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128 results for “age determination”
DETERMINING AGES OF APOGEE GIANTS WITH KNOWN DISTANCES - Full PDFs
<p>Supplementary data to Feuillet+ (2016, ApJ, arXiv:1511.04088):</p> <p>Using the APOGEE survey instrument and the NMSU 1m telescope, we observe a sample of bright, nearby, red giant stars with known distances measured by Hipparcos. By applying Bayesian analysis and hierarchical modeling, we determine individual stellar ages and the star formation histories of single alpha-abundance subsamples. The probability distribution functions (PDFs) and modeled star formation histories (SFHs) of all stars analyzed in this paper.</p> <p>Data included: APOGEE/2MASS ID, the age values for PDFs in log(age), the isochrone matching likelihood function as given in Equation 4 of paper, the age PDF of a Bayesian analysis using a flat SFH (Section 4.3), the hierarchically modeled SFH using an alpha-abundance dependent Gaussian+uniform model (Section 4.6), and the age PDF of an empirical Bayesian analysis using the hierarchically modeled SFH (Section 4.6). Individual ages of stars in the paper are taken as the mean of the age PDF, however, most PDFs are non-Gaussian, which introduces complicated errors into the selection of a single age.</p>
Introducing MADYS: the Manifold Age Determination for Young Stars | Full model database
<p>Complete database of stellar and substellar evolutionary models employed in the Manifold Age Determination for Young Stars (MADYS). For a description of the tool, please refer to the main paper. For a detailed description of individual files, it is advised to use the ad-hoc functions provided within the published package.</p> <p>Bibliographic reference: arxiv:2206.02446</p> <p>GitHub repository: https://github.com/vsquicciarini/madys</p>
Fig. 2 in Age Determination And Some Growth Parameters Of A Rana Ridibunda Population In Turkey
Fig. 2. Cross-section taken at the middle of the diaphysis of Rana ridibunda, a male, 58 mm SVL, 4 RL (scale: 108 µm)
Older than 4 Fig. 1 in Comparison Of Two Age Determination Methods Of The European Hares (Lepus Europaeus Pallas, 1778) In Southwest Lithuania
Older than 4 Fig. 1. The measurements of ulna-radius of the years European hares. 3-4 year old 18 a – juvenile, b- adult; 1 - length of epiphyses ossification in ulna; 2- width of epiphysis cartilage 2-3 year old 8 in ulna; 3- high of epiphyses in in ulna-radius; 4 - width of ulna-radius at the thickest part. Juvenile (under 1 year old)
Figure 2. – Panel showing A in A new maximum length of Labrus bergylta (Labriformes: Labridae) with notes on age determination for the species
Figure 2. – Panel showing A: Photo of the whole otoliths and B: Photo of the sectioned left otolith, with blue dots indicating annual growth rings.
Figure 1. – Panel showing A in A new maximum length of Labrus bergylta (Labriformes: Labridae) with notes on age determination for the species
Figure 1. – Panel showing A: Map of the capture location (red dot); B: Photograph of the whole specimen and C: Stomach content.
Figure 5 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 5. Illustration of the linear relationships between fetal age and growth measurements of biparietal diameter (BPD: top graph), thoracic diameter (TD: middle graph) and thoracic circumference (TC: bottom graph) in belugas.
Figure 4 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 4. Comparisons of regression curves of TL growth during the first (●) and second half (○) of gestation (top graph) and the early (●), mid (○) and late (▲) pregnancy (bottom graph). The slopes of the regression lines for first half of gestation (F = 63.31, P <0.0001, df1 = 1, df2 = 33) and for early (F = 50.05, P <0.0001, df1 = 1, df2 = 37) and mid pregnancy (F = 135.04, P <0.0001, df1 = 1, df2 = 32) were different than those for the second half of gestation and late pregnancy, respectively. Note that the animals double in length during late pregnancy (315–473 d).
Figure 3 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 3. Individual growth rate data from three animals (Animal 1, 2, 3). Regression line slopes during the first two-thirds of pregnancy (top graph) were similar (F = 0.48, P = 0.62, df1 = 2, df2 =18), while regression slopes where different (F = 15.13, P = 0.03, df1 = 2, df2 =3) from the second half to term. Animal 1 (▲) did not have any TL data beyond the first half of gestation so TL length data were used from the farthest in gestation and then again at term. Note that while growth rates were similar during the first two-thirds of pregnancy, fetuses were already different in size when initially detected.
Figure 2 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 2. Fetal growth curve comparison illustrating different growth rates resulting in wide range in estimated gestation length as compared to known gestation length determined in this study. Data from Heide-Jørgensen and Teilmann (1994; dotted line) predicts a gestation length of 310 d for a 150 cm calf and similar to our study used a 2nd order polynomial regression to describe their data. Kleinenberg et al. ([1964] 1969: dashed line) developed a curve of the average monthly embryo/fetal growth. They did not provide the curve, only the predicted age at TL, which we then used to fit to a growth curve, which predicts 150 cm calf as 338 d.
Figure 1 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management
Figure 1. Ultrasonographic images of beluga fetuses. All images have yellow caliper lines used to measure dimensions. Biparietal diameter (A, B) at two different stages of gestation show the ovoid shaped skull and echo produced from falx (arrows) located midline between the parietal bones (arrowheads). The thoracic diameter (C) as measured between the yellow caliper marks (arrowheads) on the lateral side of the fetal thorax (d1 = 6.66 cm) at the level of the heart (white arrow) and thoracic circumference (c = 24.04 cm) determined by using the elliptical measurement caliper function to include the dorsal to ventral diameter (d2 = 8.67 cm). The total length of a fetus (D) which is bent in utero, thus requiring the addition of two separate measurements (arrowheads), 1) 8.38 cm from the cranial most aspect of the skull to mid abdomen and 2) 6.91 cm from mid abdomen to distal most portion of the peduncle for a total length of 15.29 cm.
Fig. 4 in Age determination of the Mongolian wild ass (Equus hemionus Pallas, 1775) by the dentition patterns and annual lines in the tooth cementum
Fig. 4. The times of eruption, development of cementum and formation of the annual lines in the first upper incisor (I1) at the labial (left) and lingual side (right).
Crater Catalogue for Age Determination of the Chang'e-6 Mare Unit and Key Ejecta Source Craters
<p><strong>This page archives data used in the following article:<br></strong></p> <p><strong>Xu, L., Qiao, L., Xie., M., Wang, Y., Zhu, M-H., & Yan, J. (2024). Chronology, Local Stratigraphy, and Foreign Ejecta Materials at the Chang'e-6 Landing Site: Constraints on the Provenance of Samples Returned from the Moon's Farside. <em>Geophysical Research Letters</em>, 51. DOI: <span>10.1029/2024GL111311</span></strong></p> <p> </p> <p><strong>They include the Crater Catalogue for Age Determination of the Chang'e-6 Mare Unit and Key Ejecta Source Craters:</strong></p> <p><strong>CE6_MareUnit.zip: ArcGIS shapefile for the CRATER and AREA of the mare unit;</strong></p> <p><strong>CE6_5kmCircle_NAC.zip: ArcGIS shapefile for the CRATER and AREA of a 5 km-sized circle across the Chang'e-6 landing site;</strong></p> <p><strong>ChaffeeS.zip, White.zip, Vavilov.zip, and White'.zip: ArcGIS shapefile for the CRATER and AREA of key ejecta source craters Chaffee S, White, Vavilov, and White', respectively.</strong></p>
Example of determination of C. niloticus embryonic age through the egg band during an artificial incubation at 30.5°C
<p>Example of determination of C. niloticus embryonic age through the egg band during an artificial incubation at 30.5°C.</p> <p>(valid only for eggs incubated at a temperature around 30.5°C)</p>
Data from: Changes in age-structure over four decades were a key determinant of population growth rate in a long-lived mammal
<p>1. A changing environment directly influences birth and mortality rates, and thus population growth rates. However, population growth rates in the short-term are also influenced by population age-structure. Despite its importance, the contribution of age-structure to population growth rates has rarely been explored empirically in wildlife populations with long-term demographic data.</p> <p>2. Here, we assessed how changes in age-structure influenced short-term population dynamics in a semi-captive population of Asian elephants (Elephas maximus).</p> <p>3. We addressed this question using a demographic dataset of female Asian elephants from timber camps in Myanmar spanning 45 years (1970-2014). First, we explored temporal variation in age-structure. Then, using annual matrix population models, we used a retrospective approach to assess the contributions of age-structure and vital rates to short-term population growth rates with respect to the average environment.</p> <p>4. Age-structure was highly variable over the study period, with large proportions of juveniles in the years 1970 and 1985, and made a substantial contribution to annual population growth rate deviations. High adult birth rates between 1970-1980 would have resulted in large positive population growth rates, but these were prevented by a low proportion of reproductive-aged females.</p> <p>5. We highlight that an understanding of both age-specific vital rates and age-structure is needed to assess short-term population dynamics. Furthermore, this example from a human-managed system suggests that the importance of age-structure may be accentuated in populations experiencing human disturbance where age-structure is unstable, such as those in captivity or for endangered species. Ultimately, changes to the environment drive population dynamics by influencing birth and mortality rates, but understanding demographic structure is crucial for assessing population growth.</p>
Data from: Seasonality determines patterns of growth and age structure over a geographic gradient in an ectothermic vertebrate
<p>Environmental variation connected with sea- sonality is likely to affect the evolution of life-history strategies in ectotherms, but there is no consensus as to how important life-history traits like body size are influ- enced by environmental variation along seasonal gradients. We compared adult body size, skeletal growth, mean age, age at first reproduction and longevity among 11 common frog (<em>Rana temporaria</em>) populations sampled along a 1,600-km-long latitudinal gradient across Scandinavia. Mean age, age at first reproduction and longevity increased linearly with decreasing growth season length. Lifetime activity (i.e. the estimated number of active days during life-time) was highest at mid-latitudes and females had on average more active days throughout their lives than males. Variation in body size was due to differences in lifetime activity among populations—individuals (especially females) were largest where they had the longest cumula- tive activity period—as well as to differences between populations in skeletal growth rate as determined by skel- etochronological analyses. Especially, males grew faster at intermediate latitudes. While life-history trait variation was strongly associated with latitude, the direction and shape of these relationships were sex- and trait-specific. These context-dependent relationships may be the result of life- history trade-offs enforced by differences in future reproductive opportunities and time constraints among the populations. Thus, seasonality appears to be an important environmental factor shaping life-history trait variation in common frogs.</p>
Figure 4 in Suitability of analytical methods to determine tebuconazole, propiconazole and permethrin in aged wood samples
Figure 4. Two pioneers in the study of functional anatomy. 1, Heinrich Wilhelm Gottfried von Waldeyer-Hartz (1836-1921) of Hehlen an der Weser and Berlin. He introduced the terms chromosome and neuron to science. 2, Libbie Henrietta Hyman (1888-1969) of Iowa and New York. Her six-volume The Invertebrates is still a classic.
Figure 1 in Suitability of analytical methods to determine tebuconazole, propiconazole and permethrin in aged wood samples
Figure 1. Some representative animals. Today the term animal refers specifically to a metazoan, but traditionally this has also included a range of motile, single-celled protists like Euglena and Paramecium. None of the animals shown here should be thought of as primitive, as they all represent groups that been evolving for hundreds of millions of years. 1, Bigfin reef squid, Sepioteuthis lessoniana. 2, Jumping spider, Plexippus paykulli. 3, Brooding anemone, Epiactis prolifera. 4, Feather star, Himerometra robustipinna. 5, Grey heron (Ardea cinerea) with prey. Both are members of the large clade of bony fish (Osteichthyes) that includes most living vertebrates. 6, Oriental garden lizard, Calotes versicolor.
Figure 3. Contemporaries. 1 in Suitability of analytical methods to determine tebuconazole, propiconazole and permethrin in aged wood samples
Figure 3. Contemporaries. 1, Trofim Denisovich Lysenko (1898-1976) of the Russian Empire and Soviet Union, in 1938. 2, Karl Ritter von Frisch (1886-1982) of Austria-Hungary and West Germany, ca. 1920.
Fig. 3 in Age Determination And Some Growth Parameters Of A Rana Ridibunda Population In Turkey
Fig. 3. The relationship between SVL and age (in males; y = 77.04–32.53e–0.49(x–1))
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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)
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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
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