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9,152 results for “Networking”
Fig. 1 in Molecular networking-driven isolation of 8 -Glycosylated biscoumarins from Cruciata articulata
Fig. 1. Molecular network of Cluster G1. Red: Parent ions of isolated new biscoumarins (1–4); Yellow: Parent ions for proposed structures. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Supplementary code and data for "Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks"
<p>Supplementary code and data for the conference article</p> <blockquote> <p>"Tackling universal properties of minimal trap spaces of boolean networks" Sara Riva, Jean-Marie Lagniez, Gustavo Magaña López, and Loïc Paulevé Proceedings of CMSB 2023, LCNS, Springer.<a href="https://doi.org/10.1007/978-3-031-42697-1_11"> https://doi.org/10.1007/978-3-031-42697-1_11</a></p> </blockquote> <p>Requirements</p> <ul> <li> <p>Python at least 3.9</p> </li> <li> <p><a href="https://github.com/bnediction/bonesis">bonesis</a> (v0.5.6) - <a href="https://doi.org/10.5281/zenodo.7984628">https://doi.org/10.5281/zenodo.7984628</a></p> <p>With pip:</p> <ul> <li><code>pip install bonesis==0.5.6</code></li> </ul> <p>With conda:</p> <ul> <li><code>conda install -c potassco -c colomoto bonesis=0.5.6</code></li> </ul> </li> <li> <p>for the synthesis: <a href="https://github.com/sybila/biodivine-aeon-py/">AEON.py</a></p> <p>With pip:</p> <ul> <li><a href="https://rustup.rs">https://rustup.rs</a></li> <li><code>pip install biodivine-aeon</code></li> </ul> <p>With conda</p> <ul> <li><code>conda install -c daemontus biodivine_aeon</code></li> </ul> </li> </ul> <p> </p> <p>Usage</p> <ul> <li> to execute the CEGAR approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_cegar.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>The first parameter is the .bnet file, the second one is the marker (in JSON), and the third is the maximum number k of components in a perturbation. To fix the marker, it is also possible to use a special node (ex: p) in the bnet file and require a marker '{"p":1}'. In the Moon22 dataset, information about uncontrollable components are available, one can use <code>--exclude '["Apoptosis","Growth_Arrest","Proliferation"]'</code>. If the marker is given as a component, it must be involed in the uncontrollable list.</p> <p>Moreover, the markers of the Moon22 dataset are contained in the bnet files in a special component <code>p</code>. Concerning the Trappist dataset, the possible markers are contained in files .markers, the user can choose between them.</p> <p>The dataset synthetic_bns contains random generated BNs. Also in this case, different possible markers are contained in files .markers.</p> <ul> <li>to execute the Complementary approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_complementary.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the Enumeration & Filtering approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_trapspaces_naive.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the CEGAR approach for a synthesis problem:</li> </ul> <pre><code>python synthesis.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.aeon '{"p":1}' </code></pre> <p>The first parameter is the prior knowledge graph in an .aeon file and the second one is the marker (in JSON). Another option is <code>--maxclause</code> which allow one to specify the maximum integer number of clauses authorised in local functions (by default is 128). The option <code>--no-canonic</code> leads to specify the criteria for the refinement of the under-approximation (3 for TS(y)|=M ; 1 for \exists y s.t. TS(y)!=TS(x) or 0 to obtain just a different candidate solution).</p>
A platform for dissecting force sensitivity and multivalency in actin networks (supplemental)
<p>These data support the supplemental figures in our publication "<strong>A platform for dissecting force sensitivity and multivalency in actin networks</strong>". Unzip for a README file explaining data. See DOI 8239706 for main figure data. BioRxiv: https://www.biorxiv.org/content/10.1101/2023.08.15.553463v1</p>
Fig. 2 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)
Fig. 2. (A) Molecular network of the molecular family of flavonoid glycosides and phenylethanoid extracted from the MN of the fruit extract of Crescentia cujete. (B) Proposed fragmentation pathway observed in the MS/MS spectrum naringin (33).
Fig. 1 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)
Fig. 1. UPLC-MS/MS based molecular networking in negative ionization mode of the fruit extract of Crescentia cujete. AG: alkyl glycosides, BC: benzoyl and cinnamoyl derivatives, FG1-3: flavonoid glucosides, PE: phenylethanoid derivatives, IG1-2: iridoids glycosides. Node text indicates the parent ion, node color shows the chemical group (green: n-alkyl sugars, sky blue: benzoyl derivatives, dark blue: cinnamoyl derivatives, red: flavonoids glycosides, purple: phenylpropanoids derivatives, and gold: iridoid glycosides). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)
Fig. 3. Chemical structures of iridoid glycosides (11-14, 21 and 24) isolated from the fruit of Crescentia cujete.
Dataset and code for the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'
<p>This repository contains the code and data used in the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'. <br> Manuscript authors: Dr. Aakash Sane, Dr. Brandon G. Reichl, Dr. Alistair Adcroft, Dr. Laure Zanna<br> Manuscript preprint link: https://doi.org/10.48550/arXiv.2306.09045</p>
Fig. 1. Overall molecular network obtained from MZmine2 in Streamlined targeting of Amaryllidaceae alkaloids from the bulbs of Crinum scillifolium using spectrometric and taxonomically-informed scoring metabolite annotations
Fig. 1. Overall molecular network obtained from MZmine2-preprocessed HPLC-MS2 data of C. scillifolium bulbs crude alkaloid extract and first chromatographic fractions. Triangle-shaped nodes are tentatively-tagged against ISDB and benefit from a taxonomical re-ranking of the tentative candidates (their structures are provided in Fig. S1, Supporting Information). Nodes highlighted in orange correspond to the targeted structures (1–4) in the phytochemical workflow. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Data for three power distribution systems are presented in this document. The first system consists of 53 nodes and 61 branches. The second is composed of 217 nodes and 219 branches. Finally, the third system consists of 404 nodes and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</p>
Data for "Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity"
<p>The trained 600-member ensemble convolutional neural networks (CNNs) for seasonal tropical cyclone (TC) activity to allow future studies. Please refer Fu et al. (2023; <em>Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity</em>) for more details.</p>
Dataset accompanying the paper "Nudging cooperation among agents in an experimental social network"
<p>The dataset corresponding to the paper mentioned. </p>
Posterior default mode network activity underlies distracted interoceptive attention and provides an objective basis for low mindfulness and depression
<p>Interoceptive attention to internal sensory signals is fundamental to mindfulness. However, it is difficult to measure objectively, and the vast majority of investigations of the neural correlates of mindfulness-based attention rely on subjective and retrospective measures of distraction. In this study, we measured consistency of response times on a breath-monitoring task (instruction to respond every 2 breath cycles) to objectively measure interoceptive attention in individuals across the lifespan (15–91 years of age, n=324). Consistently timed responses were indicative of on-task attention, whereas variable delayed responses indicated distraction or mind-wandering. Signifying the importance of interoceptive attention consistency, we observed that this measure was positively correlated across subjects with performance across several exteroceptive cognitive tasks. Electroencephalographic (EEG) source reconstruction demonstrated that distraction on the eyes-closed task was associated with elevated alpha band (8–12 Hz) brain activity, particularly in posterior default mode network (pDMN) brain regions. The neural data revealed four important findings: 1) pDMN brain activity was inversely linked with functional connectivity to the fronto-parietal network (FPN), suggesting that pDMN-FPN interactions play a key role in the regulation of pDMN activity during interoceptive attention. 2) pDMN activity on distracted trials was inversely correlated with trait-level mindfulness. 3) A key hub of the pDMN, the isthmus region of the posterior cingulate cortex (ICC), showed elevated activity on distracted trials in individuals with self-reported depressive symptoms. 4) In an independent second experiment, pDMN activity was adaptively modulated by task difficulty that invoked different levels of distraction. These results identify an EEG-based neurophysiological marker (elevated pDMN alpha activity) underlying interoceptive distraction, which is correlated with subjective markers of mindfulness and depression and that can be adaptively modulated. Our findings, thus, provide a direct link between a neurophysiological substrate of interoceptive attention, and subjective ratings of mindfulness and depressed mood.</p>
Our processed EndoVis2017 dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed EndoVis2017 dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Our processed Kvasir-Instrument dataset in the paper A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention
<p>Our processed Kvasir-Instrument dataset is used for the paper "A Lightweight Segmentation Network for Endoscopic Surgical Instruments Based on Edge Refinement and Efficient Self-Attention".</p>
Dataset QCA Retail Strategic Networks
<p>This dataset contains information about the governance and management of 20 Brazilian retail Strategic Networks. </p>
SNANA curves preproccessed for Neural Network
<p>SN light curves generated by SNANA, preprocessed for use in RNN architecture</p>
Eletronic Companion - Optimal Protection System Planning for Distribution Networks: a MILP Approach
<p>This release is associated with a paper entitled "Optimal Protection System Planning for Distribution Networks: a MILP Approach".</p> <p>The attached document provides the data adopted to obtain the results presented in the paper.</p>
Dataset for "Exploring the distribution of phylogenetic networks generated under a birth-death-hybridization process"
<p>Contains all simulation scripts, simulated data, and supplemental materials</p>
MetalHawk: Enhanced Classification Of Metal Coordination Geometries by Artificial Neural Networks
<p>The data used to train and validate the CSD-NN and PDB-NN models. These neural networks built in Scikit-learn and are trained to recognize the geometry and coordination number of a metal site from the geometric features including distances and angles of the six atoms closest to the metal. The sites are stored as .pdb files composed of all atoms falling within 10 angstroms of the central metal atom and they include both metal complexes deposited in the Cambridge Structural Database (CSD, Version 5.42-5.43) and bioinorganic sites deposited in the Protein Data Bank (PDB), retrieved through the MetalPDB interface (up to the end of 2018). The sites are divided in seven geometry classes: linear (LIN), trigonal planar (TRI), tetrahedral (TET), square planar (SPL), square pyramidal (SQP), trigonal bipyramidal (TBP) and octahedral (OCT).</p> <p>The number of metal sites in each file is the following:</p> <p>CSD_dataset_pdbs.zip - 110K </p> <p>CSD_validation_dataset_pdbs.zip - 1369</p> <p>PDB_dataset_pdbs.zip file - 2960</p> <p>PDB_validation_dataset_pdbs.zip - 106</p> <p>The file images_and_data_analysis.zip contains all data and code required to replicate the figures shown in the paper.</p> <p>The benchmark_fp.zip file contains code for the benchmark of Metalhawk and its comparison to Findgeo, another tool for coordination geometry classification.</p>
Metnet Auxiliary Route Network Illustrations
<p><strong>Metnet Auxiliary Route Network Illustrations</strong></p> <ul> <li> <p>This repository contains interactive illustrations for the auxiliary networks at 25ºC and 37ºC defined in Gopalakrishnan Meena and Lane et al. PNAS Nexus 2023.</p> </li> </ul> <p><strong>Interactive Site:</strong></p> <ul> <li> <p>The interactive visualization can be <a href="https://web.eecs.utk.edu/~mlane42/">found here.</a></p> </li> </ul> <p><strong>Authors:</strong></p> <p>Matthew J. Lane and Muralikrishnan Gopalakrishnan Meena </p> <p><strong>Reference</strong>:</p> <p>Gopalakrishnan Meena and Lane et al., "A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs", PNAS Nexus, 2023 <a href="https://doi.org/10.1093/pnasnexus/pgad322">https://doi.org/10.1093/pnasnexus/pgad322</a></p>
ScienceDex guides
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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.