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9,152 results for “Networking”
road network inference algorithms
<p>The document includes the Chicago trajectory dataset and several map inference algorithms.</p>
Data from: Using relatedness networks to infer contemporary dispersal: application to the endangered mammal Galemys pyrenaicus
Information about the degree of contemporary dispersal is important when trying to understand how populations interchange individuals and identify the specific barriers that prevent these movements. In the case of endangered species, this can represent crucial information when designing appropriate strategies that favor natural genetic exchange between populations. Here we analyze the parentage relationships between individuals from different localities and use these data to infer dispersal occurred in recent generations. We applied this approach to the Pyrenean desman (Galemys pyrenaicus), a semiaquatic and endangered species endemic to the Iberian Peninsula. We studied this species in four primary rivers in the Iberian Range, where two ancient mitochondrial lineages are separated by a strict contact zone but whose populations are more homogeneous at the genome level, suggesting the existence of complex dispersal patterns. Using next generation sequencing, we obtained 912 SNPs from each sample and estimated relatedness values between them. While relatedness networks were very dense within each river, we found surprisingly few relationships between individuals from different rivers despite their close proximity in some cases, indicating that dispersal between rivers is extremely low compared to dispersal within a single river. In agreement with this, the degree of inbreeding was exceedingly high in most individuals. These data show that relatedness information can be crucial to understand the contemporary dispersal patterns and conservation status of specific populations of endangered species.
A new structural theory of the elasticity of particle-filled networks
<p>A new structural theory of the elasticity of particle-filled networks</p>
Multiscale investigation of fat crystal networks
<p>Multiscale investigation of fat crystal networks</p>
Data from: Comprehensive experimental fitness landscape and evolutionary network for small RNA
The origin of life is believed to have progressed through an RNA world, in which RNA acted as both genetic material and functional molecules. The structure of the evolutionary fitness landscape of RNA would determine natural selection for the first functional sequences. Fitness landscapes are the subject of much speculation, but their structure is essentially unknown. Here we describe a comprehensive map of a fitness landscape, exploring nearly all of sequence space, for short RNAs surviving selection in vitro. With the exception of a small evolutionary network, we find that fitness peaks are largely isolated from one another, highlighting the importance of historical contingency and indicating that natural selection would be constrained to local exploration in the RNA world.
Data from: Collective forces of tumor spheroids in three-dimensional biopolymer networks
<p>We describe a method for quantifying the contractile forces that tumor spheroids collectively exert on highly nonlinear three-dimensional collagen networks. While three-dimensional traction force microscopy for single cells in a nonlinear matrix is computationally complex due to the variable cell shape, here we exploit the spherical symmetry of tumor spheroids to derive a scale-invariant relationship between spheroid contractility and the surrounding matrix deformations. This relationship allows us to directly translate the magnitude of matrix deformations to the total contractility of arbitrarily sized spheroids. We show that our method is accurate up to strains of 50% and remains valid even for irregularly shaped tissue samples when considering only the deformations in the far field. Finally, we demonstrate that collective forces of tumor spheroids reflect the contractility of individual cells for up to 1h after seeding, while collective forces on longer timescales are guided by mechanical feedback from the extracellular matrix.</p>
Data from: Quantitative analysis of connectivity in populations of a semi-aquatic mammal using kinship categories and network assortativity
Analyzing the impact of anthropogenic and natural river barriers on the dispersal of aquatic and semi-aquatic species may be critical for their conservation, but no adequate genetic methods have been developed for quantifying the effect of specific barriers on current connectivity. Knowledge of kinship relationships between individuals and reconstructions of pedigrees obtained using genomic data can be extremely useful, not only for studying the social organization of animals, but also inferring how the last few generations of offspring have dispersed. In this study, we used kinship data to analyze connectivity patterns in a small semi-aquatic mammal, the Pyrenean desman, in an area comprising two river systems with close headwaters and dams of various sizes. Using a large SNP dataset from 70 specimens, we obtained kinship categories and reconstructed pedigrees. To quantify the barrier effect of specific obstacles, we constructed kinship networks and devised a method based on the assortativity coefficient, which measures the proportion between observed and expected kinship relationships across a barrier. The estimation of this parameter enabled us to infer that the most important barrier in the area was the watershed divide between the rivers, followed by a dam on one of the rivers. Other barriers did not significantly reduce the expected number of kinship relationships across them. This strategy and the information obtained with it may be crucial in determining the most important connectivity problems in an area and help develop conservation plans aimed at improving genetic exchange between populations of threatened species.
Data from: RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells
The Golgi apparatus has many important physiological functions, including sorting of secretory cargo and biosynthesis of complex glycans. These functions depend on the intricate and compartmentalized organization of the Golgi apparatus. To investigate the mechanisms that regulate Golgi architecture, we developed a quantitative morphological assay using three different Golgi compartment markers and quantitative image analysis, and performed a kinome- and phosphatome-wide RNAi screen in HeLa cells. Depletion of 159 signaling genes, nearly 20% of genes assayed, induced strong and varied perturbations in Golgi morphology. Using bioinformatics data, a large regulatory network could be constructed. Specific subnetworks are involved in phosphoinositides regulation, acto-myosin dynamics and mitogen activated protein kinase signaling. Most gene depletion also affected Golgi functions, in particular glycan biosynthesis, suggesting that signaling cascades can control glycosylation directly at the Golgi level. Our results provide a genetic overview of the signaling pathways that control the Golgi apparatus in human cells.
Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network
<p>Dataset of the fMRI study in healthy subjects from "Robot-induced hallucinations in Parkinson’s disease depend on altered sensorimotor processing in fronto-temporal network"</p> <p>It contains the fMRI data from the healthy subjects that performed the robotic stimulation task inducing the presence hallucination.</p>
Scoring of research priorities for the Philippine Liver Research Network
<p>Health research networks are viewed as an avenue through which complex health problems can be solved. Networks apply multidisciplinary expertise and methodologies to strengthen current evidence for a particular disease or condition. In 2019, the Philippine Council for Health Research Development signed a bilateral agreement with the Fondazione Italiana Fegato. Recognizing the need for liver research in the Philippines, one of their main areas of collaboration is the creation of the Philippine Liver Research Network. This exercise aimed to develop a research agenda for the network. This research priority-setting exercise followed steps based on the Philippine National Health Research System Guidelines for Health Research Prioritization and the James Lind Alliance framework: 1) identify stakeholders to participate, 2) map existing liver studies in the Philippines, 3) generation of the interim list of priorities, 4) research priority-setting consultation, and 5) seeking feedback on the research agenda from stakeholders. Seven stakeholders participated in the research priority-setting exercise. Soliciting research questions from stakeholders resulted in sixty-five initial research priorities. The final research agenda was selected through criteria selection, weight assignment, and scoring of each research priority in a consultation. The Philippine Liver Research Network aims to consolidate and capitalize on multi-sectoral and mult-disciplinary collaboration opportunities to advance liver health research in the country. With the eventual establishment of the network, the interdisciplinary research agenda presents an important opportunity for further funding. Although the network is currently in its early stages of formation, this exercise may serve to aid in further organizing the network and its stakeholders under the research agenda for 2020-2022.</p>
Data from: Comparative developmental transcriptomics reveals rewiring of a highly conserved gene regulatory network during a major life history switch in the sea urchin genus Heliocidaris
The ecologically significant shift in developmental strategy from planktotrophic (feeding) to lecithotrophic (nonfeeding) development in the sea urchin genus Heliocidaris is one of the most comprehensively studied life history transitions in any animal. Although the evolution of lecithotrophy involved substantial changes to larval development and morphology, it is not known to what extent changes in gene expression underlie the developmental differences between species, nor do we understand how these changes evolved within the context of the well-defined gene regulatory network (GRN) underlying sea urchin development. To address these questions, we used RNA-seq to measure expression dynamics across development in three species: the lecithotroph Heliocidaris erythrogramma, the closely related planktotroph H. tuberculata, and an outgroup planktotroph Lytechinus variegatus. Using well-established statistical methods, we developed a novel framework for identifying, quantifying, and polarizing evolutionary changes in gene expression profiles across the transcriptome and within the GRN. We found that major changes in gene expression profiles were more numerous during the evolution of lecithotrophy than during the persistence of planktotrophy, and that genes with derived expression profiles in the lecithotroph displayed specific characteristics as a group that are consistent with the dramatically altered developmental program in this species. Compared to the transcriptome, changes in gene expression profiles within the GRN were even more pronounced in the lecithotroph. We found evidence for conservation and likely divergence of particular GRN regulatory interactions in the lecithotroph, as well as significant changes in the expression of genes with known roles in larval skeletogenesis. We further use coexpression analysis to identify genes of unknown function that may contribute to both conserved and derived developmental traits between species. Collectively, our results indicate that distinct evolutionary processes operate on gene expression during periods of life history conservation and periods of life history divergence, and that this contrast is even more pronounced within the GRN than across the transcriptome as a whole.
Dataset used in "Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network" by Kamrava et al. in Geophysical Research Letters.
<p>Morphology of Heterogeneous Porous Media</p>
Preliminary Evaluation of Hai-Nan Lightning Detection Network (HNLDN)
<p>Lightning location data for "Preliminary Evaluation of Hai-Nan Lightning Detection Network (HNLDN)"</p>
Data from: Using local ecological knowledge to build mutualistic networks in hyper-diverse and logistically challenging ecosystems
<p>1. Collecting interaction data to build frugivory or seed dispersal networks is logistically challenging in ecosystems that have very high plant and animal diversity and/or where fieldwork is difficult or dangerous. Consequently, the majority of available networks are from ecosystems with low species diversity or they represent a sub-set of the community. </p> <p>2. Here, we propose an approach using local ecological knowledge (LEK) of indigenous communities to build interaction databases and weighted networks that would otherwise be difficult to achieve with direct observations. Indigenous communities live and work in many hyper-diverse ecosystems and the people within these communities often have detailed knowledge of ecological processes. </p> <p>3. Working in a Sundaland biodiversity hotspot – Royal Belum State Park, Peninsular Malaysia – we used field data, visually-oriented interviews with indigenous people (Orang Asli, in the Jahai and Temiar ethnic subgroups), and published records to collate interactions, and their frequency of occurrence of animal fruit consumption and seed dispersal. </p> <p>4. We documented 2060 fruit consumption and 1330 seed dispersal interactions among 164 plant species and 34 animal taxa, the latter representing groups of closely related species or individual species. The majority of the interactions (97%) were identified by the LEK interviews, with the additional methods (field data and published records) used to support and marginally expand the interview data. The metrics for the networks we built reflect those of networks structured by biological mechanisms, supporting the validity of our novel method. </p> <p>5. Local ecological knowledge is highly relevant for building detailed databases for mutualistic interactions in hyper-diverse and/or challenging ecosystems. Such ecosystems are among the most vulnerable on earth, harbouring ecological interactions that are often poorly documented at a community-level. We show how LEK can broaden our knowledge of such sensitive ecosystems, but our approach is useful for any ecosystem where people retain rich local ecological knowledge.</p>
Data from: Insights into the ecology and evolution of polyploid plants through network analysis
Polyploidy is a widespread phenomenon throughout eukaryotes, with important ecological and evolutionary consequences. Although genes operate as components of complex pathways and networks, polyploid changes in genes and gene expression have typically been evaluated as either individual genes or as a part of broad-scale analyses. Network analysis has been fruitful in associating genomic and other 'omic'-based changes with phenotype for many systems. In polyploid species, network analysis has the potential not only to facilitate a better understanding of the complex 'omic' underpinnings of phenotypic and ecological traits common to polyploidy, but also to provide novel insight into the interaction among duplicated genes and genomes. This adds perspective to the global patterns of expression (and other 'omic') change that accompany polyploidy and to the patterns of recruitment and/or loss of genes following polyploidization. While network analysis in polyploid species faces challenges common to other analyses of duplicated genomes, present technologies combined with thoughtful experimental design provide a powerful system to explore polyploid evolution. Here, we demonstrate the utility and potential of network analysis to questions pertaining to polyploidy with an example involving evolution of the transgressively superior cotton fibres found in polyploid Gossypium hirsutum. By combining network analysis with prior knowledge, we provide further insights into the role of profilins in fibre domestication and exemplify the potential for network analysis in polyploid species.
Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion
<p>With the concept of 'riverscapes' long pending to be acknowledged in the 'landscape-centric' legislative framework of Himalayan nations, conservation of native riverine species stays practically unheeded. This necessitates urgent prioritization of stream networks to conserve the lotic taxa under invasion pressures. Himalayan riverscapes are pervaded with the invasive-exotic brown trout <i>Salmo trutta</i>,<i> </i>posing serious threats to the co-occurring native, the snow trout <i>Schizothorax richardsonii</i>. Using intensive surveys (218.7km) and geostatistical stream network models (n=537), we contrasted snow trout in two stream networks with and without invasives, for assessing differences in their spatial distribution. Our models indicate invasion-induced relegations of natives from the river mainstem into headwaters, with large sections of mainstem occupied by invasives. Furthermore, a concerningly small percentage of potential habitat left for natives to occupy in the mainstem is threatened, where a 100% overlap of native and invasive trout distributions is predicted. With a higher presence probability for the natives in headwaters of invaded watershed as compared to the non-invaded watershed, we highlight the headwater streams as<b> </b>potential refugia for the natives under invasion.</p> <p><i>Synthesis and Applications: </i>Our approach of basin-scale dendritic prioritization provides immediate management solutions to tackle brown trout invasion threats in Himalaya. We inform decisions on delineation of headwaters as invasion refugia for native fish, with assisted recovery of their fragmented populations in the river mainstems through targeted management of invasives</p>
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
<p><strong>Abstract:</strong><br> To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC estimates over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. We further show that our MC estimates correctly match the distribution of ground-truth observations, thus making the PGA paradigm amenable to physically grounded uncertainty quantification.</p>
Opinion of ethics committee representatives on the use of social networks and new technologies in clinical trials. Nominal group
<p>Nominal group database to find out the opinion of ethics committee representatives on the use of social networks and new technologies in clinical trials.</p>
Figure 1 from: Ćwiek-Kupczyńska H, Krajewski P (2021) Polish network of research infrastructure for plant phenotyping. Research Ideas and Outcomes 7: e73858. https://doi.org/10.3897/rio.7.e73858
Figure 1 The results of a survey conducted among Polish units involved in plant phenotyping in the part concerning national and international cooperation (n = 32).
Pollen transport networks reveal highly diverse and temporally stable plant-pollinator interactions in an Appalachian floral community
<p>Floral visitation alone has been typically used to characterize plant-pollinator interaction networks even though it ignores differences in the quality of floral visits (e.g. transport of pollen) and thus may overestimate the number and functional importance of pollinating interactions. However, how network structural properties differ between floral visitation and pollen transport networks is not well understood. Furthermore, the strength and frequency of plant-pollinator interactions may vary across fine temporal scales (within a single season) further limiting our predictive understanding of the drivers and consequences of plant-pollinator network structure. Thus, evaluating the structure of pollen transport networks and how they change within a flowering season may help increase our predictive understanding of the ecological consequences of plant-pollinator network structure. Here we compare plant-pollinator network structure using floral visitation and pollen transport data and evaluate within-season variation in pollen transport network structure in a diverse plant-pollinator community. Our results show that pollen transport networks provide a more accurate representation of the diversity of plant-pollinator interactions in a community but that floral visitation and pollen transport networks do not differ in overall network structure. Pollen transport network structure was relatively stable throughout the flowering season despite changes in plan and pollinator species composition. Overall, our study highlights the need to improve our understanding of the drivers of plant-pollinator network structure in order to more fully understand the process that govern the assembly of these interactions in nature.</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.