| 1. |
- Evangelou, Evangelos, et al.
(författare)
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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits.
- 2018
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 50:10, s. 1412-1425
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Tidskriftsartikel (refereegranskat)abstract
- High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.
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| 2. |
- Surendran, Praveen, et al.
(författare)
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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals
- 2020
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 52:12, s. 1314-1332
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Tidskriftsartikel (refereegranskat)abstract
- Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to similar to 1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency <= 0.01) variant BP associations (P < 5 x 10(-8)), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were similar to 8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.
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| 3. |
- Shimchenko, Marina, 1994-
(författare)
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Optimizing Energy Efficiency of Concurrent Garbage Collection
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The increasing energy consumption of the Information and Communication Technology sector amid climate change concerns underscores the urgency for energy efficiency improvements in computing. This thesis focuses on optimizing the energy efficiency of Java, a widely used programming language, and its implementation in OpenJDK. Specifically, our focus is on enhancing the energy efficiency of concurrent garbage collection.As a starting point for our work, we assessed the energy consumption of various garbage collection algorithms within OpenJDK, establishing concurrent garbage collectors as the least energy-efficient. This prompted further investigation into methods to enhance their energy consumption. We investigated methods like dynamically adjusting the memory size required by an application based on how much of the computer's processors one wants to use for garbage collection. We also looked into scheduling garbage collection tasks to run on specific types of computer cores that use less energy and running these tasks when the computer is not being actively used. We implemented all the abovementioned strategies in one of Java’s concurrent garbage collectors, ZGC. Through our experiments, we showed that these techniques can significantly reduce the amount of energy used by garbage collection without slowing down the performance of the programs running on the computer. Overall, our research contributes to making computing more environmentally friendly by finding ways to use less energy while still getting the same results.
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| 4. |
- Liu, Yu, 1987-
(författare)
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Using an Artificial Ecosystem to Understand Living Ecosystems
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Community ecosystems at very different levels of biological organization often have similar properties. Coexistence of multiple species, cross-feeding, biodiversity and fluctuating population dynamics are just a few of the properties that arise in a range of ecological settings. Here we develop a bottom-up model of consumer-resource interactions, in the form of an artificial ecosystem “number soup”. Our model reflects basic properties of many bacterial and other community ecologies. We demonstrate four key properties of the number soup model: (1) Communities self-organize so that all available resources are fully consumed; (2) Reciprocal cross-feeding is a common evolutionary outcome, which evolves in a number of stages, and many transitional species are involved; (3) The evolved ecosystems are often “robust yet fragile”, with keystone species required to prevent the whole system from collapsing; (4) Nonequilibrium dynamics and chaotic patterns are general properties, readily generating rich biodiversity. These properties have been observed in empirical ecosystems, ranging from bacteria to rainforests. Establishing similar properties in an evolutionary model as simple as the number soup suggests that these four properties are ubiquitous features of all community ecosystems, and raises questions about how we interpret ecosystem structure in the context of natural selection. In Chapter 1, the motivation of the model and other researchers’ works are described. Chapter 2 is the paper about the number soup model, in a journal format. In Chapter 3, I elaborate all the mathematical details of the model, which were not fully discussed in that paper in Chapter 2. In Chapter 4, I list some of the intriguing questions and points related to the number soup model, and give a description of the whole plan for my PhD studies and the future wok that will be done in the remaining PhD study.
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| 5. |
- Liu, Yu, 1987-
(författare)
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Modelling Evolution : From non-life, to life, to a variety of life
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Life is able to replicate itself, e.g., a microorganism is able to divide into two identical ones, and a single plant is able to forest a whole island. But life is the only example of self-replication (note that a computer virus seems able to replicate itself, but it needs the assistance of a processor such as a CPU, and thus not a truly self-replicating entity). So before the appearance of life, nothing can self-replicate. How does life, a truly self-replicating entity, evolve from substances which is not able to self-replicate? Why can it ever happen? Is there a general underlying mechanism that governs how self-replicating entities can develop de novo on Earth, or even other plants?As long as the first life appears, it has the potential to cover the whole plant. But one single life form cannot do the job. Life has branched into a huge number of biological classes and species. Different species interact with each other, and with their environment, which, as a whole, is defined as an ecosystem. Distinct ecosystems are found at different scales and different places, e.g., microbes cross-feed and compete for resources within natural communities; and different types of cells interact by exchanging metabolite within an organism body. But, why sometimes we consider an ecosystem as an individual (such as the human body which is, in fact, an ecosystem inhabited by a huge number of microorganisms without which we cannot survive) while sometimes not? What really distinguishes an individual-level life from a system-level life? Are there general properties only a system-level life has, emerged from the interactions among its compositional individual-level life?This thesis is to investigate these two questions by mathematical models. For the evolution from non-life to life, namely the origin of life, we build an artificial chemistry model to investigate why an independent self-replicating entity can develop spontaneously from some chemical reaction system in which no reaction is self-replicating. For the evolution from life to a variety of life, we build an artificial ecosystem model to investigate general properties of ecosystems.
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