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ShareScore release 0.7.1
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144 results for “Complex networks”
Dataset - A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress
<p>Plant immunity is often negatively impacted by heat stress. However, the underlying molecular mechanisms remain poorly characterized. Based on a genome-wide association mapping approach, this study aims to identify in <em>Arabidopsis thaliana</em> the genetic bases of robust resistance mechanisms to the devastating pathogen<em> Ralstonia solanacearum</em> under heat stress. A local mapping population was phenotyped against the <em>R. solanacearum</em> GMI1000 strain at 27 and 30 °C. To obtain a precise description of the genetic architecture underlying natural variation of quantitative disease resistance (QDR), we applied a genome-wide local score analysis. Alongside an extensive genetic variation found in this local population at both temperatures, we observed a playful dynamics of quantitative trait loci along the infection stages. In addition, a complex genetic network of interacting loci could be detected at 30 °C. As a first step to investigate the underlying molecular mechanisms, the atypical meiotic cyclin <em>SOLO DANCERS</em> gene was validated by a reverse genetic approach as involved in QDR to <em>R. solanacearum </em>at 30 °C. In the context of climate change, the complex genetic architecture underlying QDR under heat stress in a local mapping population revealed candidate genes with diverse molecular functions.</p>
A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions
<p>Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ~380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands, and binding partners for 'orphan' receptors. New PPIs include proteins expressed on multiple cell types, and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance, and may offer new therapeutic drug targets.</p>
Data from: Genetic specificity of a plant-insect food web: implications for linking genetic variation to network complexity
Theory predicts that intraspecific genetic variation can increase the complexity of an ecological network. To date, however, we are lacking empirical knowledge of the extent to which genetic variation determines the assembly of ecological networks, as well as how the gain or loss of genetic variation will affect network structure. To address this knowledge gap, we used a common garden experiment to quantify the extent to which heritable trait variation in a host plant determines the assembly of its associated insect food web (network of trophic interactions). We then used a resampling procedure to simulate the additive effects of genetic variation on overall food-web complexity. We found that trait variation among host-plant genotypes was associated with resistance to insect herbivores, which indirectly affected interactions between herbivores and their insect parasitoids. Direct and indirect genetic effects resulted in distinct compositions of trophic interactions associated with each host-plant genotype. Moreover, our simulations suggest that food-web complexity would increase by 20% over the range of genetic variation in the experimental population of host plants. Taken together, our results indicate that intraspecific genetic variation can play a key role in structuring ecological networks, which may in turn affect network persistence.
Data from: NetView P: a network visualization tool to unravel complex population structure using genome-wide SNPs
Network-based approaches are emerging as valuable tools for the analysis of complex genetic structure in both wild and captive populations. NetView P combines data quality control with the construction of population networks based on mutual k-nearest-neighbours thresholds applied to genome-wide SNPs. The program is cross-platform compatible, open-source and efficiently operates on data ranging from hundreds to hundreds of thousands of SNPs through multiprocessing in Python. We used the pipeline for the analysis of pedigree data from simulated (n = 750, SNPs = 1279) and captive Silver-lipped Pearl Oysters (n = 415, SNPs = 1107), wild populations of the European Hake from the Atlantic and Mediterranean (n = 834, SNPs = 380) and Gray Wolves from North America (n = 239, SNPs = 86,103). The population networks effectively visualize large- and fine-scale genetic structure within and between populations, including family-level structure and relationships. NetView P comprises a network-based addition to other population analysis tools and provides user-friendly access to a complex network analysis pipeline through implementation in Python.
Ecological network complexity scales with area
<p><span><span><span><span><span><span><span><span><span><span><span>Larger geographical areas contain more species–an observation raised to a law in ecology. Less explored is whether biodiversity changes are accompanied by a modification of interaction networks. We use data from 32 spatial interaction networks from different ecosystems to analyse how network structure changes with area. We find that basic community structure descriptors (number of species, links, and links per species) increase with area following a power-law. Yet, the distribution of links per species varies little with area, indicating that the fundamental organization of interactions within networks is conserved. Our null model analyses suggest that the spatial scaling of network structure is determined by factors beyond species richness and the number of links. We demonstrate that biodiversity–area relationships can be extended from species counts to higher levels of network complexity. Therefore, the consequences of anthropogenic habitat destruction may extend from species loss to wider simplification of natural communities.</span></span></span></span></span></span></span></span></span></span></span></p>
Fig. 2 Median joining network for the seven cytochrome b in Ecomorphology of a generalist freshwater gastropod: complex relations of shell morphology, habitat, and fecundity
Fig. 2 Median joining network for the seven cytochrome b haplotypes found in 979 New Zealand Potamopyrgus antipodarum and sampling sites. Each branch represents a single nucleotide substitution and short transversal lines as well as small black circles unsampled haplotypes. Size of circles is proportional to number of individuals per haplotype
The data for "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework"
<p>The data is for supporting our research paper titled "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework".</p>
Calcium-rich parent materials enhance multiple soil functions and bacterial network complexity
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Control Network Neuromodulation to Enhance Cognitive Training in Complex Traumatic Brain Injury
ClinicalTrials.gov study NCT05236010. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Microsites of seed arrival: spatio-temporal variations in complex seed-disperser networks
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Dataset - A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress
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Data from: Genetic specificity of a plant-insect food web: implications for linking genetic variation to network complexity
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Data from: A causal role for the precuneus in network-wide theta and gamma oscillatory activity during complex memory retrieval
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Data from: NetView P: a network visualization tool to unravel complex population structure using genome-wide SNPs
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A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions
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Ecological network complexity scales with area
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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>
Data from: Maximum parsimony inference of phylogenetic networks in the presence of polyploid complexes
<p>Phylogenetic networks provide a powerful framework for modeling and analyzing reticulate evolutionary histories. While polyploidy has been shown to be prevalent not only in plants but also in other groups of eukaryotic species, most work done thus far on phylogenetic network inference assumes diploid hybridization. These inference methods have been applied, with varying degrees of success, to data sets with polyploid species, even though polyploidy violates the mathematical assumptions underlying these methods. Statistical methods were developed recently for handling specific types of polyploids and so were parsimony methods that could handle polyploidy more generally yet while excluding processes such as incomplete lineage sorting.</p> <p>In this paper, we introduce a new method for inferring most parsimonious phylogenetic networks on data that include polyploid species. Taking gene trees as input, the method seeks a phylogenetic network that minimizes deep coalescences while accounting for polyploidy. The method could also infer trees, thus potentially distinguishing between auto- and allo-polyploidy. We demonstrate the performance of the method on both simulated and biological data. The inference method as well as a method for evaluating given phylogenetic networks are implemented and publicly available in the PhyloNet software package.</p>
Data from: A model to identify urban traffic congestion hotspots in complex networks
The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities' road networks, considering, in some experiments, real- traffic data, show that the proposed model is capable of identifying susceptible junctions that might becomes hotspots if mobility demand increases.
Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
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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.