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Light Sheet Fluorescence Microscopy (LSFM) of whole organs, in particular the brain, offers a plethora of biological data imaged in 3D. This technique is however often hindered by cumbersome non-Automated analysis methods. Here we describe an approach to fully automate the analysis by integrating with data from the Allen Institute of Brain Science (AIBS), to provide precise assessment of the distribution and action of peptide-based pharmaceuticals in the brain. To illustrate this approach, we examined the acute central nervous system effects of the glucagon-like peptide-1 (GLP-1) receptor agonist liraglutide. Peripherally administered liraglutide accessed the hypothalamus and brainstem, and led to activation in several brain regions of which most were intersected b

A computerized method for automatic evaluation and comparison of crossed immunoelectrophoretic and crossed radioimmunoelectrophoretic patterns that requires limited hardware resources has been developed. For the initial reading of the plates an ordinary video camera is used. Feature extractors that allow the computer to recognize a point on the precipitation curve as being a peak point have been developed. After this automatic procedure the program allows for an interactive menu-driven proofreading phase during which it is possible to force the system to take into consideration any number of extra points along the precipitation curve in the curve-fitting process. The system has been tested on crossed immunoelectrophoretic patterns as well as crossed radioimmunoelectrophoretic patterns and it has been shown that the system can recognize the same precipitation curves on different immunoplates and autoradiographs. In addition, the system reports the mean, the variance, and the area of the precipitation curves, thus allowing not only a qualitative comparison of two or more plates but also a quantitation of individual antigens or antibodies.

A method of classifying an integrated data base consisting of data from different sources is presented. The data consist of Landsat multispectral scanner imagery, structural information, geochemical measurements, radiometric measurements and aeromagnetic readings. The data are interpolated to the same grid. This, however, gives an immense amount of data, making data reduction necessary. Information was used to preserve statistics to describe the data within squares of 5x5 km. Some of the squares are known to have a high or a low potential for mineralisation. These squares were then used as training areas in a multivariate classification, making it possible to classify the rest of the squares as having a high or low potential for mineralisation. The method is evaluated in southern Greenland, and a geologic evaluation of the classifications shows a good agreement between the classified results and the geologic evaluation., A method of classifying an integrated data base consisting of data from different sources is presented. The data consist of Landsat multispectral scanner imagery, structural information, geochemical measurements, radiometric measurements and aeromagnetic readings. The data are interpolated to the same grid. This, however, gives an immense amount of data, making data reduction necessary. Information was used to preserve statistics to describe the data within squares of 5x5 km. Some of the squares are known to have a high or a low potential for mineralisation. These squares were then used as training areas in a multivariate classification, making it possible to classify the rest of the squares as having a high or low potential for mineralisation. The method is evaluated in southern Greenland, and a geologic evaluation of the classifications shows a good agreement between the classified results and the geologic evaluation.