| 11. |
- Gauld, Jethro G., et al.
(författare)
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Hotspots in the grid : Avian sensitivity and vulnerability to collision risk from energy infrastructure interactions in Europe and North Africa
- 2022
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Ingår i: Journal of Applied Ecology. - : John Wiley & Sons. - 0021-8901 .- 1365-2664. ; 59:6, s. 1496-1512
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Tidskriftsartikel (refereegranskat)abstract
- Wind turbines and power lines can cause bird mortality due to collision or electrocution. The biodiversity impacts of energy infrastructure (EI) can be minimised through effective landscape-scale planning and mitigation. The identification of high-vulnerability areas is urgently needed to assess potential cumulative impacts of EI while supporting the transition to zero carbon energy. We collected GPS location data from 1,454 birds from 27 species susceptible to collision within Europe and North Africa and identified areas where tracked birds are most at risk of colliding with existing EI. Sensitivity to EI development was estimated for wind turbines and power lines by calculating the proportion of GPS flight locations at heights where birds were at risk of collision and accounting for species' specific susceptibility to collision. We mapped the maximum collision sensitivity value obtained across all species, in each 5 x 5 km grid cell, across Europe and North Africa. Vulnerability to collision was obtained by overlaying the sensitivity surfaces with density of wind turbines and transmission power lines. Results: Exposure to risk varied across the 27 species, with some species flying consistently at heights where they risk collision. For areas with sufficient tracking data within Europe and North Africa, 13.6% of the area was classified as high sensitivity to wind turbines and 9.4% was classified as high sensitivity to transmission power lines. Sensitive areas were concentrated within important migratory corridors and along coastlines. Hotspots of vulnerability to collision with wind turbines and transmission power lines (2018 data) were scattered across the study region with highest concentrations occurring in central Europe, near the strait of Gibraltar and the Bosporus in Turkey. Synthesis and applications. We identify the areas of Europe and North Africa that are most sensitive for the specific populations of birds for which sufficient GPS tracking data at high spatial resolution were available. We also map vulnerability hotspots where mitigation at existing EI should be prioritised to reduce collision risks. As tracking data availability improves our method could be applied to more species and areas to help reduce bird-EI conflicts.
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| 12. |
- Jax, Elinor, et al.
(författare)
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Health monitoring in birds using bio-loggers and whole blood transcriptomics
- 2021
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring and early detection of emerging infectious diseases in wild animals is of crucial global importance, yet reliable ways to measure immune status and responses are lacking for animals in the wild. Here we assess the usefulness of bio-loggers for detecting disease outbreaks in free-living birds and confirm detailed responses using leukocyte composition and large-scale transcriptomics. We simulated natural infections by viral and bacterial pathogens in captive mallards (Anas platyrhynchos), an important natural vector for avian influenza virus. We show that body temperature, heart rate and leukocyte composition change reliably during an acute phase immune response. Using genome-wide gene expression profiling of whole blood across time points we confirm that immunostimulants activate pathogen-specific gene regulatory networks. By reporting immune response related changes in physiological and behavioural traits that can be studied in free-ranging populations, we provide baseline information with importance to the global monitoring of zoonotic diseases.
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| 13. |
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| 14. |
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| 15. |
- Sprenger, HG, et al.
(författare)
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Cellular pyrimidine imbalance triggers mitochondrial DNA-dependent innate immunity
- 2021
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Ingår i: Nature metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 3:5, s. 636-
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Tidskriftsartikel (refereegranskat)abstract
- Cytosolic mitochondrial DNA (mtDNA) elicits a type I interferon response, but signals triggering the release of mtDNA from mitochondria remain enigmatic. Here, we show that mtDNA-dependent immune signalling via the cyclic GMP–AMP synthase‒stimulator of interferon genes‒TANK-binding kinase 1 (cGAS–STING–TBK1) pathway is under metabolic control and is induced by cellular pyrimidine deficiency. The mitochondrial protease YME1L preserves pyrimidine pools by supporting de novo nucleotide synthesis and by proteolysis of the pyrimidine nucleotide carrier SLC25A33. Deficiency of YME1L causes inflammation in mouse retinas and in cultured cells. It drives the release of mtDNA and a cGAS–STING–TBK1-dependent inflammatory response, which requires SLC25A33 and is suppressed upon replenishment of cellular pyrimidine pools. Overexpression of SLC25A33 is sufficient to induce immune signalling by mtDNA. Similarly, depletion of cytosolic nucleotides upon inhibition of de novo pyrimidine synthesis triggers mtDNA-dependent immune responses in wild-type cells. Our results thus identify mtDNA release and innate immune signalling as a metabolic response to cellular pyrimidine deficiencies.
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| 16. |
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| 17. |
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| 18. |
- Yu, Z., et al.
(författare)
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Power Generation Prediction of Residential Photovoltaic Equipment Based on Online Transfer Learning Model : A Case Study of a Residential Solar Power System
- 2021
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Ingår i: ACM Int. Conf. Proc. Ser.. - New York, NY, USA : Association for Computing Machinery. - 9781450385091 ; , s. 58-65
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Konferensbidrag (refereegranskat)abstract
- Power generation prediction of residential photovoltaic systems has always been a more and more crucial topic when such new types of energy have been widely applied in people's daily life. In this paper, the four seasons are identified more scientifically by studying the variation of solar altitude angles in a year, and hence the meteorological factors hidden in the data collected from a PV system are extracted by clustering and used in the model. Combined with the advantages of the online learning and transfer learning approach, the online transfer learning model is developed to predict power generation. Finally, our experimental results show that the proposed online transfer learning model outperforms the other learning methods. © 2021 ACM.
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