TY - THES A1 - Pietschmann, Thomas T1 - Molekularbiologische Untersuchungen zur Funktion des Hüllproteins des Humanen Foamyvirus T1 - Molecular studies on the envelope protein of the human foamy virus N2 - Im Rahmen dieser Arbeit wurde gezeigt, dass fremde virale Hüllproteine wie das Env Protein des murinen Leukämievirus (MLV) oder das Glykoprotein des Virus der vesiklären Stomatitis (VSV) nicht in der Lage sind, die Funktion des homologen HFV Hüllproteins in Bezug auf die Viruspartikelfreisetzung des Humanen Foamyvirus zu übernehmen. Offenbar werden für die HFV Viruspartikelmorphogenese und -freisetzung spezifische Interaktionen zwischen dem Kapsid und dem homologen Hüllprotein benötigt. Mutationsanalysen ergaben, dass die membranspannende Domäne des HFV Hüllproteins in diesem Zusammenhang spezifische Aufgaben erfüllt, die nicht durch heterologe Formen der Membranverankerung übernommen werden können. Die Analyse der Fusionsaktivität verschiedener Hüllproteinmutanten zeigte, dass die zytoplasmatische Domäne des Proteins nicht essentiell für die Fusionsaktivität benötigt wird. Umfangreichere Deletionen, die auch Teile der langen membranspannenden Domäne des Proteins einschlossen, führten dagegen zum Verlust der Fusionseigenschaften des Hüllproteins. Innerhalb der membranspannenden Domäne des HFV Hüllproteins befindet sich ein konserviertes Lysin-Prolin Motiv, dessen Mutation sich auf den zellulären Transport und auf die Fusionsaktivität des Proteins auswirkte. Es zeichnet sich ab, dass die lange membranspannende Domäne des HFV Hüllproteins nicht nur als Membranverankerung dient, sondern zusätzlich für verschiedene Funktionen des Hüllproteins von Bedeutung ist. N2 - In the course of these studies it was shown that heterologous viral envelope proteins like the Env protein of MLV (murine leukemia virus) and the glycoprotein of VSV (vesicular stomatitis virus) are not able to substitute for the HFV Env protein in HF virus (human foamy virus) particle morphogenesis. These data suggest that HFV capsids require specific interactions with their homologous envelope protein in order to be enveloped and released from the cell. A mutational analysis revealed that the long membrane-spanning domain (MSD) of HFV Env plays a key role in this respect, since it cannot be replaced by alternative forms of membrane anchorage. The analysis of fusion activity of various HFV env mutants showed that the cytoplasmic domain (CyD) is not required to mediate membrane fusion. However, fusogenicity was lost when C-terminal parts of the MSD were deleted. Furthermore, it was shown, that mutations of the conserved lysine-proline motif within the MSD result in altered transport and fusion activity of HFV Env. Together, these data imply that the MSD of HFV Env does not only function as a domain that anchors the protein in the lipid bilayer. Instead, it appears that it adopts a specific conformation that is required to mediate different functions of the HFV Env protein. KW - Spumaviren KW - Hüllproteine KW - Molekularbiologie KW - Foamyvirus KW - Retroviren KW - Hüllprotein KW - Morphogenese KW - Fusion KW - foamy virus KW - retro virus KW - envelope protein KW - morphogenesis KW - fusion Y1 - 2000 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-1879 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Steffan-Dewenter, Ingolf A1 - Zhang, Jie A1 - Ullmann, Tobias T1 - Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria JF - Remote Sensing N2 - The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. KW - Landsat KW - Sentinel-2 KW - NDVI KW - fusion KW - agriculture KW - grassland KW - forest KW - urban KW - water Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323471 SN - 2072-4292 VL - 14 IS - 3 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Liepa, Adomas A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany JF - Remote Sensing N2 - Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. KW - MOD13Q1 KW - precision agriculture KW - fusion KW - sustainable agriculture KW - decision making KW - winter wheat KW - oil-seed rape KW - crop models Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311092 SN - 2072-4292 VL - 15 IS - 6 ER -