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308 results for “Dynamic Network”
Data from: Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods
Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting, and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.
Unconsciousness reconfigures modular brain network dynamics
<p>Time-dependent adjacency matrices per state of consciousness</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: 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: Accurate measurements of dynamics and reproducibility in small genetic networks
Quantification of gene expression has become a central tool for understanding genetic networks. In many systems the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a two-fold increase in profile reproducibility due to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes.
Data from: The architecture of river networks can drive the evolutionary dynamics of aquatic populations
It is widely recognized that physical landscapes can shape genetic variation within and between populations. However, it is not well understood how riverscapes, with their complex architectures, affect patterns of neutral genetic diversity. Using a spatially explicit agent-based modeling (ABM) approach, we evaluate the genetic consequences of dendritic river shapes on local population structure. We disentangle the relative contribution of specific river properties to observed patterns of genetic variation by evaluating how different branching architectures and downstream flow regimes affect the genetic structure of populations situated within river networks. Irrespective of the river length, our results illustrate that the extent of river branching, confluence position, and levels of asymmetric downstream migration dictate patterns of genetic variation in riverine populations. Indeed, comparisons between simple and highly branched rivers show a 20-fold increase in the overall genetic diversity and a 7-fold increase in the genetic differentiation between local populations. Given that most rivers have complex architectures, these results highlight the importance of incorporating riverscape information into evolutionary models of aquatic species and could help explain why riverine fishes represent a disproportionately large amount of global vertebrate diversity per unit of habitable area.
Data from: Dynamic antagonism between phytochromes and PIF-family bHLHs induces selective reciprocal responses to light and shade in a rapidly responsive transcriptional network in Arabidopsis
Plants respond to shade-modulated light-signals, via the phytochrome (phy) system, by adaptive changes, collectively termed the shade avoidance syndrome (SAS). To examine the roles of the Phy-Interacting bHLH Factors, PIF1, 3, 4 and 5, in relaying this information to the transcriptional network, we compared the genome-wide expression profiles of wild-type and quadruple pif (pifq) mutants in response to shade. The data identify a subset of genes, enriched in transcription-factor-encoding loci, that respond rapidly (within 1 h), in a PIF-dependent manner, to the shade signal, and that contain promoter-located G-box-sequence motifs (CACGTG), known to be preferred PIF binding sites. These genes are thus potential direct targets of phy-PIF signaling that function in the primary transcriptional circuitry controlling downstream response-elaboration. A second subset of PIF-dependent, early-response genes, lacking G-box motifs, are enriched for auxin-responsive loci, suggestive of being indirect targets of phy-PIF signaling involved in the rapid cell-expansion known to be induced by shade. A meta-analysis comparing deetiolation- and shade-responsive transcriptomes identifies a further subset of G-box-containing genes that reciprocally display rapid repression and induction in response to light and shade signals at the inception of deetiolation and shade-avoidance, respectively. Collectively, these data define a core set of transcriptional and hormonal (auxin, cytokinin) processes that appear to be dynamically poised to react rapidly to changes in the light environment via perturbations in the mutually antagonistic actions of the phys and PIFs. Data from comparative analysis of the quadruple pifq and all triple pif-mutant combinations in response to light and shade, confirm that the PIF-quartet members act with overlapping redundancy on seedling morphogenesis and transcriptional regulation, but that the individual PIFs contribute differentially to these responses.
Data from: Managing seagrass resilience under cumulative dredging affecting light: predicting risk using dynamic Bayesian networks
Coastal development is contributing to ongoing declines of ecosystems globally. Consequently, understanding the risks posed to these systems, and how they respond to successive disturbances, is paramount for their improved management. We study the cumulative impacts of maintenance dredging on seagrass ecosystems as a canonical example. Maintenance dredging causes disturbances lasting weeks to months, often repeated at yearly intervals. We present a risk-based modelling framework for time varying complex systems centred around a dynamic Bayesian network (DBN). Our approach estimates the impact of a hazard on a system's response in terms of resistance, recovery and persistence, commonly used to characterise the resilience of a system. We consider whole-of-system interactions including light reduction due to dredging (the hazard), the duration, frequency and start time of dredging, and ecosystem characteristics such as the life-history traits expressed by genera and local environmental conditions. The impact on resilience of dredging disturbances is evaluated using a validated seagrass ecosystem DBN for meadows of the genera Amphibolis (Jurien Bay, WA, Australia), Halophila (Hay Point, Qld, Australia) and Zostera (Gladstone, Qld, Australia). Although impacts varied by combinations of dredging parameters and the seagrass meadows being studied, in general, 3 months of duration or more, or repeat dredging every 3 or more years, were key thresholds beyond which resilience can be compromised. Additionally, managing light reduction to less than 50% can significantly decrease one or more of loss, recovery time and risk of local extinction, especially in the presence of cumulative stressors. Synthesis and applications. Our risk-based approach enables managers to develop thresholds by predicting the impact of different configurations of anthropogenic disturbances being managed. Many real-world maintenance dredging requirements fall within these parameters, and our results show that such dredging can be successfully managed to maintain healthy seagrass meadows in the absence of other disturbances. We evaluated opportunities for risk mitigation using time windows; periods during which the impact of dredging stress did not impair resilience.
Dysfunctions-of-Multiscale-Dynamic-Brain-Functional-Networks-in-Subjective-Cognitive-Decline
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Data for the publication "Mechanical communication within the microtubule through network-based analysis of tubulin dynamics"
<p>Repository containing all the necessary data to replicate the study "Mechanical communication within the microtubule through network-based analysis of tubulin dynamics", published in Biomechanics and Modeling in Mechanobiology (https://doi.org/10.1007/s10237-023-01792-5).</p>
The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading
<p>Dataset for the paper "The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading "</p> <p>- NullModels_NoOverlap.xlsx : Index of data files and summary of results for the Null Model and No Overlap Analyses</p> <p>- EphemeralGroups.xlsx: Index of data files and summary of results of a selection of tested configurations for #G=(1,10,100,1000). The corresponding zip files contain the data files for each #G</p>
Network topology and patch connectivity affect dynamics in experimental and model metapopulations
<p>Biological populations are rarely isolated in space and instead interact with others via dispersal in metapopulations. Theory predicts that network connectivity patterns can have critical effects on network robustness, as certain topologies, such as scale-free networks, are more tolerant to disturbances than other patterns. However, at present, experimental evidence of how these topologies affect population dynamics in a metapopulation framework is lacking. We used experimental metapopulations of the aquatic protist <i>Paramecium tetraurelia</i> to determine how network topology influences occupation patterns. We created metapopulations engineered to be comparable in linkage density, but differing in their degree distribution. We compared random networks to scale-free networks by evaluating local population occupancy and abundance throughout 18-30 protist generations. In parallel, we used simulations to explore differences in patch occupation patterns among topologies. Under one scenario, random metapopulations of P. tetraurelia reached higher abundance and higher occupancy (proportion of occupied patches) compared to scale-free systems in both experimental and simulated systems, while in the other both types of metapopulations performed similarly. Increasing patch degree (i.e., number of connections per patch) reduced the probability of extinction of local populations in both types of networks. We suggest the interaction between colonization/extinction rates and network topology alters the likelihood of rescue effects which results in differential patterns of occupancy and abundance in metapopulations.</p>
Data from: Anderson lab experiments from synthesizing the effects of spatial network structure on predator prey dynamics
<p>Predator-prey persistence is thought to be enhanced by spatial heterogeneity. Theory predicts that metacommunity size, spatial connectivity, network synchrony, predator identity, and productivity influence predator-prey persistence, through a variety of mechanisms such as statistical stabilization, colonization-extinction dynamics, and trophic interactions. However, comparative tests and synthesis of the multiple factors and mechanisms across different spatial networks are needed to understand which factors and mechanisms of spatial network structure promote predator-prey persistence. To address this gap between theory and empirical work, we synthesized data from 22 microcosm experiments of protist predator-prey communities differing the productivity, connectivity, and size of spatial habitat structure. Prey time to extinction was better explained by productivity and spatial factors than predator time to extinction. At the local and regional scale, metacommunity size and productivity had positive effects on prey occupancy, whereas connectivity negatively influenced prey occupancy. For predators, metacommunity size and connectivity had positive effects on predator occupancy, network synchrony had negative influences, and productivity showed a hump-shaped relationship with predator occupancy. Further, trophic interactions drove variation in the way species were spatially structured, where the strength and direction of predator and prey occupancy relationships varied among productivity levels and predator-prey combinations. In predator-prey interactions that were stronger, prey occupancy showed negative relationship with predator occupancy regardless of productivity. However, in predator-prey interactions that were weaker, prey occupancy was positively related to predator occupancy at low productivity, and this relationship disappeared at higher productivity treatments where prey occupancy was high regardless of predator occupancy. Predictions from metapopulation theory explained predator occupancy, while prey were better explained by trophic dynamics. Taken together, these results highlight that spatial network structure has a complex, spatially contingent relationship with predator-prey dynamics.</p>
research data supporting "Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"
<p>This repository contains all the data generated and analyzed including the starting structures, the input files, the trajectory files, the output data from cpptraj and network analyses, and in-house scripts used to prepare the network and module files shown in the paper <strong>"Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"</strong> published in <strong>Journal of Chemical Information and Modeling</strong> (DOI: 10.1021/acs.jcim.2c00101). </p>
Dynamic multi-dose vaccination with initial immunity on higher-order networks
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Multiple stressors drive multitrophic biodiversity and ecological network dynamics in a shrinking sandy lake
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Data from: Stimulation-based control of dynamic brain networks
The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.
Smartphone-Based Incentive Framework for Dynamic Network-Level Traffic Congestion Management Project H3
<p>Task 2 numerical experiment results</p>
Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
<p>This is training dataset for our publication with title "Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics" at arXiv https://doi.org/10.48550/arXiv.2404.14021</p>
ScienceDex guides
Understand access before you commit
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.