Identification of the neighborhood and CA rules from spatio-temporal CA patterns

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule

Neighborhood detection and rule selection from cellular automata patterns

Using genetic algorithms (GAs) to search for cellular automation (CA) rules from spatio-temporal patterns produced in CA evolution is usually complicated and time-consuming when both, the neighborhood structure and the local rule are searched simultaneously. The complexity of this problem motivates the development of a new search which separates the neighborhood detection from the GA search. In the paper, the neighborhood is determined by independently selecting terms from a large term set on the basis of the contribution each term makes to the next state of the cell to be updated. The GA search is then started with a considerably smaller set of candidate rules pre-defined by the detected neighhorhood. This approach is tested over a large set of one-dimensional (1-D) and two-dimensional (2-D) CA rules. Simulation results illustrate the efficiency of the new algorith

Extracting Boolean rules from CA patterns

A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois

Identification of probabilistic cellular automata

The identification of probabilistic cellular automata (PCA) is studied using a new two stage neighborhood detection algorithm. It is shown that a binary probabilistic cellular automaton (BPCA) can be described by an integer-parameterized polynomial corrupted by noise. Searching for the correct neighborhood of a BPCA is then equivalent to selecting the correct terms which constitute the polynomial model of the BPCA, from a large initial term set. It is proved that the contribution values for the correct terms can be calculated independently of the contribution values for the noise terms. This allows the neighborhood detection technique developed for deterministic rules in to be applied with a larger cutoff value to discard the majority of spurious terms and to produce an initial presearch for the BPCA neighborhood. A multiobjective genetic algorithm (GA) search with integer constraints is then evolved to refine the reduced neighborhood and to identify the polynomial rule which is equivalent to the probabilistic rule with the largest probability. A probability table representing the BPCA can then be determined based on the identified neighborhood and the deterministic rule. The new algorithm is tested over a large set of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) BPCA rules. Simulation results demonstrate the efficiency of the new method

Characterisation of Parkinson's disease-associated genes and their regulation.

Parkinson's disease is a highly prevalent neurodegenerative disorder. Several genes have been shown to be associated with familial Parkinson's disease and they usually lead to Parkinson's disease due to the presence of mutations that affect protein function. It has been suggested that variations in the expression of the wild type genes may also lead to Parkinson's disease. The causes of idiopathic Parkinson's disease remain unknown. Several factors may contribute to its onset, including: susceptibility genes, environmental stress and aging. This study aimed to characterize the influence of oxidative stresses on the regulation of genes associated with Parkinson's disease. The effects of oxidative stress on a- synuclein, parkin and PINK1 were investigated in a cell culture model. Both ot- synuclein and parkin were similarly up-regulated when cells were exposed to stresses such as dopamine and l-methyl-4-phenylpyridinium (MPP+). In constrast, PINK1 levels were up-regulated only by MPP+, and were down-regulated in both dopamine and MG132 treatments. This work confirmed and extended previous reports that oxidative stresses are implicated in Parkinson's disease, and also revealed the complexity of the regulation by these stresses. A further study into the regulation of a-synuclein showed a novel interaction between the a-synuclein promoter and an Early Growth Response transcription factor family member in oxidative stress conditions. Moreover, this work demonstrated that several other neuronally expressed transcription factors influenced the regulation of a- synuclein, such as the product of the Parkinson's disease associated gene, Nurrl. The decreased expression of this gene increased a-synuclein transcription. This is of interest, as variations in the levels of either of these genes can cause Parkinson's disease and such an interaction was novel. This work further demonstrated that the POU family trancription factor, Brn3a, was involved in this pathway. Brn3a appeared to function antagonistically to Nurrl in a-synuclein regulation. In addition to studies of gene regulation, mutational and/or protein analysis were performed on Nurrl and PINK1. Studies of PINK 1 protein established the functional importance of cleavage of precursor PINK1 and also provided a better estimation of the location of the cleavage site. These genes are more recent discoveries compared to a-synuclein and parkin, thus, such studies will give important insights into their Parkinson's disease properties

Molecular evolution of the sheep prion protein gene

Transmissible spongiform encephalopathies (TSEs) are infectious, fatal neurodegenerative diseases characterized by aggregates of modified forms of the prion protein (PrP) in the central nervous system. Well known examples include variant Creutzfeldt-Jakob Disease (vCJD) in humans, BSE in cattle, chronic wasting disease in deer and scrapie in sheep and goats. In humans, sheep and deer, disease susceptibility is determined by host genotype at the prion protein gene (PRNP). Here I examine the molecular evolution of PRNP in ruminants and show that variation in sheep appears to have been maintained by balancing selection, a profoundly different process from that seen in other ruminants. Scrapie eradication programs such as those recently implemented in the UK, USA and elsewhere are based on the assumption that PRNP is under positive selection in response to scrapie. If, as these data suggest, that assumption is wrong, eradication programs will disrupt this balancing selection, and may have a negative impact on the fitness or scrapie resistance of national flocks

Identification of the Neighbourhood and CA Rules from Spatio-temporal CA Patterns

Extracting the rules from spatio-temporal patterns generated by the evolution of Cellular Automata (CA) usually produces a CA rule tablet without providing a clear understanding of the structure of the neighbourhood or the CA rule. In the present paper a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighbourhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McClauskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of one-, two-and higher-dimensional binary CA's are used to illustrate the new algorithm and simulation results show that the CA-OLS algorithm can quickly select both the correct neighbourhood structure and the corresponding rule

Extracting Boolean Rules From CA Patterns

A multi-objective GA algorithm is introduced to identify both the neighbourhood and the rule set in the form of a parsimonious Boolean expression for both one-and-two-dimensional cellular automata. Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic noise

Identification of Probabilistic Cellular Automata

The identification of Probabilistic Cellular Automata (PCA) is studied using a new two stage neighbourhood detection algorithm. It is shown that a Binary Probabilistic Cellular Automaton (BPCA) can be described by an integer-parameterised polynomial customised by noise. Searching for the correct neighbourhood of a BPCA is then equivalent to selecting the correct terms, which constitute the polynomial model of the BPCA from a large initial term set. It is proved that the contribution values for the correct terms can be calculated independently of the contribution values for noise terms. This allows the neighbourhood detection technique developed for deterministic rules in [16] to be applied with with a larger cutoff value to discard the majority of spurious terms and to produce an initial pre-search for the BPCA neighbourhood. A multi-objective GA search with integer constraints is then evolved to refine the reduced neighbourhood and to identify the polynomial rule, which is equivalent to the probabilistic rule with the largest probability. A probability table representing the BPCA can then be determined based on the identified neighbourhood and the deterministic rule. The new algorithm is tested over a large set of 1-D,2-D and 3-D BPCA rules. Simulation results demonstrate the efficiency of the new method

Numerical study on the response of shoreline change to the tidal channel after a beach nourishment project on an embayed beach

Beach erosion is a severe problem worldwide and beach nourishment is widely regarded today as an environmentally acceptable method to protect and enlarge beaches. In many beach nourishment projects on headland-bay beaches, artificial headlands were constructed on the natural headlands to form an embayed beach in static equilibrium to protect the beach more effectively. However, the construction of artificial headland would weaken the water exchange in the bay and make the water quality easy to deteriorate. In a beach nourishment project in Qinhuangdao, China to dispose this discrepancy an engineering measure was conducted: to reserve a tidal channel between the artificial headland and the natural headland to allow the tidal current to pass. In this paper, a shoreline change model was set up based on GENESIS model to evaluate the influence of the reserve of the tidal channel on the shoreline change after the project. The model was verified by reproducing the post-project shoreline. Four different project schemes with different scales of tidal channel were simulated and discussion was given based on the analysis of simulated results. The numerical evaluation of various scheme options indicates that it is feasible to involve the tidal channel in beach nourishment projects with artificial headland and the scale of the tidal channel should be designed based on the hydrodynamic processes and the state of the beach