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Figure 1 in Age, growth and mortality of the pearly razorfish Xyrichtys novacula (Linnaeus, 1758) (Labridae) in the Gulf of Tunis
Figure 1. – Surface view of otoliths from different ages of Xyrichtys novacula (A: TL12.2 cm; B: TL14.8 cm; C: TL 16 cm; D: TL 16.9 cm; E: TL17.6 cm; F: TL19.2 cm). The dashed vertical lines in red indicate the hyaline areas counted to determine the age group.
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 2 in Biodiversity patterns across the Late Paleozoic Ice Age
FIGURE 2. Turnover rates during the LPIA with clades plotted individually and merged. Mean sea-surface temperature (SST) after Song et al. (2019). The two main phases of the glaciation indicated with snowflakes.
Figure 2 in The connection of the intensity of the plankton community luminescence and the age distribution of horse mackerel in the coastal waters of the south-western Crimea
Figure 2. The average monthly intensity of glow organisms 1 – in the winter (January-February); 2 – in the spring (May); 3 — in the summer (June-July) in the 2010-2015.
Figure 3 in The connection of the intensity of the plankton community luminescence and the age distribution of horse mackerel in the coastal waters of the south-western Crimea
Figure 3. Relative quantity of the age groups the Trachurus mediterraneus (1-yearlings. 2 - two-year-olds. 3 - threeyear-olds. 4 - four-year-olds. 5 - five-year-olds) in the spring-summer period in the coastal waters of the south-western Crimea.
Figure 4 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 4. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) with different surrounding habitats (sugarcane + sesame, monoculture, sesame) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 3 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 3. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) at different crop developmental stages (crop phenology) of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 2 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 2. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 1 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 1. Percent numbers of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during 2018 at Layyah, Punjab, Pakistan.
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. 3 in Effects of male age and mating status on response to the female sex pheromone of Copitarsia decolora (Lepidoptera: Noctuidae)
Fig. 3. Activation latencies (black bars) and landing latencies (gray bars) (Q1 <Median <Q2) of virgin and mated Copitarsia decolora males to female sex pheromone extract (3FE) in wind tunnel bioassays. Mated males were tested 24 h and 48 h afer mating (24 AM and 48 AM, respectively). Bars within a behavior headed by the same letter are not significantly different (Tukey's mean separation test, n = 10, P <0.05).
Fig. 1 in Effects of male age and mating status on response to the female sex pheromone of Copitarsia decolora (Lepidoptera: Noctuidae)
Fig. 1. Activation latencies (black bars) and landing latencies (gray bars) (Q1 <Median <Q2) by age group of virgin males in response to a glandular extract of female sex pheromone (3FE) in wind tunnel bioassays. Bars within a behavior headed by the same letter are not significantly different (Tukey's mean separation test, n = 10, P <0.05).
Fig. 4 in Effects of male age and mating status on response to the female sex pheromone of Copitarsia decolora (Lepidoptera: Noctuidae)
Fig. 4. Depolarization (mean ± SEM) of antennae in response to a glandular extract of female sex pheromone (3FE) of virgin and mated males. Mated males were tested 24 h and 48 h afer mating (24 AM and 48 AM, respectively). Bars headed by the same letter are not significantly different (Tukey's mean separation test, n = 6, P <0.05).
Fig. 3 in Pre-courtship behavior and the effect of age on its duration in Diatraea magnifactella (Lepidoptera: Crambidae)
Fig. 3. Analysis of the effect of age on the duration of pre-courtship in Diatraea magnifactella females.
Fig. 1 in Pre-courtship behavior and the effect of age on its duration in Diatraea magnifactella (Lepidoptera: Crambidae)
Fig. 1. Routes and probabilities of the transition between successful steps of pre-courtship behavior in Diatraea magnifactella females (P <0.05; n = 60). The values indicate the probability of the occurrence of the next step.
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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.