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1,145 results for “somatic”
Data from: Detection of somatic epigenetic variation in Norway spruce via targeted bisulfite sequencing
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Data from: Crosstalk between growth and somatic maintenance in young animals
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Data from: A test of somatic mosaicism in the androgen receptor gene of Canada lynx (Lynx canadensis)
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Heterogeneity in risk-sensitive allocation of somatic reserves in a long-lived mammal
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Data from: Somatic deleterious mutation rate in a woody plant: estimation from phenotypic data
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Data from: Carry-over effects of early thermal conditions on somatic and germline oxidative damages are mediated by compensatory growth in sticklebacks
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[DATA_SCIENCE] Interviews: The Catalogue of Somatic Mutations in Cancer (COSMIC)
<p>This is a set of interview transcripts executed by Niccolò Tempini between May 2016 and July 2017, as part of the ERC project "The Epistemology of Data-Intensive Science", and in the context of a case study of COSMIC. Please read the "Notes on transcript editing" document for further information.</p> <p>Papers that specifically make use of these interviews and have been published as of February 2021:</p> <ul> <li> <p>Tempini, N., 2020. Data curation-research: practices of data standardization and exploration in a precision medicine database. New Genetics and Society. <a href="https://doi.org/10.1080/14636778.2020.1853513">https://doi.org/10.1080/14636778.2020.1853513</a></p> </li> <li> <p>Tempini, N., Leonelli, S., 2021. Actionable Data for Precision Oncology: Framing Trustworthy Evidence for Exploratory Research and Clinical Diagnostics. Social Science & Medicine. <a href="https://doi.org/10.1016/j.socscimed.2021.113760">https://doi.org/10.1016/j.socscimed.2021.113760</a></p> </li> </ul> <p>The transcripts document COSMIC researchers' experience of infrastructure development and data curation and re-use practices. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter.<br> You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu.</p>
Data from: Regular bottlenecks and restrictions to somatic fusion prevent the accumulation of mitochondrial defects in Neurospora
The replication and segregation of the multi-copy mitochondrial DNA (mtDNA) are not under strict control of the nuclear DNA. Within-cell selection may thus favour variants with an intracellular selective advantage but a detrimental effect on cell fitness. High relatedness among the mtDNA variants of an individual is predicted to disfavour such deleterious selfish genetic elements, but experimental evidence for this hypothesis is scarce. We studied the effect of mtDNA relatedness on the opportunities for suppressive mtDNA variants in the fungus Neurospora carrying the mitochondrial mutator plasmid pKALILO (pKAL). During growth, this plasmid integrates into the mitochondrial genome, generating suppressive mtDNA variants. These mtDNA variants gradually replace the wild-type mtDNA, ultimately culminating in growth arrest and death. We show that regular sequestration of mtDNA variation is required for effective selection against suppressive mtDNA variants. First, bottlenecks in the number of mtDNA copies from which a 'Kalilo' culture started significantly increased the maximum lifespan and variation in life span among cultures. Second, restrictions to somatic fusion among fungal individuals, either by using anastomosis-deficient mutants or by generating allotype diversity, prevented the accumulation of suppressive mtDNA variants. We discuss the implications of these results for the somatic accumulation of mitochondrial defects during ageing.
Data from: Density regulation in Northeast Atlantic fish populations: density dependence is stronger in recruitment than in somatic growth
1. Population regulation is a central concept in ecology, yet in many cases its presence and the underlying mechanisms are difficult to demonstrate. The current paradigm maintains that marine fish populations are predominantly regulated by density-dependent recruitment. 2. While it is known that density-dependent somatic growth can be present too, its general importance is unknown and most practical applications neglect it. This study aimed to close this gap by for the first time quantifying and comparing density dependence in growth and recruitment over a large set of fish populations. 3. We fitted density-dependent models to time series data on population size, recruitment and age-specific weight from commercially exploited fish populations in the Northeast Atlantic Ocean and the Baltic Sea. Data were standardised to enable a direct comparison within and among populations, and estimated parameters were used to quantify the impact of density regulation on population biomass. 4. Statistically significant density dependence in recruitment was detected in a large proportion of populations (70%) whereas for density dependence in somatic growth the prevalence of density dependence depended heavily on the method (26% and 69%). Despite age-dependent variability, the density dependence in recruitment was consistently stronger among age groups and between alternative approaches that use weight-at-age or weight increments to assess growth. Estimates of density-dependent reduction of biomass underlined these results: 97% of populations with statistically significant parameters for growth and recruitment showed a larger impact of density-dependent recruitment on population biomass. 5. The results reaffirm the importance of density-dependent recruitment in marine fishes, yet they also show that density dependence in somatic growth is not uncommon. Further, the results are important from an applied perspective because density dependence in somatic growth affects productivity and catch composition, and therefore the benefits of maintaining fish populations at specific densities.
Data from: Beyond species recognition: somatic state affects long distance sex pheromone communication
Long-range sex pheromones have been subjected to substantial research with a particular focus on their biosynthesis, peripheral perception, central processing and the resulting orientation behaviour of perceivers. Fundamental to the research on sex attractants was the assumption that they primarily coordinate species recognition. However, especially when they are produced by the less limiting sex (usually males), the evolution of heightened condition dependence might be expected and long-range sex pheromones might, therefore, also inform about a signaller's quality. Here we provide, to our knowledge, the first comprehensive study of the role of a male's long-range pheromone in mate choice that combines chemical analyses, video observations and field experiments with a multifactorial manipulation of males' condition. We show that the emission of the long-distance sex pheromone of the burying beetle, Nicrophorus vespilloides is highly condition-dependent and reliably reflects nutritional state, age, body size and parasite load—key components of an individual's somatic state. Both, the quantity and ratio of the pheromone components were affected but the time invested in pheromone emission was largely unaffected by a male's condition. Moreover, the variation in pheromone emission caused by the variation in condition had a strong effect on the attractiveness of males in the field, with males in better nutritional condition, of older age, larger body size and bearing less parasites being more attractive. That a single pheromone is influenced by so many aspects of the somatic state and causes such variation in a male's attractiveness under field conditions was hitherto unknown and highlights the need to integrate indicator models of sexual selection into pheromone research.
RNAIndel: discovering somatic coding indels from tumor RNA-Seq data (data package)
<p>Data package to run RNAIndel (> v.3.3.1) </p>
RNAIndel: discovering somatic coding indels from tumor RNA-Seq data
<ol> <li>Data package for GRCh38.</li> <li>Data package for GRCh37.</li> <li>Sample dataset (GRCh38).</li> </ol>
Inferring clonal somatic mutations directed by X chromosome inactivation status in single cells
<p>This repository contains large files for the reproduction of results described in the manuscript <strong>Inferring clonal somatic mutations directed by X chromosome inactivation status in single cells</strong>.</p>
Fig. 2. Pleuronema grolierei after protargol impregnation. A, C. Ventral views showing a typical body shape and infraciliature. B. Dorsal view showing somatic kineties. D. Nuclear apparatus, arrow denotes a in Taxonomy of four scuticociliates (Protozoa: Ciliophora) from coastal waters of South Korea
Fig. 2. Pleuronema grolierei after protargol impregnation. A, C. Ventral views showing a typical body shape and infraciliature. B. Dorsal view showing somatic kineties. D. Nuclear apparatus, arrow denotes a micronucleus. CC, caudal cilia; Ma, macronucleus; M1, 3, membranelles 1, 3; M2a, anterior part of membranelle 2; M2b, posterior part of membranelle 2; PM, paroral membrane; PK; preoral kineties; PoK, postoral kinety; SK, somatic kineties. Scale bar = 30 μm.
FIGURE 3. Somatic chromosomes and karyotypes. a, b in Crocus tuna-ekimii (Iridaceae), a new species from Turkey
FIGURE 3. Somatic chromosomes and karyotypes. a, b: C. tuna-ekimii. c, d: C. sozenii.
Table 2 in Ultrastructure of somatic embryo development and plant propagation for Lachenalia montana
<p><b>Table 2</b> Enhancement of somatic embryos and germination frequency in cell suspension culture from friable embryogenic callus (FEC) of L. montana.</p><table><tbody><tr><th>Sucrose (g L <i>−</i> 1) + PGR (μM) in solid MS medium</th><th>PGR (μM) in liquid MS medium</th><th>Liquid MS medium (MSL)</th><th>Number of SEs/SCV developmental stage</th><th>Germination (%)</th></tr></tbody><tbody><tr><th>6 weeks of culture</th><td>4 weeks of culture</td><td>2 weeks of culture</td><td>Globular</td><td>Different stages of embryo*</td><td></td></tr><tr><th>35 + 10 picloram + 2 BA</th><td>1 picloram</td><td>MSL</td><td>10.0 e</td><td>6.4 e</td><td>0</td></tr><tr><td>1 2,4-D</td><td>MSL</td><td>14.4 cd</td><td>9.0 cd</td><td>0</td></tr><tr><td>1 2,4-D + 0.5 BA</td><td>MSL</td><td>15.0 cd</td><td>9.8 cd</td><td>7.2 e</td></tr><tr><td>1 2,4-D + 0.5 <i>m</i> TR</td><td>MSL</td><td>7.8 f</td><td>6.0 e</td><td>8.6 de</td></tr><tr><td>1 2,4-D + 0.5 TDZ</td><td>MSL</td><td>18.0 b</td><td>12.4 b</td><td>14.0 ab</td></tr><tr><th>35 + 10 2,4-D + 2 TDZ</th><td>1 picloram</td><td>MSL</td><td>6.8 fg</td><td>3.0 f</td><td>0</td></tr><tr><td>1 2,4-D</td><td>MSL</td><td>16.0 cd</td><td>6.8 e</td><td>0</td></tr><tr><td>1 2,4-D + 0.5 BA</td><td>MSL</td><td>18.4 b</td><td>10.0 c</td><td>11.4 c</td></tr><tr><td>1 2,4-D + 0.5 <i>m</i> TR</td><td>MSL</td><td>10.2 e</td><td>6.8 e</td><td>14.3 ab</td></tr><tr><td>1 2,4-D + 0.5 TDZ</td><td>MSL</td><td>21.4 a</td><td>14.6 a</td><td>15.2 a</td></tr></tbody></table><p>PGR = Plant growth regulator. <sub>MSL</sub> = Liquid MS medium.SEs = Somatic embryos.SCV = Settled cell volume.*Different stages of embryo = pear-shaped, early torpedo-shaped, torpedoshaped and cotyledonary-stage embryos. Values with the means derived from 5 replicate (each 500 mg FEC) with 1000 μL SCV of embryogenic suspension cells per replicate. Mean values followed by same letters in each column are not significantly different according to the Duncan's multiple range test at 5% level.</p>
Table 1 in Ultrastructure of somatic embryo development and plant propagation for Lachenalia montana
<p><b>Table 1</b> Effect of sucrose and growth regulators on friable embryogenic callus (FEC) production for somatic embryo (SE) development with 6 weeks of incubation in cell suspension culture of L. montana.</p><table><tbody><tr><th>Sucrose (g L <i>−</i> 1) + PGR (μM) in solid MS medium</th><th>Number of SEs/SCV developmental stage</th><th>Germination (%)</th></tr></tbody><tbody><tr><th>6 weeks of culture</th><td>Globular</td><td>Different stages of embryo*</td><td></td></tr><tr><th>Control</th><td>0</td><td>0</td><td>0</td></tr><tr><th>30 + 10 2,4-D</th><td>4.0 ef</td><td>3.0 gh</td><td>0</td></tr><tr><th>35 + 10 2,4-D</th><td>6.2 de</td><td>4.6 ef</td><td>0</td></tr><tr><th>40 + 10 2,4-D</th><td>7.8 cd</td><td>6.4 de</td><td>0</td></tr><tr><th>35 + 10 2,4-D + 2 BA</th><td>9.4 c</td><td>8.0 c</td><td>0</td></tr><tr><th>35 + 10 2,4-D + 2 TDZ</th><td>14.8 ab</td><td>9.8 a</td><td>0</td></tr><tr><th>30 + 10 picloram</th><td>3.6 f</td><td>2.0 h</td><td>0</td></tr><tr><th>35 + 10 picloram</th><td>7.2 cd</td><td>5.0 ef</td><td>0</td></tr><tr><th>40 + 10 picloram</th><td>10.4 c</td><td>6.6 de</td><td>0</td></tr><tr><th>35 + 10 picloram + 2 BA</th><td>16.2 a</td><td>9.2 ab</td><td>0</td></tr><tr><th>35 + 10 picloram + 2 TDZ</th><td>9.8 c</td><td>7.0 cd</td><td>0</td></tr></tbody></table><p>FEC = Friable embryogenic callus. PGR = Plant growth regulator.SEs = Somatic embryos. SCV = Settled cell volume. *Different stages of embryo = pear-shaped, early torpedoshaped, torpedo-shaped and cotyledonary-stage embryos. Values with the means derived from 5 replicate (each 500 mg FEC) with 1000 μL SCV of embryogenic suspension cells per replicate. Mean values followed by same letters in each column are not significantly different according to the Duncan's multiple range test at 5% level.</p>
Table 3 in Direct somatic embryogenesis of drought resistance pistachio (Pistacia vera L.) and expression analysis of somatic embryogenesis-related genes
<p><b>Table 3</b> Effect of different plant growth regulators on induction of somatic embryos in three pistachio genotypes' explants (immature zygotic embryos) under light condition.</p><table><tbody><tr><th></th><th>Induction of somatic embryos %</th><th></th><th>Number of embryos/explant</th><th></th></tr></tbody><tbody><tr><th></th><td>Ghazvini</td><td>Badami Riz</td><td>Sarakhs</td><td>Ghazvini</td><td>Badami Riz</td><td>Sarakhs</td></tr><tr><th>E1</th><td>23.22 ± 3.39 c</td><td>22.74 ± 1.46d</td><td>24.67 ± 1.45 c</td><td>4.3 ± 1.2b</td><td>12.33 ± 1.45b</td><td>8 ± 0.58b</td></tr><tr><th>E2</th><td>34.25 ± 0.92b</td><td>34.66 ± 0.22 c</td><td>35.31 ± 0.85b</td><td>12.33 ± 1.45a</td><td>45 ± 2.88 a</td><td>11.33 ± 1.45a</td></tr><tr><th>E6</th><td>43.75 ± 3.6ab</td><td>43.58 ± 0.87b</td><td>42.83 ± 2.80a</td><td>5.33 ± 0.88b</td><td>10 ± 1.15b</td><td>7.67 ± 0.88b</td></tr><tr><th>E11</th><td>50.00 ± 4.12a</td><td>49.95 ± 1.19a</td><td>48.67 ± 2.03a</td><td>4.67 ± 0.88b</td><td>9.33 ± 1.20b</td><td>6 ± 0.58b</td></tr></tbody></table><p>Data within a column followed by different letters indicate significantly differences according to the LSD test at p <.05.</p>
Table 2 in Direct somatic embryogenesis of drought resistance pistachio (Pistacia vera L.) and expression analysis of somatic embryogenesis-related genes
<p><b>Table 2</b> PCR primer sequences of selected somatic embryogenesis-related genes for qRT in pistachio.</p><table><tbody><tr><th>Gene name</th><th>Forward primer sequence 5′-3′</th><th>Reverse primer sequence 5′-3′</th><th>Tm (̊C)</th><th>Amplicon length (bp)</th></tr></tbody><tbody><tr><th><i>ACT</i></th><td>GTATCCACGAGACCACCTACA</td><td>GGAGCAACGACCTTGATCTTC</td><td>60</td><td>157</td></tr><tr><th><i>AGL15</i></th><td>GGAGCAATCAAGAGTACAGGAACAG</td><td>CACTTCAGGACTTGCAGAACTATGG</td><td>60</td><td>187</td></tr><tr><th><i>LEC</i></th><td>GATGTGGGGTCTCTTGGAAGGA</td><td>AGCCTGTTTTGCTTTACGAAATCTC</td><td>60</td><td>209</td></tr><tr><th><i>BBM</i></th><td>TCCCAATTAGCAACTACGAGAAAGAG</td><td>CTGATGATGCCTTGTAACACCAC</td><td>60</td><td>140</td></tr><tr><th><i>PKL</i></th><td>GTAGACATCGCACCCTCTTAACTG</td><td>GAGCCAGCATTTTATGAAGTCTTGA</td><td>60</td><td>174</td></tr></tbody></table>
Table 1 in Direct somatic embryogenesis of drought resistance pistachio (Pistacia vera L.) and expression analysis of somatic embryogenesis-related genes
<p><b>Table 1</b> Summary of different responses of zygotic immature embryos to different basal medium (BM1 and BM2), combination and concentration of plant growth regulators (PGRs), and light and dark conditions.The response of explants was in the form of callus (C), somatic embryogenesis (SE), and adventitious shoot (AS).The Basal media were BM1: MS mineral salts,B5 vitamins, 30 g L <i>−</i> 1 sucrose, BM2: half strength MS macro salts, B5 vitamins, 100 mg L <i>−</i> 1 L-glutamine, 500 mg L <i>−</i> 1 casein hydrolysate, and 30 g L <i>−</i> 1 sucrose.</p><table><tbody><tr><th>Coding</th><th>PGRs combinations (mg L <i>−</i> 1)</th><th>Light</th><th>Dark</th></tr></tbody><tbody><tr><th></th><td></td><td>BM1</td><td>BM2</td><td>BM1</td><td>BM2</td><td>BM1</td><td>BM2</td><td>BM1</td><td>BM2</td><td>BM1</td><td>BM2</td><td>BM1</td><td>BM2</td></tr><tr><th></th><td></td><td>C</td><td></td><td>SE</td><td></td><td>AS</td><td></td><td>C</td><td></td><td>SE</td><td></td><td>AS</td><td></td></tr><tr><th>E1</th><td>0</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E2</th><td>0.01 IBA</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E3</th><td>0.01 IBA + 1 BA</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E4</th><td>0.01 IBA + 2 BA</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td></tr><tr><th>E5</th><td>0.01 IBA + 3 BA</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E6</th><td>0.01 2,4-D</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E7</th><td>0.01 2,4-D + 0.1 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E8</th><td>0.01 2,4-D + 0.25 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E9</th><td>0.01 2,4-D + 0.5 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E10</th><td>0.01 2,4-D + 1 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E11</th><td>0.01 NAA</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E12</th><td>0.01 NAA + 1 BA</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>+</td></tr><tr><th>E13</th><td>0.01 NAA + 2 BA</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>+</td></tr><tr><th>E14</th><td>0.01 IBA + 0.1 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E15</th><td>0.01 IBA + 0.25 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E16</th><td>0.01 IBA + 0.5 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th>E17</th><td>0.01 IBA + 1 TDZ</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>+</td><td>–</td><td>–</td></tr></tbody></table><p>«–» and « + » represent nonexistence and existence the response on explants that cultured in intended media, respectively.</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.