CRISPRals: a web database for assessing CRISPR defense system in Ralstonia solanacearum species complex to avoid phage resistance.

CRISPR has been widely characterized as a defense system against phages and other invading elements in bacteria and archaea. A low percentage of Ralstonia solanacearum species complex (RSSC) strains possess the CRISPR array and the CRISPR-associated proteins (Cas) that would confer immunity against various phages. In order to provide a wide-range screen of the CRISPR presence in RSSC, we analyzed 378 genomes of RSSC strains to find the CRISPR locus. We found that 20.1%, 14.3%, and 54.5% of the R. solanacearum, R. pseudosolanacearum, and R. syzygii strains respectively possess the CRISPR locus. In addition, we performed further analysis to identify the respective phages that are restricted by the CRISPR arrays. We found 252 different phages infecting different strains of RSSC, by means of the identification of similarities between the protospacers in phages and spacers in bacteria. We compiled this information in a database with web access called CRISPRals (https://crisprals.yachaytech.edu.ec/). Additionally, we made available a number of tools to detect and identify CRISPR array and Cas genes in genomic sequences that could be uploaded by users. Finally, a matching tool to relate bacteria spacer with phage protospacer sequences is available. CRISPRals is a valuable resource for the scientific community that contributes to the study of bacteria-phage interaction and a starting point that will help to design efficient phage therapy strategies.

Evolutionary design optimization of traffic signals applied to Quito city.

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.