Abstract

Abstract

Abstract
This chapter proposes a new way to detect and represent the probability of ramping events in short-term wind power forecasting. Ramping is one notable characteristic in a time series associated with a drastic change in value in a set of consecutive time steps. Two properties of a ramp event forecast, that is, slope and phase error, are important from the point of view of the system operator (SO): they have important implications in the decisions associated with unit commitment or generation scheduling, especially if there is thermal generation dominance in the power system. Unit commitment decisions, generally taken some 12-48 h in advance, must prepare the generation schedule in order to smoothly accommodate forecasted drastic changes in wind power availability. © Springer India 2013.

Abstract
Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single one achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning classifiers. © Springer International Publishing Switzerland 2013.

Abstract
Transposable Elements (TE) are sequences of DNA that move and transpose within a genome. TEs, as mutation agents, are quite important for their role in both genome alteration diseases and on species evolution. Several tools have been developed to discover and annotate TEs but no single tool achieves good results on all different types of TEs. In this paper we evaluate the performance of several TEs detection and annotation tools and investigate if Machine Learning techniques can be used to improve their overall detection accuracy. The results of an in silico evaluation of TEs detection and annotation tools indicate that their performance can be improved by using machine learning constructed classifiers.

Abstract
Understanding the molecular basis of within and between species phenotypic variation is one of the main goals of Biology. In Drosophila, most of the work regarding this issue has been performed in D. melanogaster, but other distantly related species must also be studied to verify the generality of the findings obtained for this species. Here, we make the case for D. americana, a species of the virilis group of Drosophila that has been diverging from the model species, D. melanogaster, for approximately 40 Myr. To determine the suitability of this species for such studies, polymorphism and recombination estimates are presented for D. americana based on the largest nucleotide sequence polymorphism data set so far analyzed (more than 100 data sets) for this species. The polymorphism estimates are also compared with those obtained from the comparison of the genome assembly of two D. americana strains (H5 and W11) here reported. As an example of the general utility of these resources, we perform a preliminary study on the molecular basis of lifespan differences in D. americana. First, we show that there are lifespan differences between D. americana populations from different regions of the distribution range. Then, we perform five F2 association experiments using markers for 21 candidate genes previously identified in D. melanogaster. Significant associations are found between polymorphism at two genes (hep and Lim3) and lifespan. For the F2 association study involving the two sequenced strains (H5 and W11), we identify amino acid differences at Lim3 and Hep that could be responsible for the observed changes in lifespan. For both genes, no large gene expression differences were observed between the two strains.