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12,871 results for “aging;”
The number of HAdV species B and HAdV species C positive cases by age group, and their positivity rates among ARTI cases.
<p><strong><span>The number of HAdV species B and HAdV species C positive cases by age group, and their positivity rates among ARTI cases.</span></strong></p>
Supporting Data for "Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries"
<p>This dataset provides the raw data to the manuscript</p> <p><strong>"Why Half‐Cell Samples Provide Limited Insight Into the Aging Mechanisms of Potassium Batteries"</strong></p> <p>published in Advanced Energy Materials, <strong>2024</strong>, DOI 10.1002/aenm.202403811</p> <p><a href="https://doi.org/10.1002/aenm.202403811">Link to Publisher</a></p> <p> </p> <p>Comments:</p> <ul> <li>HAXPES synchrotron data is provided as IGOR file (IGOR Pro (v6.37, WaveMaterics Inc.). The file contains the original 2D data and the converted 1D datasets used for analysis</li> <li>In-House XPS data is provided with corresponding peak fits as excel spreadsheet</li> <li>peak data (binding energy (after referencing), intensity, area, FWHM and at.%) is provided in separate excel spreadsheet.</li> </ul> <p> </p>
Resources for BMF CP 84: Gender, age, income, immersiveness, and space tourism intention
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>How are gender, age, and income associated with the intention to try space tourism?</span></li> <li><span>How are gender, age, and income associated with the intention to try space tourism when conditional on the level of immersiveness in information on social media?</span></li> </ul>
Sequential Bayesian Inference of Finite-strain Visco-elastic Visco-plastic model parameters of 22-month aged PA12 bulk material printed along different directions
<p>These are the data related to aged PA12 (22 months) following the methodology described in the following publication in which non-aged PA12 has been tested:</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br>journal = "International Journal of Solids and Structures",<br>year = "2023",<br>volume = "283",<br>pages = "112470",<br>doi = "10.1016/j.ijsolstr.2023.112470",<br>author = "Wu, Ling and Anglade, Cyrielle and Cobian, Lucia and Monclus, Miguel and Segurado, Javier and Karayagiz, Fatma and Freitas, Ubiratan and Noels Ludovic"</p> <p>Contrarily to the non-aged material, since high-strain-rate tests are not available, only 5 Maxwell's branches are considered herein.</p> <h1>Description</h1> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a></p> <p>The sequential BI is described in [WU23] The experimental protocol is reported in [COB22,COB22b] but is herein applied on aged PA12</p> <p>To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a></p> <p>If you use these data or model, we would be grateful if you could cite the related papers:</p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: <a href="https://doi.org/10.1016/j.ijsolstr.2023.112470" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.ijsolstr.2023.112470</a></li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: <a href="https://doi.org/10.1016/j.polymertesting.2022.107556" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1016/j.polymertesting.2022.107556</a> (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” <a href="http://dx.doi.org/10.5281/zenodo.6136935" target="_blank" rel="nofollow noreferrer noopener">http://dx.doi.org/10.5281/zenodo.6136935</a> (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; <a href="https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008" target="_blank" rel="nofollow noreferrer noopener">https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008</a>, Open access: <a href="https://orbi.uliege.be/handle/2268/197898" target="_blank" rel="nofollow noreferrer noopener">https://orbi.uliege.be/handle/2268/197898</a></li> </ul> <h1>Directories</h1> <p>All the codes and experimental results are in three directories:</p> <ol> <li>Experiment_PA12_AGED: experimental data of aged material, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters<br>2.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range<br>2.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.<br>2.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>2.1.3. Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI<br>2.2. VE_V and VE_H: BI for viscoelastic properties of "V" specimen (VE_V) and "H" specimen (VE_H)<br>2.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat<br>2.2.2. WarmStart = True is used to restart an inference<br>2.2.3. MCMC_VE_....dat in the VE_V and VE_H directories are the BI results<br>2.3. CheckBayRes: to visualize predictions of a BI sample and experimental curves 2.3.1. MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line 7)<br>2.3.2. ResKGEmu.py plots the evolution of elastic properties with time<br>2.3.3. uses as input VE_V/MCMC_VE_....dat or VE_H/MCMC_VE_....dat<br>2.3.4. uses local ViscoElasticTest.py, line.geo, line. msh as interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code<br>2.3.5. uses local functions plotExp.py<br>2.4. ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V and VE_H to call the VEVP model</li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters<br>3.1. PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges<br>3.1.1. Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.<br>3.1.2. PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py<br>3.1.3. Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI<br>3.2. VP_V and VP_H: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V) and "H" specimen (VP_H)<br>3.2.1. BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat<br>3.2.2. WarmStart = True is used to restart an inference 3.2.3. It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3. CheckBayRes: visualize predictions of a BI sample and experimental curves<br>3.3.1. MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples (inclusing MAP, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.2. plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?<em>1Step.dat (? being H or V)<br>3.3.3. Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP</em>?/MCMC_VP_?_1Step.dat (? being H or V) 3.3.4. ResKGEmu.py plots the evolution of elastic properties with time<br>3.4. VEVPTest.py: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> <h1>Figures (reference to the number in [WU23] but for aged PA12)</h1> <ul> <li>Fig. 5 (Selected observations): From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,14,18,22,23,24,25]</li> <li>Fig. 8 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9 (Predictions of 3 inference realisations): BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves/PrintDir_? (? being H or V), run python3 plotExp_T.py or plotExp_C.py</li> <li>Fig. 15B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 16B: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18B: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,10,11,12,13] and [14,15,16,17,18,19,20,21]</li> <li>Fig. 19B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20B: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> </ul> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015.</p>
Spleen STARmap PLUS dataset of Transient hepatic reconstitution of thymic factors enhances aged immunity
<p>Here is the STARmap PLUS dataset of Spleen section included in "<strong>Transient hepatic reconstitution of thymic factors enhances aged immunity</strong>" from Friedrich et al. </p>
A new joint 1-km China's forest age dataset
<p>This dataset combines China's forest age data of Xu et al., 2023, Cheng et al., 2024 and Shang et al., 2023 to obtain a more reliable forest age layer. We calculated the average and standard deviation (uncertainty) of these data. Spatial gaps in these data were imputed using the ‘missRanger’ package in R.</p>
Annex B – Occurrence data on brominated phenols and their derivatives in food submitted to EFSA, dietary surveys per country and age group available in the EFSA Comprehensive Database considered in the exposure assessment, and the detailed results of the chronic dietary exposure assessment to 2,4,6-TBP and the contribution of different food groups to the dietary exposure
<p>This Annex contains the occurrence data submitted to EFSA, the dietary surveys per country and age group, and the detailed results of the chronic dietary exposure assessment to 2,4,6-TBP and the contribution of different food groups to the dietary exposure related to the Update of the risk assessment of brominated phenols and their derivatives in food.</p>
Supplementary data for: "Emergence of Potential Anadromous Arctic Charr (Salvelinus alpinus) Habitats in the Svalbard Archipelago after the End of the Little Ice Age"
<p><strong>Supplementary data for: “Emergence of Potential Anadromous Arctic Charr (<em>Salvelinus alpinus</em>) Habitats in the Svalbard Archipelago after the End of the Little Ice Age” </strong></p> <p><a href="https://doi.org/10.1029/2024JG008367">https://doi.org/10.1029/2024JG008367</a></p> <p> </p> <p>Abstract: Glaciers in the Svalbard Archipelago are retreating rapidly in response to climate change. The retreat of glaciers leads to alteration of the hydrological and thermal regimes of the freshwater ecosystems. In this delicate context, existing anadromous Arctic charr (<em>Salvelinus alpinus</em>) populations are at severe risk and might disappear from the archipelago. However, the retreat of glaciers also promotes the formation of new lake systems that might be suitable for colonization by anadromous Arctic charr. These systems may provide a substantial opportunity for the establishment of new populations of anadromous charr, potentially buffering the decline in existing systems. To date, there is a lack of information on the number of recently deglaciated lake systems that have emerged since the end of the Little Ice Age (ca. 1920) that might be suitable for charr colonization. Therefore, the goal of this paper is to provide an initial assessment of the number of these lakes. To this end, and in accordance with previously published research, this study assesses whether a recently deglaciated lake system is potentially open to colonization based on gradient, river length, and lake surface area. Depending on the applied threshold (four in total), up to 24 lake systems are classified as potentially open to colonization by anadromous Arctic charr, with Spitsbergen emerging as a colonization hotspot. The findings of this paper might serve as basis for new studies and for implementing proactive management and conservation strategies to protect anadromous charr populations.</p> <p> </p> <p><strong>Data description:</strong></p> <p> </p> <p><em>Recently_Deglaciated_Systems</em> [EPGS: 25833] - Shapefile containing the information of 168 lake systems that emerged between 1936/1938 and 2020 in the Svalbard Archipelago</p> <p>ID: Identification number</p> <p>X: Easting</p> <p>Y: Northing</p> <p>Fall?: Presence of the lake system in fall (with dates when the lake was visible in satellite images)</p> <p>Spring?: Presence of the lake system in spring (with dates when the lake was visible in satellite images)</p> <p>Info: information regarding the type of system (i.e., larger lake surrounded by lakes and ponds or system of multiple lakes and ponds). If blank, lake is considered single.</p> <p> </p> <p><em>Analysis</em> [EPGS: 25833] - Shapefile containing the analysis of 125 lake systems with connection to the ocean.</p> <p>ID: Identification number</p> <p>X: Easting</p> <p>Y: Northing</p> <p>Surface (km<sup>2</sup>): Surface of the connected recently deglaciated lake system (km<sup>2</sup>)</p> <p>Elevation (m): Elevation of the outlet (m)</p> <p>Length (m): River length (m)</p> <p>Slope (%): Slope of the river (%)</p> <p>SV1: Result of the classification with SV1</p> <p>SV2: Result of the classification with SV2</p> <p>HY: Result of the classification with HY</p> <p>NO: Result of the classification with NO</p>
Data and codes for "Habitat structural complexity increases age-class coexistence and population growth rate through relaxed cannibalism in medaka fish"
<p>The zip file contains readme files, as well as data and codes to reproduce results and figures from the paper.</p>
Mapping discrete forest age classes of Mediterranean pinelands since the pre-satellite era using historical orthoimage mosaics and machine learning.
<p>Data and sripts for Journal of Forestry Research submitted manuscript. Authors: Vicent Agustí Ribas Costa, Andrew Trlica, & Aitor Gastón González.</p> <p>This data and scripts are part of Vicent's PhD project at Universidad Politécnica de Madrid, supervised by Dr. Aitor. Contact Vicent for any queries at va.ribas@upm.es.</p> <p>The PNOA LiDAR and 1956 and 2021 orthophotos are openly available at the Centro de Descargas of the Instituto Geográfico Nacional (<a href="https://centrodedescargas.cnig.es/CentroDescargas/index.jsp">https://centrodedescargas.cnig.es/CentroDescargas/index.jsp</a>). The 1989 orthophoto is available under request at the Institut Cartogràfic i Geogràfic de les Illes Balears (<a href="https://www.caib.es/webgoib/institut-cartografic-i-geografic-de-les-illes-balears-icgib-">https://www.caib.es/webgoib/institut-cartografic-i-geografic-de-les-illes-balears-icgib-</a>).</p> <p>Forest inventory data is property of the landowners and managed by Terrapi World Ltd.</p>
Does land use age influence carbon cycling in the Tibetan Plateau?
<p>Although substantial information had been generated on the effects of land use change on soil organic carbon (SOC) and total nitrogen (TN) storage, studies are absent on multifactorial effects of land use types, land use age, and elevation on SOC and TN storage. SOC and TN were therefore investigated in 30 field sites comprising natural forests, planted forests, shrub, and grasslands. SOC and TN stocks differed and correlated significantly with land use age; the C stocks correlates significantly with land use change compared the TN stocks. However, there was no relation between the C and N stocks with elevation, implying that SOC and TN are solely dependent on land use age. SOC sequestration potentials of the sampled ecosystems were 345.86, 293.19, 266.45, and 251.23 t ha<sup>−1</sup> for the natural forests, planted forests, shrub, and grasslands with total mean value of 289.18 t·ha<sup>−1</sup> (1,060.42 t·ha<sup>−1</sup> CO<sub>2−</sub>eq). A significant SOC stock loss (17.96%, 29.80%, and 37.66%) occurred in converting natural forests to planted forests, shrub, and grasslands, whereas gains (27.36%, 14.31%, and 5.71%) would occur in reconverting grassland to natural forests, planted forests, and shrublands. Therefore, the C that was lost during deforestation and conversion of natural forests into other land use types could not match the carbon gains thereafter. Our results suggest that land use change and land use age have influenced soil C and N stocks. Moreover, natural forests are better in ecological conservation and restoration of degraded lands. This study provides baseline information for C and N management in ecologically restored and degraded lands.</p>
Data from: Host genotype and age shape the leaf and root microbiomes of a wild perennial plant
Bacteria living on and in leaves and roots influence many aspects of plant health, so the extent of a plant's genetic control over its microbiota is of great interest to crop breeders and evolutionary biologists. Laboratory-based studies, because they poorly simulate true environmental heterogeneity, may misestimate or totally miss the influence of certain host genes on the microbiome. Here we report a large-scale field experiment to disentangle the effects of genotype, environment, age and year of harvest on bacterial communities associated with leaves and roots of Boechera stricta (Brassicaceae), a perennial wild mustard. Host genetic control of the microbiome is evident in leaves but not roots, and varies substantially among sites. Microbiome composition also shifts as plants age. Furthermore, a large proportion of leaf bacterial groups are shared with roots, suggesting inoculation from soil. Our results demonstrate how genotype-by-environment interactions contribute to the complexity of microbiome assembly in natural environments.
New Age of Fishes initiated by the Cretaceous−Paleogene mass extinction
<p></p><p>Ray-finned fishes (Actinopterygii) comprise nearly half of all modern vertebrate diversity, and are an ecologically and numerically dominant megafauna in most aquatic environments. Crown teleost fishes diversified relatively recently, during the Late Cretaceous and early Paleogene, although the exact timing and cause of their radiation and rise to ecological dominance is poorly constrained. Here we use microfossil teeth and shark dermal scales (ichthyoliths) preserved in deep-sea sediments to study the changes in the pelagic fish community in the latest Cretaceous and early Paleogene. We find that the Cretaceous−Paleogene (K/Pg) extinction event marked a profound change in the structure of ichthyolith communities around the globe: Whereas shark denticles outnumber ray-finned fish teeth in Cretaceous deep-sea sediments around the world, there is a dramatic increase in the proportion of ray-finned fish teeth to shark denticles in the Paleocene. There is also an increase in size and numerical abundance of ray-finned fish teeth at the boundary. These changes are sustained through at least the first 24 million years of the Cenozoic. This new fish community structure began at the K/Pg mass extinction, suggesting the extinction event played an important role in initiating the modern "age of fishes."</p><p></p>
Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes
<p>Landslides are common natural disturbances in tropical montane forests. While the geomorphic drivers of landslides in the Andes have been studied, factors controlling post-landslide forest recovery across the steep climatic and topographic gradients characteristic of tropical mountains are poorly understood.</p> <p>Here we use a LiDAR-derived canopy height map coupled with a 25-year landslide time series map to examine how landslide, topographic, and biophysical factors, along with residual vegetation, affect canopy height and heterogeneity in regenerating landslides. We also calculate aboveground biomass accumulation rates and estimate the time for landslides to recover to mature forest biomass levels.</p> <p>We find that age and elevation are the biggest determinants of forest recovery, and that the jump-start in regeneration that residual vegetation provides lasts for at least 18 years. Our estimates of time to biomass recovery (31.6-37.1 years) are surprisingly rapid, and as a result we recommend that future research pair LiDAR with hyperspectral imagery to estimate forest aboveground biomass in frequently disturbed landscapes.</p> <p>Synthesis: Using a high-resolution LiDAR dataset and a time-series inventory of 608 landslides distributed across a wide elevational gradient in Andean montane forest, we show that age and elevation are the most influential predictors of forest canopy height and canopy variability. Other features of landslides, in particular the presence of residual vegetation, shape post-landslide regeneration trajectories. LiDAR allows for a detailed analysis of forest structural recovery across large landscapes and numbers of disturbances, and provides a reasonable upper bound on aboveground biomass accumulation rates. However, because this method does not capture the effect of compositional change through succession on aboveground biomass, wherein high-wood density species gradually replace light-wooded pioneer species, it overestimates aboveground biomass. Given previously estimated stem turnover rates along this elevational gradient, we posit that aboveground biomass recovery takes at least three times as long as our recovery time estimates based on LiDAR-derived structure alone.</p>
The crater detection results (necessary data for making Figs 6 and 7) for areas A2, A4 and A7 to estimate the ages of RMDS-overlapped craters in Figs 2a-2c
<p>To estimate the ages of RMDS-overlapped craters in Figs 2a-2c ( located in areas A2, A4 and A7, respectively) with a locally calibrated Monte Carlo model, the NAC-DEM-based crater detection results are provided here, as the basis input and necessary data to make Figs 6 and 7.</p> <p>They have been introduced in the following publication, where more details can be found:</p> <p>F. Zhang, J. W. Head, C. Wöhler, A. T. Basilevsky, L. Wilson, M. Xie, R. Bugiolacchi, T. Wilhelm, S. Althoff, Y. L. Zou.</p> <p>The Lunar Mare Ring-Moat Dome Structure (RMDS) Age Conundrum: Contemporaneous with Imbrian-Aged Host Lava Flows or Emplaced in the Copernican?</p> <p>Journal of Geophysical Research: Planets, 126, e2021JE006880. https://doi. org/10.1029/2021JE006880</p>
mass spectrometry metabolomic analysis of Drosophila head extracts: 3 genotypes (control, RNAi Opa1 in muscle, RNAi Marf in muscle), 2 ages (30 days, 65 days)
<p>RNAi of the Drosophila mitochondrial fusion genes Opa1 and Marf (Mitofusin 2) in the muscle results in an extended lifespan. In order to detect metabolic changes in lipid in the neural tissue, we analysed the metabolome of fly heads by mass spectroscopy, comparing the two knock-down genotypes to a wild type control at two ages: 30 days (young flies) and 65 days (old flies).</p>
NMR metabolomic analysis of Drosophila extracts: 3 genotypes (control, RNAi Opa1 in muscle, RNAi Marf in muscle), 3 body regions (head, thorax, abdomen), 2 ages (30 days, 65 days)
<p>RNAi of the Drosophila mitochondrial fusion genes Opa1 and Marf (Mitofusin 2) in the muscle results in an extended lifespan. In order to unravel the metabolic changes in the long-living flies we analysed the metabolome by NMR spectroscopy, comparing the two knock-down genotypes to a wild type control at two ages: 30 days (young flies) and 65 days (old flies). In order to detect autonomous and non-autonomous changes, we also divided the samples in three anatomical regions: head (mostly neural tissue) thorax (mostly muscle), and abdomen (visceral, reproductive, metabolic control)</p>
Data from: Palynology of a short sequence of the Lower Devonian Beartooth Butte Formation at Cottonwood Canyon (Wyoming): Age, depositional environments and plant diversity
<p>The Beartooth Butte Formation hosts the most extensive Early Devonian macroflora of western North America. The age of the flora at Cottonwood Canyon (Wyoming) has been constrained to the Lochkovian-Pragian interval, based on fish biostratigraphy and unpublished palynological data. We present a detailed palynological analysis of the plant-bearing layers at Cottonwood Canyon. The palynomorphs comprise 32 spore, five cryptospore, two prasinophycean algae and an acritarch species. The stratigraphic ranges of these palynomorphs indicate a late Lochkovian - Pragian age, confirming previous age assignments. Analyses on samples from three different depositional environments of the plant-bearing sequence – layers with in situ lycophyte populations, flood layers that buried those populations and an organic matter accumulation zone within a flood layer – demonstrate distinct palynofacies. Comparisons between palynomorph and plant macrofossil diversity reveal some discrepancies. Whereas zosterophylls and lycophytes, most diverse and abundant among the macrofossils, have only one known corresponding spore type (assignable to zosterophylls) in the palynomorph assemblage, the trimerophytes, rare in the macrofossil assemblage, are represented by three spore types. Some of these discrepancies reflect taphonomic differences between macrofossils and palynomorphs, others could be due to the fact that the parent plants of most palynomorph types in the Cottonwood Canyon assemblage are unknown. These observations emphasize the need for concerted efforts to bring together the knowledge of macro- and microfloras within Early Devonian localities. Nevertheless, given the palaeophytogeographic significance of the Beartooth Butte Formation flora, its palyno- and macrofossil assemblages, taken together, provide new data relevant to future discussions of Early Devonian biogeography.</p>
Raw data for the publication entitled -"Local disorder in calcined kaolinitic clays for pozzolanic early-age reactivity understanding: A Synchrotron Pair Distribution Function study-"
<p>Raw data for the publication entitled -"Local disorder in calcined kaolinitic clays for pozzolanic early-age reactivity understanding: A Synchrotron Pair Distribution Function study-". This includes: raw data for calorimetry, SXRPD,LXRPD,NMR and PSD.</p>
Figure 43 in Atlas of Myriapod Biogeography. I. Indigenous Ordinal and Supra-Ordinal Distributions in the Diplopoda: Perspectives on Taxon Origins and Ages, and a Hypothesis on the Origin and Early Evolution of the Class
Figure 43. Distribution of Chordeumatida in Europe, North Africa, and the Middle East.
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.