Identification and classification of 16 new kinesin superfamily (KIF) proteins in mouse genome

KIF (kinesin superfamily) proteins are microtubule-dependent molecular motors that play important roles in intracellular transport and cell division. The extent to which KIFs are involved in various transporting phenomena, as well as their regulation mechanism, are unknown. The identification of 16 new KIFs in this report doubles the existing number of KIFs known in the mouse. Conserved nucleotide sequences in the motor domain were amplified by PCR using cDNAs of mouse nervous tissue, kidney, and small intestine as templates. The new KIFs were studied with respect to their expression patterns in different tissues, chromosomal location, and molecular evolution. Our results suggest that (i) there is no apparent tendency among related subclasses of KIFs of cosegregation in chromosomal mapping, and (ii) according to their tissue distribution patterns, KIFs can be divided into two classes–i.e., ubiquitous and specific tissue-dominant. Further characterization of KIFs may elucidate unknown fundamental phenomena underlying intracellular transport. Finally, we propose a straightforward nomenclature system for the members of the mouse kinesin superfamily.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.