O-glycosylation of mammalian proteins is one of the important posttranslational modifications. We applied a support vector machine (SVM) to predict whether Ser or Thr is glycosylated, in order to elucidate the O-glycosylation mechanism. O-glycosylated sites were often found clustered along the sequence, whereas other sites were located sporadically. Therefore, we developed two types of SVMs for predicting clustered and isolated sites separately. We found that the amino acid composition was effective for predicting the clustered type, whereas the site-specific algorithm was effective for the isolated type. The highest prediction accuracy for the clustered type was 74%, while that for the isolated type was 79%. The existence frequency of amino acids around the O-glycosylation sites was different in the two types: namely, Pro, Val and Ala had high existence probabilities at each specific position relative to a glycosylation site, especially for the isolated type. Independent component analyses for the amino acid sequences around O-glycosylation sites showed the position-specific existences of the identified amino acids as independent components. The O-glycosylation sites were preferentially located within intrinsically disordered regions of extracellular proteins: particularly, more than 90% of the clustered O-GalNAc glycosylation sites were observed in intrinsically disordered regions. This feature could be the key for understanding the non-conservation property of O-glycosylation, and its role in functional diversity and structural stability.

Intrinsically disordered (ID) proteins (IDPs) are abundant in eukaryotes but are scarce in prokaryotes. Mitochondria, cellular organelles that descended from Rickettsia‐like α‐proteobacteria, are at the intersection between prokaryotes and eukaryotes. Although IDPs are reportedly as rare in mitochondria as in bacteria, these details remained to be clarified. Human mitochondrial proteins (n = 706) were obtained from the UniProt database, and information on ID regions of all human proteins was extracted from the DICHOT database. A BLAST search carried out against all α‐proteobacterial proteins identified two types of mitochondrial proteins: those with (B) and without (E) bacterial homologues. The B‐type proteins (n = 387) descended from a bacterial ancestor, whereas the E‐type proteins (n = 319) were more recently added to the mitochondria via the host cell during the early evolution of eukaryotes. The average ID ratios of B‐type/E‐type proteins are 10.3% and 21.4%, respectively. The 706 proteins were further classified into four groups based on the mitochondrial subcompartment, namely, the matrix, intermembrane space, inner membrane, or outer membrane. The ID ratios in these different locations suggest that the frequency of IDPs in mitochondria might be due to the evolutionary origin (B‐type/E‐type) of the protein, rather than differences in its functional environment.

The phase synchronization in the complex-valued Hopfield network has been shown to be effective for a signal control in an area-wide urban traffic flow control. The basic idea of the original method is to attain the global effectiveness as a weighted summation of the local effectiveness. And the complex-valued Hopfield network is designed to converge to such an optimal state through the appropriate interaction between the neurons which model the traffic signals. As the result, the network possesses the energy function which expresses the global effectiveness, whose leading term is given by the summation of the substantial traffic flows under the given offset. Thus, it is a bottom-up approach to optimize the global effectiveness as the total of the local effectiveness. In this paper, two different approaches are introduced, and added to or compared with the above approach. The first approach is the feedback from the real time information of local traffics. The purpose of the feedback is to decrease the differences of the disadvantages among conflicting flows, which are measured by a congestion or the number of waiting vehicles. The addition of the feedback to the original method shows that the local feedback works as a pinpoint control on a local congestion, while keeping the total effectiveness especially in regular traffic patterns. The second is a top-down approach to attain the global optimization by real-coded genetic algorithms. The proposed GA directly searches the effective offset using a traffic simulator to calculate the average traveling time for the evaluation. Therefore, genetic operations are designed for a small size population and a real-code in a torus space. The best offsets obtained by GA reduce the average traveling time by 2%~7% compared with the results obtained by the original approach.

The phase synchronization in the complex-valued Hopfield network has been shown to be effective for a signal control in an area-wide urban traffic flow control. The basic idea of the original method is to attain the global effectiveness as a weighted summation of the local effectiveness. And the complex-valued Hopfield network is designed to converge to such an optimal state through the appropriate interaction between the neurons which model the traffic signals. As the result, the network possesses the energy function which expresses the global effectiveness, whose leading term is given by the summation of the substantial traffic flows under the given offset. Thus, it is a bottom-up approach to optimize the global effectiveness as the total of the local effectiveness. In this paper, two different approaches are introduced, and added to or compared with the above approach. The first approach is the feedback from the real time information of local traffics. The purpose of the feedback is to decrease the differences of the disadvantages among conflicting flows, which are measured by a congestion or the number of waiting vehicles. The addition of the feedback to the original method shows that the local feedback works as a pinpoint control on a local congestion, while keeping the total effectiveness especially in regular traffic patterns. The second is a top-down approach to attain the global optimization by real-coded genetic algorithms. The proposed GA directly searches the effective offset using a traffic simulator to calculate the average traveling time for the evaluation. Therefore, genetic operations are designed for a small size population and a real-code in a torus space. The best offsets obtained by GA reduce the average traveling time by 2%~7% compared with the results obtained by the original approach.