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1,721 results for “network data”
Data from: A bidirectional network for appetite control in larval zebrafish
Medial and lateral hypothalamic loci are known to suppress and enhance appetite, respectively, but the dynamics and functional significance of their interaction have yet to be explored. Here we report that, in larval zebrafish, primarily serotonergic neurons of the ventromedial caudal hypothalamus (cH) become increasingly active during food deprivation, whereas activity in the lateral hypothalamus (LH) is reduced. Exposure to food sensory and consummatory cues reverses the activity patterns of these two nuclei, consistent with their representation of opposing internal hunger states. Baseline activity is restored as food-deprived animals return to satiety via voracious feeding. The antagonistic relationship and functional importance of cH and LH activity patterns were confirmed by targeted stimulation and ablation of cH neurons. Collectively, the data allow us to propose a model in which these hypothalamic nuclei regulate different phases of hunger and satiety and coordinate energy balance via antagonistic control of distinct behavioral outputs.
Data from: Neuron-specific knockouts indicate the importance of network communication to Drosophila rhythmicity
<p>Animal circadian rhythms persist in constant darkness and are driven by intracellular transcription-translation feedback loops. Although these cellular oscillators communicate, isolated mammalian cellular clocks continue to tick away in darkness without intercellular communication. To investigate these issues in Drosophila, we assayed behavior as well as molecular rhythms within individual brain clock neurons while blocking communication within the ca. 150 neuron clock network. We also generated CRISPR-mediated neuron-specific circadian clock knockouts. The results point to two key clock neuron groups: loss of the clock within both regions but neither one alone has a strong behavioral phenotype in darkness; communication between these regions also contributes to circadian period determination. Under these dark conditions, the clock within one region persists without network communication. The clock within the famous PDF-expressing s-LNv neurons however was strongly dependent on network communication, likely because clock gene expression within these vulnerable sLNvs depends on neuronal firing or light</p>
Replication data for Convolutional neural networks with hierarchical context transfer for high-resolution spatiotemporal predictions
<p>Data represents number of posts from Instagram in area for six large cities (New York, London, Moscow, Vienna, Tokyo, and Saint Petersburg) covering 2017 and 2018. We aggregated data by hours and split each city using hierarchical area split by 10x10 grid with three levels. The size of the cell on the micro-level equals to 50 meters. Thus, we get 8760 examples for each year. Cells with zero number of posts in an hour are not listed.</p> <p>Data stored in JSON files with the following format:<br> {"2018-12-13 04:00:00":<br> {"(4, 4)":<br> {"sum":1,<br> "data":<br> {"(9, 6)":<br> {"sum":1,<br> "data":{"(2, 4)":1}}}}}}</p>
Resilience of patterned vegetation in the Sahel - network centrality data
Input data for a time-series analysis of the connectedness of vegetation from several locations within the Sahel. The analysis uses NDVI images from the Sentinel 2 satellite, from ESA's Copernicus program, downloaded from Google Earth Engine.
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
<p>Dataset for ICLR 2021 submission "Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling".</p>
Data from: Non-rhythmic head-direction cells in the parahippocampal region are not constrained by attractor network dynamics
Computational models postulate that HD cells are part of an attractor network integrating angular head turns. This network requires inputs from visual landmarks to anchor the HD signal to the external world. We investigated whether information about HD and visual landmarks is integrated in the medial entorhinal cortex and parasubiculum, resulting in neurons expressing a conjunctive code for HD and visual landmarks. We found that parahippocampal HD cells could be divided into two classes based on their theta-rhythmic activity: non-rhythmic and theta-rhythmic HD cells. Manipulations of the visual landmarks caused tuning curve alterations in most HD cells, with the largest visually driven changes observed in non-rhythmic HD cells. Importantly, the tuning modifications of non-rhythmic HD cells were often non-coherent across cells, refuting the notion that attractor-like dynamics control non-rhythmic HD cells. These findings reveal a new population of non-rhythmic HD cells whose malleable organization is controlled by visual landmarks.
Data from: Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection
Identifying local adaptation is crucial in conservation biology in order to define ecotypes and establish management guidelines. Local adaptation is often inferred from the detection of loci showing a high differentiation between populations, the so-called FST outliers. Methods of detection of loci under selection are reputed to be robust in most spatial population models. However, using simulations we showed that FST outlier tests provided a high rate of false positives (up to 60%) in fractal environments such as river networks. Surprisingly, the number of sampled demes was correlated with parameters of population genetic structure, such as the variance of FSTs, and hence strongly influenced the rate of outliers. This unappreciated property of river networks therefore needs to be accounted for in genetic studies on adaptation and conservation of river organisms.
Data from: Who is spreading avian influenza in the moving duck flock farming network of Indonesia?
Duck populations are considered to be a reservoir of Highly pathogenic avian influenza (HPAI) virus H5N1 in some agricultural production systems, as they are able to shed the virus for several days without clinical signs. Countries endemically affected with HPAI in Asia are characterised by production systems where ducks are fed on post-harvest spilled rice. During this scavenging process it is common for ducks to come into contact with other duck flocks or wild birds, thereby providing opportunities for virus spread. Effective risk management for HPAI has been significantly compromised by a limited understanding of management of moving duck flocks in these countries, despite of a small number of recent investigations. Here, for the first time, we described the management of moving duck flocks and the structure of the moving duck flock network in quantitative terms so that factors influencing the risk of HPAIV transmission can be identified. By following moving duck flock farmers over a period of 6 months in Java, Indonesia, we were able to describe the movement of flocks and to characterise the network of various types of actors associated with the production system. We used these data to estimate the basic reproductive number for HPAI virus spread. Our results suggest that focussing HPAI prevention measures on duck flocks alone will not be sufficient. Instead, the role of transporters of moving duck flocks, hatcheries and rice paddy owners, in the spread of the HPAI virus needs to be recognised.
Data from: Refuges from fire maintain pollinator-plant interaction networks
Fire is a major disturbance factor in many terrestrial ecosystems, leading to landscape transformation in fire‐prone areas. Species in mutualistic interactions are often highly sensitive to disturbances like fire events, but the degree and complexity of their responses are unclear. We use bipartite insect–flower interaction networks across a recently burned landscape to explore how plant–pollinator interaction networks respond to a recent major fire event at the landscape level, and where fire refuges were present. We also investigate the effectiveness of these refuges at different elevations (valley to hilltop) for the conservation of displaced flower‐visiting insects during fire events. Then, we explore how the degree of specialization of flower‐visiting insects changes across habitats with different levels of fire impact. We did this in natural areas in the Greater Cape Floristic Region (GCFR) biodiversity hotspot, which is species rich in plants and pollinators. Bees and beetles were the most frequent pollinators in interactions, followed by wasps and flies. Highest interaction activity was in the fire refuges and least in burned areas. Interactions also tracked flower abundance, which was highest in fire refuges in the valley and lowest in burned areas. Interactions consisted mostly of specialized flower visitors, especially in refuge areas. The interaction network and species specialization were lowest in burned areas. However, species common to at least two fire classes showed no significant difference in species specialization. We conclude that flower‐rich fire refuges sustain plant–pollinator interactions, especially those involving specialized species, in fire‐disturbed landscape. This may be an important shelter for specialized pollinator species at the time that the burned landscape goes through regrowth and succession as part of ecosystem recovery process after a major fire event.
Data from: The evolution of generalized reciprocity on social interaction networks
Generalized reciprocity ("help anyone, if helped by someone") is a minimal strategy capable of supporting cooperation between unrelated individuals. Its simplicity makes it an attractive model to explain the evolution of reciprocal altruism in animals that lack the information or cognitive skills needed for other types of reciprocity. Yet, generalized reciprocity is anonymous and thus defenseless against exploitation by defectors. Recognizing that animals hardly ever interact randomly, we investigate whether social network structure can mitigate this vulnerability. Our results show that heterogeneous interaction patterns strongly support the evolution of generalized reciprocity. The future probability of being rewarded for an altruistic act is inversely proportional to the average connectivity of the social network when cooperators are rare. Accordingly, sparse networks are conducive to the invasion of reciprocal altruism. Moreover, the evolutionary stability of cooperation is enhanced by a modular network structure. Communities of reciprocal altruists are protected against exploitation, because modularity increases the mean access time, i.e., the average number of steps that it takes for a random walk on the network to reach a defector. Sparseness and community structure are characteristic properties of vertebrate social interaction patterns, as illustrated by network data from natural populations ranging from fish to primates.
Data from: Modularity induced gating and delays in neuronal networks
Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition.
Data from: Modeling of kidney hemodynamics: probability-based topology of an arterial network
Through regulation of the extracellular fluid volume, the kidneys provide important long-term regulation of blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms that both produce oscillations. The nephrons are arranged in a complex branching structure that delivers blood to each nephron and, at the same time, provides a basis for an interaction between adjacent nephrons. The functional consequences of this interaction are not understood, and at present it is not possible to address this question experimentally. We provide experimental data and a new modeling approach to clarify this problem. To resolve details of microvascular structure, we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney. Using these results together with published micro-computed tomography (μCT) data we develop an algorithm for generating the renal arterial network. We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network. The model includes an implementation of electrical signal propagation along a vascular wall. Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons.
Data from: Mortality risk and social network position in resident killer whales: sex differences and the importance of resource abundance
An individual's ecological environment affects their mortality risk, which in turn has fundamental consequences for life history evolution. In many species social relationships are likely to be an important component of an individual's environment, and therefore their mortality risk. Here we examine the relationship between social position and mortality risk in resident killer whales (Orcinus orca) using over three decades of social and demographic data. We find that the social position of male, but not female, killer whales in their social unit predicts their mortality risk. More socially integrated males have a significantly lower risk of mortality than socially peripheral males, particularly in years of low prey abundance, suggesting that social position mediates access to resources. Male killer whales are larger and require more resources than females, increasing their vulnerability to starvation in years of low salmon abundance. More socially integrated males are likely to have better access to social information and food sharing opportunities which may enhance their survival in years of low salmon. Our results show that observable variation in the social environment is linked to variation in mortality risk, and highlight how sex differences in social effects on survival may be linked to sex differences in life-history evolution.
Data from: Flower preferences and pollen transport networks for cavity nesting solitary bees: implications for the design of agri-environment schemes
Floral foraging resources are valuable for pollinator conservation on farmland, and their provision is encouraged by agri-environment schemes in many countries. Across Europe, wildflower seed mixtures are widely sown on farmland to encourage pollinators, but the extent to which key pollinator groups such as solitary bees exploit and benefit from these resources is unclear. We used high-throughput sequencing of 164 pollen samples extracted from the brood cells of 6 common cavity nesting solitary bee species (Osmia bicornis, Osmia caerulescens, Megachile versicolor, Megachile ligniseca, Megachile centuncularis and Hylaeus confususi) which are widely distributed across the UK and Europe. We documented their pollen use across 19 farms in southern England, UK, revealing their forage plants and examining the structure of their pollen transport networks. Of the 32 plant species included currently in sown wildflower mixes, 15 were recorded as present within close foraging range of the bees on the study farms, but only Ranunculus acris L. was identified within the pollen samples. Rosa canina L. was the most commonly found of the 23 plant species identified in the pollen samples, suggesting that, in addition to providing a nesting resource for Megachile leafcutter bees, it may be an important forage plant for these species. Higher levels of connectance and nestedness were characteristic of pollen transport networks on farms with abundant floral resources, which may increase resilience to species loss. Our data suggest that plant species promoted currently by agri-environment schemes are not optimal for solitary bee foraging. If a diverse community of pollinators is to be supported on UK and European farmland, additional species such as Rosa canina should be encouraged to meet the foraging requirements of solitary bees.
Data from: Exploring the potential pharmacodynamic material basis and pharmacologic mechanism of the Fufang-Xialian-Capsule in chronic atrophic gastritis by network pharmacology approach based on the components absorbed into the blood
In this study, a new network pharmacology approach based on the components absorbed into the blood was utilized to investigate the pharmacodynamic material basis and the pharmacologic mechanism of the Fufang-Xialian-Capsule (FXL) in treating chronic atrophic gastritis (CAG). Initially, we confirmed the components absorbed into the blood by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Then, the network approach, which was based on the results of components absorbed into the blood, was utilized to analyze the pharmacodynamic material basis and the pharmacologic mechanism of FXL on treating CAG. As a result, 22 absorbed components were found in the rat's plasma. Given the results of the absorption analysis of the components, 8 pathways associated with CAG development were found. The targets linked to these pathways are the drug targets of FXL in CAG treatment. The components associated with these targets are the potential pharmacodynamic material basis and exert synergy in regulating pathways during CAG treatment.
Data from: Vocal networks remain stable after a disturbance in Emei music frogs
Social network analysis has been widely used to investigate the dynamics of social interactions and the evolution of social complexity across a range of taxa. Anuran species are highly dependent on vocal communication in mate choice; however, these species have rarely been the subject of social network analysis. The present study used social network analysis to investigate whether vocal network structures are consistent in Emei music frog (Babina daunchina) after the introduction of a simulated exotic rival of varying competitiveness into the social group. We broadcasted six categories of artificial calls (either highly sexually attractive calls produced from inside male nests or calls of low sexual attractiveness produced outside nests with three, five or seven notes, respectively) to simulate an intruder with different levels of competitiveness. We then constructed vocal networks for two time periods (before and after the disturbance) and quantified three network metrics (strength, closeness and betweenness) that measure different aspects of individual-level position. We used the mean values of these network metrics to evaluate group-level changes in network structure. We found that the mean strength, mean closeness and mean betweenness were consistent between two time periods in all ponds, despite the fact that the positions of some individuals had changed markedly after disturbance. In addition, there was no significant interaction effect between period and numbers of notes on the three network metrics. These finding suggest that the structure of vocal networks in Emei music frogs remain stable at the group level after a conspecific disturbance, regardless of the intruder's competitiveness.
Data from: It's all about connections: hubs and invasion in habitat networks
During the early stages of invasion, the interaction between the features of the invaded landscape, notably its spatial structure, and the internal dynamics of an introduced population, has a crucial impact on establishment and spread. By approximating introduction areas as networks of patches linked by dispersal, we characterized their spatial structure with specific metrics and tested their impact on two essential steps of the invasion process: establishment and spread. By combining simulations with experimental introductions of Trichogramma chilonis (Hymenoptera: Trichogrammatidae) in artificial laboratory microcosms, we demonstrated that spread was hindered by clusters and accelerated by hubs but was also affected by small-population mechanisms prevalent for invasions, such as Allee effects. Establishment was also affected by demographic mechanisms, in interaction with network metrics. These results highlight the importance of considering the demography of invaders as well as the structure of the invaded area to predict the outcome of invasions.
Data from: Mirrored STDP implements autoencoder learning in a network of spiking neurons
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.
Data from: Abnormal thalamo-cortical network dynamics in migraine
Objective: To investigate the dynamic functional connectivity of thalamo-cortical networks in interictal migraine patients and whether clinical features are associated with abnormal connectivity. Methods: We investigated dynamic functional network connectivity (dFNC) of the migraine brain in 89 interictal migraine patients and 70 healthy controls. We focused on the temporal properties of thalamocortical connectivity using sliding window cross-correlation, clustering state analysis, and graph-theory methods. Relationships between clinical symptoms and abnormal dFNC were evaluated using a multivariate linear regression model. Results: Five dFNC brain states were identified to characterize and compare dynamic functional connectivity patterns. We demonstrated that migraineurs spent more time in a strongly interconnected between-network state, but they spent less time in a sparsely-connected state. Interestingly, we found that abnormal posterior thalamus (pulvinar nucleus) dFNC with the visual cortex and the precuneus were significantly correlated with headache frequency of migraine. Further topological measures revealed migraineurs had significantly lower efficiency of information transfer in both global and local dFNC. Conclusion: Our results demonstrated a transient pathological state with atypical thalamo-cortical connectivity in migraineurs and extended current findings regarding abnormal thalamo-cortical networks and dysrhythmia in migraine.
Data from: ConfeitoGUI: a toolkit for size-sensitive community detection from a correlation network
Analysis of the large amounts of data accumulated in public databanks can facilitate a more comprehensive understanding of molecular biological processes. Community detection from molecular biological data is paramount in characterizing evolutionary and functional traits of organisms based on gene homology and co-expression, respectively. Although there are common tools to detect local communities from a large network, no toolkit exists for detecting communities that include an element of interest based on size sensitivity, i.e., functionality to obtain local communities with preferred sizes. Herein, we present the ConfeitoGUI toolkit for detecting local communities from a correlation network involving size sensitivity. We compared the toolkit with other common tools for detection in reconstructing communities of microarray experiments of mice. In the results, ConfeitoGUI was observed to be preferable for detecting communities whose sizes are similar to those of original communities compared to other common tools. By changing simple parameters representing sizes for the toolkit, a user can obtain local communities with preferred sizes, which is beneficial for further analysis of members belonging to the communities.
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.