New records of Haemaphysalis leporispalustris in the Transmexican Volcanic Belt province of Mexico with detection of rickettsial infection.

Haemaphysalis leporispalustris is a hard tick species that have been recorded mainly parasitizing rabbits and birds across the Nearctic and Neotropical regions. Particularly in Mexico, most of the records come from historical collection journeys from before the 1960s. In this paper, we bring new geographical records for this species in Mexico to provide the first genetic data in the country through the amplification of the 16S, COI, and 18S genes, and the detection of a rickettsial agent as well.

A feature selection strategy for gene expression time series experiments with hidden Markov models.

Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well.