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144 results for “complex networks”
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Benchmark data sets of CDPred as described in</p> <p><strong>Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks</strong></p> <p>Zhiye Guo<sup>1</sup>, Jian Liu<sup>1</sup>, Jeffrey Skolnick<sup>2</sup>, Jianlin Cheng<sup>1*</sup></p> <p><sup>1 </sup>Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211</p> <p><sup>2 </sup>School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000</p> <p>*Corresponding author (chengji@missouri.edu)</p> <p>There is four test dataset in this package, each test dataset contains four different folders and one list file. The <strong>afpred_pdb</strong> includes all the corresponding monomer structures predicted by alphafold. The <strong>cdpred_output </strong>includes the prediction results of our tool CDPred for each dataset. The <strong>pre_gen_a3m </strong>includes the multiple sequence alignments file used by CDPred to generate prediction results. And the <strong>true_pdb </strong>includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure.</p> <p>HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13</p> <p>HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14</p> <p>HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14</p> <p>HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021</p>
Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks
<pre>The data are part of the doctoral work entitled: Water quality of the Jaguari and Atibaia rivers and relationships among parameters: a study based on Complex Networks.</pre>
Resolving higher-level phylogenetic networks with repeated hybridization in a complex of polytypic salamanders (Plethodontidae: Desmognathus)
<p><span>Repeated hybridization between incipient lineages is a common feature of ecological speciation and ecomorphological diversification. However, computational constraints currently limit our ability to reconstruct network radiations from gene-tree data. Available methods are limited to level-1 networks wherein reticulations do not share edges, and higher-level networks may be non-identifiable in many cases. We present a heuristic method to recover information from higher-level networks across a range of potentially identifiable empirical scenarios, supported by a theorem and success in simulated data. When extrinsic information indicating the location and direction of recent or ancestral hybridization events is available, our method can yield successful estimates of non-level-1 networks, or at least a reduced possible set thereof. We apply this technique to the Pisgah clade of <em>Desmognathus</em> salamanders, which contains four to seven species exhibiting two discrete phenotypes, aquatic "shovel-nosed" and semi-aquatic "black-bellied" forms in the southern Appalachian Mountains of the eastern United States. Phylogenomic data strongly support a single backbone topology with up to five overlapping hybrid edges. These results suggest an unusual mechanism of ecomorphological hybrid speciation, wherein a binary threshold trait causes hybrids to shift between two microhabitat niches, promoting ecological divergence between sympatric hybrids and parentals. This contrasts with other well-known systems in which hybrids exhibit intermediate, novel, or transgressive phenotypes. Geographically proximate populations of both phenotypes exhibit admixture, and at least two black-bellied lineages have been produced via reticulations between shovel-nosed parentals, suggesting complex transmission dynamics. The genetic basis of these phenotypes is unclear and further data are needed to clarify the nature of selection and speciation in the group. </span></p>
Small-world complex network generation on a digital quantum processor
<p>Quantum cellular automata (QCA) evolve qubits in a quantum circuit depending only on the states of their neighborhoods and model how rich physical complexity can emerge from a simple set of underlying dynamical rules. The inability of classical computers to simulate large quantum systems hinders the elucidation of quantum cellular automata, but quantum computers offer an ideal simulation platform. Here, we experimentally realize QCA on a digital quantum processor, simulating a one-dimensional Goldilocks rule on chains of up to 23 superconducting qubits. We calculate calibrated and error-mitigated population dynamics and complex network measures, which indicate the formation of small-world mutual information networks. These networks decohere at fixed circuit depth independent of system size, the largest of which corresponding to 1,056 two-qubit gates. Such computations may enable the employment of QCA in applications like the simulation of strongly-correlated matter or beyond-classical computational demonstrations.</p>
Deciphering complex networks via topology-encoded latent hyperbolic geometry
<p><span>Complex networks, which are the abstraction of many real-world systems, present a persistent challenge across disciplines for people to decipher their underlying information. Recently, hyperbolic geometry of latent spaces has gained traction in network analysis, due to its ability to preserve certain local intrinsic properties of the nodes. In this study, we explore the problem from a much broader perspective: understanding the impact of nodes' global topological structures on latent space placements. Our investigations reveal a direct correlation between the topological structure of nodes and their positioning within the latent space. Building on this deep and strong connection between node distance and network topology, we propose a novel embedding framework called Topology-encoded Latent Hyperbolic Geometry (TopoLa) for analyzing complex networks. With the encoded topological information in the latent space, TopoLa is capable of enhancing both conventional and low-rank networks, using the singular value gap to clarify the mathematical principles behind this enhancement. Meanwhile, we show that the equipped TopoLa distance can also help augment pivotal deep learning models encompassing knowledge distillation and contrastive learning.</span></p>
Other Supplementary Material for 'Myosin-I Synergizes with Arp2/3 Complex to Enhance Pushing Forces of Branched Actin Networks' by Xu et al.
<div> <p>Other Supplementary Material for "Myosin-I Synergizes with Arp2/3 Complex to Enhance Pushing Forces of Branched Actin Networks" by Xu, Rutkowski, Rebowski, Boczkowska, Pollard, Dominguez, Vavylonis, and Ostap, bioRxiv, <a href="https://doi.org/10.1101/2024.02.09.579714" rel="nofollow">https://doi.org/10.1101/2024.02.09.579714</a> </p> <p> </p> </div>
Videos for "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground"
<p>Supplementary videos for the paper "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground" by S. Scher and G. Messori, Geoscientific Model Development 2019</p>
Multilayer social networks reveal the social complexity of a cooperatively breeding bird
<p>Focal observations of Arabian babblers (<em>Argya squamiceps</em>). The folder contains adjacency matrices of nine social groups observed between 2017 and 2020. Some of the groups were observed multiple times, the complete list of group rounds is also provided. We recorded six different interaction types, hence the folder contains a total of 114 adjacency matrices.</p>
Data supporting: Impacts of extreme climatic events on trophic network complexity and multidimensional stability
<p>Data used to produce the results presented in the manuscript entitled "Impacts of extreme climatic events on trophic network complexity and multidimensional stability", published in the journal "Ecology". The data were obtained from an outdoor pond mesocosm experiment where freshwater communities were exposed to two different heatwave scenarios. </p>
The social network of target of rapamycin complex 1 in plants
<p>The target of rapamycin complex 1 (TORC1) is a highly conserved serine–threonine protein kinase crucial for coordinating growth according to nutrient availability in eukaryotes. It works as a central integrator of multiple nutrient inputs such as sugar, nitrogen, and phosphate and promotes growth and biomass accumulation in response to nutrient sufficiency. Studies, especially in the past decade, have identified the central role of TORC1 in regulating growth through interaction with hormones, photoreceptors, and stress-signaling machinery in plants. In this review, we comprehensively analyse the interactome and phosphoproteome of the Arabidopsis TORC1 signaling network. Our analysis highlights the role of TORC1 as a central hub kinase communicating with the transcriptional and translational apparatus, ribosomes, chaperones, protein kinases, metabolic enzymes, and autophagy and stress response machinery to orchestrate growth in response to nutrient signals. This analysis also suggests that along with the conserved downstream components shared with other eukaryotic lineages, plant TORC1 signaling underwent several evolutionary innovations and co-opted many lineage-specific components. Based on the protein–protein interaction and phosphoproteome data, we also discuss several uncharacterized and unexplored components of the TORC1 signaling network, highlighting potential links for future studies.</p>
Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial & nuclear biology
<p>This online depository corresponds to manuscript : <em>Protein network analysis links the NSL complex to Parkinson’s disease via mitochondrial & nuclear biology.</em></p> <p><strong>Authors: </strong><em>Katie Kelly, Patrick A. Lewis, Helene Plun-Favreau, Claudia Manzoni</em></p> <p>Whilst the majority (~90-95%) of PD cases are sporadic, much of our understanding of the pathophysiological basis of disease can be traced back to the study of rare, monogenic forms of disease. However, in the past decade, the availability of Genome-Wide Association Studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring an increased risk of developing PD across the population. </p> <p>A recently developed mitophagy screening assay of GWAS candidates, has functionally implicated the non-specific lethal (NSL) complex, a chromatin remodeler, in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the interactome of the NSL complex, to unpick its relevance to PD progression. The mitochondrial interactome of the NSL complex has been built, mining 3 separate repositories: PINOT, HIPPIE and MIST, for curated, literature-derived protein-protein interaction (PPI) data. A multi-layered approach has been taken to; i) build the ‘mitochondrial’ NSL interactome, applying PD gene-set enrichment analysis to explore the relevance of the NSL mitochondrial interactome to PD and, ii) build the PD-oriented NSL interactome, using functional enrichment, to uncover biological pathways underpinning the NSL /PD association.</p>
Data for: Capturing synchronization with complexity measure of ordinal pattern transition network constructed by Crossplot
<p><span>To evaluate the synchronization of bivariate time series has been a hot topic and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network (OPTN) into the crossplot,</span> <span>a new method for measuring the synchronisation of bivariate time series is proposed.</span> <span>After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy (CPTE) of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model</span> <span>and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitable for short time series. Finally, EEG data from auditory evoked potential EEG-Biometric dataset are investigated, and some useful and interesting results are obtained.</span></p>
Data from: Assessing behavioral associations in a hybrid zone through social network analysis: complex assortative behaviors structure associations in a hybrid quail population
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Data from: Non-native species spread in a complex network: the interaction of global transport and local population dynamics determines invasion success
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Small-world complex network generation on a digital quantum processor
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The social network of target of rapamycin complex 1 in plants
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Variations in ecosystem-scale methane fluxes across a boreal mire complex assessed by a network of flux towers
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Data from: Social complexity during early development has long-term effects on neuroplasticity in the social decision-making network
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Resolving higher-level phylogenetic networks with repeated hybridization in a complex of polytypic salamanders (Plethodontidae: Desmognathus)
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Data for: Capturing synchronization with complexity measure of ordinal pattern transition network constructed by Crossplot
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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.