| 1. |
- Cerritelli, Giulia, et al.
(författare)
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Assessing reliance on vector navigation in the long-distance oceanic migrations of green sea turtles
- 2019
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Ingår i: Behavioral Ecology. - : Oxford University Press (OUP). - 1045-2249 .- 1465-7279. ; 30:1, s. 68-79
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Tidskriftsartikel (refereegranskat)abstract
- Vector navigation, i.e., maintaining a constant heading for a given amount of time, is hypothesized to provide a viable basis for the navigational feats of a number of long-distance animal migrants. Since animals following this strategy are subject to drift by wind or by ocean current, performing long migrations relying on vector navigation is particularly challenging. We tested whether vector navigation could be involved in the migrations of green turtles (Chelonia mydas) that migrate between the remote Ascension Island and Brazil. To this aim, a novel approach was followed using individual-based numerical models to simulate migratory trajectories of virtual turtles that were compared to actual routes reconstructed by satellite. Simulated postnesting migrations from Ascension revealed that weak currents enabled modeled turtles to reach the Brazilian coast, but only for a limited range of headings around due West. This conclusion was corroborated by comparing modeled trajectories with the actual routes of previously tracked turtles, with a beeline vector navigation strategy providing the best fit, although a true-navigation strategy directed to the landfall site produced similar results. Finally, we tested if a vector navigational strategy was feasible for the prebreeding migration from Brazil towards Ascension, but modeled routes mostly failed to reach the island or a larger area around it, with individuals drifting away under the influence of currents. We conclude that Ascension turtles can take advantage of vector navigation when migrating towards a wide target like the Brazilian coast, while the demanding prebreeding migration likely requires more complex navigational systems.
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| 2. |
- de Bel, Thomas, et al.
(författare)
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Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning
- 2022
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Ingår i: npj Breast Cancer. - : Nature Portfolio. - 2374-4677. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (+/- 0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted kappa = 0.747 +/- 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p < 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution.
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| 3. |
- Hays, Graeme C., et al.
(författare)
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Island-finding ability of marine turtles
- 2003
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Ingår i: Royal Society of London. Proceedings B. Biological Sciences. - : The Royal Society. - 1471-2954. ; 270:Suppl. 1, Aug 7, s. 5-7
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Tidskriftsartikel (refereegranskat)abstract
- Green turtles (Chelonia mydas) swim from foraging grounds along the Brazilian coast to Ascension Island to nest, over 2200 km distant in the middle of the equatorial Atlantic. To test the hypothesis that turtles use wind-borne cues to locate Ascension Island we found turtles that had just completed nesting and then moved three individuals 50 km northwest (downwind) of the island and three individuals 50 km southeast (upwind). Their subsequent movements were tracked by satellite. Turtles released downwind returned to Ascension Island within 1, 2 and 4 days, respectively. By contrast, those released upwind had far more difficulty in relocating Ascension Island, two eventually returning after 10 and 27 days and the third heading back to Brazil after failing to find its way back to the island. These findings strongly support the hypothesis that wind-borne cues are used by turtles to locate Ascension Island.
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