@article{SteiningerAbelZiegleretal.2023, author = {Steininger, Michael and Abel, Daniel and Ziegler, Katrin and Krause, Anna and Paeth, Heiko and Hotho, Andreas}, title = {ConvMOS: climate model output statistics with deep learning}, series = {Data Mining and Knowledge Discovery}, volume = {37}, journal = {Data Mining and Knowledge Discovery}, number = {1}, issn = {1384-5810}, doi = {10.1007/s10618-022-00877-6}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324213}, pages = {136-166}, year = {2023}, abstract = {Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.}, subject = {Klima}, language = {en} } @phdthesis{Kaymak2019, author = {Kaymak, Irem}, title = {Identification of metabolic liabilities in 3D models of cancer}, doi = {10.25972/OPUS-18154}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-181544}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2019}, abstract = {Inefficient vascularisation of solid tumours leads to the formation of oxygen and nutrient gradients. In order to mimic this specific feature of the tumour microenvironment, a multicellular tumour spheroid (SPH) culture system was used. These experiments were implemented in p53 isogenic colon cancer cell lines (HCT116 p53 +/+ and HCT116 p53-/-) since Tp53 has important regulatory functions in tumour metabolism. First, the characteristics of the cells cultured as monolayers and as spheroids were investigated by using RNA sequencing and metabolomics to compare gene expression and metabolic features of cells grown in different conditions. This analysis showed that certain features of gene expression found in tumours are also present in spheroids but not in monolayer cultures, including reduced proliferation and induction of hypoxia related genes. Moreover, comparison between the different genotypes revealed that the expression of genes involved in cholesterol homeostasis is induced in p53 deficient cells compared to p53 wild type cells and this difference was only detected in spheroids and tumour samples but not in monolayer cultures. In addition, it was established that loss of p53 leads to the induction of enzymes of the mevalonate pathway via activation of the transcription factor SREBP2, resulting in a metabolic rewiring that supports the generation of ubiquinone (coenzyme Q10). An adequate supply of ubiquinone was essential to support mitochondrial electron transport and pyrimidine biosynthesis in p53 deficient cancer cells under conditions of metabolic stress. Moreover, inhibition of the mevalonate pathway using statins selectively induced oxidative stress and apoptosis in p53 deficient colon cancer cells exposed to oxygen and nutrient deprivation. This was caused by ubiquinone being required for electron transfer by dihydroorotate dehydrogenase, an essential enzyme of the pyrimidine nucleotide biosynthesis pathway. Supplementation with exogenous nucleosides relieved the demand for electron transfer and restored viability of p53 deficient cancer cells under metabolic stress. Moreover, the mevalonate pathway was also essential for the synthesis of ubiquinone for nucleotide biosynthesis to support growth of intestinal tumour organoids. Together, these findings highlight the importance of the mevalonate pathway in cancer cells and provide molecular evidence for an enhanced sensitivity towards the inhibition of mitochondrial electron transfer in tumour-like metabolic environments.}, subject = {Tumor}, language = {en} } @phdthesis{Vogt2014, author = {Vogt, Gernot}, title = {Future changes and signal analyses of climate means and extremes in the Mediterranean Area deduced from a CMIP3 multi-model ensemble}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-117369}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2014}, abstract = {Considering its social, economic and natural conditions the Mediterranean Area is a highly vulnerable region by designated affections of climate change. Furthermore, its climatic characteristics are subordinated to high natural variability and are steered by various elements, leading to strong seasonal alterations. Additionally, General Circulation Models project compelling trends in specific climate variables within this region. These circumstances recommend this region for the scientific analyses conducted within this study. Based on the data of the CMIP3 database, the fundamental aim of this study is a detailed investigation of the total variability and the accompanied uncertainty, which superpose these trends, in the projections of temperature, precipitation and sea-level pressure by GCMs and their specific realizations. Special focus in the whole study is dedicated to the German model ECHAM5/MPI-OM. Following this ambition detailed trends and mean values are calculated and displayed for meaningful time periods and compared to reanalysis data of ERA40 and NCEP. To provide quantitative comparison the mentioned data are interpolated to a common 3x3° grid. The total amount of variability is separated in its contributors by the application of an Analysis of Variance (ANOVA). For individual GCMs and their ensemble-members this is done with the application of a 1-way ANOVA, separating a treatment common to all ensemble-members and variability perturbating the signal given by different initial conditions. With the 2-way ANOVA the projections of numerous models and their realizations are analysed and the total amount of variability is separated into a common treatment effect, a linear bias between the models, an interaction coefficient and the residuals. By doing this, the study is fulfilled in a very detailed approach, by considering yearly and seasonal variations in various reasonable time periods of 1961-2000 to match up with the reanalysis data, from 1961-2050 to provide a transient time period, 2001-2098 with exclusive regard on future simulations and 1901-2098 to comprise a time period of maximum length. The statistical analyses are conducted for regional-averages on the one hand and with respect to individual grid-cells on the other hand. For each of these applications the SRES scenarios of A1B, A2 and B1 are utilized. Furthermore, the spatial approach of the ANOVA is substituted by a temporal approach detecting the temporal development of individual variables. Additionally, an attempt is made to enlarge the signal by applying selected statistical methods. In the detailed investigation it becomes evident, that the different parameters (i.e. length of temporal period, geographic location, climate variable, season, scenarios, models, etc…) have compelling impact on the results, either in enforcing or weakening them by different combinations. This holds on the one hand for the means and trends but also on the other hand for the contributions of the variabilities affecting the uncertainty and the signal. While temperature is a climate variable showing strong signals across these parameters, for precipitation mainly the noise comes to the fore, while for sea-level pressure a more differentiated result manifests. In turn, this recommends the distinguished consideration of the individual parameters in climate impact studies and processes in model generation, as the affecting parameters also provide information about the linkage within the system. Finally, an investigation of extreme precipitation is conducted, implementing the variables of the total amount of heavy precipitation, the frequency of heavy-precipitation events, the percentage of this heavy precipitation to overall precipitation and the mean daily intensity from events of heavy precipitation. Each time heavy precipitation is defined to exceed the 95th percentile of overall precipitation. Consecutively mean values of these variables are displayed for ECHAM5/MPI-OM and the multi-model mean and climate sensitivities, by means of their difference between their average of the past period of 1981-2000 and the average of one of the future periods of 2046-2065 or 2081-2100. Following this investigation again an ANOVA is conducted providing a quantitative measurement of the severity of change of trends in heavy precipitation across several GCMs. Besides it is a difficult task to account for extreme precipitation by GCMs, it is noteworthy that the investigated models differ highly in their projections, resulting partially in a more smoothed and meaningful multi-model mean. Seasonal alterations of the strength of this behaviour are quantitatively supported by the ANOVA.}, subject = {Klimaschwankung}, language = {en} } @phdthesis{Paxian2012, author = {Paxian, Andreas}, title = {Future changes in climate means and extremes in the Mediterranean region deduced from a regional climate model}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72155}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2012}, abstract = {The Mediterranean area reveals a strong vulnerability to future climate change due to a high exposure to projected impacts and a low capacity for adaptation highlighting the need for robust regional or local climate change projections, especially for extreme events strongly affecting the Mediterranean environment. The prevailing study investigates two major topics of the Mediterranean climate variability: the analysis of dynamical downscaling of present-day and future temperature and precipitation means and extremes from global to regional scale and the comprehensive investigation of temperature and rainfall extremes including the estimation of uncertainties and the comparison of different statistical methods for precipitation extremes. For these investigations, several observational datasets of CRU, E-OBS and original stations are used as well as ensemble simulations of the regional climate model REMO driven by the coupled global general circulation model ECHAM5/MPI-OM and applying future greenhouse gas (GHG) emission and land degradation scenarios.}, subject = {Mittelmeerraum}, language = {en} } @phdthesis{Zeeshan2012, author = {Zeeshan, Ahmed}, title = {Bioinformatics Software for Metabolic and Health Care Data Management}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-73926}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2012}, abstract = {Computer Science approaches (software, database, management systems) are powerful tools to boost research. Here they are applied to metabolic modelling in infections as well as health care management. Starting from a comparative analysis this thesis shows own steps and examples towards improvement in metabolic modelling software and health data management. In section 2, new experimental data on metabolites and enzymes induce high interest in metabolic modelling including metabolic flux calculations. Data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific strengths is now possible by an advantageous combination of specific software. How can available software for metabolic modelling be improved from a computational point of view? A number of available and well established software solutions are first discussed individually. This includes information on software origin, capabilities, development and used methodology. Performance information is obtained for the compared software using provided example data sets. A feature based comparison shows limitations and advantages of the compared software for specific tasks in metabolic modeling. Often found limitations include third party software dependence, no comprehensive database management and no standard format for data input and output. Graphical visualization can be improved for complex data visualization and at the web based graphical interface. Other areas for development are platform independency, product line architecture, data standardization, open source movement and new methodologies. The comparison shows clearly space for further software application development including steps towards an optimal user friendly graphical user interface, platform independence, database management system and third party independence especially in the case of desktop applications. The found limitations are not limited to the software compared and are of course also actively tackled in some of the most recent developments. Other improvements should aim at generality and standard data input formats, improved visualization of not only the input data set but also analyzed results. We hope, with the implementation of these suggestions, metabolic software applications will become more professional, cheap, reliable and attractive for the user. Nevertheless, keeping these inherent limitations in mind, we are confident that the tools compared can be recommended for metabolic modeling for instance to model metabolic fluxes in bacteria or metabolic data analysis and studies in infection biology. ...}, subject = {Stoffwechsel}, language = {en} }