@article{KaltdorfSrivastavaGuptaetal.2016,
  author    = {Kaltdorf, Martin and Srivastava, Mugdha and Gupta, Shishir K. and Liang, Chunguang and Binder, Jasmin and Dietl, Anna-Maria and Meir, Zohar and Haas, Hubertus and Osherov, Nir and Krappmann, Sven and Dandekar, Thomas},
  title     = {Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach},
  series = {Frontiers in Molecular Bioscience},
  volume    = {3},
  journal   = {Frontiers in Molecular Bioscience},
  doi       = {10.3389/fmolb.2016.00022},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147396},
  pages     = {22},
  year      = {2016},
  abstract  = {New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness ("hubs"), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.},
  language  = {en}
}
@phdthesis{Fritsch2013,
  author    = {Fritsch, Sebastian},
  title     = {Spatial and temporal patterns of crop yield and marginal land in the Aral Sea Basin: derivation by combining multi-scale and multi-temporal remote sensing data with alight use efficiency model},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-87939},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 \%. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 \% for cotton and 30.4 \% for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 \% to 23.8 \% for cotton and from -0.4 \% to -19.4 \% for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation.},
  subject      = {Fernerkundung},
  language  = {en}
}
@article{KoehlerKuenzer2020,
  author    = {Koehler, Jonas and Kuenzer, Claudia},
  title     = {Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs12213513},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216285},
  year      = {2020},
  abstract  = {Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.},
  language  = {en}
}
@phdthesis{Balzer2018,
  author    = {Balzer, Christian},
  title     = {Adsorption-Induced Deformation of Nanoporous Materials — in-situ Dilatometry and Modeling},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157145},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {The goal of this work is to improve the understanding of adsorption-induced deformation in nanoporous (and in particular microporous) materials in order to explore its potential for material characterization and provide guidelines for related technical applications such as adsorption-driven actuation. For this purpose this work combines in-situ dilatometry measurements with in-depth modeling of the obtained adsorption-induced strains. A major advantage with respect to previous studies is the combination of the dilatometric setup and a commercial sorption instrument resulting in high quality adsorption and strain isotherms. The considered model materials are (activated and thermally annealed) carbon xerogels, a sintered silica aerogel, a sintered hierarchical structured porous silica and binderless zeolites of type LTA and FAU; this selection covers micro-, meso- and macroporous as well as ordered and disordered model materials. All sample materials were characterized by scanning electron microscopy, gas adsorption and sound velocity measurements. In-situ dilatometry measurements on mesoporous model materials were performed for the adsorption of N2 at 77 K, while microporous model materials were also investigated for CO2 adsorption at 273 K, Ar adsorption at 77 K and H2O adsorption at 298 K. Within this work the available in-situ dilatometry setup was revised to improve resolution and reproducibility of measurements of small strains at low relative pressures, which are of particular relevance for microporous materials. The obtained experimental adsorption and strain isotherms of the hierarchical structured porous silica and a micro-macroporous carbon xerogel were quantitatively analyzed based on the adsorption stress model; this approach, originally proposed by Ravikovitch and Neimark, was extended for anisotropic pore geometries within this work. While the adsorption in silica mesopores could be well described by the classical and analytical theory of Derjaguin, Broekhoff and de Boer, the adsorption in carbon micropores required for comprehensive nonlocal density functional theory calculations. To connect adsorption-induced stresses and strains, furthermore mechanical models for the respective model materials were derived. The resulting theoretical framework of adsorption, adsorption stress and mechanical model was applied to the experimental data yielding structural and mechanical information about the model materials investigated, i.e., pore size or pore size distribution, respectively, and mechanical moduli of the porous matrix and the nonporous solid skeleton. The derived structural and mechanical properties of the model materials were found to be consistent with independent measurements and/or literature values. Noteworthy, the proposed extension of the adsorption stress model proved to be crucial for the correct description of the experimental data. Furthermore, it could be shown that the adsorption-induced deformation of disordered mesoporous aero-/xerogel structures follows qualitatively the same mechanisms obtained for the ordered hierarchical structured porous silica. However, respective quantitative modeling proved to be challenging due to the ill-shaped pore geometry of aero-/xerogels; good agreement between model and experiment could only be achieved for the filled pore regime of the adsorption isotherm and the relative pressure range of monolayer formation. In the intermediate regime of multilayer formation a more complex model than the one proposed here is required to correctly describe stress related to the curved adsorbate-adsorptive interface. Notably, for micro-mesoporous carbon xerogels it could be shown that micro- and mesopore related strain mechanisms superimpose one another. The strain isotherms of the zeolites were only qualitatively evaluated. The result for the FAU type zeolite is in good agreement with other experiments reported in literature and the theoretical understanding derived from the adsorption stress model. On the contrary, the strain isotherm of the LTA type zeolite is rather exceptional as it shows monotonic expansion over the whole relative pressure range. Qualitatively this type of strain isotherm can also be explained by the adsorption stress model, but a respective quantitative analysis is beyond the scope of this work. In summary, the analysis of the model materials' adsorption-induced strains proved to be a suitable tool to obtain information on their structural and mechanical properties including the stiffness of the nonporous solid skeleton. Investigations on the carbon xerogels modified by activation and thermal annealing revealed that adsorption-induced deformation is particularly suited to analyze even small changes of carbon micropore structures.},
  subject      = {Nanopor{\"o}ser Stoff},
  language  = {en}
}