Slow parameter drift is common in many systems (e.g., the amount of greenhouse gases in the terrestrial atmosphere is increasing). In such situations, the attractor on which the system trajectory lies can be destroyed, and the trajectory will then go to another attractor of the system. We consider the case where there are more than one of these possible final attractors, and we ask whether we can control the outcome (i.e., the attractor that ultimately captures the trajectory) using only small controlling perturbations. Specifically, we consider the problem of controlling a noisy system whose parameter slowly drifts through a saddle-node bifurcation taking place on a fractal boundary between the basins of multiple attractors. We show that, when the noise level is low, a small perturbation of size comparable to the noise amplitude applied at a single point in time can ensure that the system will evolve toward a target attracting state with high probability. For a range of noise levels, we find that the minimum size of perturbation required for control is much smaller within a time period that starts some time after the bifurcation, providing a "window of opportunity" for driving the system toward a desirable state. We refer to this procedure as tipping point control.
To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.
The study of collective dynamics in complex networks has emerged as a next frontier in the science of networks. This Focus Issue presents the latest developments on this exciting front, focusing in particular on synchronous and cascading dynamics, which are ubiquitous forms of network dynamics found in a wide range of physical, biological, social, and technological systems.
Model reduction is a common goal in the study of complex systems, consisting of many components with a complex interaction structure. The quality of such reduction, however, may not be reflected correctly in the stepwise prediction error in the model since it ignores the global geometry of the dynamics. Here we introduce a general two-step framework, consisting of dimensionality reduction of the time series followed by modeling of the resulting time series, and propose the use of the shadowing distance to measure the quality of the second step. Using coupled oscillator networks as a prototypical example, we demonstrate that our approach can outperform those based on stepwise error and suggest that it sheds light on the problem of identifying and modeling low-dimensional dynamics in large-scale complex systems.
S100B is a calcium-binding protein produced by astroglia in the brain and has been used as a marker of neuronal damage after brain trauma. We investigated the utility of S100B in cerebrospinal fluid (CSF) measured during the early phase of carbon monoxide (CO) poisoning in predicting the subsequent clinical course.
Synchronization, in which individual dynamical units keep in pace with each other in a decentralized fashion, depends both on the dynamical units and on the properties of the interaction network. Yet, the role played by the network has resisted comprehensive characterization within the prevailing paradigm that interactions facilitating pairwise synchronization also facilitate collective synchronization. Here we challenge this paradigm and show that networks with best complete synchronization, least coupling cost, and maximum dynamical robustness, have arbitrary complexity but quantized total interaction strength, which constrains the allowed number of connections. It stems from this characterization that negative interactions as well as link removals can be used to systematically improve and optimize synchronization properties in both directed and undirected networks. These results extend the recently discovered compensatory perturbations in metabolic networks to the realm of oscillator networks and demonstrate why "less can be more" in network synchronization.
The interaction of guanidine-type cationic surfactants with bovine serum albumin (BSA) and liposome were investigated. Dodecylguanidine hydrochloride (C(12)A(0)G) and several dodecanoylamide alkylguanidine hydrochlorides (C(12)A(m)G, m = 2, 3, 4, 6) were used as the guanidine-type surfactants. In the interaction of these surfactants with BSA as a model protein, the binding isotherms of the surfactants to BSA were analysed. The structural change of the protein was also examined on the basis of circular dichroism and UV absobance data. In the interaction of these surfactants with liposome as a lipid bilayer model, we studied the effects of the surfactants on the solubilisation of liposomes and the release of carboxyfluorescein from liposomes. In addition, the effect of the surfactant molecular structure on the skin irritation was evaluated in connection with the interactions of the surfactants with BSA and liposome. It was found that small amounts of binding of C(12)A(0)G caused both a partial destruction of a-helix and an aggregation of BSA. C(12)A(0)G also induced the aggregation of liposomes, whereas C(12)A(m)G showed no such action. The presence of A(m) group in C(12)A(m)G appeared to reduce the skin irritation in parallel with the weakening of the interaction of the guanidine group with the protein and the lipid bilayer.
Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme eigenvalues of the graph Laplacian when small but arbitrary set of links are added or removed from the network. We demonstrate the effectiveness of our approximation schemes using both real and artificial networks, showing in particular that we can accurately obtain the spectral ranking of small subgraphs. We also propose a local iterative scheme which computes the relative ranking of a subgraph using only the connectivity information of its neighbors within a few links. Our results may not only contribute to our theoretical understanding of dynamical processes on networks, but also lead to practical applications in ranking subgraphs of real complex networks.
In order to confirm the existence of reactive metabolites by LC-MS/MS analysis, they should be modified into stable compounds, because some reactive metabolites generated by biotransformation induce drug toxicity; however, they are unstable, with very short lives, and cannot be detected in their intact forms. To overcome these problems, electrochemical oxidation of troglitazone was performed in nonaqueous medium, since such reactive compounds are stable in the absence of water. Troglitazone, an antidiabetic agent, was withdrawn from the market because of serious hepatotoxicity in some patients. It has been considered that one or more reactive metabolites are involved in hepatotoxicity, although the mechanism of the adverse reaction is unclear. Using our method of electrochemical oxidation in nonaqueous medium, we obtained a product of troglitazone derivative that may be a clue to clarify the mechanism of toxicity. The product in the reaction mixture was separated by HPLC without chemical modification and detected using UV and ESI-MS. The mass spectrum of its molecular ion showed that it was an o-quinone methide derivative of troglitazone and identified as a reactive metabolite generated by liver microsome oxidation of the drug. The product was stable over 24 h at room temperature in anhydrous acetonitrile, but it reacted with N-(tert-butoxycarbonyl)-L-cystein methylester to produce an adduct that could be identified by its m/z value. Thus, the method of electrochemical oxidation in nonaqueous medium is considered to be useful to prepare and predict reactive metabolites of drugs that are unstable in aqueous medium or in vivo.
CIBIC plus-J is the Japanese language version equivalent to CIBIC plus. Variability of CIBIC plus-J arises among raters in accordance with their experience and their memories of patients conditions at baseline. Therefore, in a multicenter trial of Alzheimers disease, CIBIC plus-J interviews with Alzheimers disease patients were videotaped, and the tapes were assessed by central raters as a means to improve the reliability of CIBIC plus-J assessment.
Eucommia ulmoides Oliver is one of a few woody plants capable of producing abundant quantities of trans-polyisoprene rubber in their leaves, barks, and seed coats. One cDNA library each was constructed from its outer stem tissue and inner stem tissue. They comprised a total of 27,752 expressed sequence tags (ESTs) representing 10,520 unigenes made up of 4,302 contigs and 6,218 singletons. Homologues of genes coding for rubber particle membrane proteins that participate in the synthesis of high-molecular poly-isoprene in latex were isolated, as well as those encoding known major latex proteins (MLPs). MLPs extensively shared ESTs, indicating their abundant expression during trans-polyisoprene rubber biosynthesis. The six mevalonate pathway genes which are implicated in the synthesis of isopentenyl diphosphate (IPP), a starting material of poly-isoprene biosynthesis, were isolated, and their role in IPP biosynthesis was confirmed by functional complementation of suitable yeast mutants. Genes encoding five full-length trans-isoprenyl diphosphate synthases were also isolated, and two among those synthesized farnesyl diphosphate from IPP and dimethylallyl diphosphate, an assumed intermediate of rubber biosynthesis. This study should provide a valuable resource for further studies of rubber synthesis in E. ulmoides.
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