnormalities and the probability of seizure recurrence after epilepsy surgery: supplementary material
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Comprehensive dataset of heterogeneous network structures in traditional Chinese medicine research
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Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
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Data from: Ecosystem engineers shape ecological network structure and stability: a framework and literature review
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Data from: The structure of the Mini-K and K-SF-42: a psychological network approach
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The role of the Arp2/3 complex in shaping the dynamics and structures of branched actomyosin networks
<p>Actomyosin networks give cells the ability to move and divide. These networks contract and expand while being driven by active energy-consuming processes such as motor protein walking and actin polymerization. Actin dynamics is also regulated by actin-binding proteins, such as the actin-related protein 2/3 (Arp2/3) complex. This complex generates branched filaments thereby changing the overall organization of the network. In this work, the spatiotemporal patterns of dynamical actin assembly accompanying the branching-induced reorganization caused by Arp2/3 were studied using a computational model (MEDYAN); this model simulates actomyosin network dynamics as a result of chemical reactions whose rates are modulated by rapid mechanical equilibration. We show that branched actomyosin networks relax significantly more slowly than do unbranched networks. Also, branched networks undergo rare convulsive movements, “avalanches”, that release strain in the network. These avalanches are associated with the more heterogeneous distribution of mechanically-linked filaments displayed by branched networks. These far-from equilibrium events arising from the marginal stability of growing actomyosin networks provide a possible mechanism of the “cytoquakes” recently seen in experiments.</p>
Molecular ecological network analyses: An effective conservation tool for the assessment of biodiversity, trophic interactions, and community structure
<p>Global biodiversity is threatened by the anthropogenic restructuring of animal communities, which rewires species interaction networks in real-time as individuals are extirpated or introduced. Conservation science and adaptive ecosystem management demands more rapid, quantitative, and non-invasive technologies for robustly capturing changing biodiversity and quantifying species interactions. Here we develop molecular ecological network analyses (MENA) as an ecosystem assessment tool to address these needs. To construct the ecological network, we used environmental DNA from feces to identify the plant and mammal diet of two carnivores: puma (<i>Puma concolor</i>) and bobcat (<i>Lynx rufus</i>); two omnivores: coyote (<i>Canis latrans</i>) and gray fox (<i>Urocyon cinereoargenteus</i>); and two herbivores: black-tailed deer (<i>Odocoileus hemionus</i>) and black-tailed jackrabbit (<i>Lepus californicus)</i> in a well-studied Californian reserve<i>. </i>To evaluate MENA as a comprehensive biodiversity tool, we applied our framework to identify the structure of the network, patterns of trophic interactions, key species, and to assess its utility in capturing the biodiversity of the area. The high dietary taxonomic resolution enabled the assessment of species diversity, niche breadth and overlap. The network analysis revealed a dense ecological network with a high diversity of weakly connected species and a community that is highly modular and non-nested. The significant prevalence of tri-trophic chain and exploitative competition patterns indicates (i) the removal or reintroduction of a top predator would trigger a trophic cascade within this community, directly affecting their prey and indirectly the plant communities, and (ii) the potential impact of indirect effects between two predators that consume the same prey. These results suggest that the recent resurgence of puma in the study area may impact the herbaceous and woody vegetation and the population size of other predators. This effect of fluctuating predator populations and plant communities could be predicted through MENA's fine-scale assessment of the diet selection and the identified keystone species. Although just using a subset of species, MENA more rapidly, accurately, and effectively captured the broader biodiversity of the area in comparison to other methodologies. MENA reconstructed and unveiled the hidden complexity in trophic structure and interaction networks within the community, providing a promising toolkit for biodiversity and ecosystem management.</p>
Kinship does not predict the structure of a shark social network
<p>Genetic relatedness in animal societies is often a factor that drives the structure of social groups. In the marine world, most studies which have investigated this question have focused on marine mammals such as whales and dolphins. For sharks, recent studies have demonstrated preferential associations among individuals from which social communities emerge. Assortment patterns have been found according to phenotypic or behavioural traits but the role of genetic relatedness in shaping the social structure of adult shark populations has, to the best our knowledge, never been investigated. Here, we used a social network analysis crossed with DNA microsatellite genotyping to investigate the role of the genetic relatedness in the social structure of a blacktip reef shark (<i>Carcharhinus melanopterus</i>) population. Based on data from 156 groups of sharks, we used generalized affiliation indices to isolate social preferences from non-social associations, controlling for the contribution of sex, size, gregariousness, spatial and temporal overlap on social associations, to test for the influence of genetic relatedness on social structure. A double permutation procedure was employed to confirm our results and account for issues arising from potentially elevated type I and type II error rates. Kinship was not a predictor of associations and affiliations among sharks at the dyad or community levels as individuals tended to associate independently of the genetic relatedness among them. The lack of parental care in this species may contribute to the breakdown of family links in the population early in life, thereby preventing the formation of kin-based social networks.</p>
Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks
<p>Files for reproducing results from Kelkar et al. (JPCB 2020) - Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks</p> <p> </p> <p>This folder contains simulations starter files and also plug-and-play datasets to test ML algorithms on molecular dynamics (MD) simulation data.</p> <p> </p> <p>All analysis scripts can also be found on GitLab on this link: https://gitlab.com/atharva-kelkar/kelkar_et_al_jpcb_2020</p>
Data from: Indirect interactions influence contact network structure and diffusion dynamics
Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT) based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than SPST models for the study of various airborne diseases outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases.
Data from: Validation of network communicability metrics for the analysis of brain structural networks.
Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.
Data from: How the online social networks are used: dialogs-based structure of MySpace
Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors such as user's psychology profiles and their reasons to use the online contacts. In this study, we have compiled and analysed two datasets from MySpace. The data contain networked dialogues occurring within a specified time depth, high temporal resolution and texts of messages, in which the emotion valence is assessed by using the SentiStrength classifier. Performing a comprehensive analysis, we obtain three groups of results: dynamic topology of the dialogues-based networks have a characteristic structure with Zipf's distribution of communities, low link reciprocity and disassortative correlations. Overlaps supporting 'weak-ties' hypothesis are found to follow the laws recently conjectured for online games. Long-range temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion; patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and interactivity times longer than 1 day. Taken together, these results give a new insight into the functioning of online social networks and unveil the importance of the amount of information and emotion that is communicated along the social links. All data used in this study are fully anonymized.
Data from: Network structure and local adaptation in coevolving bacteria-phage interactions
Numerous theoretical and experimental studies have investigated antagonistic coevolution between parasites and their hosts. Although experimental tests of theory from a range of biological systems are largely concordant regarding the influence of several driving processes, we know little as to how mechanisms acting at the smallest scales (individual molecular and phenotypic changes) may result in the emergence of structures at larger scales, such as coevolutionary dynamics and local adaptation. We capitalized on methods commonly employed in community ecology to quantify how the structure of community interaction matrices, so called 'bipartite networks', reflected observed coevolutionary dynamics, and how phages from these communities may or may not have adapted locally to their bacterial hosts. We found a consistent nested network structure for two phage types, one previously demonstrated to exhibit arms race coevolutionary dynamics and the other fluctuating coevolutionary dynamics. Both phages increased their host ranges through evolutionary time, but we found no evidence for a trade off with impact on bacteria. Finally, only bacteria from the arms race phage showed local adaptation, and we provide preliminary evidence that these bacteria underwent (sometimes different) molecular changes in the wzy gene associated with the LPS receptor, while bacteria coevolving with the fluctuating selection phage did not show local adaptation and had partial deletions of the pilF gene associated with Type IV pili. We conclude that the structure of phage-bacteria interaction networks is not necessarily specific to coevolutionary dynamics, and discuss hypotheses for why only one of the two phages was, nevertheless, locally adapted.
Data from: Linking topological structure and dynamics in ecological networks
Interaction networks are basic descriptions of ecological communities and are at the core of community dynamics models. Knowledge of their structure should enable us to understand dynamical properties of ecological communities. However, the relationships between dynamical properties of communities and qualitative descriptors of network structure remain unclear. To improve our understanding of such relationships, we develop a framework based on the concept of strongly connected components, which are key structural components of networks necessary to explain stability properties such as persistence and robustness. We illustrate this framework for the analysis of qualitative empirical food webs and plant-plant interaction networks. Both types of networks exhibit high persistence (on average, 99% and 80% of species, respectively, are expected to persist) and robustness (only 0.2% and 2% of species are expected to disappear following the extinction of a species). Each of the networks is structured as a large group of interconnected species accompanied by much smaller groups that most often consist of a single species. This low-modularity configuration can be explained by a negative modularity-stability relationship. Our results suggest that ecological communities are not typically structured in multispecies compartments and that compartmentalization decreases robustness.
Data from: Structure-based network analysis of activation mechanisms in the ErbB family of receptor tyrosine kinases: the regulatory spine residues are global mediators of structural stability and allosteric interactions
The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced "superacceptor" activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues.
Data from: Spatially structured statistical network models for landscape genetics
A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape-genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non-binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape-genetic data. We further explore a set of spatial-statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape-genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape-genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.