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12,871 results for “aging;”
Figure 5 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 5 - Relative abundance across the forest age gradient for a representative species from each of the indicator classes (see text for definitions of indicator classes).
Figure 4 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 4 - Proportions of carabid populations brachypterous and macropterous for five forest age classes. Significant differences occurred for all forest age classes except the zero age class (χ2 < 0.05).
Figure 3 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 3 - Results of Non-Metric Multidimensional Scaling (NMDS) analysis for 33 study sites. The analysis was based on the 17 most common carabid beetle species from five forest age classes (0, 10, 50, 85, and 150 years). Each the five polygons represent different forest age classes, as indicated by different symbols.
Figure 2 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 2 - Carabid beetle species accumulation curves for five forest age classes. Vertical line indicates species richness of each curve at n = 233 individuals.
Figure 4 from: Šerić Jelaska L, Dumbovich V, Kucinic M (2011) Carabid beetle diversity and mean individual biomass in beech forests of various ages. ZooKeys 100: 393-405. https://doi.org/10.3897/zookeys.100.1536
Figure 4 - Dendrogram of cluster analyses among forest sites using presence/absence carabid beetle data. Two distinct clusters are formed at roughly 30% similarity. Marks 60y 1–3, 80y 1–3 and 150y. 1–3 denote investigated sites placed in the 60-, 80- and 150-year-old forests.
Figure 1 from: Šerić Jelaska L, Dumbovich V, Kucinic M (2011) Carabid beetle diversity and mean individual biomass in beech forests of various ages. ZooKeys 100: 393-405. https://doi.org/10.3897/zookeys.100.1536
Figure 1 - Position of investigated sites labelled according to the age of the forest ("60y 1–3" denote sites 1 to 3 in the 60-year-old forest, "80y 1–3" denote sites 1 to 3 in the 80-year-old forest and "150y 1–3" denote sites 1 to 3 in the 150-year-old forest). Insert: location of Papuk Nature Park in Croatia.
Figure 4 from: Yosef R, Pande S, Pawashe A, Mahajan M, Mahabal A, Dahanukar N (2011) Biometry based ageing of nestling Indian Spotted Owlets (Athene brama brama). ZooKeys 132: 75-88. https://doi.org/10.3897/zookeys.132.1346
Figure 4 - Decision tree based on exhaustive CHAID algorithm. Bar diagrams show the number of individuals in each age class 1 to 5 weeks of nestling Spotted Owlets (Athene brama brama). Numbers on the bars indicate individuals in the given age class.
Figure 3 from: Yosef R, Pande S, Pawashe A, Mahajan M, Mahabal A, Dahanukar N (2011) Biometry based ageing of nestling Indian Spotted Owlets (Athene brama brama). ZooKeys 132: 75-88. https://doi.org/10.3897/zookeys.132.1346
Figure 3 - Discriminant Factor Analysis of the age classes based on five biometric characters of nestling Spotted Owlets (Athene brama brama). The figure shows the factor scores of observations. Factor loading of variables is given in the inset. Percentages in parenthesis are percent variation explained by each factor.
Figure 2 from: Yosef R, Pande S, Pawashe A, Mahajan M, Mahabal A, Dahanukar N (2011) Biometry based ageing of nestling Indian Spotted Owlets (Athene brama brama). ZooKeys 132: 75-88. https://doi.org/10.3897/zookeys.132.1346
Figure 2 - Identification key for ageing nestling Spotted Owlets (Athene brama brama) based on biometrics. Highlighted in grey are those parameters that are most reliable for field use.
Figure 1 from: Yosef R, Pande S, Pawashe A, Mahajan M, Mahabal A, Dahanukar N (2011) Biometry based ageing of nestling Indian Spotted Owlets (Athene brama brama). ZooKeys 132: 75-88. https://doi.org/10.3897/zookeys.132.1346
Figure 1 - Biometry ofbody mass, wing chord length, talon length, tarsus length and culmen length plotted against the age of nestling Spotted Owlets (Athene brama brama). The smoothed curve is the logistic growth model fitted to the data.
Figure 5 from: Yosef R, Pande S, Pawashe A, Mahajan M, Mahabal A, Dahanukar N (2011) Biometry based ageing of nestling Indian Spotted Owlets (Athene brama brama). ZooKeys 132: 75-88. https://doi.org/10.3897/zookeys.132.1346
Figure 5 - Flight formula for different age groups of nestling Spotted Owlets (Athene brama brama). Box plot of flight formula index are plotted for different age groups. When the flight formula approaches unity birds are ready to fly.
Figure 3 from: Ober K, Matthews B, Ferrieri A, Kuhn S (2011) The evolution and age of populations of Scaphinotus petersi Roeschke on Arizona Sky Islands (Coleoptera, Carabidae, Cychrini). ZooKeys 147: 183-197. https://doi.org/10.3897/zookeys.147.2024
Figure 3 - Phylogeny of Scaphinotus petersi dated using a Bayesian relaxed molecular clock in BEAST. Outgroups are removed to show greater detail. Specimen numbers are removed, but the mountain range from which they were collected is indicated. Branches are proportional to time in thousands of years. Shading indicates the two most recent glacial maxima. 95% confidence intervals for the ages of major clades in the tree are indicated with blue bars. The capital letters indicate population fragmentation between mountain ranges (see Table 3).
Figure 1 from: Ober K, Matthews B, Ferrieri A, Kuhn S (2011) The evolution and age of populations of Scaphinotus petersi Roeschke on Arizona Sky Islands (Coleoptera, Carabidae, Cychrini). ZooKeys 147: 183-197. https://doi.org/10.3897/zookeys.147.2024
Figure 1 - Study location A Scaphinotus petersi distribution is circled area. Habitat above 1830m is shown in black and between 1500 and 1830m is shown in grey B Shaded relief map of study area. Black dots denote sampling localities of Scaphinotus petersi used in this study (see Table 1) abbreviated as follows: P, Pinal Mountains; SC, Santa Catalina Mountains; PN, Pinaleño Mountains; and H, Huachuca Mountains. Figure courtesy of Sara Mitchell.
Figure 2 from: Ober K, Matthews B, Ferrieri A, Kuhn S (2011) The evolution and age of populations of Scaphinotus petersi Roeschke on Arizona Sky Islands (Coleoptera, Carabidae, Cychrini). ZooKeys 147: 183-197. https://doi.org/10.3897/zookeys.147.2024
Figure 2 - Maximum likelihood tree of Scaphinotus petersi populations from combined COI and ND1 data. Outgroups are removed to show greater detail. Specimen numbers are removed, but the mountain range from which they were collected is indicated. Support for branches is indicated by Bayesian Posterior Probability/Maximum Likelihood bootstrap values. Scale bar units are substitutions per site.
SF6 and CFC-12 measurements and mean age along HALO flight tracks during PGS, WISE and SouthTRAC
<p>This dataset belongs to the paper " Mean age from observations in the lowermost stratosphere: an improved method and interhemispheric differences", submitted to ACP in 2022.</p> <p>It contains</p> <ul> <li>SF<sub>6</sub> and CFC-12 measurements from the GhOST instrument (ECD-Channel),</li> <li>mean age calculated using the conventional TR-only method (mA_fit),</li> <li>mean age calculated using the extended exTR-TR method (mA_fit_fracts)</li> <li>and flight track information (delta theta, equivalent latitude, latitude, longitude</li> </ul> <p>in csv format for the three aircraft campaigns PGS (PGS_age.csv), WISE (WISE_age.csv) and SouthTRAC (ST_age.csv).</p> <p>Mean age values have not been corrected for mesospheric loss of SF<sub>6</sub>.</p> <p>The Python code tools for reproducing mean age calculations are available at</p> <pre>https://doi.org/10.5281/zenodo.7267114</pre> <pre>https://doi.org/10.5281/zenodo.7267203</pre> <p> </p>
Conta AGE Jovem - Snapshot
<p>Repository of image, part of PhD Thesis in Management.</p>
Quality of CAD-CAM inlays placed on aged resin-based composite restorations used as deep margin elevation
<p>This file comprises the dataset generated and analyzed in an investigation assessing the impact of the age of resin-based composite restorations used as deep margin elevation on the marginal quality and fracture behavior of CAD-CAM inlays</p>
photochemical aging of cooking and burning emissions
<p>Data of photochemical aging of cooking and burning emissions </p>
age-period-cohort effects on the temporal trend mortality from male suicide in Brazil from 1980 to 2019
<p>The available files represent the records of deaths by suicide in men in Brazil and its major regions in the period from 1980 to 2019, in addition to the number of deaths after the correction process for the quality and coverage of deaths, for total suicides and suicides by weapon. of fire. The database for total and firearm suicides, represents the number of corrected deaths presented in age groups and periods grouped into five-year periods, which were used to carry out the analyzes of the effect of age, period and cohort, for total suicides and by weapon. of fire for Brazil and its great regions.</p> <p>.</p>
Quality of life among Old age people in Bahir Dar City, Northwest Ethiopia, 2021
<p>Introduction: Reliable quality of life assessment is critical for identifying health issues, evaluating health interventions, and establishing the best health policies and care packages. The World Health Organization Quality of Life-Old Module is a tool for assessing the subjective quality of life in old age people. It's validated and available in more than 20 languages, except Amharic. Hence, this study was intended to translate it into Amharic language and validate it among old age people in Bahir Dar City, Ethiopia.<br> Methods: A cross-sectional study was conducted among 180 community-dwelling old age people in Bahir Dar City, Northwestern Ethiopia, from January 16 to March 13, 2021. Psychometric validation was achieved through Cronbach’s alpha of the internal consistency reliability test, and construct validity from confirmatory factor analysis.<br> Results: The study participants aged from 60 to 90 years old with a mean age of 69.44. Females made up 61.7% of the population and 40% of them could not read and write. The results showed a relatively low level of quality of life, with the total transformed score of 58.58 ± 23.15. The Amharic version of the World Health Organization Quality of Life-Old Module showed a Cronbach’s Alpha value of 0.96 and corrected item-total correlations of more than 0.74. Confirmatory factor analysis confirmed the six-factor model with a chi-square (X2) of 341.98 with a p-value less than 0.001. The comparative fit index (CFI) was 0.98, Tucker-Lewis’s index (TCL) was 0.97, and the root mean square error of approximation (RMSEA) was 0.046.<br> Conclusion: The Amharic version of the World Health Organization Quality of Life-Old Module indicated good internal consistency reliability and construct validity. The tool can be utilized to provide care to Ethiopian community-dwelling old age people.</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.