Oxidative Stress, Disease And Cancer - 11 Angebote vergleichen

Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Despatched same working day before 3pm.
Motor neurone disease (MND) is a progressive condition that damages the nervous system, leaving muscles wasted and weak, and causing loss of mobility, and difficulties with speech, swallowing and breathing. MND tends to affect people over 40 and is most common between the ages of 50 and 70. There are about 5,000 people with MND at any one time in the UK. The cause remains a mystery and there is no cure. The third edition of this book, which sells primarily via the Motor Neurone Disease Association, gives a full update of treatments and resources available to help those diagnosed live life to the full. Topics include what the disease is, what the doctors will do, and how to cope with the difficulties. This new edition also examines the latest on benefits, and up to date thinking on drug trials. Dr David Oliver, a leading expert on MND, shows how to treat not just the physical effects but also the emotional ones for the whole family. Dr Oliver also explains the vital role of the Motor Neurone Disease Association.

Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Despatched same working day before 3pm.
This book aims to provide a comprehensive review of the most up-to-date knowledge of the sources and molecular mechanisms of oxidative stress, and its role in disease and cancer.It also focuses on the novel agents and methods that can be employed to prevent oxidative stress and associated diseases.The authors first review the most recent data on the basic mechanisms of oxidative stress.The second section discusses oxidative stress leading to several diseases and cancers, and in the third section, the strategies employed in the prevention and treatment of oxidative stress-related diseases are discussed.

Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, NDS, Germany.
This item is printed on demand - Print on Demand Titel. Neuware - Hyperspectral Imagery combines imaging and spectroscopy in single systems which often include large data sets and require new processing methods. The spatial resolution of most space borne Hyperspectral (HSI) sensors is much coarser than the size of the objects being observed. Therefore the measured spectral signature is a mixture of the signatures of the objects in the field of view (FOV) of the sensor. One of the tedious and time consuming tasks related to hyperspectral data analysis is the identification of library candidates. The high spectral resolution can be effectively used to decompose measured spectra into its constituents by way of spectral unmixing. Spectral Unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra (or endmembers), and a set of corresponding fractions (or abundances). Thus spectral unmixing allows us to detect and classify sub-pixel target objects by their contribution to the measured spectral signature. The application area includes Agriculture, Forestry, Geology, Hydrology, Sea ice and Water, Environment, Land cover, Mapping, Ocean and Coastal, Military Operations, Geo-explorations, and Bio-sciences. 264 pp. Englisch.

Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Titel. Neuware - Hyperspectral Imagery combines imaging and spectroscopy in single systems which often include large data sets and require new processing methods. The spatial resolution of most space borne Hyperspectral (HSI) sensors is much coarser than the size of the objects being observed. Therefore the measured spectral signature is a mixture of the signatures of the objects in the field of view (FOV) of the sensor. One of the tedious and time consuming tasks related to hyperspectral data analysis is the identification of library candidates. The high spectral resolution can be effectively used to decompose measured spectra into its constituents by way of spectral unmixing. Spectral Unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra (or endmembers), and a set of corresponding fractions (or abundances). Thus spectral unmixing allows us to detect and classify sub-pixel target objects by their contribution to the measured spectral signature. The application area includes Agriculture, Forestry, Geology, Hydrology, Sea ice and Water, Environment, Land cover, Mapping, Ocean and Coastal, Military Operations, Geo-explorations, and Bio-sciences. 264 pp. Englisch.

Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, 11, zzgl. Versandkosten.
Hyperspectral Imagery combines imaging and spectroscopy in single systems which often include large data sets and require new processing methods. The spatial resolution of most space borne Hyperspectral (HSI) sensors is much coarser than the size of the objects being observed. Therefore the measured spectral signature is a mixture of the signatures of the objects in the field of view (FOV) of the sensor. One of the tedious and time consuming tasks related to hyperspectral data analysis is the identification of library candidates. The high spectral resolution can be effectively used to decompose measured spectra into its constituents by way of spectral unmixing. Spectral Unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra (or endmembers), and a set of corresponding fractions (or abundances). Thus spectral unmixing allows us to detect and classify sub-pixel target objects by their contribution to the measured spectral signature. The application area includes Agriculture, Forestry, Geology, Hydrology, Sea ice and Water, Environment, Land cover, Mapping, Ocean and Coastal, Military Operations, Geo-explorations, and Bio-sciences.

Lieferung aus: Deutschland, Versandkostenfrei.
SPECTRAL IMAGING ANALYSIS MODELS: Hyperspectral Imagery combines imaging and spectroscopy in single systems which often include large data sets and require new processing methods. The spatial resolution of most space borne Hyperspectral (HSI) sensors is much coarser than the size of the objects being observed. Therefore the measured spectral signature is a mixture of the signatures of the objects in the field of view (FOV) of the sensor. One of the tedious and time consuming tasks related to hyperspectral data analysis is the identification of library candidates. The high spectral resolution can be effectively used to decompose measured spectra into its constituents by way of spectral unmixing. Spectral Unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra (or endmembers), and a set of corresponding fractions (or abundances). Thus spectral unmixing allows us to detect and classify sub-pixel target objects by their contribution to the measured spectral signature. The application area includes Agriculture, Forestry, Geology, Hydrology, Sea ice and Water, Environment, Land cover, Mapping, Ocean and Coastal, Military Operations, Geo-explorations, and Bio-sciences. Englisch, Taschenbuch.

Von Händler/Antiquariat, English-Book-Service - A Fine Choice [1048135], Waldshut-Tiengen, Germany.
This item is printed on demand for shipment within 3 working days.

Lieferung aus: Spanien, Normalmente se despacha en 24 horas.
Von Händler/Antiquariat, Amazon.es.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen

Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen