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.
The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, ...Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The second volume includes 41 papers organized in topical sections on pattern recognition and learning, soft computing, systems theory, support vector machines, and bioinformatics.
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.
The ICANNGA series of Conferences has been organised since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientific ...community and is a reference for established workers in this area. Starting in Innsbruck, in Austria (1993), then to Ales in Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague in the Czech Republic (2001) and finally Roanne, in France (2003), the ICANNGA series has established itself for experienced workers in the field. The series has also been of value to young researchers wishing both to extend their knowledge and experience and also to meet internationally renowned experts. The 2005 Conference, the seventh in the ICANNGA series, will take place at the University of Coimbra in Portugal, drawing on the experience of previous events, and following the same general model, combining technical sessions, including plenary lectures by renowned scientists, with tutorials.
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.
Data clustering is the fundamental data analysis method, widely used for solving problems in the field of machine learning. Numerous clustering algorithms exist, based on various theories and ...approaches, one of them being the well-known Kohonen’s self-organizing map (SOM). Unfortunately, after training the SOM there is no explicitly obtained information about clusters in the underlying data, so another technique for grouping SOM units has to be applied afterwards. In this paper, a contribution towards clustering of the SOM is presented, employing principles of Gravitational Law. On the first level of the proposed algorithm, SOM is trained on the input data and prototypes are extracted. On the second level, each prototype acts as a unit-mass point in a feature space, in which presence of gravitational force is simulated, exploiting information about connectivity gained on the first level. The proposed approach is capable of discovering complex cluster shapes, not only limited to the spherical ones, and is able to automatically determine the number of clusters. Experiments with synthetic and real data are conducted to show performance of the presented method in comparison with other clustering techniques.
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