Modern biology has provided many examples of large networks describing the interactions between multiple species of bio-molecules. It is believed that the dynamics of molecular activities based on such networks are the origin of biological functions. On the other hand, we have a limited understanding for dynamics of molecular activity based on networks. To overcome this problem, we have developed two structural theories, by which the important aspects of the dynamical properties of the system are determined only from information on the network structure, without assuming other quantitative details. The first theory, named Linkage Logic, determines a subset of molecules in regulatory networks, by which any long-term dynamical behavior of the whole system can be identified/controlled. The second theory, named Structural Sensitivity Analysis, determines the sensitivity responses of the steady state of chemical reaction networks to perturbations of the reaction rate. The first and second theories investigate the dynamical properties of regulatory and reaction networks, respectively. The first theory targets the attractors of the regulatory network systems, whereas the second theory applies only to the steady states of the reaction network systems, but predicts their detailed behavior. To demonstrate the utility of our methods several biological network systems, and show they are practically useful to analyze behaviors of biological systems.
Cosmic-ray muons (CRM) arriving from the sky on the surface of the earth are now known to be used as radiography purposes to explore the inner-structure of large-scale objects and landforms, ranging in thickness from meter to kilometers scale, such as volcanic mountains, blast furnaces, nuclear reactors etc. At the same time, by using muons produced by compact accelerators (CAM), advanced radiography can be realized for objects with a thickness in the sub-millimeter to meter range, with additional exploration capability such as element identification and bio-chemical analysis. In the present report, principles, methods and specific research examples of CRM transmission radiography are summarized after which, principles, methods and perspective views of the future CAM radiography are described.
Our chemical syntheses and related scientific investigations of natural products with complex architectures and powerful biological activities are described, focusing on the very large 3 nm-long polycyclic ethers called the ciguatoxins, highly strained and labile chromoprotein antitumor antibiotics featuring nine-membered enediyne cores, and extremely potent anthelmintic macrolides called the avermectins.
Diffraction properties of photoinduced gratings recorded by overlapping two coherent beams at 532 nm in nematic liquid crystals doped with Disperse Red 1 were investigated with a probe beam at 632.8 nm. The grating was formed due to the alignment of dye molecules that leaded to the reorientation of the liquid crystal phase. The diffraction efficiency of the photoinduced grating was found to increase rapidly when the sample temperature was close to the clearing point in the nematic phase and a nearly 30-fold enhancement of the first-order diffraction efficiency was obtained. The pretransitional enhancement of the diffraction efficiency was discussed in terms of the reorientation of liquid crystals, optical nonlinearity effects and the onset of critical opalescence near the nematic-isotropic phase transition. Moreover, a peak shift of diffraction efficiency towards the lower temperature was observed with the increase of recording light intensity, which was attributed to laser induced photochemical disordering.
We introduce a new analysis method to deal with stationary non-Gaussian noises in gravitational wave detectors in terms of the independent component analysis. First, we consider the simplest case where the detector outputs are linear combinations of the inputs, consisting of signals and various noises, and show that this method may be helpful to increase the signal-to-noise ratio. Next, we take into account the time delay between the inputs and the outputs. Finally, we extend our method to nonlinearly correlated noises and show that our method can identify the coupling coefficients and remove non-Gaussian noises. Although we focus on gravitational wave data analysis, our methods are applicable to the detection of any signals under non-Gaussian noises.
Memory retrieval requires both accuracy and speed. Olfactory learning of the fruit fly Drosophila melanogaster serves as a powerful model system to identify molecular and neuronal substrates of memory and memory-guided behavior. The behavioral expression of olfactory memory has traditionally been tested as a conditioned odor response in a simple T-maze, which measures the result, but not the speed, of odor choice. Here, we developed multiplexed T-mazes that allow video recording of the choice behavior. Automatic fly counting in each arm of the maze visualizes choice dynamics. Using this setup, we show that the transient blockade of serotonergic neurons slows down the choice, while leaving the eventual choice intact. In contrast, activation of the same neurons impairs the eventual performance leaving the choice speed unchanged. Our new apparatus contributes to elucidating how the speed and the accuracy of memory retrieval are implemented in the fly brain.
Beginning in April 2016, a series of shallow, moderate to large earthquakes with associated strong aftershocks struck the Kumamoto area of Kyushu, SW Japan. An Mj 7.3 mainshock occurred on 16 April 2016, close to the epicenter of an Mj 6.5 foreshock that occurred about 28 hours earlier. The intense seismicity released the accumulated elastic energy by right-lateral strike slip, mainly along two known, active faults. The mainshock rupture propagated along multiple fault segments with different geometries. The faulting style is reasonably consistent with regional deformation observed on geologic timescales and with the stress field estimated from seismic observations. One striking feature of this sequence is intense seismic activity, including a dynamically triggered earthquake in the Oita region. Following the mainshock rupture, postseismic deformation has been observed, as well as expansion of the seismicity front toward the southwest and northwest.
Recent advances in biology have been driven by chemical analyses of the substances that form living organisms. Such analyses are extremely powerful as way of learning about the static properties of molecular species, but relatively powerless for understanding their dynamic behaviors even though this dynamism is essential for organisms to perform various biological processes that perpetuate their lives. Thus, attempts to identify individual species and molecular interaction cascades that drive specific responses to external stimuli or environmental changes often fail. Here I propose a general strategy to address this problem. The strategy comprises two key elements: functional manipulation of a given protein molecule coupled with close monitoring of its biological effect, and construction of a knowledge base tailored for conjecture-driven experimentation. The original idea for this strategy co-evolved with and greatly helped a series of studies we recently performed to discover critical signal cascades and cellular components that regulate the cell cycle transition from G1 to S phase.