Abstract
Part of the reason for rejecting aquatic environments as possible vectors for the transmission of Helicobacter pylori has been the preference of this microorganism to inhabit the human stomach and hence use a direct oral-oral route for transmission. On the other hand, most enteric bacterial pathogens are well known for being able to use water as an environmental reservoir. In this work, we have exposed 13 strains of seven different Helicobacter spp. (both gastric and enterohepatic) to water and tracked their survival by standard plating methods and membrane integrity assessment. The influence of different plating media and temperatures and the presence of light on recovery was also assessed. There was good correlation between cultivability and membrane integrity results (Pearson's correlation coefficient = 0.916), confirming that the culture method could reliably estimate differences in survival among different Helicobacter spp. The species that survived the longest in water was H. pylori (> 96 It in the dark at 25 degrees C), whereas H. felis appeared to be the most sensitive to water (<6 h). A hierarchical cluster analysis demonstrated that there was no relationship between the enterohepatic nature of Helicobacter spp. and an increased time of survival in water. This work assesses for the first time the survival of multiple Helicobacter spp., such has H. mustelae, H. muridarum, H. felis, H. canadensis, H. pullorum, and H. canis, in water under several conditions and concludes that the roles of water in transmission between hosts are likely to be similar for all these species, whether enterohepatic or not.

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
In this paper we derive a set of approximate but general bilinear Kalman filter equations for a multiinput multi-output bilinear stochastic system driven by general autocorrelated inputs. The derivation is based on a convergent Picard sequence of linear stochastic state-space subsystems. We also derive necessary and sufficient conditions for a steady-state solution to exist. Provided all the eigenvalues of a chain of structured matrices are inside the unit circle, the approximate bilinear Kalman filter equations converge to a stationary value. When the input is a zero-mean white noise process, the approximate bilinear Kalman filter equations coincide with those of the well known bilinear Kalman filter model operating under white noise inputs.