Influence of dielectric fluctuation on light-scattering properties of random copolymers in bulk

Polarized light scattering in bulk polymers is positively correlated with the intensity of dielectric fluctuation and the size of the heterogeneous structure, which is expressed by a correlation length. In this study, the influence of dielectric fluctuation was independently investigated for the first time on the basis of the difference in the light-scattering properties between two random copolymers – methyl methacrylate (MMA)/pentafluorophenyl methacrylate (PFPMA) and MMA/phenyl methacrylate (PMA). These copolymers have similar correlation lengths but show different intensities of dielectric fluctuation. When the difference of the mean-square average of the fluctuations of all dielectric constants between the copolymers was 105 × 10⁻¹⁰, the difference in isotropic light-scattering loss was 425 dB/km at 633 nm.     

Effect of trehalose accumulation on response to saline stress in Saccharomyces cerevisiae

To examine the effect of trehalose accumulation on response to saline stress in Saccharomyces cerevisiae, we constructed deletion strains of all combinations of the trehalase genes ATH1, NTH1 and NTH2 and examined their growth behaviour and intracellular trehalose accumulation under non‐stress and saline‐stress conditions. Saline stress was induced in yeast cells by NaCl addition at the exponential growth phase. All deletion strains showed similar specific growth rates and trehalose accumulation to their parent strain under non‐stress conditions. However, under the saline stress condition, one single deletion strain, nth1Δ, two double deletion strains, nth1Δ ath1Δ and nth1Δ nth2Δ, and the triple deletion strain nth1Δnth2Δ ath1Δ, all of which carry the nth1Δ deletion, showed increased trehalose accumulation as compared to the parent and other deletion strains. In particular, our statistical analysis revealed that the triple deletion strain showed a higher growth rate under the saline stress condition than the parent strain. Moreover, some deletion strains showed further trehalose accumulation under non‐stress conditions by overexpression of the TPS1 or TPS2 genes encoding the enzymes related to trehalose biosynthesis at the mid‐exponential phase. Such increased trehalose accumulation prior to NaCl addition could improve the growth of these strains under saline stress. Our results indicate that high trehalose accumulation prior to NaCl addition, rather than after NaCl addition, is necessary to achieve high growth activity under stress conditions. Copyright © 2009 John Wiley & Sons, Ltd.     

Artificial neural networks paddy-field classifier using spatiotemporal remote sensing data

Monitoring changes in a paddy-field area is important since rice is a staple food and paddy agriculture is a major cropping system in Asia. For monitoring changes in land surface, various applications using different satellites have been researched in the field of remote sensing. However, monitoring a paddy-field area with remote sensing is difficult owing to the temporal changes in the land surface, and the differences in the spatiotemporal characteristics in countries and regions. In this article, we used an artificial neural network to classify paddy-field areas using moderate resolution sensor data that includes spatiotemporal information. Our aim is to automatically generate a paddy-field classifier in order to create localized classifiers for each country and region.     

α‐Complementation in an Artificial Genome Replication System in Liposomes

Genome size is considered one of the limiting factors for the replication of primitive life forms. However, the relationship between genome size and replication efficiency has not been tested experimentally. In this study, we examined the effect of genome size on genome replication by using an artificial cell model: a self‐replicating RNA genome encapsulated in a liposome. For the reduced genome size we used α‐complementation of the lacZ gene. We first characterized α‐complementation in the purified translation system and then applied α‐complementation to the genome replication system. The reduction in the genome size together with the addition of ω‐fragment increased the replication efficiency approximately eightfold. This result provides experimental evidence that genome size can be a limiting factor for primitive self‐replication systems; it also implies that this artificial cell model could be a useful experimental model to identify possible mechanisms of genome enlargement.     

Relationship between noise characteristics in protein expressions and regulatory structures of amino acid biosynthesis pathways

Gene regulatory dynamics involves several stochastic chemical reactions. As a consequence, the copy number of given protein varies greatly among cells even in the case of isogeneic cells. Recently, the characteristics of noise in gene expressions were studied by using simple artificial gene networks. However, the noise characteristics in natural regulatory networks having complex interactions still remain unclear. In this study, we have focused on the noise in natural regulatory networks to understand the relationship between the characteristics of the noise and the structures of regulatory interactions. We targeted the expressions of genes related to amino acid biosynthesis (AAB) because of their well known regulatory structures. By measuring the noise of AAB genes in isogeneic Escherichia coli cells using flow cytometry, we found the noise amplitude in AAB genes to depend on the structure of the regulatory network. We categorised the regulatory networks with feedback regulation into two cases. In one case, the gene expression is negatively regulated by the final products of the AAB pathway known as feedback repression, whereas in another case, the gene expression is negatively regulated as a result of depletion of the substrate that is located upstream of the AAB pathway and activates the expression of the corresponding gene. Our data revealed that the noise amplitude of AAB genes in the former case is significantly smaller than the noise amplitude in the latter case. Furthermore, we found that the response time as a result of environmental changes is generally longer in the former case. This result provides a basis for understanding the role of natural regulatory networks better.     

Screening of Peptide Ligands for Pyrroloquinoline Quinone Glucose Dehydrogenase Using Antagonistic Template-Based Biopanning

We have developed a novel method, antagonistic template-based biopanning, for screening peptide ligands specifically recognizing local tertiary protein structures. We chose water-soluble pyrroloquinoline quinone (PQQ) glucose dehydrogenase (GDH-B) as a model enzyme for this screening. Two GDH-B mutants were constructed as antagonistic templates; these have some point mutations to induce disruption of local tertiary structures within the loop regions that are located at near glucose-binding pocket. Using phage display, we selected 12-mer peptides that specifically bound to wild-type GDH-B but not to the antagonistic templates. Consequently, a peptide ligand showing inhibitory activity against GDH-B was obtained. These results demonstrate that the antagonistic template-based biopanning is useful for screening peptide ligands recognizing the specific local tertiary structure of proteins.     

Some considerations on state equations of linear active networks

It is shown that if there equivalently exist the virtual resistive elements in parallel with the inductive elements of the over-normal tree of a given linear active network or in series with the capacitive elements of the corresponding co-tree, an increase in the number of state variables arises. It is also shown that when a virtual resistor equivalently appears in parallel with the distinct resistor in a tree or in series with the distinct resistor in the co-tree, a decrease in the number of state variables may arise. This is, however, a rare case in the usual types of network. Two algebraic methods for obtaining the state equation of linear active networks are presented. One is useful for the networks in which the decrease in the number of state variables does not arise. From the other, the output equation for the required variables is obtained at the same time as the state equation. Further, the initial values are simply determined without iteration in many cases.     

An attempt to stabilize nilvadipine solid dispersion by the use of ternary systems

Firstly, we investigated the physical stability of nilvadipine (NIL)/crospovidone (cl-PVP) solid dispersion during storage (40°C, 75% relative humidity) with powder x-ray diffraction, differential scanning calorimetry (DSC) and dissolution test. These studies indicated that recrystallization occurred during storage and that the dissolution of NIL greatly decreased, compared with that of the initial finding. Secondly, to improve the amorphous form physical stability of NIL, methylcellulose (MC) was added to NIL/cl-PVP solid dispersions as a dispersion carrier and NIL/cl-PVP/MC ternary solid dispersion systems were obtained by the solvent method. Powder x-ray diffraction and DSC studies indicated that the amorphous form physical stability of NIL clearly improved in the NIL/cl-PVP/MC solid dispersion systems during storage. Moreover, the dissolution properties of NIL/cl-PVP/MC solid dispersion systems were characterized by cl-PVP markedly enhancing the dissolution of NIL and MC inhibiting the change of the dissolution of NIL during storage. Finally, we obtained an ideal solid dispersion that was accompanied by a consistently higher rate of dissolution.     

A genetic network programming with learning approach for enhanced stock trading model

In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading decision-making. In order to create more efficient judgment functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that the stock trading model using GNP-Sarsa outperforms all the other methods.