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10,331 results for “Signaling”
FeedBES - FeedBack signals from Episodic and Semantic memories.
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Data and code of the article: "Early Warning Signals of the Termination of the African Humid Period(s)"
<p>Data and MATLAB Code of the article Trauth, M.H., Asrat, A., Fischer, M.L., Hopcroft, P.O., Foerster, V., Kaboth-Bahr, S., Kindermann, K., Lamb, H.F., Marwan, N., Maslin, M.A., Schaebitz, F., Valdes, P.J. (2024) Early Warning Signals of the Termination of the African Humid Period(s), Nature Communications, https://doi.org/10.1038/s41467-024-47921-1. The individual directories contain the data and the MATLAB code used to generate Fig. 1 and 2 and Supplementary Fig. 1 to 7 published with the article.</p>
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Abrupt hippocampal remapping signals resolution of memory interference.
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Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations
<p><strong>Description of the Supplementary Data</strong></p> <p>This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “<i>A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations</i>,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor.</p> <p>This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code><strong>[CHANNEL]</strong></code> refers to the annihilation channel used, i.e. <i>e<sup>+</sup> e<sup>-</sup></i>, <i>μ<sup>+</sup> μ<sup>-</sup></i>, <i>τ<sup>+</sup> τ<sup>-</sup></i>, <i>b b̄</i>, <i>c c̄</i>, <i>t t̄</i>, <i>g g</i>, <i>W<sup>+</sup> W<sup>-</sup></i>, and <i>Z Z</i>. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files.</p> <ul> <li>One-dimensional limits on <i><σ v></i>. The files <code>oneD_frequentist_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass <i>m<sub>χ</sub></i> tabulated there. The files <code>oneD_Bayesian_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass <i>m<sub>χ</sub></i> tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of <i>m<sub>χ</sub></i> and <i><σ v></i>. The files <code>twoD_posterior_probability_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <i><σ v></i>) together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. The tabulated values of <i>m<sub>χ</sub></i> and <i><σ v></i> correspond to the centres of the respective bins in <i>m<sub>χ</sub></i> and <i><σ v></i> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> <p>Please contact the authors if you require different data or have any questions regarding this data set.</p>
Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong></p> <p>Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.</p>
Investigation of the properties of conductivity signals in BK channels by Empirical Mode Decomposition
<p>The idea of the project is the comprehensive time-frequency analysis of ion current data registered from BK channels of the different cell lines and measured under the different experimental conditions. Decomposition of signals into individual frequency modes and application of non-linear measures in the form of Information Entropy or Hurst exponent to individual signal components will allow for a more detailed analysis of the information hidden behind the complex ionic conduction sequences. The sample data contains patch-clamp sequences. </p>
Supplemental Information - Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling
<p>This folder contains</p> <p>1) maps of plasmids</p> <p>2) files of phylogenetic analysis </p> <p>3) Replication information</p> <p>4) Image cropping information</p> <p>5) Gene IDs and protein sequences</p> <p>that are part of the manuscript "Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling"</p>
Inhibition of epithelial cell YAP-TEAD/LOX signaling attenuates pulmonary fibrosis "
<table> <tbody> <tr> <td> <p>Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disease characterized by excessive extracellular matrix (ECM) deposition. Current IPF therapies slow disease progression but do not stop or reverse it. The (myo)fibroblasts are thought to be the main cellular contributors to excessive ECM production in IPF. Here we report that fibrotic AT2 cells regulate production and crosslinking of ECM via the co-transcriptional activator YAP. YAP leads to increase expression of Lysyloxidase (LOX) and subsequent LOX mediated crosslinking by fibrotic AT2 cells. Pharmacological YAP inhibition reverses fibrotic AT2 cell reprogramming and LOX expression in experimental lung fibrosis <span>in vivo</span><span> and in human fibrotic </span><span>tissue ex vivo</span><span>. We thus identify YAP-TEAD/LOX inhibition in AT2 cells as a promising potential new therapy for IPF patients.<span> </span></span></p> <p><span><span>In</span></span></p> </td> </tr> </tbody> </table>
Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals
<p><strong>Supplementary Material for:</strong></p> <p>Emerling C.A., Springer M.S., Gatesy J., Jones Z., Hamilton D., Xia-Zhu D., Collin M.A., and Delsuc F. (2021). Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals.<strong><em> Open Research Europe</em></strong> 1:75. doi:10.12688/openreseurope.13795.1.</p> <p> </p> <p><strong>Supplementary File Legends:</strong></p> <p><strong>- Supplementary_Figure_S1.pdf:</strong> <em>AANAT</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 24 ratio” in Supplementary Table S7.</p> <p><strong>- Supplementary_Figure_S2.pdf:</strong> <em>ASMT</em> PAML ‘master model’ showing branch categories, corresponding to “Model 2: 24 ratio” in Supplementary Table S8.</p> <p><strong>- Supplementary_Figure_S3.pdf:</strong> <em>MTNR1A</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 27 ratio” in Supplementary Table S9.</p> <p><strong>- Supplementary_Figure_S4.pdf:</strong> <em>MTNR1B</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 46 ratio” in Supplementary Table S10.</p> <p><strong>- Supplementary_Figure_S5.pdf:</strong> RAxML <em>AANAT</em> gene tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S6.pdf: </strong>RAxML <em>ASMT</em> gene tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S7.pdf: </strong>RAxML <em>MTNR1A</em>+<em>MTNR1B</em> tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S8.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> exon 2 in cetaceans. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S9.pdf: </strong>Supporting data showing the inactivation of <em>ASMT</em> in spalacids and <em>Fukomys damarensis</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S10.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in hyracoids and <em>Cyclopes didactylus</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S11.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in sirenians. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S12.pdf: </strong>Supporting data showing the inactivation of <em>AANAT</em> in sirenians and a polymorphic premature stop codon in exon 5 of <em>ASMT</em> in <em>Trichechus manatus</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S13.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in <em>Condylura cristata</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S14.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in <em>Phataginus tricuspis</em>. Read Supplementary Table S14 for further details.</p> <p><strong>- Supplementary_Figure_S15.pdf: </strong>PAML <em>AANAT</em> results, Model 1: 24 ratio (see Supplementary Table S7).</p> <p><strong>- Supplementary_Figure_S16.pdf: </strong>PAML <em>ASMT</em> results, Model 2: 24 ratio (see Supplementary Table S8).</p> <p><strong>- Supplementary_Figure_S17.pdf: </strong>PAML <em>MTNR1A</em> results, Model 1: 27 ratio (see Supplementary Table S9).</p> <p><strong>- Supplementary_Figure_S18.pdf: </strong>PAML <em>MTNR1B</em> results, Model 1: 46 ratio (see Supplementary Table S10).</p> <p><strong>- Supplementary_Table_S1.xlsx: </strong>List of species examined in this study and the sources of the genes. Source key: WGS: Sequences derived from NCBI's Whole Genome Shotgun database, with accession prefix provided; Whole Genome Sequencing of Short Reads: whole genomes were sequenced using short-read technologies. The methodologies varied for the species, and will be or have been published with other projects, so please contact the author(s) for information on the specific methodology and samples used (Xenarthrans, <em>Proteles cristatus</em>, <em>Otocyon megalotis</em>: Frédéric Delsuc, e-mail: Frederic.Delsuc@umontpellier.fr; Crocodylians: John Gatesy, e-mail: jgatesy@amnh.org; <em>Dugong dugon</em>: Mark Springer, e-mail: mark.springer@ucr.edu; SRA: sequences derived from NCBI's Sequence Read Archive; GenBank: sequences derived from NCBI's nucleotide collection; Bowhead Whale Genome Resource: sequences derived from http://www.bowhead-whale.org; Ensembl: sequences derived from Ensembl genome browser (www.ensembl.org)l; Discovar de novo: sequences derived genomes assembled via Discovar de novo (<a href="https://software.broadinstitute.org/software/discovar/blog/">https://software.broadinstitute.org/software/discovar/blog/</a>). Coverage: indicates coverage of the whole genome (reported in NCBI or other source) or individual genes (derived from short read mapping). Scaffold and contig N50: reported in NCBI or other source.</p> <p><strong>- Supplementary_Table_S2.xlsx: </strong>Accession numbers and functionality of <em>AANAT</em> in species examined. If Accession # indicated as “New”, sequence generated for this study and can be found in Supplementary Dataset S1. Parentheses after accession number indicates coordinates for sequence on the contig / scaffold. Exon colors code for the following: green = putatively functional; yellow = missing (e.g., negative BLAST results, negative mapping results); pink = one or more inactivating mutations found. Abbreviations for mutations are as follows: del = deletion; ins = insertion; start = start codon mutation; stop = premature stop codon; ? = ambiguity whether the mutation is shared among all members of the clade. Abbreviations in brackets following an inactivating mutation indicate shared inactivating mutation. Key for each abbreviation follows: Bacu = <em>Balaenoptera acutorostrata</em>; BALA = Balaenidae; BALAEN = Balaenopteridae; Bbon = <em>Balaenoptera bonaerensis</em>; CAB = <em>Cabassous</em>; Ccap = <em>Cebus capucinus</em>; CETA = Cetacea; CHLAM = Chlamyphoridae; CHOL = <em>Choloepus</em>; Cjac = <em>Callithrix jacchus</em>; CING = Cingulata; DASY = Dasypodidae; DELP = Delphinidae; DERM = Dermoptera; Erob = <em>Eschrichtius robustus</em>; INIA = <em>Inia</em>; FOLI = Folivora; GALE = <em>Galeopterus</em>; LIPO = <em>Lipotes</em>; Lobl = <em>Lagenorhynchus obliquidens</em>; MANI = Manidae; MONO = Monodontidae; MYRM = Myrmecophagidae; MYST = Mysticeti; NPP = Not present in <em>Platanista</em> or Physeteroidea, but present in other Odontocetes; NPZ = Not present in Ziphiidae, but present in other Odontocetes; Oorc = <em>Orcinus orca</em>; PEUT = Tolypeutinae; PHOC = Phocoenidae; PHOL = Pholidota; PHOR = Chlamyphorinae; PILO = Pilosa; PHYS = Physeteroidea; PONT = <em>Pontoporia</em>; Schi = <em>Sousa chinensis</em>; SIRE = Sirenia; Tadu = <em>Tursiops aduncus</em>; TOLY = <em>Tolypeutes</em>; VERM = Vermilingua; XEN = Xenarthra.</p> <p><br> <strong>- Supplementary_Table_S3.xlsx: </strong>Accession numbers and functionality of <em>ASMT</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S4.xlsx: </strong>Accession numbers and functionality of <em>MTNR1A</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S5.xlsx: </strong>Accession numbers and functionality of <em>MTNR1B</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S6.xlsx: </strong>Codon frequency model selection. These are the results from one ratio dN/dS analyses using different codon frequency models. AIC = Akaike Information Criterion.</p> <p><strong>- Supplementary_Table_S7.xlsx: </strong>Results of <em>AANAT</em> PAML dN/dS analyses for mammals. Model: BG = branch(es) grouped with background; fixed 1 = branch(es) fixed at 1. p’-value: p-value after Holm-Bonferroni correction for multiple testing. Model Comparison: if model comparison yields statistically significant differences (p < 0.05), model comparison bolded and given green background; if model comparison is still significant after Holm-Bonferroni correction, asterisk (*) added. For most models, w only shown for branch(es) of interest. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S1.</p> <p><strong>- Supplementary_Table_S8.xlsx: </strong>Results of <em>ASMT</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S2.</p> <p><strong>- Supplementary_Table_S9.xlsx: </strong>Results of <em>MTNR1A</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S3.</p> <p><strong>- Supplementary_Table_S10.xlsx: </strong>Results of <em>MTNR1B</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S4.</p> <p><strong>- Supplementary_Table_S11.xlsx: </strong>Results of PAML analyses for sauropsids.</p> <p><strong>- Supplementary_Table_S12.xlsx: </strong>Results of BLASTing and mapping short reads from <em>Alligator mississippiensis</em> RNA sequencing experiments.</p> <p><strong>- Supplementary_Table_S13.xlsx: </strong>Supporting data for validating putative inactivating mutations. Validating data came from four general sources of information: mutations shared by more than one species within a clade, mutations shared by two sources of sequencing data for the same species, mutations validated by coverage of mapped short reads and statistically elevated dN/dS ratio estimates. For additional details, see Supplementary Tables S2–S5 and S7–S10, as well as Figure 2 and Supplementary Figures S8–S18.</p> <p><strong>- Supplementary_Dataset_S1.txt:</strong><strong> </strong>Genomic alignments in fasta format used to determine the pseudogene/functional status of all four melatonin genes in different taxonomic groups.</p> <p><strong>- Supplementary_Dataset_S2.txt:</strong><strong> </strong>Alignment of <em>AANAT</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S3.txt: </strong>Alignment of <em>ASMT</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S4.txt: </strong>Alignment of <em>MTNR1A</em> and <em>MTNR1B</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S5.txt:</strong><strong> </strong>Codon alignments of <em>AANAT</em> used in selection pressure analyses with PAML. </p> <p><strong>- Supplementary_Dataset_S6.txt: </strong>Codon alignments of <em>ASMT</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S7.txt:</strong><strong> </strong>Codon alignments of <em>MTNR1A</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S8.txt: </strong>Codon alignments of <em>MTNR1B</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S9.txt: </strong>Tree topologies in newick format used in selection pressure analyses with PAML.</p>
Eco-evolutionary processes underlying early warning signals of population declines
<p>Datasets for the paper appearing in Journal of Animal ecology : "Eco-evolutionary processes underlying early warning signals of population declines". Also GitHub repository link :<a href="https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA">https://github.com/GauravKBaruah/ECO-EVO-EWS-DATA</a></p>
The relevance of signal timing in human-robot collaborative manipulation
<p><em><strong>Dataset version 1.0.1. The data collected here are attached to the following journal article: F. Cini*, T. Banfi*, G. Ciuti, L. Craighero, M. Controzzi, The relevance of signal timing in human-robot collaborative manipulation. Science Robotics Vol. 6 Issue 58, 2021. DOI: 10.1126/scirobotics.abg1308</strong></em></p> <p>To achieve a seamless human-robot collaboration, it is crucial that robots express their intentions without perturbating or interrupting the task that a human partner is performing at that moment. Although it has not received much attention so far, this issue is important when robots assist humans in physical and manipulation tasks. The main question addressed here is whether there is a more appropriate time to inform a human partner that a robot is requesting to pass them an object. This question is posed in a reference scenario where human individuals are involved in a continuous pick-and-place task that cannot be interrupted. Our findings showed that providing a cue at the beginning of a reach-to-grasp movement could severely interfere with the ongoing human action,<br> increasing the number of errors made by humans, slowing down and degrading the smoothness of their arm movement, and deflecting their gaze. These disruptive interferences strongly decreased, until they disappeared, when the robot provided the cue to the human partners shortly after the participants picked up an object, identifying this as the best signaling timing. The results of this work showed how the signaling timing may have a decisive influence on the performances of the human-robot teamwork and contribute to understating the mechanisms underpinning the phenomenon of cognitive-motor interference in humans.</p>
Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence
<p>RNA sequencing, SomaLogic proteomics and flow cytometry data were generated for two cohorts of end-stage kidney disease patients with COVID-19. The Wave 1 cohort consists of samples collected from patients during the first wave of COVID-19 in early 2020, while samples were collected for the Wave 2 cohort in the following year.</p> <p>This data deposition includes the RNA-seq counts, SomaScan proteomics, flow cytometry and clinical metadata associated with the study. For further information about the study and data, see the associated GitHub repository (https://github.com/jackgisby/covid-longitudinal-multi-omics) or our pre-print (https://doi.org/10.1101/2022.04.29.22274267). The repository also contains code to replicate our analysis of the data.</p> <p>The raw RNA-seq reads were processed using the nf-core RNA-seq v3.2 pipeline before htseq-count was used to generate a raw counts matrix, which is included in this deposition (<code>htseq_counts.csv</code>). Three files make up the proteomics data: <code>sample_technical_meta.csv</code>, <code>feature_meta.csv</code> and <code>soma_abundance.csv</code>. The first two files contain metadata columns for the samples and protein features, respectively. The final file includes the unprocessed protein abundance data. The files <code>general_panel.csv</code> and <code>t_cell_panel.csv</code> contain the flow cytometry data, split into the general and T-cell panels, respectively. Finally, clinical metadata is available for the two cohorts described in this study (<code>w1_metadata.csv</code>, <code>w2_metadata.csv</code>).</p> <p>The features in the clinical metadata include:</p> <table> <thead> <tr> <th>Column Name</th> <th>Data Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>sample_id</td> <td>Character</td> <td>Unique identifier for samples</td> </tr> <tr> <td>individual_id</td> <td>Character</td> <td>Unique identifier for individuals</td> </tr> <tr> <td>ethnicity</td> <td>Character</td> <td>The individual's ethnicity (asian, white, black or other)</td> </tr> <tr> <td>sex</td> <td>Character</td> <td>The individual's sex (M or F)</td> </tr> <tr> <td>calc_age</td> <td>Integer</td> <td>Age in years</td> </tr> <tr> <td>ihd</td> <td>Character</td> <td>Information on coronary heart disease</td> </tr> <tr> <td>previous_vte</td> <td>Character</td> <td>Whether individuals have had venous thromboembolism</td> </tr> <tr> <td>copd</td> <td>Character</td> <td>Whether individuals have chronic obstructive pulmonary disease</td> </tr> <tr> <td>diabetes</td> <td>Character</td> <td>Whether individuals have diabetes, and, if so, the type of diabetes</td> </tr> <tr> <td>smoking</td> <td>Character</td> <td>Smoking status</td> </tr> <tr> <td>cause_eskd</td> <td>Character</td> <td>Cause of ESKD</td> </tr> <tr> <td>WHO_severity</td> <td>Character</td> <td>The peak (WHO) severity for the patient over the disease course</td> </tr> <tr> <td>WHO_temp_severity</td> <td>Character</td> <td>The (WHO) severity at time of sampling</td> </tr> <tr> <td>fatal_disease</td> <td>Logical</td> <td>Whether the disease was fatal</td> </tr> <tr> <td>case_control</td> <td>Character</td> <td>Whether the individual was COVID-19 <code>POSITIVE</code> or <code>NEGATIVE</code> at time of sampling. Convalescent patients are denoted by the label <code>RECOVERY</code></td> </tr> <tr> <td>radiology_evidence_covid</td> <td>Character</td> <td>Evidence of COVID-19 from radiology</td> </tr> <tr> <td>time_from_first_symptoms</td> <td>Integer</td> <td>The number of days since the individual first experienced COVID symptoms at time of sampling</td> </tr> <tr> <td>time_from_first_positive_swab</td> <td>Integer</td> <td>The number of days since the individual's first positive swab was taken at time of sampling</td> </tr> </tbody> </table>
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
MCR LTER: Coral Reef: Finding Signals in the Noise of Coral Recruitment, data for Edmunds 2021 Coral Reefs
These data, looking at coral recruitment were collected in Moorea, French Polynesia, measured over 13 years, and tested for associations with environmental conditions. Recruitment of spawning pocilloporid corals was recorded using settlement tiles immersed for ~ 6 months at 10 m and 17 m depth, biannually, and the environment was quantified through seawater clarity (Kd490), surface and bottom flow speeds, coral cover, and temperature. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2021). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Differences in Chemo-signaling Compound-Evoked Brain Activity in Male and Female Young Adults: A Pilot Study in the Role of Sexual Dimorphism in Olfactory Chemo-Signaling
Open the record for dataset details and reuse information.
'MARSH' - respiratory signal repository
<p>This dataset, 'MARSH', includes respiratory signals as part of the study "Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography."<br> It is meant to support academic research, particularly on algorithm development tools.</p> <p>Contents:<br> - Data.txt (age, gender, height, weight, systole, diastole, [respectively])<br> - ECG.mat (raw ECG data)<br> - ECG_annot.mat (annotations for the R peaks in ECG data)<br> - IP.mat (Raw IP data)<br> - IP_annot.mat (annotations for the local maxima of IP data [end of inspiration phase])<br> - NASAL.mat (Thermistor mask data)<br> - NASAL_annot.mat (annotations for the local maxima of thermistor mask data [end of inspiration phase])<br> - PPG.mat (Raw PPG signal data)</p> <p>When referring to this dataset, please consider including the following reference:</p> <p>Mikko Pirhonen and Vehkaoja Antti, Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography. Biomedical Signal Processing and Control. 2020</p>
A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection
<p>This data-set consists of antenna-wise received signal strength (RSS) and channel state information (CSI) data. Both types of data have been captured using the <a href="https://dhalperi.github.io/linux-80211n-csitool/">Intel CSI Tools</a>. The RSS data have been used in our paper "Detecting Human Movement from Ambient Wi-Fi Signal Strength".</p> <p>This release extends the README with a data dictionary for the annotations. We hope to add more information about the data acquisition process (e.g., data acquisition protocols).</p>
Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification
<p>Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.</p>
Associated dataset for "Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental and perceptual evaluation utilize the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. However, the provided execution configurations (see below) are probably not exactly in accordance with the latest ReTiSAR code base. Hence, the at the time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.ICASSP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way you should obtain exactly the same Python setup as utilized in the instrumental and perceptual evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_ICASSP_freeze</code></pre> <p>Directory "SNR":</p> <ul> <li>Tools for instrumental evaluation (Section 4)</li> <li>Shell script to capture input and output signals of rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all specified configurations</li> <li>Matlab script to analyse captured signal and generate system transfer plots (Figure 1 to Figure 3 and further configurations)</li> </ul> <p>Directory "Relative Output Levels":</p> <ul> <li>Tools for preparation of perceptual evaluation (Section 5)</li> <li>Shell script to capture rendered uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse and level align captured signals and generate plot result plot (Figure 4)</li> </ul> <p>Directory "Absolute Output Levels":</p> <ul> <li>Tools for specification of perceptual evaluation (Section 5)</li> <li>Shell script to capture reproduced uniformly contributing noise signals for all specified configurations</li> <li>Matlab script to analyse the calibrated captured signals yielding the average level in the ear signals of 58.2 dBSPL (Section 5.1)</li> </ul> <p>Files in base directory and directory "Study Results":</p> <ul> <li>Tools for perceptual evaluation / user study (Section 5)</li> <li>Matlab GUI to conduct perceptual user study (employ by executing "ICASSP_gui.m", respective ReTiSAR instances are started and remote controlled by the GUI, raw study results will be stored in "results" directory)</li> <li>Matlab script to "calculate_conclusion.m" to analyse the raw study results and generate individual and conclusive result plots (Figure 5, Figure 6 and more)</li> </ul>
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.