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Coming dissertations at Uppsala university

  • Fångsamhället som inte skulle finnas : Överlevnad och anpassning i fängelse under åren 1890–1920 Author: Viktor Englund Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393320 Publication date: 2019-10-18 11:43

    This dissertation studies how prisoners could affect and influence their life in two different prison systems and what it meant for how the systems worked in practice. The systems in question are the Philadelphia system (the separate system) and the Auburn system (the congregate system). To a large extent, these are studied from the example of the central prison at Långholmen, which used both systems. Other Swedish prisons also form a part of the study, mainly through prison biographies. The research period is 1890–1920. This was when the separation system peaked in Sweden with the longest isolation penalties.

    The main question of the thesis is: in what ways did prisoners try to manage and influence their life in prison, how did those actions affect their everyday situation in prison, and how does the importance of those effects appear for the prisoner?

    In earlier research we can, to some extent, observe a hidden world behind the prison walls where it is obvious that things differed significantly from how they were supposed to work. In order to reach this hidden world, a prisoner-centered perspective has been used, which in this book means a systematic focus on the prisoners' actions and experiences. The result of this approach can be summed up in what I call a prison community that should not have existed. To a large extent, it is this community that we see in the prisoners' actions documented in interrogation protocols and described in prisoners’ biographies.

    The most important result of this dissertation is that there was a prison community even among isolated prisoners. This is important because the separate system was built upon the idea of isolation, it was the very foundation of the model, and it was a system widely spread internationally. The prisoners' forbidden acts produced a community that was not meant to exist. The dissertation has also studied other ways for prisoners to affect their situation, for example: simulations of ill health, self-harm actions, stimulation strategies, smuggling and illegal production, bribes and thefts.

  • Greenhouse gas emission from tropical reservoirs : Spatial and temporal dynamics Author: Annika Linkhorst Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393433 Publication date: 2019-10-18 10:53

    The emission of methane (CH4) and carbon dioxide (CO2) from reservoirs has been estimated to make up for about 1.3% of the global anthropogenic greenhouse gas emission. The impoundment of a river leads to the accumulation of sediment that is brought in from inflowing rivers, and the sediment organic matter is degraded to CH4 and CO2. CH4 is of particular concern as its global warming potential is 34 times stronger than that of CO2. In the tropics, high temperatures and high availability of fresh organic matter from high net primary production fuel CH4 and CO2 production. As the construction of hydropower plants is currently undergoing a boom, especially in the tropics, reservoir emission is probably bound to increase.

    The emission of CH4 and CO2 from reservoir surfaces is, however, highly variable, which makes current estimates uncertain. This thesis is built on the hypothesis that the spatial and temporal variability of greenhouse gas emission in tropical reservoirs, particularly of CH4 ebullition (the emission via gas bubbles), is so large that the sampling strategy affects whole-system estimates of greenhouse gas emission.

    This thesis shows that greenhouse gas emission from the four studied tropical reservoirs in Brazil varied greatly at different timescales – over 24 hours, between days and between seasons. Seasonal variability was identified as the most important temporal scale to be covered for CH4 ebullition inventories. In addition, the spatial variability of gas emission was large for all pathways. The variability of CH4 ebullition across space, for example, was estimated to be almost as large as its variability between seasons, and patterns of spatial variability in diffusive CH4 and CO2 emission differed between seasons. For both ebullition and diffusion, river inflow areas were prone to elevated greenhouse gas emission.

    This thesis shows that for retrieving solid emission estimates, there is no alternative to time-consuming measurements in the field. Measurements should be repeated at least once during each hydrological season (i.e. falling and rising water level). The seasonal surveys should cover space at a high resolution, including areas with and without river inflows, and different water column depths. CH4 ebullition made up for 60–99% of the total CO2-equivalent emission from the whole water surface of the studied reservoirs, with the highest contribution in the most productive reservoir. The most variable greenhouse gas emission pathway is therefore the most important one to be measured at appropriate resolution, particularly in productive reservoirs.

  • Integrating multi-omics for type 2 diabetes : Data science and big data towards personalized medicine Author: Klev Diamanti Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393440 Publication date: 2019-10-18 08:49

    Type 2 diabetes (T2D) is a complex metabolic disease characterized by multi-tissue insulin resistance and failure of the pancreatic β-cells to secrete sufficient amounts of insulin. Cells recruit transcription factors (TF) to specific genomic loci to regulate gene expression that consequently affects the protein and metabolite abundancies. Here we investigated the interplay of transcriptional and translational regulation, and its impact on metabolome and phenome for several insulin-resistant tissues from T2D donors. We implemented computational tools and multi-omics integrative approaches that can facilitate the selection of candidate combinatorial markers for T2D.

    We developed a data-driven approach to identify putative regulatory regions and TF-interaction complexes. The cell-specific sets of regulatory regions were enriched for disease-related single nucleotide polymorphisms (SNPs), highlighting the importance of such loci towards the genomic stability and the regulation of gene expression. We employed a similar principle in a second study where we integrated single nucleus ribonucleic acid sequencing (snRNA-seq) with bulk targeted chromosome-conformation-capture (HiCap) and mass spectrometry (MS) proteomics from liver. We identified a putatively polymorphic site that may contribute to variation in the pharmacogenetics of fluoropyrimidines toxicity for the DPYD gene. Additionally, we found a complex regulatory network between a group of 16 enhancers and the SLC2A2 gene that has been linked to increased risk for hepatocellular carcinoma (HCC). Moreover, three enhancers harbored motif-breaking mutations located in regulatory regions of a cohort of 314 HCC cases, and were candidate contributors to malignancy.

    In a cohort of 43 multi-organ donors we explored the alternating pattern of metabolites among visceral adipose tissue (VAT), pancreatic islets, skeletal muscle, liver and blood serum samples. A large fraction of lysophosphatidylcholines (LPC) decreased in muscle and serum of T2D donors, while a large number of carnitines increased in liver and blood of T2D donors, confirming that changes in metabolites occur in primary tissues, while their alterations in serum consist a secondary event. Next, we associated metabolite abundancies from 42 subjects to glucose uptake, fat content and volume of various organs measured by positron emission tomography/magnetic resonance imaging (PET/MRI). The fat content of the liver was positively associated with the amino acid tyrosine, and negatively associated with LPC(P-16:0). The insulin sensitivity of VAT and subcutaneous adipose tissue was positively associated with several LPCs, while the opposite applied to branch-chained amino acids. Finally, we presented the network visualization of a rule-based machine learning model that predicted non-diabetes and T2D in an “unseen” dataset with 78% accuracy.

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