| 1. |
- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 2. |
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| 5. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 6. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 7. |
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| 8. |
- McFarlane, G., et al.
(författare)
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Enamel daily secretion rates of deciduous molars from a global sample of children
- 2021
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Ingår i: Archives of Oral Biology. - : Elsevier BV. - 0003-9969. ; 132
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To investigate and describe the variation in enamel daily secretion rates (DSRs) of naturally exfoliated deciduous molars (n = 345) from five modern-day populations (Aotearoa New Zealand, Britain, Canada, France, and Sweden). Design: Each tooth was thin sectioned and examined using a high-powered Olympus BX51 microscope and DP25 digital microscope camera. Mean DSRs were recorded for the inner, mid, and outer regions of cuspal and lateral enamel, excluding enamel nearest the enamel-dentin junction and at the outermost crown surface. Results: Mean DSRs did not vary significantly between populations, or by sex. Cuspal enamel grew slightly faster than lateral enamel (mean difference 0.16 mu m per day; p < 0.001). The trajectory of DSRs remained relatively constant from inner to outer cuspal enamel and increased slightly in lateral enamel (p = 0.003). Conclusions: The DSRs of deciduous molars from modern-day children are remarkably consistent when compared among populations. While growth rates are faster in cuspal than lateral enamel, the trajectory of enamel formation changes only slightly from inner to outer regions. The trajectory of DSRs for deciduous molars differs to that of permanent molar enamel, which typically display a steep increase in matrix deposition from inner to outer enamel.
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| 9. |
- Munoz-Gil, G., et al.
(författare)
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Objective comparison of methods to decode anomalous diffusion
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers. Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics but often difficult to characterize. Here the authors compare approaches for single trajectory analysis through an open competition, showing that machine learning methods outperform classical approaches.
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