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186 results for “Application method”
Data from: Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture
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Data from: Herbicide screening and application method development for sustainable weed management in Tagetes erecta L. fields
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Fig. 2. A B in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 2. A B-spline solid is a closed object whose shape can be adjusted by moving control points (dark points) that deforms the local portion of the object near the control point. The initial cylindrical shape in A is adjusted (B and C) by pulling out the points at the ends and drawing the points in the middle closer to the axis.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Data from: Linking genetic kinship and demographic analyses to characterize dispersal: methods and application to Blanding's turtle
Characterizing how frequently, and at what life stages and spatial scales, dispersal occurs can be difficult, especially for species with cryptic juvenile periods and long reproductive life spans. Using a combination of mark–recapture information, microsatellite genetic data, and demographic simulations, we characterize natal and breeding dispersal patterns in the long-lived, slow-maturing, and endangered Blanding's turtle (Emydoidea blandingii), focusing on nesting females. We captured and genotyped 310 individual Blanding's turtles (including 220 nesting females) in a central Wisconsin population from 2010 to 2013, with additional information on movements among 3 focal nesting areas within this population available from carapace-marking conducted from 2001 to 2009. Mark–recapture analyses indicated that dispersal among the 3 focal nesting areas was infrequent (<0.03 annual probability). Dyads of females with inferred first-order relationships were more likely to be found within the same nesting area than split between areas, and the proportion of related dyads declined with increasing distance among nesting areas. The observed distribution of related dyads for nesting females was consistent with a probability of natal dispersal at first breeding between nearby nesting areas of approximately 0.1 based on demographic simulations. Our simulation-based estimates of infrequent female dispersal were corroborated by significant spatial genetic autocorrelation among nesting females at scales of <500 m. Nevertheless, a lack of spatial genetic autocorrelation among non-nesting turtles (males and females) suggested extensive local connectivity, possibly mediated by male movements or long-distance movements made by females between terrestrial nesting areas and aquatic habitats. We show here that coupling genetic and demographic information with simulations of individual-based population models can be an effective approach for untangling the contributions of natal and breeding dispersal to spatial ecology.
FIGURE 8 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 8. Scatter plots showing the volume (A), length (B), width (C) and depth (D) of zooidal and avicularian chambers automatically detected and measured on the virtual dataset (N = 16) for Smittina imragueni n. sp. As in Fig. 4, the horizontal axis represents an arbitrary numbering by the software Amira. A separation between zooidal (large values on the left) and avicularian chambers (small values on the right) is clearly visible.
FIGURE 9 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 9. Scatter plots showing length (A) and width (B) of orifices and frontal pores automatically detected and measured on the virtual dataset (N = 503) for Smittina imragueni, showing a distinction between those two entities, with some datapoints plotting in between (see Discussion). As in Fig. 4, the horizontal axis represents an arbitrary numbering by the software Amira.
FIGURE 7 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 7. Virtual reconstructions of the internal structure of Smittina imragueni n. sp. (paratype SMF 40010). A, broad side of a branch. B, narrow side of a branch. C, single autozooid with suboral avicularium. D, same as C, without the suboral avicularium. E, a single suboral avicularian chamber; the two frontal protuberances are frontal pores. F, automatically detected orifices and frontal pores. Scalebars: A, B, F, 500 µm; C, D, 250 Μm; E, 50 Μm.
FIGURE 6 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 6. SEM micrographs of Smittina imragueni n. sp. A, aspect of colony fragments (right, holotype SMF 40005; left, paratype SMF 40006). B, cross-section through a branch, showing thick secondary calcification and different profiles of zooidal chambers (paratype SMF 40007). C, interior view of primary orifice and suboral avicularium (SMF 40007). D, autozooids showing a relatively early developmental stage of the peristome (SMF 40010). E, secondary orifice with an intermediately developed peristome (SMF 40006). F, immersed secondary orifice late in ontogeny; note the vertical orientation of the avicularium (SMF 40007). G, ancestrular area (paratype SMF 40008). H, transition between encrusting and erect growth (paratype SMF 40009). Scalebars: A, 2 mm; B, G, 500 µm; C, E, 100 µm; D, F, H, 200 Μm.
FIGURE 5 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 5. Scatter plot showing the width of opesial (+) and rhizoidal (×) openings sorted by zooid rows (A) and columns (B) (N = 37) for Cellaria bafouri n. sp. The datapoints for opesiae not aligned to innermost or outermost columns, respectively, are those at a position in between.
FIGURE 4 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 4. Scatter plot showing the width (A) and length (B) of opesial and rhizoidal openings detected and measured from the virtual datamodel (N = 37) for Cellaria bafouri n. sp. The horizontal axis represents an arbitrary numbering of the measured entities by the Amira software.
FIGURE 3 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 3. Virtual reconstructions of the internal structure of Cellaria bafouri n. sp. (paratype SMF 40003). A–C, three views from different angles. In B, the branch is bent away from the viewer. D, enlargement of the centrodistal zooid in B. E, the same autozooid as in D, but from a different angle, showing the form of the rhizoidal tubules. Scalebars: A–C, 500 µm; D, E, 200 µm.
FIGURE 1 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 1. Charts showing the positions of all stations investigated based on MSM 16-3 multibeam surveys. Stations in bold font are those where either C. bafouri n. sp. or S. imragueni n. sp. occurred. Black areas in the insets A–H denote data gaps. The bathymetry of the overview map is from CleanTOPO2, http://www.shadedrelief.com/cleantopo2/.
FIGURE 2 in Two new species of erect Bryozoa (Gymnolaemata: Cheilostomata) and the application of non-destructive imaging methods for quantitative taxonomy
FIGURE 2. SEM micrographs of Cellaria bafouri n. sp. A, infertile segment (paratype SMF 40003). B, fertile autozooid (holotype SMF 40001). C, distal tip of an internode, showing the bases of the joints (SMF 40001). D, internode with distal autozooids having rhizoidal pores (SMF 40003). E, close-up of rhizoidal pores (SMF 40003). Scalebars: A, B, 250 µm; C, E, 100 µm; D, 500 µm.
Fig. 1 in Fig. 2 in Fishing for Tigers: A Method for Collecting Tiger Beetle Larvae Holds Useful Applications for Biology and Conservation
Fig. 1. Degree of dimorphism for all body and leg traits. Trait means ranged from 0.1– 39.6% larger in males than females. Results for tests of significance of difference in trait size are found in Table 1.
Fig. 2 in Fig. 2 in Fishing for Tigers: A Method for Collecting Tiger Beetle Larvae Holds Useful Applications for Biology and Conservation
Fig. 2. Frequency distribution of canonical discriminant scores for male and female Laccophilus maculosus. Female mean 5 0.315, male mean 5 20.587.
Epidermal club cell densities in fathead minnows: assessment of method and application to a case study
<p><strong>Abstract</strong></p> <p>Many fishes possess epidermal club cells that are the presumptive source of chemical alarm cues and also play a role in innate immune defense. Club cell density has been estimated in some studies but a standardized method for quantifying these cells is lacking.</p> <p>Here, we assess the repeatability of estimating club cell density in fathead minnows (<em>Pimephales promelas</em>). Thin-sectioned histological samples of fathead minnows were stained and mounted on slides and then digitally scanned for scoring. We estimated epidermal area using the segment tool in ImageJ to simulate the traditional method of scoring microscope slides using an ocular micrometer, where epidermal area was estimated by the lengths of straight-line segments of epidermal thickness and length of the tissue sample. The second approach measured epidermal area using the freehand tool in ImageJ.</p> <p>The R<sup>2</sup> value for repeated estimates of club cell density (club cells/mm<sup>2</sup>) ranged from 0.959 and 0.969, depending on the method used to estimate epidermal area. Measurement error in estimates of epidermal area was greater than measurement error in cell counts and the freehand tool was more repeatable than the segment tool as a method to measure epidermal area.</p> <p>We applied these methods to test differences in club cell densities between two sources of fathead minnows; wild-caught fish versus lab-reared fish provided by the Environmental Protection Agency. Lab-reared fish had higher densities of club cells than wild-caught fish did, likely reflecting differences in body condition.epidermal club cells</p>
Novel thermal management methods to improve the performance of the Li-ion batteries in high discharge current applications
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Quantifying Mozambique's Peace Dividend: An Application of the Synthetic Control Method
<p>The data used for the study is publicly available data. It was accessed from the following sources:</p> <p> </p> <p>World Development Indicators (WDI) - Please see the following link for more information on WDI</p> <p>https://databank.worldbank.org/source/world-development-indicators</p> <p> </p> <p> </p> <p>Penn World Tables - please see the following link for Penn World Tables:</p> <p>https://www.rug.nl/ggdc/productivity/pwt/?lang=en</p> <p> </p> <p> </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.