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364 results for “Network interaction”
Changes in the structure of seed dispersal networks when including interaction outcomes from both plant and animal perspectives
<p>Interaction frequency is the most common currency in quantitative ecological networks, although interaction quality can also affect benefits provided by mutualisms. Here, we evaluate if interaction quality can modify network topology, species' role and whether such changes affect community vulnerability to species loss. We use a well-examined study system (bird-lizard and fleshy-fruited plants in the 'thermophilous' woodland of the Canary Islands) to compare network and species-level metrics from a network based on fruit consumption rates (Interaction Frequency, IF), against networks reflecting functional outcomes: a Seed Dispersal Effectiveness network (SDE) quantifying recruitment, and a Fruit Resource Provisioning network (FRP), accounting for the nutrient supply of fruits. Nestedness decreased in the FRP and the SDE networks, due to the lack of association between fruit consumption rates and (1) nutrient content, and (2) recruitment at the seed deposition sites, respectively. The FRP network showed lower niche overlap due to resource use complementarity among frugivores. Interaction evenness was lower in the SDE network, in response to a higher dominance of lizards in the recruitment of heliophilous species. Such changes, however, did not result in enhanced vulnerability against extinctions. At the plant species level, strength changed in the FRP network in frequently consumed or highly nutritious species. The number of effective partners decreased for species whose seeds were deposited in unsuitable places for recruitment. In frugivores, strength was consistent across networks (SDE vs IF), showing that consumption rates outweighed differences in dispersal quality. In the case of lizards, the increased importance of nutrient-rich species resulted in a higher number of effective partners.</p> <p>Our work shows that although frequency strongly impacts interaction effects, accounting for quality improves our inferences about interaction assembly and species role. Thus, future studies including interaction outcomes from both partners' perspectives will provide valuable insights about the net effects of mutualistic interactions.</p>
An innovative approach combining metabarcoding and ecological interaction networks for selecting candidate biological control agents
<p>Classical biological control (CBC) can be used to decrease the density of invasive species to below an acceptable ecological and economic threshold. Natural enemies specific to the invasive species are selected from its native range and released into the invaded range. This approach has drawbacks, despite the performance of specificity tests to ensure its safety, because the fundamental host range defined under controlled conditions does not represent the actual host range <em>in natura, </em>and these tests omit indirect interactions within community.</p> <p>We focus on <em>Sonchus oleraceus</em> (Asteraceae), a weed species originating from Western Palearctic that is invasive in many countries and notably in Australia. We explore how analyses of interaction network within its native range can be used to 1) inventory herbivores associated to the target plant, 2) characterize their ecological host ranges, and 3) guide the selection of candidate biocontrol agents considering interactions with species from higher trophic levels. Arthropods were collected from plant community sympatric to <em>S. oleraceus</em>, in three bioclimatic regions, and interactions were inferred by a combination of molecular and morphological approaches.</p> <p>The networks reconstructed were structured in several trophic levels from basal species (plant community), to intermediate and top species (herbivorous arthropods and their natural enemies). The subnetwork centered on <em>S. oleraceus</em> related interactions contained 116 taxa and 213 interactions. We identified 47 herbivores feeding on <em>S. oleraceus</em>, 15 of which were specific to the target species. Some discrepancies with respect to published findings or conventional specificity tests suggested possible insufficient sampling effort for the recording of interactions or the existence of cryptic species. Among potential candidate agents, 6 exhibited interactions with natural enemies.</p> <p>Synthesis and applications: Adopting a network approach as prerequisite step of the classical biological control program can provide a rapid screening of potential agents to be tested in priority. Once ecological host range defined, we suggest that priority should be given to agent used by a minimum species, and, when they exist, to agents that possess enemies from the most distant taxonomical group from those occurring in the range of introduction.</p>
OMEN data set: Tokheim pancancer data set with genome-wide interaction network
<p>OMEN is a Network-based Driver Gene Identification method that exploits Mutual Exclusivity.<br> This repository stores the data used in a pancancer experiment showcasing this method.</p> <p>It consists of</p> <p>- [tokheim_pancancer_somatic_CADD.pl] A file containing CADD probabilities for gene-patient pairs (derived from the pancancer data set in Tokheim, Collin J., et al. "Evaluating the evaluation of cancer driver genes." <em>Proceedings of the National Academy of Sciences</em> 113.50 (2016): 14330-14335.) formatted to be used by OMEN.<br> - [tokheim_pancancer_somatic_coverage_ranks.pl] A file containing gene coverage data (derived from the CADD data) formatted to be used by OMEN.<br> - [network.pl] A file containing a genome-wide interaction network consisting of high quality metabolic interactions from Recon X and literature curated interactions from Intact. Recon X and Intact data was acquired from Pathway Commons version 8.<br> The resulting network covers 7901 samples, and contains 15.694 nodes and 178.051 edges.</p>
Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks
<p>Seed dispersal by animals is one of the most important ecological processes in tropical forests, entailing millions of years of evolutionary adaptations of plants and frugivorous animals forming networks of interactions that, ultimately, contribute to the resilience of such forests. We analyze 29 seed dispersal networks in the threatened Atlantic Forest biodiversity hotspot, with data on the frequency of feeding visits by birds to fruiting plants to answer: (1) which are the effects of forest cover and landscape connectivity on the maintenance of phylogenetic diversity (PD) of interacting birds and plants and the evolutionary distinctiveness of the interactions (EDi) between them; and (2) how EDi and plant/bird PD affects the robustness of the interaction networks? We found that forest cover positively influences both plant and bird PD and EDi. Landscape connectivity is an important predictor of bird PD, but not plant PD, suggesting that the spatial arrangement of forest remnants is essential for guaranteeing bird movement among forest fragments. Furthermore, interaction networks of areas with higher PD and EDi had great robustness to the simulated extinction of species, which underscore the importance of larger forest blocks for conserving evolutionary information and, consequently, the health and natural resistance of seed dispersal networks against environmental change.</p>
Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"
<p>This dataset contains the untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community". The dataset has also been submitted to MetaboLights repository with ID "MTBLS3535". Please refer to the MetaboLights repository for the most up-to-date datasets. </p> <p>Publication abstract:</p> <p>A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM<sup>12</sup>) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM<sup>12</sup> interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, <em>Enterococcus faecalis</em> KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM<sup>12</sup> interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.</p>
Integrating nocturnal and diurnal interactions in a Neotropical pollination network
<p><span>Plants establish pollination interactions with different groups of animals, including nocturnal ones that establish interactions with economically valuable and culturally important crops, as well as wild plants of conservation concern. Despite the considerable number of studies addressing the structure and dynamic of pollination networks, nocturnal interactions have been relatively overlooked. Using a multilayer network approach and considering diurnal and nocturnal interactions, we aimed to understand how interactions at different periods of the day are integrated and contribute to the network structural pattern. We also aimed to highlight how multilayer networks may give a more nuanced assessment of species importance across layers.</span></p> <p><span>We assembled a pollination network of an intensively studied Neotropical area by standardizing interaction data from 16 previous studies into a presence/absence (binary) network. Then we used a multilayer network approach to evaluate the network modularity and plant </span><span>species'</span><span> roles in these different temporal layers. Plants were classified as nocturnal or diurnal according to the onset of floral opening and pollinators were classified according to their foraging period. </span></p> <p><span>The network consisted of 178 pollinator species and 158 plant species, with 870 links. Among plant species, 135 species have diurnal floral opening while 23 species are nocturnal. The multilayer network was significantly modular, and these modules differed in the composition of pollinator groups (e.g., hawk moths, bats, bees, hummingbirds), as well as of diurnal and nocturnal plants. We show that diurnal and nocturnal interactions are organized into interconnected modules in the multilayer network. Nocturnal plants had higher values of versatility and multidegree than diurnal plants, due to their role in connecting the two temporal layers.</span></p> <p><span><em>Synthesis</em></span><span>. Our study highlights the importance of integrating different pollination systems to understand the importance of distinct components that structure pollination networks. We also illustrate the value of tapping into existing information, </span><span>particularly species interaction data, </span><span>from well studied biodiversity hotspot areas, to gain a better understanding of how communities are structured. Finally, despite the relative scarcity of nocturnal pollination network studies, we showed nocturnal plants, which often make complementary use of diurnal pollinators, to be important in connecting the temporal layers.</span></p>
Data supporting: Drivers of individual-based, antagonistic interaction networks during plant range expansion
<p><span>1. Range expansion in plant populations, especially at the colonization front, can be either limited by disproportionately large effects of antagonistic interactions or facilitated by their release. How the strength of antagonistic interactions changes along successional gradients during range expansion is still poorly documented, especially when diverse assemblages of plant antagonists (rodents, invertebrates, and birds) combine within interaction networks.</span></p> <p><span>2. We study the changes in individual-based, predispersal seed-pulp predator networks along a colonization gradient in a rapidly-expanding <em>Juniperus phoenicea</em> population in Doñana National Park (SW Spain). Additionally, we analysed the role of individual plant traits and neighbourhood attributes in network configuration by using Exponential Random Graph Models.</span></p> <p><span>3. Seven seed-pulp consumer animal species varied significantly in their frequency of interaction and prevalence. While invertebrate species were well established in old and intermediately mature stands, greenfinch (<em>Chloris chloris</em>) was dominant at the colonization front. Variable species roles and spread of interactions among individual plants generated changes in the configuration of interactions during plant expansion.</span></p> <p><span>4. Individual plant traits strongly determined the topology of these networks, although with differences between stands. Increasing individual crop size and seeds per cone increased the interaction odds of individual plants, while seed viability showed the opposite effect. The network topology at the colonization front appeared less driven by individual traits, possibly because of the short interaction history of this recently established area. The disproportionately large effect of <em>C. chloris</em> in these recently established stands, potentially resulted in large seed losses during range expansion.</span></p> <p><span>5.</span><em><span> Synthesis</span></em><span>. Turnover of antagonistic interactions, characterized the colonization front, resulting in more heterogeneous interaction strengths among individual plants. We found no evidence for a complete or sizeable antagonistic release of <em>J. phoenicea</em> at the colonization front promoting this rapid expansion. It becomes necessary to explore interactions with seed dispersers to understand how antagonistic and mutualistic plant-animal interactions balance during range expansion. Our study highlights the importance of an individual-based approach in understanding how interactions are structured and driven in natural changing landscapes.</span></p>
Predicting compound-protein interaction using hierarchical graph convolutional networks
<p>This repository contains the datasets which are used in the article "Predicting Compound-Protein Interaction using Hierarchical Graph Convolutional Networks".</p>
Figure 3 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 3. Comparison of the number of interactions between frugivorous birds and plants between native forest and eucalyptus plantation in the PEIT. (a): Fecal samples interactions (P-value = 0.83, W = 3.5); (b): Focal observation interactions (P-value = 0.99, W = 4.0).
Figure 2 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 2. Interaction networks between frugivorous birds and zoochoric plants, according to the focal observations of birds in both sampled habitats. The circles represent the plant species, and the species of birds are represented by triangles. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with between species from other clusters due to its modularity (Q). (a): Fragments of native forest; (b): Fragments of eucalyptus plantation.
Figure 1 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 1. Interaction networks between frugivorous birds and zoochoric plants, according to the fecal samples of birds in the understory of the two sampled habitats. The circles represent the plant species, and the triangles are representing the species of birds. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with species from other clusters due to its modularity (Q). (a): F fragments of native forest; (b): Fragments of eucalyptus plantation.
Data Set for the Journal Article "SCINE - Software for Chemical Interaction Networks"
<p>This data archive contains all data and software described and used in the following publication:</p> <p>Thomas Weymuth, Jan P. Unsleber, Paul L. Türtscher, Miguel Steiner, Jan-Grimo Sobez, Charlotte H.<br>Müller, Maximilian Mörchen, Veronika Klasovita, Stephanie A. Grimmel, Marco Eckhoff, Katja-Sophia<br>Csizi, Francesco Bosia, Moritz Bensberg, Markus Reiher, "SCINE --- Software for Chemical Interaction<br>Networks", in preparation.</p> <p>The directory structure is as follows:</p> <ul> <li>software: contains all software used for the example exploration <ul> <li>requirements.txt: lists all Python packages needed to create the virtual environment with which the exploration has been carried out; the virtual environment was created with Python 3.6.8.</li> <li>start.py: script to initialize the database with the reactants</li> <li>step_1.py: script to create the first set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>step_2.py: script to create the second set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>puffin_1.3.0.sif: Singularity image containing a full Puffin instance (version 1.3.0) to execute all calculations of the exploration</li> <li>submit_container.sh: script to submit the Puffin image to the queueing system</li> </ul> </li> <li>data: contains a complete dump of the database created during the example exploration; additionally, this directory contains a script called "import.sh" which can be used to reimport the data into a MongoDB instance</li> </ul>
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
<p>Data used to train multiclass and binary neural networks to analyse SiPM (Silicon Photomultiplier) signals in a multiplexed array of 16 detectors and detect the signal detector origin. Data acquired using an oscilloscope. Results compared with previous anger logic methods. </p> <p>Dataset used in the publication</p> <p>"Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks"</p> <p>https://doi.org/10.1088/2057-1976/ad4f73</p>
HerpesFolds: All-versus-all protein interaction network
<p>For HSV-1, HCMV, and KSHV, all possible protein-protein combinations within the species were structurally predicted with AlphaFold 2.3. A user interface and network visualization can be found at: https://www.bosse-lab.org/herpesfolds/</p>
Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks - Supporting Dataset
<div>Supporting dataset for the numerical experiments in the manuscript <em>Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks</em>, consisting of a tar.gz archive containing the following directories:</div> <h3>learnext_dataset</h3> <div>Dataset used to train the DeepONet mesh motion model. For one period of structure deformation in the FSI benchmark problem 2 of Turek and Hron (2006), contains the harmonic mesh motion in input and biharmonic mesh motion in output, relative to the undeformed domain.</div> <h3>mesh</h3> <div>Mesh of the FSI benchmark problem 2 used to run FSI simulations to test DeepONet mesh motion.</div> <h3>Warmstart checkpoint</h3> <div>State checkpoint of FSI benchmark problem 2 run for 15 simulation seconds with trained DeepONet mesh motion. Used to warmstart the FSI simulations to verify quantities of interest produced from DeepONet mesh motion by comparing it with ones from biharmonic mesh motion.</div> <h3>grav-test</h3> <div>Dataset used in gravity-driven deformation test of DeepONet mesh motion.</div> <h3>best_run_model</h3> <div>Saved, pretrained branch and trunk networks from the best run of the hyperparameter study and problem-file needed to build the DeepONet mesh motion from it.</div>
Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations.
<p>Repository containing dataset and code for the manuscript entitled <em>Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations</em>.</p> <p>For this study, we compiled 46 empirical individual-based networks on plant-animal seed dispersal mutualism, encompassing 1037 plant individuals across 29 species from various regions. We compare the structure of individual-based networks to that of species-based networks and by extending the niche concept to interaction assemblages, we explore levels of individual plant specialization. We examine how individual variation influences network structure and how plant individuals "explore" the interaction niche of the population.</p> <p>Please refer to <strong>makefile.R</strong> for project outline, explanation and codes used, and to the <strong>README</strong> in networks folder for data structure and compilation.</p>
Protein interaction networks are substantially rewired across evolution
<p>Complete data and source code deposit for Protein interaction networks are substantially rewired across evolution.</p>
Interaction-Based Behavioral Analysis in Twitter Social Network
<p>Literature studies usually use data sets consisting of data collected from many different metrics and user counts collected over different time periods. The data set used in this article was formed using completely up-to-date data obtained as a result of metrics measured in terms of scope and efficiency, sufficient and effective user counts, and filtering processes. To classify users correctly and make the classification performance high—in addition to parameters used in the literature such as tweets, account age, follower rank, average retweets and average likes—other parameters such as diameter, density, reciprocity, centralization and modularity were used. These metrics are the parameters that focus on a different area to reveal many aspects in which social network users interact. The data used to create the data set was collected from Twitter. The metric data forming the data set was extracted using Twitter Rest API V1.1 supporting search/tweet endpoints by means of the SocialBlade and Netlytic platforms.</p>
Interactions of pharmaceutical companies with world countries, cancers and rare diseases from Wikipedia network analysis
<p>Using the English Wikipedia network of more than 5 million articles we analyze interactions and interlinks between the 34 largest pharmaceutical companies, 195 world countries, 47 rare renal diseases and 37 types of cancer. The recently developed algorithm using a reduced Google matrix (REGOMAX) allows us to take account both of direct Markov transitions between these articles and also of indirect transitions generated by the pathways between them<br> via the global Wikipedia network. This approach therefore provides a compact description of interactions between these articles that allows us to determine the friendship networks between them, as well as the PageRank sensitivity<br> of countries to pharmaceutical companies and rare renal diseases. We also show that the top pharmaceutical companies in terms of their Wikipedia PageRankvare not those with the highest market capitalization.</p>
Dataset for the paper "Network-Based Differential Abundance Analysis: Bridging Community Interactions and Host-Microbiome Dynamics."
<p>The files with extension rds are files that contain simulated data and the tsv files contain original data along with their meta data.</p>
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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.