6 Varianten organische Duengung, 1 Variante ohne organische Duengung in Kombination mit 4 Varianten Mineralduengung = 28 Varianten. Organische Varianten: I = 250 dt/ha Tiefstallmist, II = 300 dt/ha Stapelmist, III = 300 dt/Ha Frischmist, IV = 60 dt/ha Gerstenstroh, V = 250 dt/ha Kompostmist, VI = 17-stuendiger Schafpferch, VII = ohne organische Duengung. Die organische Duengung erfolgt jedes dritte Jahr zur Hackfrucht. In der Fruchtfolge Zuckerrueben - Weizen - Hafer werden folgende Parameter erfasst: Ertraege, Naehrstoffumsatz, Naehrstoffbilanz, Naehrstoffuntersuchungen im Boden, bodenbiologische Untersuchungen, Klimafaktoren.
This project is aimed at the characterization of the systemic reprogramming in barley, which modulates the compatible interaction with the biotrophic leaf pathogen Blumeria graminis f.sp. hordei upon root infestation with the mutualistic endophyte Piriformospora indica. We have recently shown that the basidiomycete P. indica - upon successful establishment in the roots - reprograms barley to salt stress tolerance, resistance to root diseases and higher yield (Waller et al., 2005). Successful powdery mildew infections in barley leaves are also disturbed by the mutualistic fungus. These processes are associated with a strong change in plant metabolism, especially with a drastic alteration of leaf and root antioxidants. On the basis of these findings we will perform an in-depth analysis of the barley metabolome (B6) and transcriptome (B7) with two specific foci: First, to elucidate the process of establishment of the mutualistic fungus within the barley roots; second, to characterize elements of the systemic response in leaves leading to an interruption or failure of compatibility processes required for successful establishment of biotrophic leaf pathogens like Blumeria. New gene candidates will be pre-selected systematically for their regulatory role in compatibility by means of transiently transformed barley leaves upon Blumeria inoculation. Stable transgenic barley and maize lines (B3) generated with verified gene candidates and genes identified by other projects (A1, A2, B5, B6) will be tested with Blumeria and P. indica. By comparing candidate genes in the different plant - microbe systems, we will identify common regulatory processes, metabolites and metabolic networks implicated in compatibility including those required for successful interactions with mutualistic fungi.
L'evaluation du risque encouru par les ouvriers exposes a des solvants peut se faire en principe par 3 approches distinctes: mesures dans l'air, surveillance biologique (solvants ou metabolites) et surveillance medicale (effets precoces reversibles). Le but du present travail de recherche est d'utiliser ces 3 approches sur un meme collectif de travailleurs afin de comparer leur validite et fiabilite dans l'idee d'une utilisation en routine. Les types d'industries visees sont: l'heliogravure, la serigraphie (tous deux des procedes d'impression), le nettoyage a sec, l'industrie du polyester. Les solvants etudies sont: aromatiques (toluene, styrene), hydrocarbures chlores (tetrachloroethylene), esters, glycols ethers, cetones. Cette recherche s'articule autour de 3 points principaux: developpements methodologiques, enquetes dans les entreprises, evaluations et interpretation des donnees recueillies. (FRA)
Es werden u.a. wissenschaftliche Workshops durchgefuehrt: 26./27. November 1981: Expoquimia Barcelona 19./20. November 1984: Expoquimia Barcelona 20./21. Maerz 1986: EPF-L, Lausanne in Zukunft werden auch biochemische und Metabolismus-Studien einbezogen.
Mit 57 Co-Autoren werden vorkommen, Eigenschaften, Analytik, Gewinnung, Verwendung, Entsorgung, Verteilung, Resorption, Stoffwechsel und Wirkungen auf Pflanzen, Tiere und Menschen von Metallverbindungen erfasst und allgemeine Zusammenhaenge zur Risikoanalyse und zum aufstellen von Grenzwerten erarbeitet, insbesondere wurden Umweltchemie und globale Kreislaeufe von Chrom-, Nickel-, Cobalt-, Beryllium-, Arsen-, Cadmium und Selenverbindungen studiert. Uebersichtsberichte ueber Metalltagungen siehe z.B. chemische Rundschau 35, Nr. 16, 9-13 (15. April 1982), Chemosphere 12 (4/5) N 28 - N 36 (1983), 12 (7/8), N 20 - N 27 (1983), 13 (3), N 4 - N 17 (1984) und 13 (7), N 5 - N 30 (1984). Weitere Berichte im Druck.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
1. Akkumulation von Cd in den Nebennieren und Wirkungen auf den Metabolismus von Nebennierenrindenhormon. 2. Biokinetik von Jod, Cobalt und Chrom bei schwangeren Saeugetieren.
Entwicklung, Verbesserung, Anpassung und Erprobung von Verfahren zur Bestimmung von Alphastrahlern und anderen Radionukliden in Luft, Wasser, Bewuchs, Boden und Nahrungsmitteln. Ueberwachung von Alpha-Strahlern, insbesondere Transuranen, in Abluft, Primaer- und Abwasser kerntechnischer Anlagen (mit BGA). Messung des natuerlichen Untergrundes einzelner Radionuklide in Luftstaub und Niederschlag (teilweise mit Usaec). Ausscheidungsanalyse von Radionukliden bei Stoffwechseluntersuchungen an Kleinkindern (mit Kinderklinik der Uni Muenchen). Ueberwachung von Elementspuren in Luftstaub durch Atomabsorptions-, Aktivierungs- und Elektroanalyse sowie Ir-Spektroskopie. Bestimmung von Nullpegel- und Intoxikationsgehalten an Pb und cd in Schlachtrindern zur Festlegung von Toleranzwerten (mit Institut fuer Nahrungsmittelkunde der Uni Muenchen) sowie in Zaehnen (mit Zahnklinik der Uni Muenchen). Ueberwachung von PO-210 in verschiedenen Nahrungsmitteln. Abgabe toxischer Elemente aus Gebrauchsgeschirr.
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