Designing higher fourier harmonics of Tegotae function using genetic algorithm-a case study with an earthworm locomotion.

'Tegotae' is a Japanese concept describing how well a perceived reaction matches a generated action. We previously proposed a novel decentralised control scheme for animals' adaptive locomotion based on a Tegotae function, a function which quantifies Tegotae and is generally given by the product of action and reaction terms. Although this scheme is helpful for extracting common control principles underlying various types of animal locomotion, there remains further room for improvement of its design. Specifically, while the previous works assumed that the action term is given by a sinusoidal function, it is expected that introducing higher Fourier harmonics in the action term improves locomotion performance. In this study, taking earthworm locomotion as an example, we design a Tegotae function whose action term includes higher Fourier harmonics by using a genetic algorithm. We demonstrate via simulations that the performance improves by implementing the higher Fourier harmonics.

Efficiently finding regulatory elements using correlation with gene expression.

We present an efficient algorithm for detecting putative regulatory elements in the upstream DNA sequences of genes, using gene expression information obtained from microarray experiments. Based on a generalized suffix tree, our algorithm looks for motif patterns whose appearance in the upstream region is most correlated with the expression levels of the genes. We are able to find the optimal pattern, in time linear in the total length of the upstream sequences. We implement and apply our algorithm to publicly available microarray gene expression data, and show that our method is able to discover biologically significant motifs, including various motifs which have been reported previously using the same data set. We further discuss applications for which the efficiency of the method is essential, as well as possible extensions to our algorithm.

Intravenous iron administration induces oxidation of serum albumin in hemodialysis patients.

BACKGROUND: Intravenous iron administration (IVIR) is effective for correcting anemia in hemodialysis (HD) patients. However, it may also enhance the generation of hydroxyl radicals. Recently, plasma proteins have been demonstrated to be extremely susceptible to oxidative stress. Therefore, we investigated the effect of IVIR on the oxidative status of albumin, a major plasma protein, in HD patients. METHODS: Eleven hemodialysis (HD) patients were treated with 40 mg of saccharated ferric oxide intravenously after every dialysis session for four weeks, and 11 age-/gender-matched HD patients were treated with vehicle. We performed high performance liquid chromatography (HPLC) analysis of serum albumin and determined the levels of reduced and oxidized albumin. Carbonyl formation of plasma proteins were also measured using an anti-2,4 dinitrophenylhydrazine antibody in patients with or without IVIR. RESULTS: IVIR resulted in an increase in both disulfide form (f(HNA-1)) and oxidized form (f(HNA-2)) of albumin in HD patients (36.0 +/- 6.03 vs. 41.7 +/- 6.27; 5.46 +/- 1.50 vs. 8.7 +/- 2.22, respectively, P < 0.05). The findings here also show that IVIR substantially increased plasma protein carbonyl content by oxidizing albumin. In addition, we found a strong correlation between plasma carbonyl content and the levels of oxidized albumin (f(HNA-1) and f(HNA-2)) in HD patients (R= 0.674 and R= 0.724, respectively, P < 0.01). CONCLUSION: The results of this study indicate that the HPLC analysis of serum albumin represents a potentially useful method for the quantitative and qualitative evaluation of oxidative stress in HD patients, and strongly suggest the possibility that oxidative stress, generated by IVIR, enhances the oxidation of albumin in those patients.

An O(N2) algorithm for discovering optimal Boolean pattern pairs.

We consider the problem of finding the optimal combination of string patterns, which characterizes a given set of strings that have a numeric attribute value assigned to each string. Pattern combinations are scored based on the correlation between their occurrences in the strings and the numeric attribute values. The aim is to find the combination of patterns which is best with respect to an appropriate scoring function. We present an O(N2) time algorithm for finding the optimal pair of substring patterns combined with Boolean functions, where N is the total length of the sequences. The algorithm looks for all possible Boolean combinations of the patterns, e.g., patterns of the form p and not q, which indicates that the pattern pair is considered to occur in a given string s, if p occurs in s, AND q does NOT occur in s. An efficient implementation using suffix arrays is presented, and we further show that the algorithm can be adapted to find the best k-pattern Boolean combination in O(Nk) time. The algorithm is applied to mRNA sequence data sets of moderate size combined with their turnover rates for the purpose of finding regulatory elements that cooperate, complement, or compete with each other in enhancing and/or silencing mRNA decay.

A string pattern regression algorithm and its application to pattern discovery in long introns.

We present a new approach to pattern discovery called string pattern regression, where we are given a data set that consists of a string attribute and an objective numerical attribute. The problem is to find the best string pattern that divides the data set in such a way that the distribution of the numerical attribute values of the set for which the pattern matches the string attribute, is most distinct, with respect to some appropriate measure, from the distribution of the numerical attribute values of the set for which the pattern does not match the string attribute. By solving this problem, we are able to discover, at the same time, a subset of the data whose objective numerical attributes are significantly different from rest of the data, as well as the splitting rule in the form of a string pattern that is conserved in the subset. Although the problem can be solved in linear time for the substring pattern class, the problem is NP-hard in the general case (i.e. more complex patterns), and we present an exact but efficient branch-and-bound algorithm which is applicable to various pattern classes. We apply our algorithm to intron sequences of human, mouse, fly, and zebrafish, and show the practicality of our approach and algorithm. We also discuss possible extensions of our algorithm, as well as promising applications, such as microarray gene expression data.