Lateral gene exchanges shape the genomes of amoeba-resisting microorganisms.

Based on Darwin's concept of the tree of life, vertical inheritance was thought to be dominant, and mutations, deletions, and duplication were streaming the genomes of living organisms. In the current genomic era, increasing data indicated that both vertical and lateral gene inheritance interact in space and time to trigger genome evolution, particularly among microorganisms sharing a given ecological niche. As a paradigm to their diversity and their survival in a variety of cell types, intracellular microorganisms, and notably intracellular bacteria, were considered as less prone to lateral genetic exchanges. Such specialized microorganisms generally have a smaller gene repertoire because they do rely on their host's factors for some basic regulatory and metabolic functions. Here we review events of lateral gene transfer (LGT) that illustrate the genetic exchanges among intra-amoebal microorganisms or between the microorganism and its amoebal host. We tentatively investigate the functions of laterally transferred genes in the light of the interaction with their host as they should confer a selective advantage and success to the amoeba-resisting microorganisms (ARMs).

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.