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10,331 results for “signaling”
Likelihoods for the CTA sensitivity to a dark matter signal from the Galactic centre (A. Acharyya et al., [arXiv:2007.16129])
<p>We present likelihoods to estimate upper limits on DM pair-annihilation in the Galactic centre, based on the<br> Cherenkov Telescope Array (CTA) consortium publication "Sensitivity of the Cherenkov Telescope Array to a<br> dark matter signal from the Galactic centre" [arXiv:2007.16129]. As explained in more detail in Sec. 5.2 of that<br> article, these likelihoods are suitable for models featuring cuspy dark matter profiles and generic gamma-ray<br> spectra produced from annihilating dark matter.</p> <p>The four files contain likelihoods that have been derived with respect to the full CTA South baseline array layout<br> and the initial construction phase of CTA South. For each of these two cases, as indicated by the filename, we<br> provide tables with and without the inclusion of systematic uncertainties (where the former refers to the benchmark<br> treatment of systematic uncertainties as described in the main publication). Further, all likelihoods are based on the<br> benchmark analysis settings with respect to masking known bright gamma-ray sources and the adopted<br> interstellar emission models.</p>
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
<p>The WIDEFT data corpus has been created to provide bursts from wireless devices in the spectrum of Bluetooth, WiFi, and other RF signals to further research of the acquisition and usage of wireless device fingerprints. WIDEFT was developed through the efforts of the Physical Science Laboratories (PSL) at New Mexico State University (NMSU). Data collection was recorded at PSL and at NMSU main campus and cataloged, maintained, and prepared for release at PSL.</p> <p>Please cite the following article:</p> <p>A. Bucker Siddik, D. Drake, T. Wilkinson, P. L. De Leon, S. Sandoval, and M. Campos, “WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research,” <em>IEEE Int. Symp. Technol. Homel. Secur. (HST)</em>, 2021.</p>
Disentangling ecological and taphonomic signals in ancient food webs
<p>Analyses of ancient food webs reveal important paleoecological processes and responses to a range of perturbations throughout Earth's history, such as climate change. These responses can inform our forecasts of future biotic responses to similar perturbations. However, previous analyses of ancient food webs rarely accounted for key differences between modern and ancient community data, particularly selective loss of soft-bodied taxa during fossilization. To consider how fossilization impacts inferences of ancient community structure we (1) analyzed node-level attributes to identify correlations between ecological roles and fossilization potential and (2) applied selective information loss procedures to food web data for extant systems. We found that selective loss of soft-bodied organisms has predictable effects on the trophic structure of "artificially fossilized" food webs, because these organisms occupy unique, consistent food web positions. Fossilized food webs misleadingly appear less stable (i.e., more prone to trophic cascades), with less predation and an overrepresentation of generalist consumers. We also found that ecological differences between soft- and hard-bodied taxa—indicated by distinct positions in modern food webs—are recorded in an Early Eocene web, but not in Cambrian webs. This suggests that ecological differences between the groups have existed for ≥ 48 million years. Our results indicate that accounting for soft-bodied taxa is vital for accurate depictions of ancient food webs. However, the consistency of information loss trends across the analyzed food webs means it is possible to predict how the selective loss of soft-bodied taxa affects food web metrics, which can permit better modeling of ancient communities.</p>
Supporting data and code for "Brown AW, Bohan Brown MM, Onken KL, Beitz DC. Short-term consumption of sucralose, a nonnutritive sweetener, is similar to water with regard to select markers of hunger signaling and short-term glucose homeostasis in women. Nutr Res. 2011 Dec;31(12):882-8. doi: 10.1016/j.nutres.2011.10.004. PMID: 22153513."
<p>Data and code to support the publication, Brown AW, Bohan Brown MM, Onken KL, Beitz DC. Short-term consumption of sucralose, a nonnutritive sweetener, is similar to water with regard to select markers of hunger signaling and short-term glucose homeostasis in women. Nutr Res. 2011 Dec;31(12):882-8. doi: 10.1016/j.nutres.2011.10.004. PMID: 22153513.</p> <ul> <li>Code was updated 2020 NOV 24 to add comments, but otherwise remains unchanged from 2011.</li> <li>Data file was updated to include a data dictionary, but otherwise remains unchanged from 2011.</li> </ul> <p>Treatment identifiers for the four-arm crossover are clarified in the SAS code.</p> <p>Other details are available in the published article.</p>
Raw data: Redox protein Memo1 coordinates FGF23-driven signaling and small Rho-GTPases in the kidney
<p>This dataset contains the raw data for "Redox protein Memo1 coordinates FGF23-driven signaling and small Rho-GTPases in the kidney". We investigated the role of Memo1 redox function in FGF23-driven receptor tyrosine kinase signaling in the kidney.</p> <p> </p> <p>Raw data which are not included here can be found using the following accession codes:</p> <p>RNAseq data: NCBI SRA, Accession: PRJNA672305 </p> <p>LC-MS/MS kidney data: proteomeXchange accession PXD022342 </p> <p>LC-MS/MS recombinant protein data: proteomeXchange PXD022382 </p>
Post-injury immunosuppression and secondary infections are caused by an AIM2 inflammasome-driven signaling cascade
<p>RAW FCS files for the manuscript:</p> <p>"Post-injury immunosuppression and secondary infections are caused by an AIM2 inflammasome-driven signaling cascade"</p> <p> </p>
Illuminating the planktonic stages of salmon lice: a unique fluorescence signal for rapid identification of a rare copepod in zooplankton assemblages.
<p>The Excitation Emission Matrix (EEM) measurements were taken with Shimadzu's proprietary software ‘LabSolutions RF’. All files are in the exported csv format with columns representing the excitation wavelengths and rows the emission wavelengths. Wavelengths range from 200-600 nm with a 2 nm increment. Fluorescence intensity was influenced by the fluctuating number of animals in the path of the excitation beam during the 5 minute measurement. We compensated for this artefact by repeating measurements of each sample five times, calculating the mean, and applying a smoothing function which found the median value within 10 nm. The fluorescence intensity was further normalized on a 0 to 1 scale by dividing intensity by the maximum fluorescence within each EEM measurement.</p> <p>Supplemental Table 1. The metadata of EEM measurements. The measurements were used for the spectrum section analysis and correspond to those depicted in Figures 2, 3, 4, & 5. See sections 3.1, 3.1.1, & 3.1.2. The Sample column indicates which lab culture cohort the sea lice came from (BGO*), which wild caught fish sample they came from (WC*), or the sampling date of non-target copepods (DDMMYY). The filename of each mean measurement is listed and indicates the first of 5 repeated measurements. Corresponding files can be found in the deposited.csv files at Zendo. In those files, data columns represent the excitation wavelengths, 200nm to 600nm increasing in 2 nm increments. Likewise, the rows represent the emission wavelengths. NaN’s are present where scattering layers were removed. All fluorescence intensity values are normalized to the maximum within each EEM.</p>
Phylogenetic signals in host-parasite associations for Neotropical bats and Nearctic desert rodents
<p>Hosts and their parasites have strong ecological and evolutionary relationships, with hosts representing habitats and resources for parasites. In the present study, we use approaches developed to evaluate the statistical dependence of species trait values on phylogenetic relationships to determine whether host–parasite relationships (i.e. parasite infections) are contingent on host phylogeny. If host–parasite relationships are contingent on the ability of hosts to provide habitat or resources to parasites, and if host phylogeny is an effective surrogate for among-host variation in habitat and resource quality, host–parasite relationships should evince phylogenetic signals (i.e. be contingent on host phylogeny). Because the strength of ecological relationships between parasites and their hosts may affect the likelihood of phylogenetic signals occurring in host–parasite relationships, we hypothesized that (1) host specificity would be positively correlated with the strength of phylogenetic signals and (2) the strength of phylogenetic signals will be greater for parasites that rely more on their host throughout their life cycle. Analyses were conducted for ectoparasites from tropical bats and for ectoparasites, helminths, and coccidians from desert rodents. Phylogenetic signals were evaluated for parasite presence and for parasite prevalence. The frequency of phylogenetic signal occurrence was similar for parasite presence and prevalence, with a signal detected in 24–27% of cases at the species level and in 67% and 15% of cases at the genus level for parasites of bats and rodents, respectively. No differences in signal strength or the likelihood of detecting a signal existed between groups of parasites. Phylogenetic signal strength was correlated with host specificity, suggesting that mechanisms increasing host specificity also increase the likelihood of a phylogenetic signal in host use by parasites. Differences in the transmission mode did not affect signal strength or the likelihood of detecting a signal, indicating that variation in host switching opportunities associated with the transmission mode does not affect signal strength.</p>
The Place of Signal processing
<p>“The Place of Signal processing” 2012. In: Workshop at Conference on Biomedical<br /> Science, by Scientific Society of Medical Engineering of Islamic Azad university of<br /> Tabriz. Presenter: Dr.Mirhadi Seyed Arabi, Dr.Ali Gouya and Dr.Hamid Mirzaei,<br /> 2012.</p>
Data and code for "A Signal-Detection Approach to Modeling Forgiveness Decisions"
<p>Contains the data and code that supplements the journal article, "A Signal-Detection Approach to Modeling Forgiveness Decisions".</p> <p>Article doi: 10.1016/j.evolhumbehav.2016.06.004</p>
Signal feeds for creating the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Wav files for the comparison of wave field synthesis, surround, and stereo listening test, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Figure 6. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 6. - Strobe models of five positions along the canonical variates indicated in Fig. 5. CV-1, CV-2, and CV-3 axes account for 79.5% of the observed between-species shape variation. Landmarks and semi-landmarks are superimposed in the figure to the right of each sequence to express the magnitudes and directions (arrows) of shape trends. In all models, the mesial margin of the coxa is depicted to the left, the lateral margin to the right.
Supplementary material 1: Appendix from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Voucher data for specimens use din morphometric analyses and supplementary figures of Canonical Variates scatterplots
Figure 5. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 5. - Results of the CVA of coxal shape data for all eight species, showing the subspaces formed by the first three discriminant axes, which together account for more than 79% of observed between-group shape variation. Within each subspace plot the black circles represent the coordinate locations for each of the five along-axis shape models depicted in Fig. 6.
Figure 4. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 4. - Scatterplots of Procrustes PCA scores for coxal shape data. The first two shape variation axes (top) together account for 62.63% of the observed shape variation; PC-2 and PC-3 axes (bottom) together account for 27.58% of the observed shape variation.
Figure 3. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 3. - Thereuopoda longicornis scatterplot of coxal shape data along the discriminant subspace formed by the first two CV axes, which together account for 74.17% of observed between-group shape variation.
Figure 2. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 2. - Landmarks (L1-L10) used in morphometric analysis. Diagonal line to L1 is the longest line from anterolateral to posteromedial corners of the coxa. Spine-bristles numbered 1-4 (blue) from interior to exterior. Throughout text, left and right coxae refer to dorsal orientation (inverted 180° relative to this ventral view).
Figure 1. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 1. - Ventral view of head and forcipules of Thereuopoda longicornis placed in a standard horizontal position. BM 1952.9.8.574-575, Kuching, Sarawak, Malaysia.
In silico identified signal peptides of Chlamydomonas reinhardtii
<p><strong>Overview</strong></p> <p><em>Chlamydomonas reinhardtii </em>theoretical signal peptides identified by<em> </em>SignalP 4.0 in a protein data set described below:</p> <ul> <li>Protein data set came from "The Genome Portal of the Department of Energy Joint Genome Institute" (http://genome.jgi.doe.gov/)</li> <li>Protein sequences were evaluated in SignalP 4.0 Server (http://www.cbs.dtu.dk/services/SignalP/)</li> </ul> <p> </p> <p><strong>File used</strong></p> <p>Chlre4_best_proteins.fasta.gz -> Protein dataset version used for analysis</p> <p> </p> <p><strong>Workflow</strong> </p> <p> ______Chlre4_best_proteins.fasta.gz_______</p> <p> | |</p> <p> Chlre4_best_proteins_fasta_protein_woSP.fasta Chlre4_best_proteins_signalPeptide.fasta</p> <p> |</p> <p> ___Chlre4_best_proteins_signalPeptide_unique.fasta___</p> <p> | |</p> <p> Chlre4_best_proteins_signalPeptide_unique.aln Signal Peptide Anotation from aligned.xlsx</p> <p> </p> <p><strong>Info</strong></p> <p>Chlre4_best_proteins_fasta_protein_woSP.fasta -> Mature protein sequences from proteins identified without signal peptide</p> <p>Chlre4_best_proteins_signalPeptide.fasta -> Identified signal peptide</p> <p>Chlre4_best_proteins_signalPeptide_unique.fasta -> Unique identified signal peptide</p> <p>Chlre4_best_proteins_signalPeptide_unique.aln -> Align signal peptides (UGENE)</p> <p>Signal Peptide Annotation from aligned.xlsx -> Signal peptide list, highlighted in orange theoretical tested.</p> <p> </p> <p><strong>Citations</strong></p> <p>For use of signal peptide dataset, please cite:</p> <p>Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433</p> <p>and </p> <p><strong>SignalP 4.0: discriminating signal peptides from transmembrane regions</strong><br> Thomas Nordahl Petersen, Søren Brunak, Gunnar von Heijne & Henrik Nielsen<br> <em>Nature Methods</em>, <strong>8</strong>:785-786, <strong>2011</strong><br> <br> doi: 10.1038/nmeth.1701<br> PMID: 21959131<br> Supplementary materials: nmeth.1701-S1.pd</p> <p>and </p> <p><strong>The genome portal of the Department of Energy Joint Genome Institute: 2014 updates</strong></p> <p>H. Nordberg, M. Cantor, S. Dusheyko, S. Hua, A. Poliakov, I. Shabalov, T. Smirnova, I. V. Grigoriev, I. Dubchak, ,</p> <p>Nucleic Acids Res. 42, 26–31. <strong>2014</strong> </p> <p>doi:10.1093/nar/gkt1069.</p> <p> </p>
2017 Solar Eclipse Doppler and signal strengths
<p>K3KO 30.210666N,81.603833W Jacksonville FL. No .wav file. Antenna Vee pointed north.K3 rx. CHU 14.670 MHz and WWV 15 MHz measured.</p> <p>Rb locked reference oscillators. Methodology described in *. doc file upload</p>
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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.