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- Ludewig, Kristin, et al.
(författare)
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Phenology and morphology of the invasive legume Lupinus polyphyllus along a latitudinal gradient in Europe
- 2022
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Ingår i: NeoBiota. - : Pensoft Publishers. - 1619-0033 .- 1314-2488. ; 78
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Tidskriftsartikel (refereegranskat)abstract
- Plant phenology, i. e. the timing of life cycle events, is related to individual fitness and species distributionranges. Temperature is one of the most important drivers of plant phenology together with day length.The adaptation of their phenology may be important for the success of invasive plant species. The presentstudy aims at understanding how the performance and the phenology of the invasive legume Lupinuspolyphyllus vary with latitude. We sampled data across a >2000 km latitudinal gradient from Centralto Northern Europe. We quantified variation in phenology of flowering and fruiting of L. polyphyllususing >1600 digital photos of inflorescences from 220 individual plants observed weekly at 22 sites. Theday of the year at which different phenological phases were reached, increased 1.3–1.8 days per degreelatitude, whereas the growing degree days (gdd) required for these phenological phases decreased 5–16 gddper degree latitude. However, this difference disappeared, when the day length of each day included inthe calculation of gdd was considered. The day of the year of the earliest and the latest climatic zone toreach any of the three studied phenological phases differed by 23–30 days and temperature requirementsto reach these stages differed between 62 and 236 gdd. Probably, the invasion of this species will furtherincrease in the northern part of Europe over the next decades due to climate warming. For invasive speciescontrol, our results suggest that in countries with a large latitudinal extent, the mowing date should shiftby ca. one week per 500 km at sites with similar elevations.
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- Reichart, Bruno, et al.
(författare)
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Pig-to-non-human primate heart transplantation : The final step toward clinical xenotransplantation?
- 2020
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Ingår i: Journal of Heart and Lung Transplantation. - : Elsevier BV. - 1053-2498. ; 39:8, s. 751-757
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Tidskriftsartikel (refereegranskat)abstract
- Background: The demand for donated human hearts far exceeds the number available. Xenotransplantation of genetically modified porcine organs provides an alternative. In 2000, an Advisory Board of the International Society for Heart and Lung Transplantation set the benchmark for commencing clinical cardiac xenotransplantation as consistent 60% survival of non-human primates after life-supporting porcine heart transplantations. Recently, we reported the stepwise optimization of pig-to-baboon orthotopic cardiac xenotransplantation finally resulting in consistent success, with 4 recipients surviving 90 (n = 2), 182, and 195 days. Here, we report on 4 additional recipients, supporting the efficacy of our procedure. Results: The first 2 additional recipients succumbed to porcine cytomegalovirus (PCMV) infections on Days 15 and 27, respectively. In 2 further experiments, PCMV infections were successfully avoided, and 3-months survival was achieved. Throughout all the long-term experiments, heart, liver, and renal functions remained within normal ranges. Post-mortem cardiac diameters were slightly increased when compared with that at the time of transplantation but with no detrimental effect. There were no signs of thrombotic microangiopathy. The current regimen enabled the prolonged survival and function of orthotopic cardiac xenografts in altogether 6 of 8 baboons, of which 4 were now added. These results exceed the threshold set by the Advisory Board of the International Society for Heart and Lung Transplantation. Conclusions: The results of our current and previous experimental cardiac xenotransplantations together fulfill for the first time the pre-clinical efficacy suggestions. PCMV-positive donor animals must be avoided.
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- Shao, Yunqi, 1993-
(författare)
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Simulating ion transport in electrolyte materials with physics-based and machine-learning models
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Electrolytes are indispensable components of electrochemical devices such as batteries, fuel cells, and supercapacitors, and the mass transport in electrolytes is one of the most important design focuses of such devices. A microscopic picture of ion transport is essential to link the chemical properties of electrolyte materials to their electrochemical applications. This thesis aims to establish such a connection through computer simulations of the transport phenomena, using a combination of physics-based and machine-learning methods.The first part of the thesis concerns the study of transport phenomena with molecular dynamics simulations, where the atomistic interactions are described by physics-based classical force fields. Guided by the principles of non-equilibrium statistical mechanics, the simulations reveal governing factors of ion transport in different systems. This is exemplified by the leading contribution of hydrodynamic interactions in the non-ideal ionic conductivity, and the qualitative distinction between transient and long-lived ion pairs. This approach also aids the interpretation and comparison of experiments and simulations, by elucidating their intrinsic constraints imposed by the reference frame, and their proper inter-conversions.The second part of the thesis aims to remedy a major limitation of the physics-based approach, namely the difficulty of accurately simulating complex reactive systems. The machine learning methods were developed to systematically generate the models from electronic structure calculations. The strength of this approach is demonstrated by showing how it correctly predicted the transport coefficients of proton-conducting materials with the desired accuracy. Limitations of this data-driven approach are also investigated, demonstrating the potential pitfall in the parameterization process, and leading to the development of an adaptive learn-on-the-fly workflow.Overall, the present thesis showcases how computer simulations can lead to insights regarding the ion transport in electrolyte materials, and how the development of machine-learning methods could empower those simulations to tackle complex and reactive systems.
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