@phdthesis{Sieber2009,
  author    = {Sieber, Maximilian},
  title     = {Evaluation of 1H-NMR and GC/MS-based metabonomics for the assessment of liver and kidney toxicity},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-43052},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2009},
  abstract  = {For the assessment of metabonomics techniques for the early, non-invasive detection of toxicity, the nephrotoxins gentamicin (s.c. administration of 0, 60 and 120 mg/kg bw 2x daily for 8 days), ochratoxin A (p.o. administration of 0, 21, 70 and 210 µg/kg bw 5 days/week for 90 days) and aristolochic acid (p.o. administration of 0, 0.1, 1.0 and 10 mg/kg bw for 12 days) were administered to rats and urine samples were analyzed with 1H-NMR and GC/MS. Urine samples from the InnoMed PredTox project were analyzed as well, thereby focusing on 1H-NMR analysis and bile duct necrosis as histopathological endpoint. 1H-NMR analysis used water supression with the following protocol: 1 M phosphate buffer, D2O as shift lock reagent, D4-trimethylsilyl­propionic acid as chemical shift reference, noesygppr1d pulse sequence (Bruker). For multivariate data analysis, spectral intensity was binned into 0.04 ppm wide bins. GC/MS analysis of urine was carried out after protein precipitation with methanol, drying, derivatization with methoxyamine hydrochloride in pyridine and with methyl(trimethylsilyl)­trifluoroacetamide on a DB5-MS column using EI ionization. The chromatograms were prepared for multivariate data analysis using the R-program based peak picking and alignment software XCMS version 2.4.0. Principal component analysis (PCA) to detect and visualize time-point and dose-dependent differences between treated animals and controls and orthogonal projection to latent structures discriminant analysis (OPLS-DA) for identification of potential molecular markers of toxicity was carried out using SIMCA P+ 11.5 1H-NMR-based markers were identified and quantified with the Chenomx NMR Suite, GC/MS based markers were identified using the NIST Mass Spectral Database and by co-elution with authentic reference standards. PCA of urinary metabolite profiles was able to differentiate treated animals from controls at the same time as histopathology. An advantage over classical clinical chemistry parameters regarding sensitivity could be observed in some cases. Metabonomic analysis with GC/MS and 1H-NMR revealed alterations in the urinary profile of treated animals 1 day after start of treatment with gentamicin, correlating with changes in clinical chemistry parameters and histopathology. Decreased urinary excretion of citrate, 2-oxoglutarate, hippurate, trigonelline and 3-indoxylsulfate increased excretion of 5-oxoproline, lactate, alanine and glucose were observed. Ochratoxin A treatment caused decreased excretion of citrate, 2-oxoglutarate and hippurate and and increased excretion of glucose, myo-inositol, N,N-dimethylglycine, glycine, alanine and lactate as early as 2 weeks after start of treatment with 210µg OTA/kg bw, correlating with changes in clinical chemistry parameters and histopathology. Integration of histopathology scores increased confidence in the molecular markers discovered. Aristolochic acid treatment resulted in decreased urinary excretion of citrate, 2-oxoglutarate, hippurate and creatinine as well as increased excretion of 5-oxoproline, N,N-dimethylglycine, pseudouridine and uric acid. No alterations in clinical chemistry parameters or histopathology were noted.Decreased excretion of hippurate indicates alterations in the gut microflora, an effect that is expected as pharmacological action of the aminoglycoside antibiotic gentamicin and that can also be explained by the p.o. administration of xenobiotica. Decreased Krebs cycle intermediates (citrate and 2-oxoglutarate) and increased lactate is associated with altered energy metabolism. Increased pseudouridine excretion is associated with cell proliferation and was observed with aristolochic acid and ochratoxin A, for which proliferative processes were observed with histopathology. 5-oxoproline and N,N-dimethylglycine can be associated with oxidative stress. Glucose, a marker of renal damage in clinical chemistry, was observed for all three nephrotoxins studied. Single study analysis with PCA of GC/MS chromatograms and 1H-NMR spectra of urine from 3 studies conducted within the InnoMed PredTox project showing bile duct necrosis revealed alterations in urinary profiles with the onset of changes in clinical chemistry and histopathology. Alterations were mainly decreased Krebs cycle intermediates and changes in the aromatic gut flora metabolites, an effect that may result as a secondary effect from altered bile flow. In conclusion, metabonomics techniques are able to detect toxic lesions at the same time as histopathology and clinical chemistry. The metabolites found to be altered are common to most toxicities and are not organ-specific. A mechanistic link to the observed toxicity has to be established in order to avoid confounders such as body weight loss, pharmacological effects etc. For pattern recognition purposes, large databases are necessary.},
  subject      = {Toxikologie},
  language  = {en}
}
@phdthesis{Uereyen2022,
  author    = {{\"U}reyen, Soner},
  title     = {Multivariate Time Series for the Analysis of Land Surface Dynamics - Evaluating Trends and Drivers of Land Surface Variables for the Indo-Gangetic River Basins},
  doi       = {10.25972/OPUS-29194},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-291941},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {The investigation of the Earth system and interplays between its components is of utmost importance to enhance the understanding of the impacts of global climate change on the Earth's land surface. In this context, Earth observation (EO) provides valuable long-term records covering an abundance of land surface variables and, thus, allowing for large-scale analyses to quantify and analyze land surface dynamics across various Earth system components. In view of this, the geographical entity of river basins was identified as particularly suitable for multivariate time series analyses of the land surface, as they naturally cover diverse spheres of the Earth. Many remote sensing missions with different characteristics are available to monitor and characterize the land surface. Yet, only a few spaceborne remote sensing missions enable the generation of spatio-temporally consistent time series with equidistant observations over large areas, such as the MODIS instrument. In order to summarize available remote sensing-based analyses of land surface dynamics in large river basins, a detailed literature review of 287 studies was performed and several research gaps were identified. In this regard, it was found that studies rarely analyzed an entire river basin, but rather focused on study areas at subbasin or regional scale. In addition, it was found that transboundary river basins remained understudied and that studies largely focused on selected riparian countries. Moreover, the analysis of environmental change was generally conducted using a single EO-based land surface variable, whereas a joint exploration of multivariate land surface variables across spheres was found to be rarely performed. To address these research gaps, a methodological framework enabling (1) the preprocessing and harmonization of multi-source time series as well as (2) the statistical analysis of a multivariate feature space was required. For development and testing of a methodological framework that is transferable in space and time, the transboundary river basins Indus, Ganges, Brahmaputra, and Meghna (IGBM) in South Asia were selected as study area, having a size equivalent to around eight times the size of Germany. These basins largely depend on water resources from monsoon rainfall and High Mountain Asia which holds the largest ice mass outside the polar regions. In total, over 1.1 billion people live in this region and in parts largely depend on these water resources which are indispensable for the world's largest connected irrigated croplands and further domestic needs as well. With highly heterogeneous geographical settings, these river basins allow for a detailed analysis of the interplays between multiple spheres, including the anthroposphere, biosphere, cryosphere, hydrosphere, lithosphere, and atmosphere. In this thesis, land surface dynamics over the last two decades (December 2002 - November 2020) were analyzed using EO time series on vegetation condition, surface water area, and snow cover area being based on MODIS imagery, the DLR Global WaterPack and JRC Global Surface Water Layer, as well as the DLR Global SnowPack, respectively. These data were evaluated in combination with further climatic, hydrological, and anthropogenic variables to estimate their influence on the three EO land surface variables. The preprocessing and harmonization of the time series was conducted using the implemented framework. The resulting harmonized feature space was used to quantify and analyze land surface dynamics by means of several statistical time series analysis techniques which were integrated into the framework. In detail, these methods involved (1) the calculation of trends using the Mann-Kendall test in association with the Theil-Sen slope estimator, (2) the estimation of changes in phenological metrics using the Timesat tool, (3) the evaluation of driving variables using the causal discovery approach Peter and Clark Momentary Conditional Independence (PCMCI), and (4) additional correlation tests to analyze the human influence on vegetation condition and surface water area. These analyses were performed at annual and seasonal temporal scale and for diverse spatial units, including grids, river basins and subbasins, land cover and land use classes, as well as elevation-dependent zones. The trend analyses of vegetation condition mostly revealed significant positive trends. Irrigated and rainfed croplands were found to contribute most to these trends. The trend magnitudes were particularly high in arid and semi-arid regions. Considering surface water area, significant positive trends were obtained at annual scale. At grid scale, regional and seasonal clusters with significant negative trends were found as well. Trends for snow cover area mostly remained stable at annual scale, but significant negative trends were observed in parts of the river basins during distinct seasons. Negative trends were also found for the elevation-dependent zones, particularly at high altitudes. Also, retreats in the seasonal duration of snow cover area were found in parts of the river basins. Furthermore, for the first time, the application of the causal discovery algorithm on a multivariate feature space at seasonal temporal scale revealed direct and indirect links between EO land surface variables and respective drivers. In general, vegetation was constrained by water availability, surface water area was largely influenced by river discharge and indirectly by precipitation, and snow cover area was largely controlled by precipitation and temperature with spatial and temporal variations. Additional analyses pointed towards positive human influences on increasing trends in vegetation greenness. The investigation of trends and interplays across spheres provided new and valuable insights into the past state and the evolution of the land surface as well as on relevant climatic and hydrological driving variables. Besides the investigated river basins in South Asia, these findings are of great value also for other river basins and geographical regions.},
  subject      = {Multivariate Analyse},
  language  = {en}
}