Abstract
Background: Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations. Results: From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs. Conclusion: In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.

Abstract
Protein sequence motifs describe, through means of enhanced regular expression syntax, regions of amino acids that have been conserved across several functionally related proteins. These regions may have an implication at the structural and functional level of the proteins. Sequence motif analysis can bring significant improvements towards a better understanding of the protein sequence-structure-function relation. In this chapter, we review the subject of mining deterministic motifs from protein sequence databases. We start by giving a formal definition of the different types of motifs and the respective specificities. Then, we explore the methods available to evaluate the quality and interest of such patterns. Examples of applications and motif repositories are described. We discuss the algorithmic aspects and different methodologies for motif extraction. A brief description on how sequence motifs can be used to extract structural level information patterns is also provided. © 2008, IGI Global.

Abstract
The existence of preserved subsequences in a set of related protein sequences suggests that they might play a structural and functional role in protein's mechanisms. Due to its exploratory approach, the mining process tends to deliver a large number of motifs. Therefore it is critical to release methods that identify relevant significant motifs. Many measures of interest and significance have been proposed. However, since motifs have a wide range or applications, how to choose the appropriate significance measures is application dependent. Some measures show consistent results being highly correlated, while others show disagreements. In this paper we review existent measures and study their behavior in order to assist the selection of the most appropriate set of measures. An experimental evaluation of the measures for high quality patterns from the Prosite database is presented.

Abstract
Understanding protein folding and unfolding mechanisms are a central problem in molecular biology. Data obtained from molecular dynamics unfolding simulations may provide valuable insights for a better understanding of these mechanisms. Here, we propose the application of an augmented version of hierarchical clustering analysis to detect clusters of amino-acid residues with similar behavior in protein unfolding simulations. These clusters hold similar global pattern behavior of solvent accessible surface area (SASA) variation in unfolding simulations of the protein Transthyretin (TTR). Classical hierarchical clustering was applied to build a dendrogram based on the SASA variation of each amino-acid residue. The dendrogram was enriched with background information on the amino-acid residues, enabling the extraction of sub-clusters with well differentiated characteristics.

Abstract
The basic statistical features of radon time series from continuous radon monitoring at the Elat granite, Israel are analysed. A similar analysis is carried out for ancillary and possibly related geophysical parameters for the Elat area. The results show that air temperature, precipitable water and longwave radiation time series exhibit constant variance over the analyzed period, while radon time series, atmospheric pressure, short-wave radiation and total electron content exhibit heteroscedasticity. Furthermore, for radon and shortwave radiation the variability is associated with the overall mean level, while for atmospheric pressure such an association is not present. The analyzed radon time series not only are non-stationary but also nonlinear, reflecting the complex dynamics of radon emanation and transport in natural subsurface systems.

Abstract
A comparative study is carried out for sea level observations in the North Atlantic from tide gauges and satellite altimetry. Monthly tide gauge records from 12 stations in both sides of the North Atlantic from January 1993 to December 2003 and monthly time series of sea level anomalies derived from TOPEX measurements are considered. The degree of association between tide gauge and altimetry observations is analysed for different scales by computing the correlation between the sea level components resulting from a multiresolution analysis based on the maximal overlap discrete wavelet transform. A similar correlation analysis is carried out to assess the relationship between the sea level observations and climate variables: sea surface temperature, precipitation rate and wind speed. The results show that altimetry and tide gauge observations are strongly correlated, as expected, but also that the relation is scale dependent, with covariability driven by the seasonal signal for most stations. For all variables the obtained correlation patterns exhibit significant spatial variability reflecting the diversity of local conditions affecting coastal sea level.