Many facts were at the origin of the present monograph. The ftrst is the beauty of maple leaves in Quebec forests in Fall. It raised the question: how does nature create and reproduce such beautiful patterns? The second was the reading of A. Lindenmayer's works on L systems. Finally came the discovery of "the secrets of DNA" together with many stimulating ex changes with biologists. Looking at such facts from the viewpoint of recursive numerical systems led to devise a simple model based on six elementary operations organized in a generating word, the analog of the program of a computer and of the genetic code of DNA in the cells of a living organism. It turned out that such a model, despite its simplicity, can account for a great number of properties of living organisms, e.g. their hierarchical structure, their ability to regenerate after a trauma, the possibility of cloning, their sensitivity to mutation, their growth, decay and reproduction. The model lends itself to analysis: the knowledge of the generating word makes it possible to predict the structure of the successive developmental stages of the system; and to synthesis: a speciftc type of structure can be obtained by systematically constructing a generating word that produces it. In fact the model here proposed is coherent with the fundamental assumptions of cellular biology and in particular with recent discoveries concerning DNA, which in the light of our model behaves like a very elaborate generating word.
Every cell has developed mechanisms to respond to changes in its environment and to adapt its growth and metabolism to unfavorable conditions. The unicellular eukaryote yeast has long proven as a particularly useful model system for the analysis of cellular stress responses, and the completion of the yeast genome sequence has only added to its power This volume comprehensively reviews both the basic features of the yeast genral stress response and the specific adapations to different stress types (nutrient depletion, osmotic and heat shock as well as salt and oxidative stress). It includes the latest findings in the field and discusses the implications for the analysis of stress response mechanisms in higher eukaryotes as well.
In-vivo imaging markers of neuronal changes related to Alzheimer’s disease (AD) are ideally suited to be employed as diagnostic markers for early and differential diagnosis of AD as well as for the assessment of neurobiological effects of medical treatments in clinical trials. Novel molecular imaging techniques enable in-vivo detection of cerebral amyloid pathology, whereas magnetic resonance imaging (MRI)-based techniques, such as volumetric MRI and diffusion tensor imaging (DTI), provide structural lesion markers that allow tracking disease progression from preclinical through predementia to clinically manifest stages of AD. However, a widespread clinical use of these imaging biomarkers is hampered by considerable multi-centric variability related to differences in scanner hardware and acquisition protocols, but also by the lack of internationally agreed upon standards for analytic design and employed quantitative metrics. Several strategies for reducing multicenter variability in imaging measures have been proposed, including homogenization of the acquisition settings across scanner platforms, stringent quality assurance procedures, and artifact removal by means of post-acquisition image processing techniques. In addition, selection of appropriate statistical models to account for remaining multicenter variability in the data can further improve the accuracy and reproducibility of study results. The first projects for international standardization of image analysis methods and derived quantitative metrics have emerged recently for volumetric MRI measures. In contrast, the standardization and establishment of DTI-derived measures within a multicenter context are less well developed. Although molecular imaging techniques are already widely used in multicenter settings, sources of variability across sites and appropriate methods to reduce multicenter effects are still not explored in detail. Comparability of neuroimaging measures as AD biomarkers in worldwide clinical settings will finally depend on the establishment of internationally agreed upon standards for image acquisition, quality assurance, and employed quantitative metrics.
Many facts were at the origin of the present monograph. The ftrst is the beauty of maple leaves in Quebec forests in Fall. It raised the question: how does nature create and reproduce such beautiful patterns? The second was the reading of A. Lindenmayer's works on L systems. Finally came the discovery of "the secrets of DNA" together with many stimulating ex changes with biologists. Looking at such facts from the viewpoint of recursive numerical systems led to devise a simple model based on six elementary operations organized in a generating word, the analog of the program of a computer and of the genetic code of DNA in the cells of a living organism. It turned out that such a model, despite its simplicity, can account for a great number of properties of living organisms, e.g. their hierarchical structure, their ability to regenerate after a trauma, the possibility of cloning, their sensitivity to mutation, their growth, decay and reproduction. The model lends itself to analysis: the knowledge of the generating word makes it possible to predict the structure of the successive developmental stages of the system; and to synthesis: a speciftc type of structure can be obtained by systematically constructing a generating word that produces it. In fact the model here proposed is coherent with the fundamental assumptions of cellular biology and in particular with recent discoveries concerning DNA, which in the light of our model behaves like a very elaborate generating word.
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