| 1. |
- Keogan, Katharine, et al.
(författare)
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Global phenological insensitivity to shifting ocean temperatures among seabirds
- 2018
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Ingår i: Nature Climate Change. - : Springer Science and Business Media LLC. - 1758-678X .- 1758-6798. ; 8:4, s. 313-318
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Tidskriftsartikel (refereegranskat)abstract
- Reproductive timing in many taxa plays a key role in determining breeding productivity(1), and is often sensitive to climatic conditions(2). Current climate change may alter the timing of breeding at different rates across trophic levels, potentially resulting in temporal mismatch between the resource requirements of predators and their prey(3). This is of particular concern for higher-trophic-level organisms, whose longer generation times confer a lower rate of evolutionary rescue than primary producers or consumers(4). However, the disconnection between studies of ecological change in marine systems makes it difficult to detect general changes in the timing of reproduction(5). Here, we use a comprehensive meta-analysis of 209 phenological time series from 145 breeding populations to show that, on average, seabird populations worldwide have not adjusted their breeding seasons over time (-0.020 days yr(-1)) or in response to sea surface temperature (SST) (-0.272 days degrees C-1) between 1952 and 2015. However, marked between-year variation in timing observed in resident species and some Pelecaniformes and Suliformes (cormorants, gannets and boobies) may imply that timing, in some cases, is affected by unmeasured environmental conditions. This limited temperature-mediated plasticity of reproductive timing in seabirds potentially makes these top predators highly vulnerable to future mismatch with lower-trophic-level resources(2).
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| 2. |
- Leps, J, et al.
(författare)
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Long-term effectiveness of sowing high and low diversity seed mixtures to enhance plant community development on ex-arable fields
- 2007
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Ingår i: Applied Vegetation Science. - 1402-2001. ; 10:1, s. 97-110
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Tidskriftsartikel (refereegranskat)abstract
- Questions: How is succession on ex-arable land affected by sowing high and low diversity mixtures of grassland species as compared to natural succession? How long do effects persist? Location: Experimental plots installed in the Czech Republic, The Netherlands, Spain, Sweden and the United Kingdom. Methods: The experiment was established on ex-arable land, with five blocks, each containing three 10 m x 10 m experiment tal plots: natural colonization, a low- (four species) and high-diversity (15 species) seed mixture. Species composition and biomass was followed for eight years. Results: The sown plants considerably affected the whole successional pathway and the effects persisted during the whole eight year period. Whilst the proportion of sown species (characterized by their cover) increased during the study period, the number of sown species started to decrease from the third season onwards. Sowing caused suppression of natural colonizing species, and the sown plots had more biomass. These effects were on average larger in the high diversity mixtures. However, the low diversity replicate sown with the mixture that produced the largest biomass or largest suppression of natural colonizers fell within the range recorded at the five replicates of the high diversity plots. The natural colonization plots usually had the highest total species richness and lowest productivity at the end of the observation period. Conclusions: The effect of sowing demonstrated dispersal limitation as a factor controlling the rate of early secondary succession. Diversity was important primarily for its 'insurance effect': the high diversity mixtures were always able to compensate for the failure of some species.
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| 3. |
- Garrucho, L, et al.
(författare)
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High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
- 2023
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Ingår i: Frontiers in oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1044496-
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Tidskriftsartikel (refereegranskat)abstract
- Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in high-resolution mammograms. The training images were split by breast density BI-RADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist.
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