Abstract: Near-infrared spectroscopy (NIRS) has been widely
used as a non-invasive method to measure brain activity, but it is
corrupted by baseline drift noise. Here we present a method to measure
regional cerebral blood flow as a derivative of NIRS output. We
investigate whether, when listening to languages, blood flow can
reasonably localize and represent regional brain activity or not. The
prefrontal blood flow distribution pattern when advanced
second-language listeners listened to a second language (L2) was most
similar to that when listening to their first language (L1) among the
patterns of mean and standard deviation. In experiments with 25
healthy subjects, the maximum blood flow was localized to the left
BA46 of advanced listeners. The blood flow presented is robust to
baseline drift and stably localizes regional brain activity.
Abstract: We propose an unconstrained measurement system for scratching motion based on mathematical model of unconstrained bed sensing method which could measure the bed vibrations due to the motion of the person on the bed. In this paper, we construct mathematical model of the unconstrained bed monitoring system; and we apply the unconstrained bed sensing method to the system for detecting scratching motion. The proposed sensors are placed under the three bed feet. When the person is lying on the bed, the output signals from the sensors are proportional to the magnitude of the vibration due to the scratching motion. Hence, we could detect the subject’s scratching motion from the output signals from ceramic sensors. We evaluated two scratching motions using the proposed system in the validity experiment as follows: 1st experiment is the subject’s scratching the right side cheek with his right hand, and; 2nd experiment is the subject’s scratching the shin with another foot. As the results of the experiment, we recognized the scratching signals that enable the determination when the scratching occurred. Furthermore, the difference among the amplitudes of the output signals enabled us to estimate where the subject scratched.
Abstract: To understand life as biological system, evolutionary
understanding is indispensable. Protein interactions data are rapidly
accumulating and are suitable for system-level evolutionary analysis.
We have analyzed yeast protein interaction network by both
mathematical and biological approaches. In this poster presentation,
we inferred the evolutionary birth periods of yeast proteins by
reconstructing phylogenetic profile. It has been thought that hub
proteins that have high connection degree are evolutionary old. But
our analysis showed that hub proteins are entirely evolutionary new.
We also examined evolutionary processes of protein complexes. It
showed that member proteins of complexes were tend to have
appeared in the same evolutionary period. Our results suggested that
protein interaction network evolved by modules that form the
functional unit. We also reconstructed standardized phylogenetic trees
and calculated evolutionary rates of yeast proteins. It showed that
there is no obvious correlation between evolutionary rates and
connection degrees of yeast proteins.