Emerging fields such as nanomedicine and nanotoxicology, demand new information on the effects of nanoparticles on biological membranes and lipid vesicles are suitable as an experimental model for ...bio-nano interaction studies. This paper describes image processing algorithms which stitch video sequences into mosaics and recording the shapes of thousands of lipid vesicles, which were used to assess the effect of CoFe
O
nanoparticles on the population of 1-palmitoyl-2-oleoyl-
-glycero-3-phosphatidylcholine lipid vesicles. The applicability of this methodology for assessing the potential of engineered nanoparticles to affect morphological properties of lipid membranes is discussed.
In this paper finite automata are treated as general discrete dynamical systems from the viewpoint of systems theory. The unconditional on-line identification of an unknown finite automaton is the ...problem considered. A generalized architecture of recurrent neural networks with a corresponding on-line learning scheme is proposed as a solution to the problem. An on-line rule-extraction algorithm is further introduced. The architecture presented, the on-line learning scheme and the on-line rule-extraction method are tested on different, strongly connected automata, ranging from a very simple example with two states only to a more interesting and complex one with 64 states; the results of both training and extraction processes are very promising.
Clustering-ensemble methods have emerged recently as an effective approach to the problem of clustering, which is one of the fundamental data-analysis tools. Data clustering with an ensemble involves ...two steps: generation of the ensemble with single-clustering methods and the combination of the obtained solutions to produce a final consensus partition of the data. In this paper we first propose a novel clustering method, based on Kohonen's self-organising map and gravitational algorithm, and, second, investigate its performance in the generation of a clustering ensemble. The proposed method is able to discover clusters of complex shapes and determines the number of clusters automatically. Furthermore, its stochastic nature is beneficial in the construction of a diverse ensemble of partitions. Promising results of the presented method were obtained in comparison with three, relevant, single-clustering algorithms over artificial and real data sets.
Electronic computer circuits consisting of a large number of connected logic gates of the same type, such as NOR, can be easily fabricated and can implement any logic function. In contrast, designed ...genetic circuits must employ orthogonal information mediators owing to free diffusion within the cell. Combinatorial diversity and orthogonality can be provided by designable DNA- binding domains. Here, we employed the transcription activator-like repressors to optimize the construction of orthogonal functionally complete NOR gates to construct logic circuits. We used transient transfection to implement all 16 two-input logic functions from combinations of the same type of NOR gates within mammalian cells. Additionally, we present a genetic logic circuit where one input is used to select between an AND and OR function to process the data input using the same circuit. This demonstrates the potential of designable modular transcription factors for the construction of complex biological information-processing devices.
This paper investigates
pre-images (ancestors or past configurations) of specified configurations of one-dimensional cellular automata. Both
counting and
listing of pre-images are discussed. The main ...graphical tools used are the
de Bruijn diagram, and its extension the
pre-image network, which is created by concatenating de Bruijn diagrams. The counting of pre-images is performed as the multiplication of topological matrices of de Bruijn diagrams. Listing of pre-images is described using two algorithms. The first algorithm traces paths in the pre-image network and focuses on local knowledge of the network. The second performs a complete analysis of the network before proceeding with listing.
In this article we research the impact of the adaptive learning process of recurrent neural networks (RNN) on the structural properties of the derived graphs. A trained fully connected RNN can be ...converted to a graph by defining edges between pairs od nodes having significant weights. We measured structural properties of the derived graphs, such as characteristic path lengths, clustering coefficients and degree distributions. The results imply that a trained RNN has significantly larger clustering coefficient than a random network with a comparable connectivity. Besides, the degree distributions show existence of nodes with a large degree or hubs, typical for scale-free networks. We also show analytically and experimentally that this type of degree distribution has increased entropy.
A type of topological approach to mobile robot navigation is discussed and experimentally evaluated. The environment as experienced by a moving robot is treated as a dynamical system. Simple types of ...reactive behavior are supplemented with eventual decisions to switch between them. When switching criteria are defined, the system may be described in the form similar to a finite state machine. Since it is embedded in the environment and dependent on the sensory flow of the robot, we introduce the term "Embedded flow state machine" (EFSM). We implemented it with a recurrent neural network, trained on a sequence of sensory contents and actions. One of the main virtues of this approach is that no explicit localization is required, since the recurrent neural network holds the state implicitly. The EFSM is applicable to multi-step prediction of sensory information and the travelled distances between decision points, given a sequence of decisions at decision points. Thus, the optimal path to a specified goal can be sought. One of the main issues is, for how many steps ahead the prediction is reliable enough. In other words, is it feasible to perform environment modelling and path planning in this manner? The approach is tested on a miniature mobile robot, equipped with proximity sensors and a color video camera. Decision 'points,' where deviations from the wall-following behavior are allowed, are based on color object recognition. In the case of an experimental environment of medium complexity, this approach was successful.PUBLICATION ABSTRACT
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